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Remote Sensing in Coastal Environments
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Remote Sensing in Coastal Environments

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 March 2016) | Viewed by 213082

Special Issue Editors

Prof. Dr. Deepak R. Mishra

E-Mail Website
Guest Editor
Department of Geography, University of Georgia, 210 Field Street, Rm 212B, Athens, GA 30602, USA
Interests: water quality (inland waters, estuaries, coastal, and open ocean waters); wetlands health, productivity, and carbon sequestration; benthic habitat mapping; cyber-innovated environmental sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Richard W. Gould, Jr.

E-Mail Website
Guest Editor
Head, Bio-Optical/Physical Processes and Remote Sensing Section, Naval Research Laboratory Code 7331, Building 1009, Stennis Space Center, MS 39529, USA
Interests: remote sensing of ocean bio-optical properties; ocean color algorithm development and uncertainty analysis; optical water mass classification; ocean vertical structure from lidar; coupled bio-optical/physical processes; linking satellite and in situ data with modeling to forecast ocean properties; coastal hypoxia; harmful algal blooms

Special Issue Information

Dear Colleagues,

Coastal ecosystems are regions of remarkable primary and secondary productivity, biodiversity, and high accessibility. Apart from supporting numerous physical and biological processes, they also act as recreational, leisure, and tourism centers. Encompassing a broad range of habitat types and harboring a wealth of species and genetic diversity, coastal ecosystems perform numerous vital ecosystem functions. In addition to serving as nursery grounds for many birds and aquatic organisms, coastal ecosystems play roles in regulating: global hydrology and climate; the biological, physical, and chemical modifications of the water column, sediment, and submerged and emergent vegetation; the storage and cycling of nutrients; the filtration of pollutants from inland freshwater systems; and the protection of shorelines from erosion and storms. Consequently, there is a need for accurate, cost effective, frequent, and synoptic methods of characterizing and monitoring these complex ecosystems.

Remote sensing from in situ, airborne, and space-borne platforms satisfies the aforementioned criteria and offers large scale data acquisition at regular temporal frequencies, so as to monitor coastal environments.  This Special Issue on “Remote Sensing in Coastal Environments” is specifically aimed at addressing challenges related to assessing, quantifying, and monitoring near-shore shallow marine and open ocean processes, ecosystem productivity and biodiversity, interrelationships between vegetation and water quality, and the impact of sea level rise. Authors are encouraged to submit articles with respect to the following topics:

  • coastal mangroves, tidal wetlands (productivity, carbon flux, up-scaling techniques)
  • coupled bio-optical/physical processes (cdom/sediment dynamics, red tide, floating algae)
  • ocean properties and algorithm development (optical water mass classification; vertical structure)
  • coastal hypoxia
  • sea level rise impact on coastal environments
  • coastal and marine biodiversity and benthic habitats (corals, seagrass, benthic algae)
  • lidar and unmanned aerial vehicles (uav) in coastal research (application of structure from motion (sfm) techniques)
  • big data remote sensing and cloud computing in coastal research
  • applications of hyperspectral and/or high spatial resolution sensors
  • integrating remote sensing into coupled coastal biophysical forecast models

Deepak R. Mishra
Richard W. Gould, Jr.
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (22 papers)

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Editorial

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176 KiB  
Editorial
Preface: Remote Sensing in Coastal Environments
by Deepak R. Mishra and Richard W. Gould
Remote Sens. 2016, 8(8), 665; https://doi.org/10.3390/rs8080665 - 17 Aug 2016
Cited by 6 | Viewed by 6410
Abstract
The Special Issue (SI) on “Remote Sensing in Coastal Environments” presents a wide range of articles focusing on a variety of remote sensing models and techniques to address coastal issues and processes ranging for wetlands and water quality to coral reefs and kelp [...] Read more.
The Special Issue (SI) on “Remote Sensing in Coastal Environments” presents a wide range of articles focusing on a variety of remote sensing models and techniques to address coastal issues and processes ranging for wetlands and water quality to coral reefs and kelp habitats. The SI is comprised of twenty-one papers, covering a broad range of research topics that employ remote sensing imagery, models, and techniques to monitor water quality, vegetation, habitat suitability, and geomorphology in the coastal zone. This preface provides a brief summary of each article published in the SI. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)

Research

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3600 KiB  
Article
The Use of Aerial RGB Imagery and LIDAR in Comparing Ecological Habitats and Geomorphic Features on a Natural versus Man-Made Barrier Island
by Carlton P. Anderson, Gregory A. Carter and William R. Funderburk
Remote Sens. 2016, 8(7), 602; https://doi.org/10.3390/rs8070602 - 16 Jul 2016
Cited by 20 | Viewed by 8270
Abstract
The Mississippi (MS) barrier island chain along the northern Gulf of Mexico coastline is subject to rapid changes in habitat, geomorphology and elevation by natural and anthropogenic disturbances. The purpose of this study was to compare habitat type coverage with respective elevation, geomorphic [...] Read more.
The Mississippi (MS) barrier island chain along the northern Gulf of Mexico coastline is subject to rapid changes in habitat, geomorphology and elevation by natural and anthropogenic disturbances. The purpose of this study was to compare habitat type coverage with respective elevation, geomorphic features and short-term change between the naturally-formed East Ship Island and the man-made Sand Island. Ground surveys, multi-year remotely-sensed data, habitat classifications and digital elevation models were used to quantify short-term habitat and geomorphic change, as well as to examine the relationships between habitat types and micro-elevation. Habitat types and species composition were the same on both islands with the exception of the algal flat existing on the lower elevated spits of East Ship. Both islands displayed common patterns of vegetation succession and ranges of existence in elevation. Additionally, both islands showed similar geomorphic features, such as fore and back dunes and ponds. Storm impacts had the most profound effects on vegetation and geomorphic features throughout the study period. Although vastly different in age, these two islands show remarkable commonalities among the traits investigated. In comparison to East Ship, Sand Island exhibits key characteristics of a natural barrier island in terms of its vegetated habitats, geomorphic features and response to storm impacts, although it was established anthropogenically only decades ago. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The MS barrier island chain, USA, showing the East Ship and Sand Island study sites.</p> Full article ">Figure 2
<p>Maximum Likelihood classifications of East Ship Island for the years: (<b>a</b>) 2007; (<b>b</b>) 2009; (<b>c</b>) 2010; and (<b>d</b>) 2012.</p> Full article ">Figure 3
<p>Maximum Likelihood classifications of Sand Island for the years: (<b>a</b>) 2007; (<b>b</b>) 2009; (<b>c</b>) 2010; and (<b>d</b>) 2012.</p> Full article ">Figure 4
<p>DTM of East Ship Island (2012) delineating the island’s minimum and maximum elevations along with geomorphic features.</p> Full article ">Figure 5
<p>DTM of Sand Island (2012) delineating the island’s minimum and maximum elevations along with geomorphic features.</p> Full article ">Figure 6
<p>Box and whisker plot showing the relationship between habitat (2012 ML classification) and elevation (2012 DTM) on both islands. The line and dot within each box represent the median and mean, respectively. Box widths represent the middle 50% of the data about the median; 75% of the data lie between the horizontal bars (“whiskers”). The dots outside the horizontal bars represent the extreme minimum and maximum 5% and 10% of the data.</p> Full article ">Figure 7
<p>NAIP imagery of East Ship Island from (<b>a</b>) 2007 and (<b>b</b>) 2009 showing geographic features before and after Hurricane Gustav in 2008. The circled area represents the partial filling of East Ship’s pond due to a foredune blowout. Arrows show an intertidal zone and berm construction.</p> Full article ">Figure 8
<p>Map showing Sand Island shoreline change from deposition of dredge material (2009–2010) and Hurricane Isaac (2012). Shorelines are represented by a solid line for 2009 (pre-deposition), large dashed line for 2010 (post-deposition, pre-Isaac) and a small dashed line for 2012 (post-Isaac).</p> Full article ">
5106 KiB  
Article
A Spatially Explicit, Multi-Criteria Decision Support Model for Loggerhead Sea Turtle Nesting Habitat Suitability: A Remote Sensing-Based Approach
by Lauren Dunkin, Molly Reif, Safra Altman and Todd Swannack
Remote Sens. 2016, 8(7), 573; https://doi.org/10.3390/rs8070573 - 6 Jul 2016
Cited by 19 | Viewed by 8048
Abstract
Nesting habitat for the federally endangered loggerhead sea turtle (Caretta caretta) were designated as critical in 2014 for beaches along the Atlantic Coast and Gulf of Mexico. Nesting suitability is routinely determined based on site specific information. Given the expansive geographic [...] Read more.
Nesting habitat for the federally endangered loggerhead sea turtle (Caretta caretta) were designated as critical in 2014 for beaches along the Atlantic Coast and Gulf of Mexico. Nesting suitability is routinely determined based on site specific information. Given the expansive geographic location of the designated critical C. caretta nesting habitat and the highly dynamic coastal environment, understanding nesting suitability on a regional scale is essential for monitoring the changing status of the coast as a result of hydrodynamic forces and maintenance efforts. The increasing spatial resolution and temporal frequency of remote sensing data offers the opportunity to study this dynamic environment on a regional scale. Remote sensing data were used as input into the spatially-explicit, multi-criteria decision support model to determine nesting habitat suitability. Results from the study indicate that the morphological parameters used as input into the model are well suited to provide a regional level approach with the results from the optimized model having sensitivity and detection prevalence values greater than 80% and the detection rate being greater than 70%. The approach can be implemented in various geographic locations to better communicate priorities and evaluate management strategies as a result of changes to the dynamic coastal environment. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
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Graphical abstract
Full article ">Figure 1
<p>Study area along the South East of Florida.</p> Full article ">Figure 2
<p>Predicted probability of <span class="html-italic">C. caretta</span> nest density based on: (<b>A</b>) beach slope; (<b>B</b>) beach width parameter; (<b>C</b>) bare earth elevation; and (<b>D</b>) dune peak parameter. Predicted probabilities determined through multinomial logistic regression run on individual parameters.</p> Full article ">Figure 3
<p>Suitability index curves for: (<b>A</b>) beach slope; (<b>B</b>) beach width; (<b>C</b>) elevation; and (<b>D</b>) dune elevation.</p> Full article ">Figure 4
<p>Rasterized model grids for: (<b>A</b>) slope; (<b>B</b>) beach width; (<b>C</b>) elevation; and (<b>D</b>) dune elevation for the area around Jupiter Inlet, FL.</p> Full article ">Figure 5
<p>Sensitivity (solid gray line), detection prevalence (dashed black line), and detection rate (gray dashed line) for predicting the occurrence of <span class="html-italic">C. caretta</span> nests for each of the 61 models tested. The weighting scheme for each model is listed in <a href="#app2-remotesensing-08-00573" class="html-app">Appendix B</a>.</p> Full article ">Figure 6
<p>Nesting suitability distribution for the study area divided into sections for: (<b>A</b>) Martin County; (<b>B</b>) northern portion of Palm Beach County (area used in the model evaluation); (<b>C</b>) southern portion of Palm Beach County; (<b>D</b>) Broward County; and (<b>E</b>) a Dade County.</p> Full article ">Figure 7
<p>Wave rose for Station 63460 off the coast of Palm Beach County.</p> Full article ">Figure 8
<p>Nesting suitability, Model 17 (<b>A</b>); approximate dune vegetation (<b>B</b>); and potential artificial light pollution (<b>C</b>).</p> Full article ">
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Article
Mangroves at Their Limits: Detection and Area Estimation of Mangroves along the Sahara Desert Coast
by Viviana Otero, Katrien Quisthoudt, Nico Koedam and Farid Dahdouh-Guebas
Remote Sens. 2016, 8(6), 512; https://doi.org/10.3390/rs8060512 - 18 Jun 2016
Cited by 16 | Viewed by 7685
Abstract
The northernmost and most arid mangrove ecosystem of West Africa is found in Mauritania, in the Parc National du Banc d’Arguin (PNBA). The existing global and regional maps of Mauritania’s mangroves have little detail, and available estimates of the mangrove area differ among [...] Read more.
The northernmost and most arid mangrove ecosystem of West Africa is found in Mauritania, in the Parc National du Banc d’Arguin (PNBA). The existing global and regional maps of Mauritania’s mangroves have little detail, and available estimates of the mangrove area differ among studies. We assessed the use of automated Remote Sensing classification techniques to calculate the extent and map the distribution of the mangrove patches located at Cap Timiris, PNBA, using QuickBird and GeoEye imagery. It was possible to detect the northernmost contiguous mangrove patches of West Africa with an accuracy of 87% ± 2% using the Maximum Likelihood algorithm. The main source of error was the low spectral difference between mangroves and other types of terrestrial vegetation, which resulted in an erroneous classification between these two types of land cover. The most reliable estimate for the mangrove area obtained in this study was 19.48 ± 5.54 ha in 2011. Moreover, we present a special validation procedure that enables a detailed and reliable validation of the land cover maps. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The study site Cap Timiris at Parc National du Banc d’Arguin (PNBA), Mauritania. The Iwik area and the Parc National du Diawling (PND) are also indicated (see text). Adapted from Google Maps (2016).</p> Full article ">Figure 2
<p>Procedure used to select the pixels for training and validation in the supervised classification based on the cross-validation technique.</p> Full article ">Figure 3
<p>Box-plot of the accuracies for the Maximum Likelihood (ML) and neural network (NN) algorithms implemented for the image from 2004 (<b>A</b>) and 2011 (<b>B</b>). The overall accuracies are named “ML” and “NN”. The specific accuracies are named “ML specific” and “NN specific”. These results were obtained with the five iterations of the cross-validation technique.</p> Full article ">Figure 4
<p>Box-plot showing the quantity, exchange, and shift differences of the image from 2004 (<b>A</b>) and 2011 (<b>B</b>). ML refers to the Maximum Likelihood and NN to the neural network algorithm. These metrics were calculated taking into account the specific accuracy.</p> Full article ">Figure 5
<p>Area (Ha) calculated with Maximum Likelihood Algorithm (ML) and the neural network (NN) for the image from 2004 (<b>A</b>) and in 2011 (<b>B</b>).</p> Full article ">Figure 6
<p>Mangroves at Cap Timiris, since mangroves in PNBA only comprise the mangrove species <span class="html-italic">Avicennia germinans</span>. There is an overestimation of mangroves in the central-east part of the area, mangroves are not found in that location.</p> Full article ">
5802 KiB  
Article
Submerged Kelp Detection with Hyperspectral Data
by Florian Uhl, Inka Bartsch and Natascha Oppelt
Remote Sens. 2016, 8(6), 487; https://doi.org/10.3390/rs8060487 - 8 Jun 2016
Cited by 33 | Viewed by 12366
Abstract
Submerged marine forests of macroalgae known as kelp are one of the key structures for coastal ecosystems worldwide. These communities are responding to climate driven habitat changes and are therefore appropriate indicators of ecosystem status and health. Hyperspectral remote sensing provides a tool [...] Read more.
Submerged marine forests of macroalgae known as kelp are one of the key structures for coastal ecosystems worldwide. These communities are responding to climate driven habitat changes and are therefore appropriate indicators of ecosystem status and health. Hyperspectral remote sensing provides a tool for a spatial kelp habitat mapping. The difficulty in optical kelp mapping is the retrieval of a significant kelp signal through the water column. Detecting submerged kelp habitats is challenging, in particular in turbid coastal waters. We developed a fully automated simple feature detection processor to detect the presence of kelp in submerged habitats. We compared the performance of this new approach to a common maximum likelihood classification using hyperspectral AisaEAGLE data from the subtidal zones of Helgoland, Germany. The classification results of 13 flight stripes were validated with transect diving mappings. The feature detection showed a higher accuracy till a depth of 6 m (overall accuracy = 80.18%) than the accuracy of a maximum likelihood classification (overall accuracy = 57.66%). The feature detection processor turned out as a time-effective approach to assess and monitor submerged kelp at the limit of water visibility depth. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>(<b>a</b>) The location of Helgoland is outlined in a map of Europe (data source: openstreetmapdata.com); (<b>b</b>) A bathymetric map based on sea chart level zero (lowest astronomical tide) of the subtidal zone of the archipelago (data source: courtesy of Federal Maritime and Hydrographic Agency of Germany 2010).</p> Full article ">Figure 2
<p>Overview of diving transects (<b>a</b>–<b>h</b>), flight stripe cover of the study area (<b>i</b>) and overview of diving transects (<b>j</b>–<b>o</b>); the colouring indicates the percentage cover of kelp mapped by the divers.</p> Full article ">Figure 3
<p>Validation of the Feature Detection (FD) and Maximum Likelihood Classification (MLC) results using a single diving transect mapping point: (<b>a</b>) percentage coverage comparison and (<b>b</b>) kelp/non-kelp detection.</p> Full article ">Figure 4
<p>Savitzky-Golay smoothed kelp (<span class="html-italic">Laminaria digitata</span>) laboratory measurements (<b>a</b>) and Savitzky-Golay ROI AISA kelp (<span class="html-italic">Laminaria hyperborea</span>) spectra with varying water cover (<b>b</b>–<b>e</b>).</p> Full article ">Figure 5
<p>Influence of water anomaly filtering (WAF performance) on imagery quality at sites influenced by sun glint. True colour RGB (639 nm/550 nm/459 nm) for two example areas. Area 1 before filtering (<b>a</b>) and after filtering (<b>b</b>) and area 2 before filtering (<b>c</b>) and after filtering (<b>d</b>).</p> Full article ">Figure 6
<p>(<b>a</b>) Stretched true colour image of selected flight stripes and (<b>b</b>) kelp detected by FD.</p> Full article ">Figure 7
<p>Kelp detection validation results with diving transect mapping (<b>a</b>–<b>h</b>), cover of the selected flight stripes (<b>i</b>) and kelp detection validation results (<b>j</b>).</p> Full article ">Figure 8
<p>Comparison of FD and MLC kelp detection results. (<b>a</b>) Spatial distribution of the classified kelp and (<b>b</b>) kelp area detected per depth.</p> Full article ">Figure 9
<p>Intertidal field mappings compared to MLC and FD kelp detection results. Intertidal field mapping results (<b>a</b>); field mapping compared to FD results (<b>b</b>); field mapping compared to MLC results (<b>c</b>).</p> Full article ">Figure 10
<p>(<b>a</b>) Stretched true colour images of all flight stripes and azimuth sun direction during data acquisition (<b>a</b>) FS 1 (<b>b</b>) FS 2 (<b>c</b>) FS 3 (<b>d</b>) FS 4 (<b>e</b>) FS 5 (<b>f</b>) FS 6 (<b>g</b>) FS 7 (<b>h</b>) FS 8 (<b>i</b>) FS 9 (<b>j</b>) mosaic of all FS (<b>k</b>) FS 10 (<b>l</b>) FS 11 (<b>m</b>) FS 12 (<b>n</b>) FS 13.</p> Full article ">
9770 KiB  
Article
Examination of Abiotic Drivers and Their Influence on Spartina alterniflora Biomass over a Twenty-Eight Year Period Using Landsat 5 TM Satellite Imagery of the Central Georgia Coast
by John P. R. O’Donnell and John F. Schalles
Remote Sens. 2016, 8(6), 477; https://doi.org/10.3390/rs8060477 - 4 Jun 2016
Cited by 55 | Viewed by 13589
Abstract
We examined the influence of abiotic drivers on inter-annual and phenological patterns of aboveground biomass for Marsh Cordgrass, Spartina alterniflora, on the Central Georgia Coast. The linkages between drivers and plant response via soil edaphic factors are captured in our graphical conceptual [...] Read more.
We examined the influence of abiotic drivers on inter-annual and phenological patterns of aboveground biomass for Marsh Cordgrass, Spartina alterniflora, on the Central Georgia Coast. The linkages between drivers and plant response via soil edaphic factors are captured in our graphical conceptual model. We used geospatial techniques to scale up in situ measurements of aboveground S. alterniflora biomass to landscape level estimates using 294 Landsat 5 TM scenes acquired between 1984 and 2011. For each scene we extracted data from the same 63 sampling polygons, containing 1222 pixels covering about 1.1 million m2. Using univariate and multiple regression tests, we compared Landsat derived biomass estimates for three S. alterniflora size classes against a suite of abiotic drivers. River discharge, total precipitation, minimum temperature, and mean sea level had positive relationships with and best explained biomass for all dates. Additional results, using seasonally binned data, indicated biomass was responsive to changing combinations of variables across the seasons. Our 28-year analysis revealed aboveground biomass declines of 33%, 35%, and 39% for S. alterniflora tall, medium, and short size classes, respectively. This decline correlated with drought frequency and severity trends and coincided with marsh die-backs events and increased snail herbivory in the second half of the study period. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
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Graphical abstract
Full article ">Figure 1
<p>Landsat 5 color infrared image of the Brunswick National Weather Service (NWS) station, Sapelo Island National Estuarine Research Reserve (SINERR), and Georgia Coastal Ecosystems Long Term Ecological Research (GCE LTER) sites in Georgia.</p> Full article ">Figure 2
<p>Monthly mean temperature and precipitation (1981–2010) for Brunswick and Sapelo Island, Georgia.</p> Full article ">Figure 3
<p>Landsat 5 color infrared image of Sapelo Island, GA and surrounding marshes highlighting polygon selection approach for extracting pixel clusters of different <span class="html-italic">S. alterniflora</span> size classes within our study site using Environment for Visualizing Images’ (ENVI) Region of Interest (ROI) procedure.</p> Full article ">Figure 4
<p>Map of Georgia Coast highlighting long-term monitoring stations and the ten abiotic drivers used in our study, along with abbreviations used in subsequent graphics.</p> Full article ">Figure 5
<p>Mean monthly <span class="html-italic">S. alterniflora</span> biomass values for tall (TS), medium (MS), and short (SS) forms on the Central Georgia Coast. Data extracted from polygons across 294 Landsat 5 TM scenes; sorted by day-of-year, binned into month, and averaged for 28-year phenology patterns. * Number of images available for each month.</p> Full article ">Figure 6
<p>Extracted biomass values from Landsat 5 data for three height classes of <span class="html-italic">S. alterniflora</span> (<b>A</b>) tall; (<b>B</b>) medium; and (<b>C</b>) short size classes over the 28 year study period. Gray line corresponds to day-of-year look up table values calculated by interpolation of the binned monthly biomass estimates in <a href="#remotesensing-08-00477-f005" class="html-fig">Figure 5</a>. Regression equations and coefficient of determination for (<b>A</b>) y = 2373.84 − 0.0389x; 0.057; (<b>B</b>) y = 1469.35 − 0.0247x; 0.067; and (<b>C</b>) y = 1030.64 − 0.0183x; 0.081. Also shown are mean monthly values for (<b>D</b>) Altamaha River Discharge at Doctortown, GA and (<b>E</b>) Palmer Drought Severity Index for region 9 in Georgia (<a href="#remotesensing-08-00477-f004" class="html-fig">Figure 4</a>).</p> Full article ">Figure 7
<p>Multivariate analysis results for tall (TS), medium (MS), and short (SS) <span class="html-italic">S. alterniflora versus</span> abiotic drivers across each of the seven-binned periods. Driver abbreviations: Altamaha River Discharge (RD), Ft. Pulaski Mean Sea Level (PMSL), Fernandina Beach Mean Sea Level (FMSL), Palmer Drought Severity Index (PDSI), Total and Mean Precipitation (TP and MP), and Maximum and Minimum Temperature (MXT and MNT). <b>*</b> Unable to identify best multivariate model due to collinearity among variables.</p> Full article ">Figure 8
<p>Conceptual model linking abiotic drivers to aboveground biomass via soil mediated interactions within the elevation gradient of the marsh platform. Gradient arrows (center) adapted from Mendelssohn and Morris [<a href="#B52-remotesensing-08-00477" class="html-bibr">52</a>].</p> Full article ">Figure 9
<p>Monthly mean values for (<b>A</b>) Altamaha River Discharge at Doctortown, GA; (<b>B</b>) Mean Sea Level relative to NAVD88 for Fort Pulaski, GA and Fernandina Beach, FL; (<b>C</b>) Palmer Drought Severity Index (PDSI) and Standard Precipitation Index (SPI) for Georgia, region nine; (<b>D</b>) Total Precipitation and (<b>E</b>) Mean Temperature at Brunswick, GA over a 28-year period. Note that SPI was multiplied by 1.5 in order to amplify the signal to better identify trends when plotted. Gaps in data represent periods of missing observations.</p> Full article ">
3030 KiB  
Article
Effects of Per-Pixel Variability on Uncertainties in Bathymetric Retrievals from High-Resolution Satellite Images
by Elizabeth J. Botha, Vittorio E. Brando and Arnold G. Dekker
Remote Sens. 2016, 8(6), 459; https://doi.org/10.3390/rs8060459 - 28 May 2016
Cited by 30 | Viewed by 6523
Abstract
Increased sophistication of high spatial resolution multispectral satellite sensors provides enhanced bathymetric mapping capability. However, the enhancements are counter-acted by per-pixel variability in sunglint, atmospheric path length and directional effects. This case-study highlights retrieval errors from images acquired at non-optimal geometrical combinations. The [...] Read more.
Increased sophistication of high spatial resolution multispectral satellite sensors provides enhanced bathymetric mapping capability. However, the enhancements are counter-acted by per-pixel variability in sunglint, atmospheric path length and directional effects. This case-study highlights retrieval errors from images acquired at non-optimal geometrical combinations. The effects of variations in the environmental noise on water surface reflectance and the accuracy of environmental variable retrievals were quantified. Two WorldView-2 satellite images were acquired, within one minute of each other, with Image 1 placed in a near-optimal sun-sensor geometric configuration and Image 2 placed close to the specular point of the Bidirectional Reflectance Distribution Function (BRDF). Image 2 had higher total environmental noise due to increased surface glint and higher atmospheric path-scattering. Generally, depths were under-estimated from Image 2, compared to Image 1. A partial improvement in retrieval error after glint correction of Image 2 resulted in an increase of the maximum depth to which accurate depth estimations were returned. This case-study indicates that critical analysis of individual images, accounting for the entire sun elevation and azimuth and satellite sensor pointing and geometry as well as anticipated wave height and direction, is required to ensure an image is fit for purpose for aquatic data analysis. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
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Graphical abstract
Full article ">Figure 1
<p>Graphic representation of the geometric relationship between the sun and satellite sensor, relative to the target, for the two image acquisitions: (1) Image 1: φ<sub>view</sub> = 106°, θ<sub>view</sub> = 77° and (2) Image 2: φ<sub>view</sub> = 175°, θ<sub>view</sub> = 57° (BRF field in the figure was generated using [<a href="#B37-remotesensing-08-00459" class="html-bibr">37</a>]).</p> Full article ">Figure 2
<p>Subset of (<b>a</b>) Image 1 (φ<sub>view</sub> = 106°, θ<sub>view</sub> = 77°) and (<b>b</b>) Image 2 (φ<sub>view</sub> = 175°, θ<sub>view</sub> = 57°), acquired one minute apart on 22 October 2011 during an outgoing tide with a gentle (16 km/h) south-easterly breeze. Gray mask: exposed land area; 1: location of the <span class="html-italic">in situ</span> depth observations (red dots) collected on 22 August 2013; 2: location of the least glint-affected image segment, selected for atmospheric correction inter-comparison; 3: location of an as homogenous as possible area of water that is as optically deep as possible, selected for NEΔrsE computation.</p> Full article ">Figure 3
<p>Comparison of atmospherically-corrected <span class="html-italic">r<sub>rs</sub></span> spectra of the first six WoldView-2 spectral bands, of a cross-track sample of a shallow water over sandy bottom in an image segment that is least affected by glint in both Image 1 and Image 2 (dashed line represents 1:1 relationship).</p> Full article ">Figure 4
<p>NEΔrsE, a standard deviation computed on a kernel of 35 × 35 pixels collected over an as homogenous as possible area of optically deep water, from Image 1 and Image 2, illustrating the effects of environmental factors due to sunglint and related sun-sensor target geometry on the inherent noise of each individual image.</p> Full article ">Figure 5
<p>(<b>a</b>) Median and 5th and 95th percentiles <span class="html-italic">r<sub>rs</sub></span> in each spectral band of a cross-track spectral profile, collected from each image (Image 1, Image 2 (uncorrected), and Image 2 (GC: glint corrected)) where <span class="html-italic">in situ</span> depth observations were collected (transect 1, <a href="#remotesensing-08-00459-f002" class="html-fig">Figure 2</a>); (<b>b</b>) median and 5th and 95th percentiles <span class="html-italic">r<sub>rs</sub></span> in each spectral band of all optically shallow (&lt;1 m water depth) observations; and (<b>c</b>) mMedian and the 5th and 95th percentiles <span class="html-italic">r<sub>rs</sub></span> in each spectral band of all optically deep (&gt;1 m water depth) observations.</p> Full article ">Figure 6
<p>Analysis of depth retrieval: scatterplot of the model-estimated depth <span class="html-italic">versus in situ</span> depth observations retrieved from (<b>a</b>) Image 1; (<b>b</b>) Image 2 and (<b>c</b>) Image 2 after glint correction (dashed line represents 1:1 relationship). Histogram of the frequency of absolute relative depth errors (<math display="inline"> <semantics> <mrow> <mrow> <mo>|</mo> <mrow> <mrow> <mo>(</mo> <mrow> <mi>m</mi> <mi>e</mi> <mi>a</mi> <mi>s</mi> <mi>u</mi> <mi>r</mi> <mi>e</mi> <mi>d</mi> <mo> </mo> <mi>z</mi> <mo>−</mo> <mi>m</mi> <mi>o</mi> <mi>d</mi> <mi>e</mi> <mi>l</mi> <mi>e</mi> <mi>d</mi> <mo> </mo> <mi>z</mi> </mrow> <mo>)</mo> </mrow> </mrow> <mo>|</mo> </mrow> <mo>/</mo> <mi>m</mi> <mi>e</mi> <mi>a</mi> <mi>s</mi> <mi>u</mi> <mi>r</mi> <mi>e</mi> <mi>d</mi> <mo> </mo> <mi>z</mi> </mrow> </semantics> </math>), retrieved from (<b>d</b>) Image 1; (<b>e</b>) Image 2; and (<b>f</b>) Image 2 after glint correction. Scatterplot of relative depth error <span class="html-italic">versus SDI</span> retrieved from (<b>g</b>) Image 1; (<b>h</b>) Image 2; and (<b>i</b>) Image 2 after glint correction.</p> Full article ">Figure 7
<p>Relative comparison of depth retrieval: scatterplot of the model-estimated depth retrieved from Image 1 <span class="html-italic">versus</span> model–estimated depth retrieved from (<b>a</b>) Image 2 and (<b>b</b>) Image 2 after glint correction (dashed line represents 1:1 relationship). Scatterplot of absolute relative depth errors (<math display="inline"> <semantics> <mrow> <mrow> <mo>|</mo> <mrow> <mrow> <mo>(</mo> <mrow> <mi>m</mi> <mi>e</mi> <mi>a</mi> <mi>s</mi> <mi>u</mi> <mi>r</mi> <mi>e</mi> <mi>d</mi> <mo> </mo> <mi>z</mi> <mo>−</mo> <mi>m</mi> <mi>o</mi> <mi>d</mi> <mi>e</mi> <mi>l</mi> <mi>e</mi> <mi>d</mi> <mo> </mo> <mi>z</mi> </mrow> <mo>)</mo> </mrow> </mrow> <mo>|</mo> </mrow> <mo>/</mo> <mi>m</mi> <mi>e</mi> <mi>a</mi> <mi>s</mi> <mi>u</mi> <mi>r</mi> <mi>e</mi> <mi>d</mi> <mo> </mo> <mi>z</mi> </mrow> </semantics> </math>), retrieved from Image 1 <span class="html-italic">versus</span> absolute relative depth errors retrieved from (<b>c</b>) Image 2 and (<b>d</b>) Image 2 after glint correction. Scatterplot of <span class="html-italic">SDI</span> retrieved from Image 1 <span class="html-italic">versus SDI</span> retrieved from (<b>e</b>) Image 2 and (<b>f</b>) Image 2 after glint correction.</p> Full article ">
5251 KiB  
Article
Spatial Assessment of the Bioclimatic and Environmental Factors Driving Mangrove Tree Species’ Distribution along the Brazilian Coastline
by Arimatéa C. Ximenes, Eduardo Eiji Maeda, Gustavo Felipe Balué Arcoverde and Farid Dahdouh-Guebas
Remote Sens. 2016, 8(6), 451; https://doi.org/10.3390/rs8060451 - 27 May 2016
Cited by 28 | Viewed by 8202
Abstract
Brazil has one of the largest mangrove surfaces worldwide. Due to a wide latitudinal distribution, Brazilian mangroves can be found within a large range of environmental conditions. However, little attention has been given to the description of environmental variables driving the distribution of [...] Read more.
Brazil has one of the largest mangrove surfaces worldwide. Due to a wide latitudinal distribution, Brazilian mangroves can be found within a large range of environmental conditions. However, little attention has been given to the description of environmental variables driving the distribution of mangrove species in Brazil. In this study, we present a novel and unprecedented description of environmental conditions for all mangroves along the Brazilian coast focusing on species limits. We apply a descriptive statistics and data-driven approach using Self-Organizing Maps and we combine data from terrestrial and marine environmental geodatabases in a Geographical Information System. We evaluate 25 environmental variables (21 bioclimatic variables, three sea surface temperature derivates, and salinity). The results reveal three groups of correlated variables: (i) air temperature derivates and sea surface temperature derivates; (ii) air temperature, potential evapotranspiration and precipitation derivates; and (iii) precipitation derivates, aridity and salinity. Our results unveil new locations of extreme values of temperature and precipitation. We conclude that Rhizophora harrisonii and Rhizophora racemosa are more limited by precipitation and aridity and that they do not necessarily follow a latitudinal gradient. Our data also reveal that the lowest air temperatures of the coldest month are not necessarily found at the southernmost limits of mangroves in Brazil; instead they are localized at the Mesoregion of Vale do Itajaí. However, the minimum sea surface temperature drops gradually with higher latitudes in the Brazilian southern hemisphere and is probably a better indicator for the decrease of species at the latitudinal limits of mangroves than the air temperature and precipitation. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Mangrove species richness. The sample point distribution of Brazilian mangroves is colored by species richness (<span class="html-italic">n</span> = 390) and the species limits are shown by yellow circles (detailed information in <a href="#remotesensing-08-00451-t001" class="html-table">Table 1</a>). Brazil has 16 coastal states with mangroves. Two-letter codes represent States, whereas dots represent our samples, both of which are given below between parentheses. In the north, we have: Amapá (AP—34) and Pará (PA—64); in the northeast: Maranhão (MA—70), Piauí (PI—3), Ceará (CE—19), Rio Grande do Norte (RN—15), Paraíba (PB—10), Pernambuco (PE—10), Alagoas (AL—13), Sergipe (SE—11) and Bahia (BA—57); in the southeast: Espírito Santo (ES—15), Rio de Janeiro (RJ—25), São Paulo (SP—16); and in the south: Paraná (PR—11) and Santa Catarina (SC—17).</p> Full article ">Figure 2
<p>Methodological framework. Framework of the methodology applied in this study following four main steps. We refer to the text for methodological details.</p> Full article ">Figure 3
<p>SOM Component planes. The component planes organized in three groups according to correlated components. High values are in red, intermediated values in yellow and green, and low values in dark blue.</p> Full article ">Figure 4
<p>SOM labels and D-Matrix overlapped with species distribution. High values are boundaries between clusters and indicate high dissimilarities. Low values can be considered as a cluster, with more similarity and homogeneity. Brazilian States demarcate the localization on the neural map and the numbers indicate their frequency in each neuron. This figure represents the geographical location of samples that have a relation with component planes in <a href="#remotesensing-08-00451-f003" class="html-fig">Figure 3</a>. At the top of the map are the northern states, in the middle the northeastern states, further down and to the right and center are the southeastern states and the lower left side shows the southern states where the mangrove reaches its limits. The red and blue polygons indicate the range of the mangrove species that are found along a section of the Brazilian coastline. Indicating the species that are found along the entire coastline would create a polygon around the entire neural map (not shown).</p> Full article ">Figure 5
<p>Detailed description of the Min temperature of coldest month (BIO6) (°C × 10) for mangroves in Brazil. (<b>a</b>) Detailed map of the south limits with their BIO6 values for each location of mangroves; (<b>b</b>) Scatterplot (<span class="html-italic">n</span> = 334) representing the BIO6 by latitude in decimal degree superimposed on species distribution limits of (vertical dashed lines from the right to the left) <span class="html-italic">R. racemosa</span> (Rr) and <span class="html-italic">R. harrisonii</span> (Rh) (at 2.6°S), <span class="html-italic">A. germinans</span> (Ag) (at 21.6°S), <span class="html-italic">C. erectus</span> (Ce) (at 22.9°S), <span class="html-italic">Rhizophora mangle</span> (Rm) (at 27.8°S), and finally <span class="html-italic">A. schaueriana</span> (As) and <span class="html-italic">L. racemosa</span> (Lr) (together at 28.5°S). The vertical dashed lines from the right to the left can therefore be seen as the increasing richness of mangrove trees species; (<b>c</b>) Cartogram map distorted by the high and low values of BIO6. Each circle represents a mangrove sample point location (however distorted), the circle size overstates the value of the variable (here BIO 6), and the circle color represents the lower outliers, the lower quartile, the inter-quartile range (split into an upper and lower part), the upper quartile and the upper outliers, in exactly the same way as a Box and Whisker plot. The legend also shows the number of samples per color between brackets; (<b>d</b>) Scatterplot of BIO6 with a detailed zoom on the surrounding of the southernmost species distribution limits (vertical dashed lines of the species limits) and the blue circle indicates the mesoregion of Vale do Itajaí with the lowest values of BIO6 in the Brazilian mangroves.</p> Full article ">Figure 6
<p>Detailed description of the Min sea surface temperature (SSTMIN) (°C) for mangroves in Brazil. (<b>a</b>) Detailed map of the south limits with their SSTMIN values for each location of mangroves; (<b>b</b>) Scatterplot (<span class="html-italic">n</span> = 334) representing the SSTMIN by latitude in decimal degree superimposed on species distribution limits (vertical dashed lines); (<b>c</b>) Cartogram map distorted by the high and low values of SSTMIN; (<b>d</b>) Scatterplot of SSTMIN with a detailed zoom of the surrounding area of the southernmost species distribution limits (vertical dashed lines of the species limits). Refer to <a href="#remotesensing-08-00451-f005" class="html-fig">Figure 5</a> for further explanations.</p> Full article ">Figure 7
<p>Detailed description of the Annual Precipitation (BIO12) and Potential Evapo-Transpiration (PET) for mangroves in Brazil. (<b>a</b>) The cartogram map distorted by the high and low values of BIO12; (<b>b</b>) Scatterplot (<span class="html-italic">n</span> = 334) representing the BIO12 by latitude in decimal degree superimposed on species distribution limits (vertical dashed lines); (<b>c</b>) Cartogram map distorted by the high and low values of PET; (<b>d</b>) Scatterplot (<span class="html-italic">n</span> = 334) representing the PET by latitude in decimal degree superimposed on species distribution limits. Refer to <a href="#remotesensing-08-00451-f005" class="html-fig">Figure 5</a> for further explanations.</p> Full article ">Figure 8
<p>Detailed description of the Precipitation of the driest quarter (BIO17) for mangroves in Brazil. (<b>a</b>) Detailed map of the northeast limits with their BIO17 values for each location of mangroves; (<b>b</b>) Scatterplot (<span class="html-italic">n</span> = 334) representing the BIO17 by latitude in decimal degree superimposed on species distribution limits of (vertical dashed lines); (<b>c</b>) Cartogram map distorted by the high and low values of BIO17; (<b>d</b>) Scatterplot of BIO17 with a detailed zoom on the surrounding area of the northeast species distribution limits (vertical dashed lines of the species limits). The horizontal dashed lines in the BIO17 scatterplots indicate a barrier of &lt;15 mm of precipitation for the <span class="html-italic">Rhizophora</span> species’ northeast limits. Refer to <a href="#remotesensing-08-00451-f005" class="html-fig">Figure 5</a> for further explanations.</p> Full article ">Figure 9
<p>Detailed description of the Aridity index for mangroves in Brazil. (<b>a</b>) Detail spatial distribution map of the northeast limits with their Aridity values for each location of mangroves; (<b>b</b>) Scatterplot (<span class="html-italic">n</span> = 334) representing the aridity by latitude in decimal degree superimposed on species distribution limits of (vertical dashed lines); (<b>c</b>) Cartogram map distorted by the high and low values of aridity index; (<b>d</b>) Scatterplot of aridity with a detailed zoom on the surrounding of the northeast species distribution limits (vertical dashed lines of the species limits). The horizontal dashed lines in the aridity index indicate the climate class (0.2–0.5—Semi-Arid; 0.5–0.65—Dry Sub-humid; &gt;0.65—Humid) [<a href="#B3-remotesensing-08-00451" class="html-bibr">3</a>], and we included an additional dashed line at aridity index 0.9, because as shown in (<b>a</b>) the two species of <span class="html-italic">Rhizophora</span> reach their southernmost limit around that value. Refer to <a href="#remotesensing-08-00451-f005" class="html-fig">Figure 5</a> for further explanations.</p> Full article ">Figure 10
<p>SSTMIN variation at the southernmost limit. Map showing the distribution and variation of SSTMIN at the mangrove limits in the state of Santa Catarina. Note that within Laguna, the SSTMIN variation ranges over 16 °C, 17 °C and 18 °C, averaging to 17 °C ± 1 °C. The yellow circle corresponds to the southernmost mangrove limits in Brazil.</p> Full article ">
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Article
Impact of Satellite Remote Sensing Data on Simulations of Coastal Circulation and Hypoxia on the Louisiana Continental Shelf
by Dong S. Ko, Richard W. Gould, Bradley Penta and John C. Lehrter
Remote Sens. 2016, 8(5), 435; https://doi.org/10.3390/rs8050435 - 23 May 2016
Cited by 6 | Viewed by 7899
Abstract
We estimated surface salinity flux and solar penetration from satellite data, and performed model simulations to examine the impact of including the satellite estimates on temperature, salinity, and dissolved oxygen distributions on the Louisiana continental shelf (LCS) near the annual hypoxic zone. Rainfall [...] Read more.
We estimated surface salinity flux and solar penetration from satellite data, and performed model simulations to examine the impact of including the satellite estimates on temperature, salinity, and dissolved oxygen distributions on the Louisiana continental shelf (LCS) near the annual hypoxic zone. Rainfall data from the Tropical Rainfall Measurement Mission (TRMM) were used for the salinity flux, and the diffuse attenuation coefficient (Kd) from Moderate Resolution Imaging Spectroradiometer (MODIS) were used for solar penetration. Improvements in the model results in comparison with in situ observations occurred when the two types of satellite data were included. Without inclusion of the satellite-derived surface salinity flux, realistic monthly variability in the model salinity fields was observed, but important inter-annual variability was missed. Without inclusion of the satellite-derived light attenuation, model bottom water temperatures were too high nearshore due to excessive penetration of solar irradiance. In general, these salinity and temperature errors led to model stratification that was too weak, and the model failed to capture observed spatial and temporal variability in water-column vertical stratification. Inclusion of the satellite data improved temperature and salinity predictions and the vertical stratification was strengthened, which improved prediction of bottom-water dissolved oxygen. The model-predicted area of bottom-water hypoxia on the Louisiana shelf, an important management metric, was substantially improved in comparison to observed hypoxic area by including the satellite data. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>NCOM-LCS model domain and grid (every second gridline shown). The model ocean topography is contoured.</p> Full article ">Figure 2
<p>Real-time daily average river flows from Army Corps of Engineers for Mississippi River at Tarbert Landing, Mississippi, and for Atchafalaya River at Simmesport, Louisiana.</p> Full article ">Figure 3
<p>Moderate Resolution Imaging Spectroradiometer (MODIS) Kd (488), 2006. (<b>A</b>) January; (<b>B</b>) April; (<b>C</b>) July; (<b>D</b>) October. Color scale units are m<sup>−1</sup>.</p> Full article ">Figure 4
<p>Tropical Rainfall Measurement Mission (TRMM) accumulated monthly rainfall, 2006. (<b>A</b>) January; (<b>B</b>) April; (<b>C</b>) July; (<b>D</b>) October. Note color scale changes for each panel, units are mm.</p> Full article ">Figure 5
<p>Monthly accumulated rainfall, averaged over the model domain, 2003–2012.</p> Full article ">Figure 6
<p>Comparison of mean transect salinity and temperature from observations during 12 cruises from 2 December 2002 to 7 August 2007 and corresponding values from models without (upper plots) and with (lower plots) satellite-derived Kd and rainfall. The legend shows the dates of cruises. In the lower plots, smaller mean bias <math display="inline"> <semantics> <mrow> <mrow> <mo>(</mo> <mrow> <mfrac> <mn>1</mn> <mi>n</mi> </mfrac> <mo>⋅</mo> <mstyle displaystyle="true"> <munderover> <mo>∑</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <mrow> <mi>M</mi> <mi>o</mi> <mi>d</mi> <mi>e</mi> <msub> <mi>l</mi> <mi>i</mi> </msub> <mo>−</mo> <mi>O</mi> <mi>b</mi> <mi>s</mi> <mi>e</mi> <mi>r</mi> <mi>v</mi> <mi>e</mi> <msub> <mi>d</mi> <mi>i</mi> </msub> </mrow> </mstyle> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> and root mean square error (RMSE) and larger coefficient of determination (<span class="html-italic">R<sup>2</sup></span>) were obtained using satellite data. The lines represent the 1:1 line.</p> Full article ">Figure 7
<p>Effect of including satellite diffuse attenuation coefficient (Kd) on thermal structure. Temperature north-south cross-section (right to left) at 91.5°W for May 2008 for (<b>a</b>) reference run (solar attenuation parameterized as Jerlov Type 1A oligotrophic water); and for (<b>b</b>) test run (solar attenuation determined from satellite Kd).</p> Full article ">Figure 8
<p>Effect of including satellite TRMM rainfall on salinity. Salinity north-south cross-section (right to left) at 90°W for May 2008 for (<b>a</b>) reference run (monthly climatology surface salinity flux); and for (<b>b</b>) test run (salinity flux estimated with TRMM rainfall).</p> Full article ">Figure 9
<p>Cumulative distribution function of maximum buoyancy frequency (N<sub>max</sub>), which is an index of the strength of vertical stratifications, for 12 cruises (20–90 stations occupied per cruise from December 2002 to August 2007. Observations (green) were compared to a reference model run without satellite data (blue) and a test model run with satellite data (red). The black dashed vertical line at N = 40 represents the buoyancy frequency at which hypoxia begins to occur [<a href="#B7-remotesensing-08-00435" class="html-bibr">7</a>].</p> Full article ">Figure 10
<p>Area of bottom hypoxia on the Louisiana shelf simulated with Simplified Dissolved Oxygen (SDO) parameterization applying model physical parameters without satellite data (blue) and with satellite data (red).</p> Full article ">Figure 11
<p>Area of bottom hypoxia on the Louisiana Shelf: blue from observation, green from the test case with satellite data and grey from the reference case without satellite data. For the model, the hypoxic area was estimated as the area where the hypoxic condition (DO &lt; 64 mmol/m<sup>3</sup>) persisted over 15 days each season.</p> Full article ">
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Article
Monitoring the Topography of a Dynamic Tidal Inlet Using UAV Imagery
by Nathalie Long, Bastien Millescamps, Benoît Guillot, Frédéric Pouget and Xavier Bertin
Remote Sens. 2016, 8(5), 387; https://doi.org/10.3390/rs8050387 - 6 May 2016
Cited by 109 | Viewed by 11754
Abstract
Unmanned Aerial Vehicles (UAVs) are being increasingly used to monitor topographic changes in coastal areas. Compared to Light Detection And Ranging (LiDAR) data or Terrestrial Laser Scanning data, this solution is low-cost and easy to use, while allowing the production of a Digital [...] Read more.
Unmanned Aerial Vehicles (UAVs) are being increasingly used to monitor topographic changes in coastal areas. Compared to Light Detection And Ranging (LiDAR) data or Terrestrial Laser Scanning data, this solution is low-cost and easy to use, while allowing the production of a Digital Surface Model (DSM) with a similar accuracy. Three campaigns were carried out within a three-month period at a lagoon-inlet system (Bonne-Anse Bay, La Palmyre, France), with a flying wing (eBee) combined with a digital camera. Ground Control Points (GCPs), surveyed by the Global Navigation Satellite System (GNSS) and post-processed by differential correction, allowed georeferencing DSMs. Using a photogrammetry process (Structure From Motion algorithm), DSMs and orthomosaics were produced. The DSM accuracy was assessed against the ellipsoidal height of a GNSS profile and Independent Control Points (ICPs) and the root mean square discrepancies were about 10 and 17 cm, respectively. Compared to traditional topographic surveys, this solution allows the accurate representation of bedforms with a wavelength of the order of 1 m and a height of 0.1 m. Finally, changes identified between both main campaigns revealed erosion/accretion areas and the progradation of a sandspit. These results open new perspectives to validate detailed morphological predictions or to parameterize bottom friction in coastal numerical models. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Location of the study area (<b>A</b>) on the Atlantic coast of France; (<b>B</b>) the Bonne-Anse Lagoon-Inlet; and (<b>C</b>) the study area (Lambert 93 Projection).</p> Full article ">Figure 2
<p>eBee UAV during a field campaign, in flight and the hardware.</p> Full article ">Figure 3
<p>General overview of methods, starting with the preparation of overflight drone campaigns until the determination of DSM and orthomosaic accuracy by GNSS data (profile and ICPs).</p> Full article ">Figure 4
<p>Orthomosaics of the three campaigns: (<b>a</b>) campaign 1; (<b>b</b>) campaign 2 and (<b>c</b>) campaign 3. Location of the GCPs (black circles), the ICPs (black triangles) and the profile used to estimate the accuracy of the DSMs. The grey areas correspond to areas where the photogrammetric processes failed due to the lack of tie points. White areas correspond to areas where no data were available. Blue areas correspond to subtidal/water areas.</p> Full article ">Figure 5
<p>Illustration of the different stages of the photogrammetric process at the flood delta sand bank: alignment of images, creation of point cloud, creation of dense point cloud, creation of model texture.</p> Full article ">Figure 6
<p>Histograms of errors of images georeferencing from GCPs for (<b>a</b>) campaign 1; (<b>b</b>) campaign 2; and (<b>c</b>) campaign 3.</p> Full article ">Figure 7
<p>Image recovery maps for the three campaigns. Black points correspond to nadir images.</p> Full article ">Figure 8
<p>DSMs obtained from the three campaigns: (<b>a</b>) campaign 1; (<b>b</b>) campaign 2; and (<b>c</b>) campaign 3. The spatial resolutions are 20 cm for (<b>a</b>) and (<b>b</b>) and 2 cm for (<b>c</b>). Isolines computed over 5-m-resolution DSMs were superimposed every 0.5 m to improve the representation of the morphology on figures <b>a</b>–<b>c</b>. Grey areas correspond to lack of tie points. White areas correspond to no data (inside the sand banks).</p> Full article ">Figure 9
<p>(<b>a</b>) Ellipsoidal heights of the GNSS profile (in black) and extracted from the DSM (in red) of campaign 2 and (<b>b</b>) histogram of the ellipsoidal height differences.</p> Full article ">Figure 10
<p>Scatter plot of ICPs’ ellipsoidal heights between DSMs data and GNSS data. The blue dots correspond to campaign 1 while the red dots correspond to campaign 2.</p> Full article ">Figure 11
<p>Horizontal differences between GNSS ICPs and orthomosaics from campaigns 1 and 2.</p> Full article ">Figure 12
<p>Ellipsoidal height difference during the studied period (from campaign 1 to campaign 2). The results are presented on the orthomosaic of the campaign 2. The shade of red corresponds to areas where erosion occurred and the shade of green corresponds to areas where accretion occurred. Light grey means that the changes are not significant according to the margin of error computed previously. Dark grey areas correspond to lack of tie points and white areas to no data. The arrow corresponds to the location of the topographic profile plotted on <a href="#remotesensing-08-00387-f013" class="html-fig">Figure 13</a>.</p> Full article ">Figure 13
<p>Ellipsoidal height profiles, extracted from the DSM of campaigns 1 (blue) and 2 (red) and difference between both (black).</p> Full article ">Figure 14
<p>(<b>a</b>) Significant wave height measured offshore of Oléron Island (red) and simulated in front of the inlet (blue) between July 2014 and December 2015; (<b>b</b>) Longshore transport estimated with wave parameters at breaking extracted from the wave hindcast.</p> Full article ">
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Article
Satellite Retrievals of Karenia brevis Harmful Algal Blooms in the West Florida Shelf Using Neural Networks and Comparisons with Other Techniques
by Ahmed El-habashi, Ioannis Ioannou, Michelle C. Tomlinson, Richard P. Stumpf and Sam Ahmed
Remote Sens. 2016, 8(5), 377; https://doi.org/10.3390/rs8050377 - 4 May 2016
Cited by 29 | Viewed by 7920
Abstract
We describe the application of a Neural Network (NN) previously developed by us, to the detection and tracking, of Karenia brevis Harmful Algal Blooms (KB HABs) that plague the coasts of the West Florida Shelf (WFS) using Visible Infrared Imaging Radiometer Suite [...] Read more.
We describe the application of a Neural Network (NN) previously developed by us, to the detection and tracking, of Karenia brevis Harmful Algal Blooms (KB HABs) that plague the coasts of the West Florida Shelf (WFS) using Visible Infrared Imaging Radiometer Suite (VIIRS) satellite observations. Previous approaches for the detection of KB HABs in the WFS primarily used observations from the Moderate Resolution Imaging Spectroradiometer Aqua (MODIS-A) satellite. They depended on the remote sensing reflectance signal at the 678 nm chlorophyll fluorescence band (Rrs678) needed for both the normalized fluorescence height (nFLH) and Red Band Difference algorithms (RBD) currently used. VIIRS which has replaced MODIS-A, unfortunately does not have a 678 nm fluorescence channel so we customized the NN approach to retrieve phytoplankton absorption at 443 nm (aph443) using only Rrs measurements from existing VIIRS channels at 486, 551 and 671 nm. The aph443 values in these retrieved VIIRS images, can in turn be correlated to chlorophyll-a concentrations [Chla] and KB cell counts. To retrieve KB values, the VIIRS NN retrieved aph443 images are filtered by applying limiting constraints, defined by (i) low backscatter at Rrs 551 nm and (ii) a minimum aph443 value known to be associated with KB HABs in the WFS. The resulting filtered residual images, are then used to delineate and quantify the existing KB HABs. Comparisons with KB HABs satellite retrievals obtained using other techniques, including nFLH, as well as with in situ measurements reported over a four year period, confirm the viability of the NN technique, when combined with the filtering constraints devised, for effective detection of KB HABs. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>(<b>a</b>) Flowchart of Bio-Optical model and the Forward Model simulations; (<b>b</b>) Architecture of VIIRS NN, one-hidden layer multilayer perceptron (MLP), trained with 10,000 set of <span class="html-italic">Rrs</span> and related IOPs.</p> Full article ">Figure 2
<p>NOAA HABSOS data [<a href="#B37-remotesensing-08-00377" class="html-bibr">37</a>] with <span class="html-italic">in situ</span> <span class="html-italic">KB</span> concentrations, for period 8 August–17 September 2014<span class="html-italic">.</span></p> Full article ">Figure 3
<p>(<b>a</b>) VIIRS <span class="html-italic">Rrs</span>551 image; (<b>b</b>) Residual <span class="html-italic">Rrs</span>551 image after F1 mask (dark grey) is applied showing residual {<span class="html-italic">Rrs</span>551 ≤ 0.006 sr<sup>−1</sup>}. Note that these images are overlaid with NOAA-HABSOS <span class="html-italic">KB</span> Cell Counts, from <a href="#remotesensing-08-00377-f002" class="html-fig">Figure 2</a> above. White areas represent cloud cover or invalid data.</p> Full article ">Figure 4
<p>(<b>a</b>) Shows retrieved NN <span class="html-italic">a<sub>ph</sub></span><sub>443</sub> (left hand scale) and NN equiv. [<span class="html-italic">Chla</span>] (right hand scale); (<b>b</b>) Same image with F1 mask applied. Cell counts classifications same for <a href="#remotesensing-08-00377-f002" class="html-fig">Figure 2</a>, <a href="#remotesensing-08-00377-f003" class="html-fig">Figure 3</a>a,b, and <a href="#remotesensing-08-00377-f004" class="html-fig">Figure 4</a>b. Dark gray represents F1 mask and white areas represent cloud cover or invalid data.</p> Full article ">Figure 5
<p>(<b>a</b>) Shows VIIRS retrieved NN <span class="html-italic">a<sub>ph</sub></span><sub>443</sub> (left hand scale) and equiv. [<span class="html-italic">Chla</span>] (right hand scale) after filter process masks F1 and F2 are applied. These residual values are therefore compatible with and show the extent of the <span class="html-italic">KB</span> blooms; (<b>b</b>) MODIS-A NN retrieved <span class="html-italic">a<sub>ph</sub></span><sub>443</sub> (left hand scale) and equiv. [<span class="html-italic">Chla</span>] (right hand scale) after filter process masks F1 and F2 are applied. Residual values are therefore compatible with and indicate <span class="html-italic">KB</span> blooms. Dark gray represents F1&amp;F2 masks and white represents cloud cover or invalid data.</p> Full article ">Figure 6
<p>(<b>a</b>) MODIS-A nFLH (left hand scale) standard ocean color product, and equiv. [<span class="html-italic">Chla</span>] (right hand scale); (<b>b</b>) Same image as <a href="#remotesensing-08-00377-f006" class="html-fig">Figure 6</a>a after filter process masks F1 and F2 are applied. Residual values are therefore compatible with and indicate <span class="html-italic">KB</span> blooms. Notes that “×” symbol in both images indicates no cell counts observed, <span class="html-italic">in situ</span> within 2 h windows from MODIS-A observation. Dark gray represents F1&amp;F2 masks and white represent cloud cover or invalid data.</p> Full article ">Figure 7
<p>(<b>a</b>) MODIS-A nFLH equiv. [<span class="html-italic">Chla</span>] retrievals against MODIS-A NN equiv. [<span class="html-italic">Chla</span>] retrievals; (<b>b</b>) Shows the same retrievals as 5a but with F1 and F2 masks applied. Color coding of the dots denotes distance to shore, with red being the closest.</p> Full article ">Figure 8
<p>Retrieved MODIS-A nFLH [<span class="html-italic">Chla</span>] against retrieved MODIS-A NN [<span class="html-italic">Chla</span>], not restricted to open ocean area A, and includes in-shore areas. Color coding of the dots denotes distance to shore, with red being the closest. Note that color bar is in log scale.</p> Full article ">Figure 9
<p>MODIS-A RBD retrieval after filter process masks F1 and F2 are applied, showing <span class="html-italic">KB</span> blooms. Dark gray represents F1&amp;F2 masks and white represent cloud cover or invalid data.</p> Full article ">Figure 10
<p>(<b>a</b>) Retrieved VIIRS OCI/OC3 [<span class="html-italic">Chla</span>] against retrieved MODIS-A NN [<span class="html-italic">Chla</span>]; (<b>b</b>) Same retrieval as 10a with Filters F1 and F2 applied; (<b>c</b>) Retrieved OCI/OC3 [<span class="html-italic">Chla</span>] against MODIS-A nFLH equiv. [<span class="html-italic">Chla</span>]; (<b>d</b>) Same as 10c but with filter F1 &amp; F2 applied. The vertical line shows the NN [<span class="html-italic">Chla</span>] <span class="html-italic">KB</span> threshold (F2 mask) consistent with the existence of <span class="html-italic">KB</span>, residual pixels of F1 and F2 masks satisfy both maximum backscatter and minimum NN [<span class="html-italic">Chla</span>] and represent values for <span class="html-italic">KB</span> blooms. Color coding of the dots denotes distance to shore, with red being the closest.</p> Full article ">Figure 11
<p>(<b>a</b>) Retrieved VIIRS RGCI [<span class="html-italic">Chla</span>] against retrieved VIIRS NN [<span class="html-italic">Chla</span>]; (<b>b</b>) Same retrieval as 11a with Filters F1 and F2 applied; (<b>c</b>) Retrieved RGCI [<span class="html-italic">Chla</span>] against MODIS-A nFLH equiv. [<span class="html-italic">Chla</span>]; (<b>d</b>) Same as 11c but with filter F1 &amp; F2 applied. The vertical line shows the NN [<span class="html-italic">Chla</span>] <span class="html-italic">KB</span> threshold (F2 mask) Color coding of the dots denotes distance to shore, with red being the closest.</p> Full article ">Figure 12
<p>Showing <span class="html-italic">in situ</span> locations of <span class="html-italic">Karenia brevis</span> cell counts cover the range 0.01–9.2·10<sup>6</sup> cells·L<sup>−1</sup> collected by Florida Fish and Wildlife Conservation Commission (FWC). Zoomed areas illustrate the extent of the underline details of <span class="html-italic">KB</span> values available in VIIRS retrievals for the period of 2012–2015.</p> Full article ">Figure 13
<p><span class="html-italic">In situ</span> observation within the same day of VIIRS image: (<b>a</b>) VIIRS NN retrieved <span class="html-italic">a<sub>ph</sub></span><sub>443</sub> against <span class="html-italic">in situ KB</span> cell counts; (<b>b</b>) VIIRS NN equiv. [<span class="html-italic">Chla</span>] against <span class="html-italic">in situ</span> cell counts; (<b>c</b>) VIIRS OCI/OC3 retrieved [<span class="html-italic">Chla</span>] against <span class="html-italic">in situ KB</span> cell counts; (<b>d</b>) VIIRS RGCI retrieved [<span class="html-italic">Chla</span>] against <span class="html-italic">in situ KB</span> cell counts. Color coding of the dots denotes distance to shore, with blue being the closest.</p> Full article ">Figure 14
<p>(<b>a</b>–<b>f</b>) Retrieved NN equiv. [<span class="html-italic">Chla</span>] and OCI/OC3 [<span class="html-italic">Chla</span>] and RGCI [Chla] against <span class="html-italic">in situ</span> cell counts for 1 h and 30 min observation time windows. Note that the vertical color bar is indicates distant (mi) from coastline with red being closest to shore.</p> Full article ">Figure 15
<p>Shows the match-ups falling within the ½ h window, with both filter thresholds.</p> Full article ">Figure 16
<p>VIIRS-NN <span class="html-italic">KB</span> HABs retrievals for blooms date (28 August 2014), showing bloom compatible <span class="html-italic">a<sub>ph</sub></span><sub>443</sub> and equiv. [<span class="html-italic">Chla</span>] values. Notes image are overlaid with cell counts for this date. White areas represent cloud cover or invalid data.</p> Full article ">Figure 17
<p>VIIRS-NN <span class="html-italic">KB</span> HABs retrievals on 2 different blooms dates, showing bloom compatible <span class="html-italic">a<sub>ph</sub></span><sub>443</sub> and equiv. [<span class="html-italic">Chla</span>] values. (<b>a</b>) 11 November 2014, bloom; (<b>b</b>) 09 October 2012, bloom. Notes all images are overlaid with cell counts corresponding for these dates. White areas represent cloud cover or invalid data.</p> Full article ">Figure 18
<p>(<b>A1</b>) Shows the retrieved <span class="html-italic">a</span><sub>ph443</sub> VIIRS NN; (<b>A2</b>) Shows the retrieved <span class="html-italic">bb<sub>p</sub></span><sub>443</sub> VIIRS NN.</p> Full article ">
11107 KiB  
Article
Spectral Classification of the Yellow Sea and Implications for Coastal Ocean Color Remote Sensing
by Huping Ye, Junsheng Li, Tongji Li, Qian Shen, Jianhua Zhu, Xiaoyong Wang, Fangfang Zhang, Jing Zhang and Bing Zhang
Remote Sens. 2016, 8(4), 321; https://doi.org/10.3390/rs8040321 - 12 Apr 2016
Cited by 30 | Viewed by 9338
Abstract
Remote sensing reflectance (Rrs) classification of coastal waters is a useful tool to monitor environmental processes and manage marine environmental resources. This study presents classification work for data sets that were collected in the Yellow Sea during six cruises (spring [...] Read more.
Remote sensing reflectance (Rrs) classification of coastal waters is a useful tool to monitor environmental processes and manage marine environmental resources. This study presents classification work for data sets that were collected in the Yellow Sea during six cruises (spring and autumn, 2003; summer and winter, 2006/2007; and spring and autumn, 2007). Specifically, we analyzed classification features of Rrs spectra and obtained spatio-temporal characteristics of reflectance and bio-optical properties in the coastal waters. Yellow Sea waters were classified into the following four typical regions based on their spatial distribution characteristics: middle of the Yellow Sea (MYS), north Yellow Sea (NYS), coastal Shandong (CS), and Jiangsu shoal (JS), and five water type categories consisting of Classes A–E were used to represent water colors from clear to very turbid. Application of this classification scheme to Medium Resolution Imaging Spectrometer (MERIS) imagery revealed seasonal variations in the data, which suggests that the water types have both significant temporal and spatial distributions. In particular, the area of Class E waters in the Jiangsu shoal tended to gradually shrink in summer and expand in winter. The spatio-temporal variability was due to the influence of various environmental factors such as currents, tidal activity, fresh water discharges, monsoon winds, and typhoons. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Locations of the <span class="html-italic">in-situ</span> sampling sites in the Yellow Sea (<b>left</b>); and coastal water depths from 0 to 120 m, which were referenced from the GEBCO_2014 Grid, <a href="http://www.gebco.net" target="_blank">www.gebco.net</a> (<b>right</b>).</p> Full article ">Figure 2
<p>Field measured remote sensing reflectance spectra and the visible–near infrared bands (B 1–15) of the Medium Resolution Imaging Spectrometer (MERIS) sensor. Red arrow covers the peak of spectra.</p> Full article ">Figure 3
<p>(<b>a</b>) Pixel reflectance spectra of one cloudless imagery MERIS 2P products (16 April 2003) corresponding to the station analyzed during the spring cruise in 2003. (<b>b</b>) Strictly match-up scatter plot of the MERIS <span class="html-italic">R</span><sub>rs</sub>(<span class="html-italic">λ</span>) <span class="html-italic">versus</span> <span class="html-italic">in-situ</span> measurements during the spring and autumn cruises in 2003; the solid line is the 1:1 line, and dashed lines are the 1:2 and 2:1 lines.</p> Full article ">Figure 4
<p>The flowchart for the maximum–minimum values for supervised classification (referred to in shortened form as the max-classification) of <span class="html-italic">R</span><sub>rs</sub> spectra. Min(<span class="html-italic">R</span><sub>rs</sub>) was used only to assist with the confirmation of each class; the minimums of Classes A and B were at 580 nm and Classes C and D were near 400 nm.</p> Full article ">Figure 5
<p>The six cruises’ average spectral shapes (<span class="html-italic">R</span><sub>rs</sub>(<span class="html-italic">λ</span>)/max(<span class="html-italic">R</span><sub>rs</sub>(<span class="html-italic">λ</span>))) for the five classes.</p> Full article ">Figure 6
<p><span class="html-italic">In-situ</span> remote sensing reflectance (<span class="html-italic">R</span><sub>rs</sub>) spectra classification for distributed features, with 10 m, 20 m, 30 m and 60 m isobaths. Four regions with typical bio-optical properties are shown; these regions include: coastal Shandong (CS, inner 30 m isobaths, north of 35°N), the north Yellow Sea (NYS, north of 37°N, where the part containing CS is excluded), the middle of the Yellow Sea (MYS, above 60 m isobaths), and Jiangsu shoal (JS, inner 20 m isobaths).</p> Full article ">Figure 7
<p>(<b>a</b>–<b>g</b>) Yellow Sea (YS) <span class="html-italic">R</span><sub>rs</sub>(490) contour diagrams in (<b>a</b>) summer and (<b>b</b>) winter; (<b>c</b>) north Yellow Sea (NYS) <span class="html-italic">K</span><sub>d</sub>(490) distribution diagram in autumn; (<b>e</b>,<b>f</b>) <span class="html-italic">a</span>(488) and <span class="html-italic">b</span><sub>b</sub>(488) contour diagrams in winter; and (<b>d</b>,<b>g</b>) Jiangsu shoal (JS) <span class="html-italic">a<sub>g</sub></span>(400) and <span class="html-italic">a</span><sub>d</sub>(400) contour diagrams in summer.</p> Full article ">Figure 8
<p>Yellow Sea water class types diagrams for spring, summer, autumn, and winter.</p> Full article ">Figure 9
<p>The classification tree used to categorize water masses into the five optical types (A–E) for MERIS data; the max is the wavelength where the peak of MERIS B1–B9 lies in.</p> Full article ">Figure 10
<p>(<b>a</b>) The color composite shows the 709, 560, and 413 nm channels as red, green, and blue, respectively. (<b>b</b>) Cloudless imagery classified into Class A (blue), Class B (green), Class C (light green), Class D (yellow), and Class E (brown); cloud, glint, and so forth are shown in black. Data for MERIS imagery (MER_RR_PRBCM_20070428_021351_Data) are from 28 April 2007 (spring).</p> Full article ">Figure 11
<p>Seasonal accumulative frequency distzributions of water types (Classes E–A, up to down) for the four seasons (summer (1 June–31 August) and winter (1 December–28 February) in 2006/2007, and spring (1 March–30 May) and autumn (1 September–30 November) in 2007) corresponding to four survey cruises in 2006–2007. The color bar at the bottom represents the number of days over the season.</p> Full article ">Figure 12
<p>Seasonal frequency distribution of the Class E area in Jiangsu shoal between 31°N–35°N over the entire MERIS archive from 2002 to 2012.</p> Full article ">Figure 13
<p>Cloudless imagery classified into Class A (blue), Class B (green), Class C (light green), Class D (yellow), and Class E (brown); cloud, glint, and so forth are shown in black: (<b>a</b>) the Mississippi region (27 April 2007); and (<b>b</b>) the English Channel (8 April 2007).</p> Full article ">
6890 KiB  
Article
Combining L- and X-Band SAR Interferometry to Assess Ground Displacements in Heterogeneous Coastal Environments: The Po River Delta and Venice Lagoon, Italy
by Luigi Tosi, Cristina Da Lio, Tazio Strozzi and Pietro Teatini
Remote Sens. 2016, 8(4), 308; https://doi.org/10.3390/rs8040308 - 6 Apr 2016
Cited by 75 | Viewed by 11935
Abstract
From leveling to SAR-based interferometry, the monitoring of land subsidence in coastal transitional environments significantly improved. However, the simultaneous assessment of the ground movements in these peculiar environments is still challenging. This is due to the presence of relatively small built-up zones and [...] Read more.
From leveling to SAR-based interferometry, the monitoring of land subsidence in coastal transitional environments significantly improved. However, the simultaneous assessment of the ground movements in these peculiar environments is still challenging. This is due to the presence of relatively small built-up zones and infrastructures, e.g., coastal infrastructures, bridges, and river embankments, within large natural or rural lands, e.g., river deltas, lagoons, and farmland. In this paper we present a multi-band SAR methodology to integrate COSMO-SkyMed and ALOS-PALSAR images. The method consists of a proper combination of the very high-resolution X-band Persistent Scatterer Interferometry (PSI), which achieves high-density and precise measurements on single structures and constructed areas, with L-band Short-Baseline SAR Interferometry (SBAS), properly implemented to raise its effectiveness in retrieving information in vegetated and wet zones. The combined methodology is applied on the Po River Delta and Venice coastland, Northern Italy, using 16 ALOS-PALSAR and 31 COSMO-SkyMed images covering the period between 2007 and 2011. After a proper calibration of the single PSI and SBAS solution using available GPS records, the datasets have been combined at both the regional and local scales. The measured displacements range from ~0 mm/yr down to ?35 mm/yr. The results reveal the variable pattern of the subsidence characterizing the more natural and rural environments without losing the accuracy in quantifying the sinking of urban areas and infrastructures. Moreover, they allow improving the interpretation of the natural and anthropogenic processes responsible for the ongoing subsidence. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Image of the Po River Delta and Venice coastland. Cosmo-SkyMed and ALOS-PALSAR frames are shown by red and blue rectangles (Track 639, Frames 890/900), respectively. Yellow dots indicate the positions of the CGPS stations (<a href="#remotesensing-08-00308-t001" class="html-table">Table 1</a>) used to reference the SAR solutions. Base map source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community.</p> Full article ">Figure 2
<p>Images graph. X-axis: temporal baseline of the acquisition. Y-axis: normal baseline of the acquisition. (<b>a</b>) COSMO-SkyMed PSI analysis; and (<b>b</b>) ALOS-PALSAR short baseline SBAS analysis.</p> Full article ">Figure 3
<p>Average calibrated land displacements (mm/yr) along the LOS in the PDV region: (<b>a</b>) 2008–2011 by PSI on Cosmo-SkyMed images; and (<b>b</b>) 2007–2010 SBAS on ALOS-PALSAR images. Positive values mean uplift, negative values mean land subsidence. The sites labeled by A, B, C, D, and E are used for satellite cross-validation. Base map source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community.</p> Full article ">Figure 4
<p>Data validation. Comparison between the LOS displacements provided by the CGPS (red dots) and the calibrated COSMO-SkyMed PT (blue dots) time series.</p> Full article ">Figure 5
<p>Northern mainland: calibrated (<b>a</b>) ALOS-PALSAR; and (<b>b</b>) COSMO-SkyMed average displacement rates (mm/yr) over the period 2007–2010 and 2008–2011, respectively; (<b>c</b>,<b>d</b>) provide the respective pdf(<span class="html-italic">v</span>) for the pixels/PTs located within the black polygon. Negative values indicate subsidence, positive mean uplift. The trace of the area is provided in <a href="#remotesensing-08-00308-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 6
<p>Northern lagoon: calibrated (<b>a</b>) ALOS-PALSAR; and (<b>b</b>) COSMO-SkyMed average displacement rates (mm/yr) over the period 2007–2010 and 2008–2011, respectively; (<b>c</b>,<b>d</b>) provide the respective pdf(<span class="html-italic">v</span>) for the pixels/PTs located within the black polygon. Negative values indicate subsidence, positive mean uplift.</p> Full article ">Figure 7
<p>Central and southern lagoon: calibrated (<b>a</b>) ALOS-PALSAR; and (<b>b</b>) COSMO-SkyMed average displacement rates (mm/yr) over the period 2007–2010 and 2008–2011, respectively; (<b>c</b>,<b>d</b>) provide the respective pdf(<span class="html-italic">v</span>) for the pixels/PTs located within the black polygon. Negative values indicate subsidence, positive mean uplift.</p> Full article ">Figure 8
<p>Littoral strips of the Venice Lagoon: calibrated (<b>a</b>) ALOS-PALSAR; and (<b>b</b>) COSMO-SkyMed average displacement rates (mm/yr) over the period 2007–2010 and 2008–2011, respectively; (<b>c</b>,<b>d</b>) provide the respective pdf(v) for the pixels/PTs located within the black polygon. Negative values indicate subsidence, positive mean uplift.</p> Full article ">Figure 9
<p>Po River Delta: calibrated (<b>a</b>) ALOS-PALSAR; and (<b>b</b>) COSMO-SkyMed average displacement rates (mm/yr) over the period 2007–2010 and 2008–2011, respectively; (<b>c</b>,<b>d</b>) provide the respective pdf(<span class="html-italic">v</span>) for the pixels/PTs located within the black polygon. Negative values indicate subsidence, positive mean uplift.</p> Full article ">Figure 10
<p>Average displacement rates (mm/yr) between January 2007 and July 2010 on ALOS-PALSAR images and from September 2008 to July 2011 on COSMO-SkyMed images. (<b>a</b>) ALOS-PALSAR outcome in the northern Venice Lagoon; (<b>b</b>) COSMO-SkyMed outcome in the northern Venice Lagoon; (<b>c</b>) ALOS-PALSAR outcome in the southern Venice Lagoon; (<b>d</b>) COSMO-SkyMed outcome in the southern Venice Lagoon; (<b>e</b>) ALOS-PALSAR outcome in the Po River Delta; (<b>f</b>) COSMO-SkyMed outcome in the Po River Delta. Negative values indicate subsidence, positive mean uplift. The image background is from Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, Swisstopo, and the GIS User Community.</p> Full article ">Figure 11
<p>Combined L- and X-band maps of the average displacement rates at the regional scale: (<b>a</b>) northern mainland; (<b>b</b>) Venice Lagoon, and (<b>c</b>) Po River Delta. Negative values indicate subsidence, positive mean uplift; The white boxes in (<b>b</b>,<b>c</b>) represent the location of the areas provided in <a href="#remotesensing-08-00308-f012" class="html-fig">Figure 12</a>. The image background is from Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, Swisstopo, and the GIS User Community.</p> Full article ">Figure 12
<p>Combined L- and X-band maps of the average displacement rates at the local scale. (<b>a</b>) ALOS-PALSAR results in the northern tip of the Venice Lagoon; (<b>b</b>) ALOS-PALSAR results in the southern edge of the Venice Lagoon; (<b>c</b>) ALOS-PALSAR results in the central basin of the Po River Delta; (<b>d</b>) COSMO-SkyMed results in the northern tip of the Venice Lagoon; (<b>e</b>) COSMO-SkyMed results in the southern edge of the Venice Lagoon; (<b>f</b>) COSMO-SkyMed results in the central basin of the Po River Delta; (<b>g</b>) combined maps in the northern tip of the Venice Lagoon; (<b>h</b>) combined maps in the southern edge of the Venice Lagoon; (<b>i</b>) combined maps in the central basin of the Po River Delta. The location of (<b>a</b>,<b>d</b>,<b>g</b>), (<b>b</b>,<b>e</b>,<b>h</b>), (<b>c</b>,<b>f</b>,<b>i</b>) areas is shown in <a href="#remotesensing-08-00308-f011" class="html-fig">Figure 11</a>. Negative values indicate subsidence, positive mean uplift. The image background is from Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, Swisstopo, and the GIS User Community.</p> Full article ">
8017 KiB  
Article
Potential of High Spatial and Temporal Ocean Color Satellite Data to Study the Dynamics of Suspended Particles in a Micro-Tidal River Plume
by Anouck Ody, David Doxaran, Quinten Vanhellemont, Bouchra Nechad, Stefani Novoa, Gaël Many, François Bourrin, Romaric Verney, Ivane Pairaud and Bernard Gentili
Remote Sens. 2016, 8(3), 245; https://doi.org/10.3390/rs8030245 - 16 Mar 2016
Cited by 59 | Viewed by 9463
Abstract
Ocean color satellite sensors are powerful tools to study and monitor the dynamics of suspended particulate matter (SPM) discharged by rivers in coastal waters. In this study, we test the capabilities of Landsat-8/Operational Land Imager (OLI), AQUA&TERRA/Moderate Resolution Imaging Spectroradiometer (MODIS) and MSG-3/Spinning [...] Read more.
Ocean color satellite sensors are powerful tools to study and monitor the dynamics of suspended particulate matter (SPM) discharged by rivers in coastal waters. In this study, we test the capabilities of Landsat-8/Operational Land Imager (OLI), AQUA&TERRA/Moderate Resolution Imaging Spectroradiometer (MODIS) and MSG-3/Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensors in terms of spectral, spatial and temporal resolutions to (i) estimate the seawater reflectance signal and then SPM concentrations and (ii) monitor the dynamics of SPM in the Rhône River plume characterized by moderately turbid surface waters in a micro-tidal sea. Consistent remote-sensing reflectance (Rrs) values are retrieved in the red spectral bands of these four satellite sensors (median relative difference less than ~16% in turbid waters). By applying a regional algorithm developed from in situ data, these Rrs are used to estimate SPM concentrations in the Rhône river plume. The spatial resolution of OLI provides a detailed mapping of the SPM concentration from the downstream part of the river itself to the plume offshore limits with well defined small-scale turbidity features. Despite the low temporal resolution of OLI, this should allow to better understand the transport of terrestrial particles from rivers to the coastal ocean. These details are partly lost using MODIS coarser resolutions data but SPM concentration estimations are consistent, with an accuracy of about 1 to 3 g·m?3 in the river mouth and plume for spatial resolutions from 250 m to 1 km. The MODIS temporal resolution (2 images per day) allows to capture the daily to monthly dynamics of the river plume. However, despite its micro-tidal environment, the Rhône River plume shows significant short-term (hourly) variations, mainly controlled by wind and regional circulation, that MODIS temporal resolution failed to capture. On the contrary, the high temporal resolution of SEVIRI makes it a powerful tool to study this hourly river plume dynamics. However, its coarse resolution prevents the monitoring of SPM concentration variations in the river mouth where SPM concentration variability can reach 20 g·m?3 inside the SEVIRI pixel. Its spatial resolution is nevertheless sufficient to reproduce the plume shape and retrieve SPM concentrations in a valid range, taking into account an underestimation of about 15%–20% based on comparisons with other sensors and in situ data. Finally, the capabilities, advantages and limits of these satellite sensors are discussed in the light of the spatial and temporal resolution improvements provided by the new and future generation of ocean color sensors onboard the Sentinel-2, Sentinel-3 and Meteosat Third Generation (MTG) satellite platforms. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
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Graphical abstract

Graphical abstract
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<p>RGB georeferenced OLI image of the study area the 23 February 2014. Black stars locate the TUCPA field campaign stations (see <a href="#sec2dot3-remotesensing-08-00245" class="html-sec">Section 2.3</a> for details), the autonomous Mesurho station is indicated by a white circle.</p> Full article ">Figure 2
<p>(<b>A</b>) Rhône River freshwater discharge (blue) measured at Beaucaire-Tarascon (65 km upstream the Rhône River mouth) as well as particulate backscattering coefficient b<sub>bp</sub>(700) (as a proxy of SPM concentration [<a href="#B8-remotesensing-08-00245" class="html-bibr">8</a>]) (red) and Chl a fluorescence (green) measured by the Wetlabs ECO-BB2FL sensor at the Mesurho buoy, from the 22 January to the 11 March (daily averaged values). The b<sub>bp</sub>(700) coefficient was calculated from the light backscattering data measured at 117° and 700 nm (β(117°,700)) following the formula b<sub>bp</sub> = 2π × 1.1 × β(117°,700) [<a href="#B29-remotesensing-08-00245" class="html-bibr">29</a>] (water light backscattering and absorption losses were neglected). Dashed black rectangle indicate data that could be altered by probe saturation. Green zone indicates study period from 17 to 23 February; (<b>B</b>) Same for the period from 17 to 23 February 2014 (green zones on (<b>A</b>)) (data averaged over 4 h). Black solid line and black dashed lines represent 4 hours averaged wind speed and direction, respectively.</p> Full article ">Figure 3
<p>(<b>A</b>) MODIS-A Rrs(645) product of the Rhône River plume for the 23 February 2014 obtained using MUMM [<a href="#B37-remotesensing-08-00245" class="html-bibr">37</a>] (top) and NIR- SWIR [<a href="#B36-remotesensing-08-00245" class="html-bibr">36</a>,<a href="#B42-remotesensing-08-00245" class="html-bibr">42</a>] (middle) atmospheric corrections. Grey areas on the western part of the plume on the MODIS-A Rrs product obtained with the NIR-SWIR correction correspond to area flagged because of atmospheric correction failures. Scatter-plot on bottom panel is obtained by comparing the six available MODIS (-A and -T) Rrs(645) products corrected with both (MUMM and NIR-SWIR) atmospheric correction algorithms; (<b>B</b>) OLI Rrs(655) product of the Rhône River plume for the 23 February 2014 obtained using NIR [<a href="#B13-remotesensing-08-00245" class="html-bibr">13</a>] (top) and SWIR [<a href="#B18-remotesensing-08-00245" class="html-bibr">18</a>] (middle) atmospheric correction for OLI. Scatter-plot on the bottom panel are obtained by comparing OLI Rrs(655) product corrected with both (NIR and SWIR) atmospheric corrections algorithm. Colors on scatter plots denote pixel density.</p> Full article ">Figure 4
<p><span class="html-italic">In situ</span> Rrs weighted by sensitivity of red, green and NIR sensors spectral bands <span class="html-italic">vs. in situ</span> SPM concentration measured during the TUCPA campaign (27 stations) for the three sensors: (<b>A</b>) OLI; (<b>B</b>) MODIS -A and -T; (<b>C</b>) SEVIRI.</p> Full article ">Figure 5
<p>Empirical relationships between <span class="html-italic">in situ</span> Rrs (weighted by sensors spectral sensitivity) in the red spectral bands of the OLI (red), MODIS (green) and SEVIRI (blue) sensors and <span class="html-italic">in situ</span> SPM concentration obtained from TUCPA campaign measurments (27 stations). Three relations are very similar and show a correlation coefficient R<sup>2</sup> ~ 0.61.</p> Full article ">Figure 6
<p>Intercomparison of Rrs obtained in the red spectral band of each satellite sensor. Scatter-plots (top) and relative differences (bottom) are shown for comparison between (<b>A</b>) SEVIRI and MODIS-A and –T; (<b>B</b>) OLI and MODIS-T; (<b>C</b>) OLI and MODIS-A; (<b>D</b>) OLI and SEVIRI. Because of the large time difference between MODIS-A and OLI data acquisitions, the corresponding Rrs products were compared considering only a polygon inside the river plume, where Rrs variations are less important than in the plume boundaries regions. For a better comparison, the same polygon is used for the comparison between Rrs derived from MODIS-T and OLI. Linear regressions are applied on each scatter plot, showing good correlation between all sensors. The median relative difference was calculated for Rrs &gt; 0.010 (sr<sup>−1</sup>) corresponding to Rrs observed in the moderately turbid waters of the Rhône River plume, and indicated by horizontal bars. The number of observations as well as the spatial resolution used for comparison are indicated in each plot.</p> Full article ">Figure 7
<p>(<b>A</b>) Full OLI image of SPM concentration over the Rhône River plume (left) and zoom on the Rhône River mouth (right) at the OLI native resolution (30 m) (top). Then averaged into coarser grids of 250 m, 500 m, 1 km and 3 × 5 km, from top to bottom row, corresponding to MODIS and SEVIRI resolutions. SPM concentrations are obtained using Rrs <span class="html-italic">vs.</span> SPM concentration relationship obtained in <a href="#sec3dot1dot1-remotesensing-08-00245" class="html-sec">Section 3.1.1</a>; (<b>B</b>) Maps of the standard deviation of SPM concentration at OLI native resolution (30 m) into aggregated pixels. Color scales are adapted to each map and illustrated with color bar. In order to better illustrate the whole variability of OLI SPM concentration into aggregated pixels, scatter-plots of OLI data at native resolution (30 m) as function of the resampled data are also presented. The color on the scatter plots denotes pixel density; (<b>C</b>) SPM concentration profile derived from the OLI native resolution image, as a function of distance from the mouth. Path used for this profile is illustrated by black stars on two OLI native resolution images in (<b>A</b>).</p> Full article ">Figure 7 Cont.
<p>(<b>A</b>) Full OLI image of SPM concentration over the Rhône River plume (left) and zoom on the Rhône River mouth (right) at the OLI native resolution (30 m) (top). Then averaged into coarser grids of 250 m, 500 m, 1 km and 3 × 5 km, from top to bottom row, corresponding to MODIS and SEVIRI resolutions. SPM concentrations are obtained using Rrs <span class="html-italic">vs.</span> SPM concentration relationship obtained in <a href="#sec3dot1dot1-remotesensing-08-00245" class="html-sec">Section 3.1.1</a>; (<b>B</b>) Maps of the standard deviation of SPM concentration at OLI native resolution (30 m) into aggregated pixels. Color scales are adapted to each map and illustrated with color bar. In order to better illustrate the whole variability of OLI SPM concentration into aggregated pixels, scatter-plots of OLI data at native resolution (30 m) as function of the resampled data are also presented. The color on the scatter plots denotes pixel density; (<b>C</b>) SPM concentration profile derived from the OLI native resolution image, as a function of distance from the mouth. Path used for this profile is illustrated by black stars on two OLI native resolution images in (<b>A</b>).</p> Full article ">Figure 8
<p>Satellite images of the SPM concentration over the Rhône River plume the 23rd February 2014. (<b>A</b>) OLI SPM product at 30m resolution acquired at 10:24; (<b>B</b>) MODIS-T and (<b>C</b>) MODIS-A SPM product at 250 m resolution acquired at 09:50 and 13:10 respectively and (<b>D</b>) SEVIRI SPM product at 3 × 5 km resolution acquired at 09:45. SPM concentrations are obtained using Rrs <span class="html-italic">vs.</span> SPM concentration relationship obtained in <a href="#sec3dot1dot1-remotesensing-08-00245" class="html-sec">Section 3.1.1</a>. Grey areas correspond to pixels masked by flags. The MODIS-T image (<b>B</b>) shows an overall low quality (<span class="html-italic">i.e.</span>, grey streak on left side) because of the location of the Rhône River plume at the border of the MODIS-T swath.</p> Full article ">Figure 9
<p>MODIS-A and -T SPM products (<b>left</b>) and SEVIRI SPM products (<b>right</b>) for the four match-ups identified with the TUCPA <span class="html-italic">in situ</span> data. Dates and times for the four match-ups are (1) 2014-02-17 10:24; (2) 2014-02-17 12:11, (3) 2014-02-20 11:07 and (4) 2014-02-20 12:51. More match-up are available with SEVIRI but are not shown here. In addition to SPM concentrations, corresponding Rrs values in the red band of the two sensors (645 nm for MODIS and 635 nm for SEVIRI) are also reported on the color scale.</p> Full article ">Figure 10
<p>Comparison between (<b>A</b>) MODIS-A and -T and (<b>B</b>) SEVIRI derived Rrs (left) and SPM concentration (right) with <span class="html-italic">in situ</span> data from the TUCPA campaign. Linear regression are applied on each comparison. The R<sup>2</sup>, median relative difference (MRD = median(|DATA<sub>sensors</sub>-DATA<sub>TUCPA</sub>|/DATA<sub>TUCPA</sub>*100) ) and RMSE values are indicated. Some SPM concentration match-up were considered as not relevant because of <span class="html-italic">in situ</span> data measurement error (see <a href="#sec3dot2dot3-remotesensing-08-00245" class="html-sec">Section 3.2.3</a>) and were excluded.</p> Full article ">Figure 11
<p>(<b>A</b>) SPM concentration variations in the river mouth over the studied period (17 to 23 February). SPM concentrations were computed from the particulate backscaterring coefficient b<sub>bp</sub>(700) measured by the Wetlabs ECO-BB2FL autonomous sensor mounted at the Mesurho station according to: SPM = b<sub>bp</sub>(700)*F, with F = 416 a local calibration factor established using field measurements during the TUCPA campaign. SPM concentration measurements are averaged over 1h in order to keep only variation significant enough to be compared to variations observed from the space at meters to kilometers scales. The grey shaded regions correspond to the SEVIRI acquisition time range from 08:00 to 16:00 and the red and blue vertical line correspond to mean acquisition time of MODIS-A (10:00) and MODIS-T (13:00) respectively; (<b>B</b>) Daily averaged SPM concentration measured by the probe (black) over a large period ranging from 22 January to the 11 March 2014, compared to the SPM concentration measured by the probe at MODIS-A (red), MODIS-T (blue) and OLI (yellow) acquisition time. Dashed black rectangle indicates data that could be altered by probe saturation.</p> Full article ">Figure 12
<p>SPM concentration contour lines for concentration &gt; 10 g m<sup>−3</sup> derived from SEVIRI and MODIS-A and -T SPM product for the 17, 20 and 23 February 2014. SEVIRI contour lines for all images acquired every 15 min are mapped with color from blue (08:00) to yellow (16:00). MODIS-A and -T contour lines are mapped in red solid line and red dashed line respectively. For an easier comparison with SEVIRI, MODIS images are ressampled to the SEVIRI resolution. For comparison, the wind speed and direction for each hour (from 5:00 to 19:00 UTC ) is also reported under each contour lines map with same color code as SEVIRI contours (grey arrows correspond to wind speed and direction for hours before and after SEVIRI and MODIS acquisition times).</p> Full article ">
7824 KiB  
Article
Application of the Geostationary Ocean Color Imager to Mapping the Diurnal and Seasonal Variability of Surface Suspended Matter in a Macro-Tidal Estuary
by Zhixin Cheng, Xiao Hua Wang, David Paull and Jianhua Gao
Remote Sens. 2016, 8(3), 244; https://doi.org/10.3390/rs8030244 - 15 Mar 2016
Cited by 40 | Viewed by 8259
Abstract
Total suspended particulate matter (TSM) in estuarine and coastal regions usually exhibits significant natural variations. The understanding of such variations is of great significance in coastal waters. The aim of this study is to investigate and assess the diurnal and seasonal variations of [...] Read more.
Total suspended particulate matter (TSM) in estuarine and coastal regions usually exhibits significant natural variations. The understanding of such variations is of great significance in coastal waters. The aim of this study is to investigate and assess the diurnal and seasonal variations of surface TSM distribution and its mechanisms in coastal waters based on Geostationary Ocean Color Imager (GOCI) data. As a case study, dynamic variations of TSM in the macro-tidal Yalu River estuary (YRE) of China were analysed. With regard to diurnal variability, there were usually two peaks of TSM in a tidal cycle corresponding to the maximum flood and ebb current. Tidal action appears to play a vital role in diurnal variations of TSM. Both the processes of tidal re-suspension and advection could be identified; however, the diurnal variation of TSM was mainly affected by a re-suspension process. In addition, spring-neap tides can affect the magnitude of TSM diurnal variations in the YRE. The GOCI-retrieved TSM results clearly showed the seasonal variability of surface TSM in this area, with the highest level occurring in winter and the lowest in summer. Moreover, although river discharge to the YRE was much greater in the wet season than the dry season, TSM concentrations were significantly higher in the dry season. Wind waves were considered to be the main factor affecting TSM seasonal variation in the YRE. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Location and bathymetry of the Yalu River estuary and its surrounding shelf region. Black points T1, P1, P4, P5 indicate selected monitoring stations. The line between P4 and P5 is the selected <a href="#sec1-remotesensing-08-00244" class="html-sec">Section 1</a>. D and H indicate the location of the Donggang Meteorological Station and Huanggou Hydrologic Station, respectively. Y03 is the location of the field observation station.</p> Full article ">Figure 2
<p>(<b>a</b>) Comparison between <span class="html-italic">in situ</span> total suspended particulate matter (TSM) in the upper layer at Y03 in August 2009 and the Geostationary Ocean Color Imager (GOCI)-retrieved TSM concentration at T1 in August 2014 and (<b>b</b>) same comparison under the tidal phase . “HW” and “LW” represent for high slack water and low slack water, respectively. “−” and “+” represent for hours “before” and “after”, respectively</p> Full article ">Figure 3
<p>Hourly maps of GOCI-retrieved TSM from 08:28–15:28 (local time) in the Yalu River estuary on 3 April 2014. The graph shows the hourly tide elevation on the same day. Red lines demark the extent of the turbidity maxima zone (&gt;15 g·m<sup>−3</sup> TSM concentration). The black line represents the location of <a href="#sec1-remotesensing-08-00244" class="html-sec">Section 1</a>.</p> Full article ">Figure 4
<p>Hourly maps of GOCI-retrieved TSM from 08:28–15:28 (local time) in the Yalu River estuary on 2 August 2014. The graph shows the hourly tide elevation on the same day. Red lines demark the extent of the turbidity maxima zone (&gt;15 g·m<sup>−3</sup> TSM concentration). The black line represents the location of <a href="#sec1-remotesensing-08-00244" class="html-sec">Section 1</a>.</p> Full article ">Figure 5
<p>TSM variations at <a href="#sec1-remotesensing-08-00244" class="html-sec">Section 1</a> from (<b>a</b>) 08:28–15:28 (local time) on 9 March 2014; (<b>b</b>) TSM variations at <a href="#sec1-remotesensing-08-00244" class="html-sec">Section 1</a> from 09:28–15:28 (local time) on 26 May 2014, corresponding tidal elevation with section-averaged TSM concentration and conditions of wind and wave height (<b>c</b>) on 9 March 2014 and (<b>d</b>) on 26 May 2014. W and S represent wind speed and significant wave height, respectively.</p> Full article ">Figure 6
<p>TSM variations at <a href="#sec1-remotesensing-08-00244" class="html-sec">Section 1</a> from (<b>a</b>) 09:28–15:28 (local time) on 6 March 2014; (<b>b</b>) TSM variations at <a href="#sec1-remotesensing-08-00244" class="html-sec">Section 1</a> from 08:28–15:28 (local time) on 2 August 2014, corresponding tidal elevation with section-averaged TSM concentration and conditions of wind and wave height (<b>c</b>) on 6 March 2014 and (<b>d</b>) on 2 August 2014. W and S represent wind speed and significant wave height, respectively.</p> Full article ">Figure 7
<p>TSM variations at <a href="#sec1-remotesensing-08-00244" class="html-sec">Section 1</a> from (<b>a</b>) 08:28–14:28 (local time) on 3 May 2014; (<b>b</b>) TSM variations at <a href="#sec1-remotesensing-08-00244" class="html-sec">Section 1</a> from 09:28–15:28 (local time) on 21 March 2014, corresponding tidal elevation with section-averaged TSM concentration and conditions of wind and wave height (<b>c</b>) on 3 May 2014 and (<b>d</b>) on 21 March 2014. W and S represent wind speed and significant wave height, respectively.</p> Full article ">Figure 8
<p>Spatial distributions of daily averaged TSM on (<b>a</b>) 30 May 2014 (spring tide) and (<b>b</b>) 8 June 2014 (neap tide). SD maps of TSM on (<b>c</b>) 30 May 2014 and (<b>d</b>) 8 June 2014.</p> Full article ">Figure 9
<p>Monthly mean TSM maps retrieved from GOCI in the Yalu River estuary from January–December 2014. Red lines demark the extent of the turbidity maxima zone (&gt;15 g·m<sup>−3</sup> TSM concentration).</p> Full article ">Figure 10
<p>Daily averaged TSM retrieved from GOCI at (<b>a</b>) P1; (<b>b</b>) P4 and (<b>c</b>) P5 from April 2011–December 2014.</p> Full article ">Figure 11
<p>Spatial distributions of monthly mean TSM in (<b>a</b>) April 2014 (dry season) and (<b>b</b>) August 2014 (wet season). SD maps of TSM on cloud-free days in (<b>c</b>) April 2014 and (<b>d</b>) August 2014.</p> Full article ">Figure 12
<p>Monthly mean TSM across <a href="#sec1-remotesensing-08-00244" class="html-sec">Section 1</a> in April and August of (<b>a</b>) 2013 and (<b>b</b>) 2014. (<b>c</b>) Monthly water discharge and sediment load in the wet season of the Yalu River from 2011–2014.</p> Full article ">Figure 13
<p>Vertical distribution of salinity at Station Y03 on (<b>a</b>) 15 August 2009 during neap tide and (<b>b</b>) 9 August 2009 during spring tide.</p> Full article ">Figure 14
<p>Relationship between area-averaged GOCI-retrieved daily averaged TSM concentration and corresponding wind speed/significant wave height for all cloud-free days at P4 in 2014.</p> Full article ">
5522 KiB  
Article
Examining the Influence of Seasonality, Condition, and Species Composition on Mangrove Leaf Pigment Contents and Laboratory Based Spectroscopy Data
by Francisco Flores-de-Santiago, John M. Kovacs, Jinfei Wang, Francisco Flores-Verdugo, Chunhua Zhang and Fernando González-Farías
Remote Sens. 2016, 8(3), 226; https://doi.org/10.3390/rs8030226 - 10 Mar 2016
Cited by 28 | Viewed by 6834
Abstract
The purpose of this investigation was to determine the seasonal relationships (dry vs. rainy) between reflectance (400–1000 nm) and leaf pigment contents (chlorophyll-a (chl-a), chlorophyll-b (chl-b), total carotenoids (tcar), chlorophyll a/b ratio) in three mangrove species (Avicennia germinans (A. germinans), [...] Read more.
The purpose of this investigation was to determine the seasonal relationships (dry vs. rainy) between reflectance (400–1000 nm) and leaf pigment contents (chlorophyll-a (chl-a), chlorophyll-b (chl-b), total carotenoids (tcar), chlorophyll a/b ratio) in three mangrove species (Avicennia germinans (A. germinans), Laguncularia racemosa (L. racemosa), and Rhizophora mangle (R. mangle)) according to their condition (stressed vs. healthy). Based on a sample of 360 leaves taken from a semi-arid forest of the Mexican Pacific, it was determined that during the dry season, the stressed A. germinans and R. mangle show the highest maximum correlations at the green (550 nm) and red-edge (710 nm) wavelengths (r = 0.8 and 0.9, respectively) for both chl-a and chl-b and that much lower values (r = 0.7 and 0.8, respectively) were recorded during the rainy season. Moreover, it was found that the tcar correlation pattern across the electromagnetic spectrum was quite different from that of the chl-a, the chl-b, and chl a/b ratio but that their maximum correlations were also located at the same two wavelength ranges for both seasons. The stressed L. racemosa was the only sample to exhibit minimal correlation with chl-a and chl-b for either season. In addition, the healthy A. germinans and R. mangle depicted similar patterns of chl-a and chl-b, but the tcar varied depending on the species. The healthy L. racemosa recorded higher correlations with chl-b and tcar at the green and red-edge wavelengths during the dry season, and higher correlation with chl-a during the rainy season. Finally, the vegetation index Red Edge Inflection Point Index (REIP) was found to be the optimal index for chl-a estimation for both stressed and healthy classes. For chl-b, both the REIP and the Vogelmann Red Edge Index (Vog1) index were found to be best at prediction. Based on the results of this investigation, it is suggested that caution be taken as mangrove leaf pigment contents from spectroscopy data have been shown to be sensitive to seasonality, species, and condition. The authors suggest potential reasons for the observed variability in the reflectance and pigment contents relationships. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Study site within the Mexican Pacific. Symbols in the legend indicate sample locations.</p> Full article ">Figure 2
<p>Spectral curves of the six mangrove classes during the dry and rainy seasons based on the means and the first standard deviations: <span class="html-italic">Avicennia germinans</span> (AG), <span class="html-italic">Laguncularia racemosa</span> (LR), and <span class="html-italic">Rhizophora mangle</span> (RM). The blue line represents the <span class="html-italic">p</span>-value between the dry and rainy season at each nanometer. <span class="html-italic">p</span> = 0.05 is shown by the flat dashed line across the bottom of each graph. (<b>a</b>) AG stressed condition; (<b>b</b>) AG healthy condition; (<b>c</b>) LR stressed condition; (<b>d</b>) LR healthy condition; (<b>e</b>) RM stressed condition; and (<b>f</b>) RM healthy condition.</p> Full article ">Figure 2 Cont.
<p>Spectral curves of the six mangrove classes during the dry and rainy seasons based on the means and the first standard deviations: <span class="html-italic">Avicennia germinans</span> (AG), <span class="html-italic">Laguncularia racemosa</span> (LR), and <span class="html-italic">Rhizophora mangle</span> (RM). The blue line represents the <span class="html-italic">p</span>-value between the dry and rainy season at each nanometer. <span class="html-italic">p</span> = 0.05 is shown by the flat dashed line across the bottom of each graph. (<b>a</b>) AG stressed condition; (<b>b</b>) AG healthy condition; (<b>c</b>) LR stressed condition; (<b>d</b>) LR healthy condition; (<b>e</b>) RM stressed condition; and (<b>f</b>) RM healthy condition.</p> Full article ">Figure 3
<p>ANOVA <span class="html-italic">p</span>-plots among reflectance from the three mangrove species during the dry and rainy seasons for both stressed and healthy conditions: <span class="html-italic">Avicennia germinans</span> (AG), <span class="html-italic">Laguncularia racemosa</span> (LR), and <span class="html-italic">Rhizophora mangle</span> (RM). The flat black dashed line at the bottom of each graph represents <span class="html-italic">p</span> = 0.05. (<b>a</b>) AG, LR, and RM stressed condition; (<b>b</b>) AG, LR, and RM healthy condition.</p> Full article ">Figure 4
<p>Chlorophyll-a, chlorophyll-b, total carotenoids, and the chlorophyll a/b ratio box plots for the six classes of mangroves during the dry and rainy seasons. Each box plot depicts the mean (small square), the 25%–75% quartiles (rectangle), and the median (line dividing the box plot): LR: <span class="html-italic">Laguncularia racemosa</span>, RM: <span class="html-italic">Rhizophora mangle</span>, AG: <span class="html-italic">Avicennia germinans</span>. An asterisk indicates a seasonal significant difference at <span class="html-italic">p</span> &lt; 0.05, based on the Mann–Whitney <span class="html-italic">U</span>-test. (<b>a</b>) Chlorophyll-a stressed condition; (<b>b</b>) chlorophyll-a healthy condition; (<b>c</b>) chlorophyll-b stressed condition; (<b>d</b>) chlorophyll-b healthy condition; (<b>e</b>) total carotenoids stressed condition, (<b>f</b>)total carotenoids healthy condition; (<b>g</b>) chlorophyll a/b ratio stressed condition; (<b>h</b>) chlorophyll a/b ratio healthy condition.</p> Full article ">Figure 5
<p>Correlograms of leaf pigments and pseudo-absorption of mangroves under stress condition for the dry and rainy seasons. <span class="html-italic">Avicennia germinans</span> (AG), <span class="html-italic">Laguncularia racemosa</span> (LR), and <span class="html-italic">Rhizophora mangle</span> (RM). The horizontal lines at ±0.12 correlation indicate the 95% confidence limits. Chl-a overlaps the chl a/b ratio line. (<b>a</b>)AG dry season; (<b>b</b>) AG rainy season; (<b>c</b>) LR dry season; (<b>d</b>) LR rainy season; (<b>e</b>) RM dry season; (<b>f</b>) RM rainy season.</p> Full article ">Figure 6
<p>Correlograms of leaf pigments and pseudo-absorption of mangroves under healthy condition for the dry and rainy seasons. <span class="html-italic">Avicennia germinans</span> (AG), <span class="html-italic">Laguncularia racemosa</span> (LR), and <span class="html-italic">Rhizophora mangle</span> (RM). The horizontal lines at ±0.12 correlation indicate the 95% confidence limit. (<b>a</b>) AG dry season, (<b>b</b>) AG rainy season; (<b>c</b>) LR dry season; (<b>d</b>) LR rainy season; (<b>e</b>) RM dry season; (<b>f</b>) RM rainy season. 3.3. Estimation of Chlorophyll-a, Chlorophyll-b, and Total Carotenoids Contents Based on Selected Vegetation Indices.</p> Full article ">Figure 7
<p>Predicted <span class="html-italic">versus</span> measured chlorophyll-a content during the dry and rainy seasons based on select regression equations from <a href="#remotesensing-08-00226-t003" class="html-table">Table 3</a> and <a href="#remotesensing-08-00226-t004" class="html-table">Table 4</a>. Hollow symbols indicate stressed mangroves while filled symbols indicate healthy mangroves. The standard error of estimate (SE) is indicated for each prediction. The dashed lines represent a 1:1 relation. (<b>a</b>) Stressed condition Vog1; (<b>b</b>) REIP; and (<b>c</b>) PRI; (<b>d</b>) healthy condition Vog1; (<b>e</b>) REIP; and (<b>f</b>) PRI.</p> Full article ">Figure 8
<p>Predicted <span class="html-italic">versus</span> measured chlorophyll-b content during the dry and rainy seasons based on select regression equations from <a href="#remotesensing-08-00226-t003" class="html-table">Table 3</a> and <a href="#remotesensing-08-00226-t004" class="html-table">Table 4</a>. Hollow symbols indicate stressed mangroves while filled symbols indicate healthy mangroves. The standard error of estimate (SE) is indicated for each prediction. The dashed lines represent a 1:1 relation. (<b>a</b>) Stressed condition Vog1; (<b>b</b>) REIP; and (<b>c</b>) PRI; (<b>d</b>) healthy condition Vog1; (<b>e</b>) REIP; and (<b>f</b>) PRI.</p> Full article ">
6538 KiB  
Article
Development of a Semi-Analytical Algorithm for the Retrieval of Suspended Particulate Matter from Remote Sensing over Clear to Very Turbid Waters
by Bing Han, Hubert Loisel, Vincent Vantrepotte, Xavier Mériaux, Philippe Bryère, Sylvain Ouillon, David Dessailly, Qianguo Xing and Jianhua Zhu
Remote Sens. 2016, 8(3), 211; https://doi.org/10.3390/rs8030211 - 5 Mar 2016
Cited by 107 | Viewed by 11860
Abstract
Remote sensing of suspended particulate matter, SPM, from space has long been used to assess its spatio-temporal variability in various coastal areas. The associated algorithms were generally site specific or developed over a relatively narrow range of concentration, which make them inappropriate [...] Read more.
Remote sensing of suspended particulate matter, SPM, from space has long been used to assess its spatio-temporal variability in various coastal areas. The associated algorithms were generally site specific or developed over a relatively narrow range of concentration, which make them inappropriate for global applications (or at least over broad SPM range). In the frame of the GlobCoast project, a large in situ data set of SPM and remote sensing reflectance, Rrs(?), has been built gathering together measurements from various coastal areas around Europe, French Guiana, North Canada, Vietnam, and China. This data set covers various contrasting coastal environments diversely affected by different biogeochemical and physical processes such as sediment resuspension, phytoplankton bloom events, and rivers discharges (Amazon, Mekong, Yellow river, MacKenzie, etc.). The SPM concentration spans about four orders of magnitude, from 0.15 to 2626 g·m?3. Different empirical and semi-analytical approaches developed to assess SPM from Rrs(?) were tested over this in situ data set. As none of them provides satisfactory results over the whole SPM range, a generic semi-analytical approach has been developed. This algorithm is based on two standard semi-analytical equations calibrated for low-to-medium and highly turbid waters, respectively. A mixing law has also been developed for intermediate environments. Sources of uncertainties in SPM retrieval such as the bio-optical variability, atmospheric correction errors, and spectral bandwidth have been evaluated. The coefficients involved in these different algorithms have been calculated for ocean color (SeaWiFS, MODIS-A/T, MERIS/OLCI, VIIRS) and high spatial resolution (LandSat8-OLI, and Sentinel2-MSI) sensors. The performance of the proposed algorithm varies only slightly from one sensor to another demonstrating the great potential applicability of the proposed approach over global and contrasting coastal waters. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Flowchart of the present work. Number in each box indicates the different steps of the study, while rectangles in black stand for activities and round-corner rectangle in blue for data set.</p> Full article ">Figure 2
<p>Location of the <span class="html-italic">in situ</span> data points used for the development and validation of the different <span class="html-italic">SPM</span> algorithms. N represents the number of <span class="html-italic">in situ</span> (<span class="html-italic">R</span><sub>rs</sub>(λ), <span class="html-italic">SPM</span>) data points for each sampled region.</p> Full article ">Figure 3
<p>Frequency distribution of (<b>a</b>) <span class="html-italic">SPM</span>; (<b>b</b>) <span class="html-italic">R</span><sub>rs</sub>(670); (<b>c</b>) <span class="html-italic">b</span><sub>bp</sub>(555); and (<b>d</b>) <span class="html-italic">b</span><sub>bp</sub>(555)/<span class="html-italic">SPM</span> in terms of logarithmically equal interval. The data used for the development and validation are represented in red and blue, respectively.</p> Full article ">Figure 4
<p>Variation of (<b>a</b>) <span class="html-italic">R</span><sub>rs</sub>(490); (<b>b</b>) <span class="html-italic">R</span><sub>rs</sub>(510); (<b>c</b>) <span class="html-italic">R</span><sub>rs</sub>(555); (<b>d</b>) <span class="html-italic">R</span><sub>rs</sub>(670); (<b>e</b>) <span class="html-italic">R</span><sub>rs</sub>(765); and (<b>f</b>) <span class="html-italic">R</span><sub>rs</sub>(865) as a function of <span class="html-italic">SPM</span> in logarithmic space. The best linear regression fit is also plotted(dashed lines) in each panel.</p> Full article ">Figure 5
<p>Variation of (<b>a</b>) <span class="html-italic">R</span><sub>rs</sub>(490)/<span class="html-italic">R</span><sub>rs</sub>(555); (<b>b</b>) <span class="html-italic">R</span><sub>rs</sub>(670)/<span class="html-italic">R</span><sub>rs</sub>(555); (<b>c</b>) <span class="html-italic">R</span><sub>rs</sub>(765)/<span class="html-italic">R</span><sub>rs</sub>(555); and (<b>d</b>) <span class="html-italic">R</span><sub>rs</sub>(865)/<span class="html-italic">R</span><sub>rs</sub>(555) as a function of <span class="html-italic">SPM</span> in logarithmic space. The best linear regression fit is also plotted(dashed line) in each panel.</p> Full article ">Figure 6
<p>Comparison of the measured and inversed <span class="html-italic">SPM</span> values when the models of (<b>a</b>) <span class="html-italic">Siswanto11</span>, (<b>b</b>) <span class="html-italic">Nechad10</span>; (<b>c</b>) <span class="html-italic">Doxaran03</span>; and (<b>d</b>) <span class="html-italic">Gohin</span><span class="html-italic">05</span> are applied to the whole <span class="html-italic">R</span><sub>rs</sub> <span class="html-italic">in situ</span> data set. Solid and dashed lines represent the 1:1 and 1.5:1, 1:1.5 lines, respectively. The corresponding statistic indicators values are given in each panel. The number of data points are not always the same due to the limitation of the model (panel (<b>b</b>,<b>d</b>)) or the unavailability of the input reflectance (panel (<b>c</b>)).</p> Full article ">Figure 7
<p>Comparison of the measured and inversed <span class="html-italic">SPM</span> values when the (<b>a</b>) <span class="html-italic">EA-BR</span>; (<b>b</b>) <span class="html-italic">SAA</span>; and (<b>c</b>) <span class="html-italic">EA-MB</span> models are applied to the DS-V <span class="html-italic">in situ</span> data set. Solid lines represent 1:1 line and dashed lines represent 1.5:1, 1:1.5 lines. The corresponding statistics indicators values are illustrated in each panel.</p> Full article ">Figure 8
<p>Comparison of the measured and inversed <span class="html-italic">SPM</span> values when (<b>a</b>) <span class="html-italic">SAA</span>; and (<b>b</b>) <span class="html-italic">EA-</span><span class="html-italic">BR</span> models are applied to the DS-V <span class="html-italic">in situ</span> data set. Open, green, and pink circles stand for the version of each model developed over the Whole, High and Low <span class="html-italic">SPM</span> ranges, respectively (see <a href="#remotesensing-08-00211-t003" class="html-table">Table 3</a>). Solid lines represent 1:1 line and dashed lines represent 1.5:1, 1:1.5 lines.</p> Full article ">Figure 9
<p>(<b>a</b>) Monthly <span class="html-italic">SPM</span> image (March 2003) obtained by applying the SAA generic algorithm (Equations (13)–(15)) to the monthly mean MERIS data collected over the Amazon coastal waters. The black line on the image represents the location of the transect over which the <span class="html-italic">SPM</span> and <span class="html-italic">R</span><sub>rs</sub>(665) data are extracted; (<b>b</b>) Spatial evolution of <span class="html-italic">SPM</span> and <span class="html-italic">R</span><sub>rs</sub>(665) along the transect. The two dashed lines indicate the adopted switching thresholds.</p> Full article ">Figure 10
<p>(<b>a</b>) Repartition of the <span class="html-italic">b</span><sub>bp</sub>/<span class="html-italic">SPM</span> values when SPM is over estimated (red dots), under-estimated (green dots), well estimated (see text); (<b>b</b>) Comparison of the measured and inversed-SAA <span class="html-italic">SPM</span> values. The points belonging to each of the four defined quartile are represented as indicated. The values of the statistical indicators are provided for each quartile. Solid lines represent 1:1 line and dashed lines represent 1.5:1, 1:1.5 lines.</p> Full article ">Figure 11
<p>Comparison of the measured and inversed <span class="html-italic">SPM</span> by <span class="html-italic">SAA</span> model for the data points of the <span class="html-italic">Q1</span>, <span class="html-italic">Q23</span>, and <span class="html-italic">Q4</span> quartiles. In each panel, black circles represent the <span class="html-italic">SPM</span> values estimated using the averaged <span class="html-italic">C</span><sup>ρ</sup> value over <span class="html-italic">DS-IOP-D</span>, and red circles represent those estimated from the averaged <span class="html-italic">C</span><sup>ρ</sup> value over each quartile-averaged. The values of the statistical indicators are provided for each quartile. Solid line represent 1:1 line and dashed lines represent 1.5:1, 1:1.5 lines. (<b>a</b>) DS-IOP-VQ1; (<b>b</b>) DS-IOP-VQ23; (<b>c</b>) DS-IOP-VQ4.</p> Full article ">Figure 12
<p>Comparison between the <span class="html-italic">R</span><sub>rs</sub> values averaged over a 10nm-bandwidth, and those calculated using the Spectral Response Functions for the (<b>a</b>) SeaWiFS; (<b>b</b>) MODIS-Aqua; (<b>c</b>) MODIS-Terra; (<b>d</b>) MERIS (<b>e</b>) VIIRS and (<b>f</b>) OLI sensors for visible and near-infrared channels, as indicated. Solid lines represent the 1:1 line.</p> Full article ">
14105 KiB  
Article
Change Detection of Submerged Seagrass Biomass in Shallow Coastal Water
by Syarifuddin Misbari and Mazlan Hashim
Remote Sens. 2016, 8(3), 200; https://doi.org/10.3390/rs8030200 - 1 Mar 2016
Cited by 45 | Viewed by 11717
Abstract
Satellite remote sensing is an advanced tool used to characterize seagrass biomass and monitor changes in clear to less-turbid waters by analyzing multi-temporal satellite images. Seagrass information was extracted from the multi-temporal satellite datasets following a two-step procedure: (i) retrieval of substrate-leaving radiances; [...] Read more.
Satellite remote sensing is an advanced tool used to characterize seagrass biomass and monitor changes in clear to less-turbid waters by analyzing multi-temporal satellite images. Seagrass information was extracted from the multi-temporal satellite datasets following a two-step procedure: (i) retrieval of substrate-leaving radiances; and (ii) estimation of seagrass total aboveground biomass (STAGB). Firstly, the substrate leaving radiances is determined by compensating the water column correction of the pre-processed data because of the inherent errors associated with the geometric and radiometric fidelities including atmospheric perturbations. Secondly, the seagrass leaving radiances were correlated to the corresponding in situ STAGB to predict seagrass biomass. The relationship between STAGB and cover percentage was then established for seagrass meadows occurring in Merambong, Straits of Johor, Malaysia. By applying the above-mentioned approach on Landsat Thematic Mapper (TM) acquired in 2009 and Operational Land Imager (OLI) data acquired in 2013, the resulting maps indicated that submerged STAGB in less clear water can be successfully quantified empirically from Landsat data, and can be utilized in STAGB change detection over time. Data validation showed a good agreement between in situ STAGB and Landsat TM (R2 = 0.977, p < 0.001) and OLI (R2 = 0.975, p < 0.001) derived water leaving radiances for the studied seagrass meadows. The STAGB was estimated as 803 ± 0.47 kg in 2009, while it was 752.3 ± 0.34 kg in 2013, suggesting a decrease of 50.7 kg within the four-year interval. This could be mainly due to land reclamation in the intertidal mudflat areas performed, with a view to increase port facilities and coastal landscape development. Statistics on dugong sightings also supports changes in STAGB. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Field samples and location of Merambong shoal. Depth (blue line) is in meter.</p> Full article ">Figure 2
<p>Study area viewed from (<b>a</b>) Landsat 5 TM; and (<b>b</b>) Landsat 8 OLI (path/row: 125/59). Both the scenes are loaded in natural color composites by layer stacking bands 3, 2, 1 for TM and bands 4, 3, 2 for OLI.</p> Full article ">Figure 3
<p>Submerged STAGB quantification flowchart using BRI.</p> Full article ">Figure 4
<p>Typical mixed seagrass species on Merambong shoal during submergence seen from underwater video (<b>left</b>) and exposed (<b>right</b>).</p> Full article ">Figure 5
<p>Common seagrass species found on Merambong shoal and its vicinity.</p> Full article ">Figure 6
<p>Visual appearance of both Landsat 5 TM (<b>a</b>) and Landsat 8 OLI (<b>b</b>) before and after atmospheric and water column corrections. The RGB color composite is created by layer stacking R: band 3, G: band 2, B: band 1 for Landsat 5 TM while R: band 4, G: band 3, B: band 2 for Landsat OLI.</p> Full article ">Figure 7
<p>Submerged seagrass occurrence (green shaded areas) at the Straits of Johor in (<b>a</b>) 2009 and (<b>b</b>) 2013.</p> Full article ">Figure 8
<p>(<b>a</b>) STAGB distribution map for 2009 (<b>left</b>) and 2013 (<b>right</b>); (<b>b</b>) STAGB changes between 2009 and 2013; and (<b>c</b>) zoomed-in view of STAGB changes for Merambong shoal. * Note: Please refer legend in (<b>c</b>).</p> Full article ">Figure 8 Cont.
<p>(<b>a</b>) STAGB distribution map for 2009 (<b>left</b>) and 2013 (<b>right</b>); (<b>b</b>) STAGB changes between 2009 and 2013; and (<b>c</b>) zoomed-in view of STAGB changes for Merambong shoal. * Note: Please refer legend in (<b>c</b>).</p> Full article ">Figure 9
<p>Relationship between STAGB from ground sampling and seagrass coverage (%).</p> Full article ">Figure 10
<p>Relationship between BRI<span class="html-italic"><sub>b</sub></span> and STAGB measured empirically from satellite image after water column correction for Landsat images acquired in 2009 and 2013.</p> Full article ">Figure 11
<p><span class="html-italic">In situ</span> seagrass biomass <span class="html-italic">versus</span> satellite-based estimation at corresponding location using 20 inductive sites and 16 test sites. Each plotted marker represents selected quadrats of seagrass sampling by 0.5 × 0.5 m quadrat, upscale to be equal to 30 × 30 m Landsat pixel.</p> Full article ">Figure 12
<p>Location and frequency of dugong sighted by fishermen; where red circles represent sighting period between 1999 and before, the yellow circle for 2000–2009 shown in and green circles for 2010–2013.</p> Full article ">Figure 13
<p>The attenuation coefficient of blue, green and red TM band.</p> Full article ">Figure 13 Cont.
<p>The attenuation coefficient of blue, green and red TM band.</p> Full article ">Figure 14
<p>The attenuation coefficient of blue, green and red OLI band.</p> Full article ">Figure 14 Cont.
<p>The attenuation coefficient of blue, green and red OLI band.</p> Full article ">Figure 15
<p>The relationship between BRI<sub>b</sub> of Landsat 5 TM and <span class="html-italic">in situ</span> STAGB.</p> Full article ">Figure 16
<p>The relationship between BRI<sub>b</sub> of Landsat 8 OLI and <span class="html-italic">in situ</span> STAGB.</p> Full article ">Figure 17
<p>BRI exponential relationships to various water depths (<span class="html-italic">n</span> = 45) in both Landsat images, L8-OLI (I) and L5-TM (II), and Seagrass mostly live in ≤5 m in this area. Thus, BRI range for seagrass can be seen through this figure. Similar to <a href="#remotesensing-08-00200-t009" class="html-table">Table 9</a>, in this range, low BRI indicates high STAGB, and <span class="html-italic">vice versa</span>. Muddy and sandy flat surface represented by very low BRI range (≤2 for TM, ≤5 for OLI). The middle range consists of shallow substrates heterogeneity at rocky area including submerged seaweed and rocks of different shapes and sizes, confirmed using underwater video in the identification of sea bottom features.</p> Full article ">
7882 KiB  
Article
Fuzzy Classification for Shoreline Change Monitoring in a Part of the Northern Coastal Area of Java, Indonesia
by Ratna Sari Dewi, Wietske Bijker, Alfred Stein and Muh Aris Marfai
Remote Sens. 2016, 8(3), 190; https://doi.org/10.3390/rs8030190 - 27 Feb 2016
Cited by 44 | Viewed by 10061
Abstract
This study presents an unsupervised fuzzy c-means classification (FCM) to observe the shoreline positions. We combined crisp and fuzzy methods for change detection. We addressed two perspectives of uncertainty: (1) uncertainty that is inherent to shoreline positions as observed from remote sensing images [...] Read more.
This study presents an unsupervised fuzzy c-means classification (FCM) to observe the shoreline positions. We combined crisp and fuzzy methods for change detection. We addressed two perspectives of uncertainty: (1) uncertainty that is inherent to shoreline positions as observed from remote sensing images due to its continuous variation over time; and (2) the uncertainty of the change results propagating from object extraction and implementation of shoreline change detection method. Unsupervised FCM achieved the highest kappa (?) value when threshold (t) was set at 0.5. The highest ? values were 0.96 for the 1994 image. For images in 2013, 2014 and 2015, the ? values were 0.95. Further, images in 2003, 2002 and 2000 obtained 0.93, 0.90 and 0.86, respectively. Gradual and abrupt changes were observed, as well as a measure of change uncertainty for the observed objects at the pixel level. These could be associated with inundations from 1994 to 2015 at the northern coastal area of Java, Indonesia. The largest coastal inundations in terms of area occurred between 1994 and 2000, when 739 ha changed from non-water and shoreline to water and in 2003–2013 for 200 ha. Changes from water and shoreline to non-water occurred between 2000 and 2002 (186 ha) and in 2013–2014 (65 ha). Urban development in flood-prone areas resulted in an increase of flood hazards including inundation and erosion leading to the changes of shoreline position. The proposed methods provided an effective way to present shoreline as a line and as a margin with fuzzy boundary and its associated change uncertainty. Shoreline mapping and monitoring is crucial to understand the spatial distribution of coastal inundation including its trend. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The study area in Sayung sub-district, Central Java Province covering four coastal villages. The RGB 532 of Landsat image 2015 is displayed as the background. A severe coastal inundation was reported leading to a large shoreline change.</p> Full article ">Figure 2
<p>Some examples of the impact of coastal inundation: (<b>a</b>) daily floods at the house yard; (<b>b</b>) an abandoned fish landing facility (the red dashed line shows the previous shoreline), (<b>c</b>) permanent inundation of several houses.</p> Full article ">Figure 3
<p>(<b>a</b>–<b>d</b>) The comparison of normal (<b>a</b>,<b>c</b>) and flooded (<b>b</b>,<b>d</b>) situations due to coastal inundation at two locations at Sayung sub-district. Over a longer period, this cyclic flood leads to a permanent inundation.</p> Full article ">Figure 4
<p>Trapezoidal membership function. Area between <span class="html-italic">b</span> and <span class="html-italic">c</span> is a core zone which has a membership value equal to 1 to the <span class="html-italic">water</span> class. Area <span class="html-italic">a-b</span> and <span class="html-italic">c-d</span> are transition zones or boundaries which have value between 0 and 1 to the <span class="html-italic">water</span> class, while the pixels with 0 memberships do not belong to the <span class="html-italic">water</span> class.</p> Full article ">Figure 5
<p>(<b>a</b>–<b>h</b>) Topological relationships between two sub-areas. Green polygons represent sub-area <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>T</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> and blue polygons represent sub-area <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>T</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics> </math>.</p> Full article ">Figure 6
<p>(<b>a</b>) Shoreline at time <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mn>1</mn> </msub> </mrow> </semantics> </math>; (<b>b</b>) Shoreline at time <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mn>2</mn> </msub> </mrow> </semantics> </math> ; (<b>c</b>) Shoreline change estimation considering two categories of changed areas, namely: (A) <span class="html-italic">water</span> to <span class="html-italic">non-water</span>, and (B) <span class="html-italic">non-water</span> to <span class="html-italic">water</span>. Solid lines represent shoreline at <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mn>1</mn> </msub> </mrow> </semantics> </math> whereas dashed lines refer to shoreline at <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mn>2</mn> </msub> </mrow> </semantics> </math> .</p> Full article ">Figure 7
<p>(<b>a</b>) <span class="html-italic">Shoreline</span> margin at time <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mn>1</mn> </msub> </mrow> </semantics> </math>; (<b>b</b>) <span class="html-italic">Shoreline</span> margin at time <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mn>2</mn> </msub> </mrow> </semantics> </math> ; (<b>c</b>) Shoreline change estimation considering six changed areas, namely: (A) <span class="html-italic">shoreline</span> to <span class="html-italic">non-water</span>, (B) <span class="html-italic">water</span> to <span class="html-italic">shoreline</span>, (C) <span class="html-italic">water</span> to <span class="html-italic">non-water</span>, (D) <span class="html-italic">non-water</span> to <span class="html-italic">shoreline</span>, (E) <span class="html-italic">shoreline</span> to <span class="html-italic">water</span>, and (F) <span class="html-italic">non-water</span> to <span class="html-italic">water</span>. Solid lines represent <span class="html-italic">shoreline</span> margins at <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mn>1</mn> </msub> </mrow> </semantics> </math> whereas dashed lines refer to <span class="html-italic">shoreline</span> margins at <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mn>2</mn> </msub> </mrow> </semantics> </math> .</p> Full article ">Figure 8
<p>The accuracy assessment results of water class images, generated by applying FCM classification followed by thresholding on the water membership image. The highest kappa (<span class="html-italic">κ</span>) values were obtained from <span class="html-italic">t =</span> 0.5 for all images, and <span class="html-italic">t =</span> 0.3 and 0.7 gave a nearly constant <span class="html-italic">κ</span> value.</p> Full article ">Figure 9
<p>(<b>a</b>–<b>n</b>) FCM results show the membership of water class (<b>a</b>,c,<b>e</b>,<b>g</b>,<b>i</b>,<b>k</b>,<b>m</b>), and classified images of water class by setting <span class="html-italic">t =</span> 0.5 (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>,<b>j</b>,<b>l</b>,<b>n</b>). The shrinking of <span class="html-italic">non-water</span> sub-areas over two decades can be identified by the change of the shape of the <span class="html-italic">non-water</span> class from wide strips to the thin elongated shapes over the series of images (see (<b>a</b>–<b>n</b>); e.g., grid cells C3). Whereas <span class="html-italic">non-water</span> sub-areas emerged when mangroves were planted (see (i) grid cells C2), and in coastal reclamation areas (see (<b>a</b>,<b>c</b>) grid cells A5).</p> Full article ">Figure 10
<p>(<b>a</b>–<b>d</b>) The illustration of shoreline as a line; (<b>a</b>) Shorelines (in red colour) created by setting t = 0.5; (<b>b</b>) the uncertainty of pixels classified as <span class="html-italic">water</span> at the uncertainty level ≤0.5. Generally, pixels closer to the shoreline have a higher uncertainty value (see (<b>d</b>) grid cells C2 and D2).</p> Full article ">Figure 11
<p>The illustration of shoreline as a margin; (<b>a</b>) <span class="html-italic">Shoreline</span> margin (blue polygons) generated by giving <span class="html-italic">t =</span> 0.3 and 0.7<math display="inline"> <semantics> <mo>;</mo> </semantics> </math> (<b>b</b>) the uncertainty of <span class="html-italic">shoreline</span> margin from Equation (12); (<b>c</b>) zooming in sub-areas in yellow rectangle based on <a href="#remotesensing-08-00190-f011" class="html-fig">Figure 11</a>a. <span class="html-italic">Shoreline</span> margin was assessed through different levels of uncertainty (<math display="inline"> <semantics> <mrow> <msub> <mi>U</mi> <mi>C</mi> </msub> </mrow> </semantics> </math> ): (<b>d</b>) ≤0.1; (<b>e</b>) ≤0.2; (<b>f</b>) ≤0.3; and (<b>g</b>) ≤0.4.</p> Full article ">Figure 12
<p>(<b>a</b>–<b>f</b>) Shoreline change analysis at <span class="html-italic">t =</span> 0.5. Two changes were identified, namely <span class="html-italic">non-water</span> to <span class="html-italic">water</span> and <span class="html-italic">water</span> to <span class="html-italic">non-water</span>. Large areas changed from <span class="html-italic">non-water</span> to <span class="html-italic">water</span> such as due to inundation and erosion which were indicated between 1994 and 2000 (<b>a</b>). Whereas large areas changed from <span class="html-italic">water</span> to <span class="html-italic">non-water</span> and were distinguished between 2000 and 2002 (<b>b</b>).</p> Full article ">Figure 13
<p>(<b>a</b>) Shoreline change uncertainty at <span class="html-italic">t =</span> 0.5; (<b>b</b>–<b>f</b>) Change uncertainty is highlighted at different levels for the period 1994–2000 for the yellow rectangle site. The number of red pixels indicates that the change uncertainty from <span class="html-italic">water</span> to <span class="html-italic">non-water</span> increase with the increase of uncertainty values, as also can be seen for the blue pixels.</p> Full article ">Figure 14
<p>(<b>a</b>–<b>f</b>) Shoreline change uncertainty at <span class="html-italic">t =</span> 0.5 and <span class="html-italic">CU ≤</span> 0.1 for the period 1994–2015. The extensive inundation has been indicated from 1994 to 2000 (<b>a</b>) and the largest change to <span class="html-italic">non-water</span> occurred in the period 2000–2002 (<b>b</b>).</p> Full article ">Figure 15
<p>(<b>a</b>–<b>f</b>) The changes of <span class="html-italic">shoreline</span> margin, <span class="html-italic">water</span> and <span class="html-italic">non-water</span>. Six changes were identified including abrupt and gradual changes. An extensive inundation has been indicated from 1994 to 2000 (<b>a</b>), while the large change to <span class="html-italic">non-water</span> occurred in the period 2000–2002 (<b>b</b>).</p> Full article ">Figure 16
<p>(<b>a</b>) Shoreline change uncertainty for the period 1994–2000; (<b>b</b>–<b>f</b>) Change uncertainty was measured at different levels for yellow rectangle site. A number of pixels (red, green, and blue) increase with the increase in the level of uncertainty. Changes from <span class="html-italic">non-water</span> to <span class="html-italic">shoreline</span> and from <span class="html-italic">water</span> to <span class="html-italic">shoreline</span> were grouped under one label and are presented in shades of green, while changes from <span class="html-italic">shoreline</span> and <span class="html-italic">water</span> to <span class="html-italic">non-water</span> are presented in shades of red. Changes from <span class="html-italic">non-water</span> and <span class="html-italic">shoreline</span> to <span class="html-italic">water</span> are represented as shades of blue.</p> Full article ">Figure 17
<p>(<b>a</b>–<b>f</b>) Change uncertainty of <span class="html-italic">shoreline</span> margins and their associated sub-areas at <span class="html-italic">CU</span> level ≤ 0.1 in the period 1994–2015. (<b>a</b>) The largest coastal inundation occurred in the period 1994–2000. It was dominated by light blue pixels indicated low change uncertainty values to <span class="html-italic">water</span>; (<b>b</b>) The largest increase in <span class="html-italic">non-water</span> occurred in the period 2000–2002 represented by light red pixels indicated low change uncertainty to <span class="html-italic">non-water</span>.</p> Full article ">
2495 KiB  
Article
Automatic Sun Glint Removal of Multispectral High-Resolution Worldview-2 Imagery for Retrieving Coastal Shallow Water Parameters
by Javier Martin, Francisco Eugenio, Javier Marcello and Anabella Medina
Remote Sens. 2016, 8(1), 37; https://doi.org/10.3390/rs8010037 - 5 Jan 2016
Cited by 55 | Viewed by 12035
Abstract
Remote sensing of coastal areas requires multispectral satellite images with a high spatial resolution. In this sense, WorldView-2 is a very high resolution satellite, which provides an advanced multispectral sensor with eight narrow bands, allowing the proliferation of new environmental monitoring and mapping [...] Read more.
Remote sensing of coastal areas requires multispectral satellite images with a high spatial resolution. In this sense, WorldView-2 is a very high resolution satellite, which provides an advanced multispectral sensor with eight narrow bands, allowing the proliferation of new environmental monitoring and mapping applications in shallow coastal ecosystems. These challenges need the accurate determination of the water radiance, which is not often valued compared to other sources such as atmosphere and specular water reflection (sun glint). In this context, the atmospheric correction and the glinting removal have demonstrated to be critical steps in the preprocessing chain of high resolution images. In this work, the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) is used to compensate the atmospheric effects and to compute part of the deglinting algorithm using the modeled direct normalized irradiance. This paper describes a novel automatic deglinting procedure, integrated in the Radiative Transfer Modeling (RTM) inversion of the shallow water environments, which allows computing the water Inherent Optical Properties (IOPs), bathymetry and seafloor albedo contributions. The proposed methodology has demonstrated a proper performance for environmental monitoring in shallow water areas. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
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Graphical abstract

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Full article ">Figure 1
<p>Schematic procedure of the proposed multispectral high resolution WorldView-2 processing chain for shallow coastal waters applications.</p> Full article ">Figure 2
<p>(<b>a</b>) Location of study area (The Canary Islands); (<b>b</b>,<b>c</b>) WV-2 images of two Canary Islands singular littoral zones: (<b>b</b>) Maspalomas (Gran Canaria Island, 11 August 2013) area; and (<b>c</b>) Corralejo-Lobo Island (Fuerteventura Island, 28 October 2010) area.</p> Full article ">Figure 3
<p>Linear fitting of the green (R3) and NIR1 (R7) WV-2 bands.</p> Full article ">Figure 4
<p>Simplified schematic diagram showing routes by which light reaches a remote sensing detector.</p> Full article ">Figure 5
<p>Specular reflection geometry on a flat surface.</p> Full article ">Figure 6
<p>Slope <span class="html-italic">b<sub>i</sub></span>, obtained by linear fitting (<b>blue</b>); and optical-band/NIR direct irradiance ratio (<b>red</b>).</p> Full article ">Figure 7
<p>(<b>a</b>) WV-2 image of Maspalomas before deglinting removal; (<b>b</b>) image after deglinting.</p> Full article ">Figure 8
<p>(<b>a</b>) <span class="html-italic">In situ</span> <span class="html-italic">Chl-a</span> samples in Maspalomas inner lake; (<b>b</b>) <span class="html-italic">Chl-a</span> concentration map by using classical + RTE deglinting algorithm; (<b>c</b>) <span class="html-italic">Chl-a</span> concentration map by using developed deglinting algorithm.</p> Full article ">Figure 8 Cont.
<p>(<b>a</b>) <span class="html-italic">In situ</span> <span class="html-italic">Chl-a</span> samples in Maspalomas inner lake; (<b>b</b>) <span class="html-italic">Chl-a</span> concentration map by using classical + RTE deglinting algorithm; (<b>c</b>) <span class="html-italic">Chl-a</span> concentration map by using developed deglinting algorithm.</p> Full article ">Figure 9
<p>Corralejo and the Lobo Island (Fuerteventura Island), biosphere reserve and natural protected area: (<b>a</b>) <span class="html-italic">k<sub>d</sub></span>(<span class="html-italic">490</span>) coefficient map; and (<b>b</b>) color composite of the seafloor albedo obtained using the new deglinting integrated in the RTE inversion algorithm.</p> Full article ">Figure 10
<p>Maspalomas coastal shallow water bathymetry: (<b>a</b>) Sonar bathymetry; and (<b>b</b>) bathymetry map by using the new deglinting algorithm integrated in the RTE model.</p> Full article ">Figure 10 Cont.
<p>Maspalomas coastal shallow water bathymetry: (<b>a</b>) Sonar bathymetry; and (<b>b</b>) bathymetry map by using the new deglinting algorithm integrated in the RTE model.</p> Full article ">
5208 KiB  
Article
Bottom Reflectance in Ocean Color Satellite Remote Sensing for Coral Reef Environments
by Martina Reichstetter, Peter R. C. S. Fearns, Scarla J. Weeks, Lachlan I. W. McKinna, Chris Roelfsema and Miles Furnas
Remote Sens. 2015, 7(12), 16756-16777; https://doi.org/10.3390/rs71215852 - 9 Dec 2015
Cited by 25 | Viewed by 9505
Abstract
Most ocean color algorithms are designed for optically deep waters, where the seafloor has little or no effect on remote sensing reflectance. This can lead to inaccurate retrievals of inherent optical properties (IOPs) in optically shallow water environments. Here, we investigate in situ [...] Read more.
Most ocean color algorithms are designed for optically deep waters, where the seafloor has little or no effect on remote sensing reflectance. This can lead to inaccurate retrievals of inherent optical properties (IOPs) in optically shallow water environments. Here, we investigate in situ hyperspectral bottom reflectance signatures and their separability for coral reef waters, when observed at the spectral resolutions of MODIS and SeaWiFS sensors. We use radiative transfer modeling to calculate the effects of bottom reflectance on the remote sensing reflectance signal, and assess detectability and discrimination of common coral reef bottom classes by clustering modeled remote sensing reflectance signals. We assess 8280 scenarios, including four IOPs, 23 depths and 45 bottom classes at MODIS and SeaWiFS bands. Our results show: (i) no significant contamination (Rrscorr < 0.0005) of bottom reflectance on the spectrally-averaged remote sensing reflectance signal at depths >17 m for MODIS and >19 m for SeaWiFS for the brightest spectral reflectance substrate (light sand) in clear reef waters; and (ii) bottom cover classes can be combined into two distinct groups, “light” and “dark”, based on the modeled surface reflectance signals. This study establishes that it is possible to efficiently improve parameterization of bottom reflectance and water-column IOP retrievals in shallow water ocean color models for coral reef environments. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Flowchart showing an overview of the input variables for the radiative transfer modeling framework used to conduct a hierarchical analysis of the class spectral separability of common bottom types. The Hydrolight model scenario setup is further described in <a href="#remotesensing-07-15852-t002" class="html-table">Table 2</a>.</p> Full article ">Figure 2
<p>Map showing sampling locations for the four inherent optical property scenarios used: Coastal, Estuarine, Lagoonal and Reef Waters of the Great Barrier Reef (adapted from Blondeau-Patissier <span class="html-italic">et al.</span> [<a href="#B36-remotesensing-07-15852" class="html-bibr">36</a>]).</p> Full article ">Figure 3
<p><span class="html-italic">In situ</span> reflectances for the eight pure endmember bottom types used in this study. Each line represents a sub-sample spectrum for the respective bottom type category.</p> Full article ">Figure 4
<p>Silhouette plots for Reef Waters at 5 m geometric depth using MODIS bands. Each cluster is represented by a different color (Cluster-1 (C1)-Black, Cluster-2 (C2)-Grey, Cluster-3 (C3)-Green, Cluster-4 (C4)-Blue and Cluster-5 (C5)-Red). The cluster statistics represent the number of bottom spectra assigned to each cluster, followed by the cluster silhouette width. Misclassified spectra are counted toward the cluster they are assigned to but represented as negative, hence to the left of the graphics.</p> Full article ">Figure 5
<p>Silhouette plots for Reef Waters at 5 m geometric depth at SeaWiFS bands. Each cluster is represented by a different color (Cluster-1 (C1)-Black, Cluster-2 (C2)-Grey, Cluster-3 (C3)-Green, Cluster-4 (C4)-Blue and Cluster-5 (C5)-Red). The cluster statistics represent the number of bottom spectra assigned to each cluster, followed by the cluster silhouette width. Misclassified spectra are counted toward the cluster they are assigned to but represented as negative, hence to the left of the graphics.</p> Full article ">Figure 6
<p>Maximum depth of detectability for light sand and seagrass under four different optical domain scenarios: Estuarine, Lagoonal, Coastal and Reef waters for depths assessed between 5 m and 49 m.</p> Full article ">Figure 7
<p>Water column-corrected (a black bottom scenario was subtracted from the model run), average surface reflectance signals for two extremes of substrate brightness: light sand (<b>left panel</b>) and seagrass (<b>right panel</b>) for the four optical water property scenarios for SeaWiFS and MODIS sensors. For light sand, the <span class="html-italic">R<sub>rscorr</sub></span> values for the Estuarine and Coastal scenarios are close to zero even at shallow depths, while for seagrass, <span class="html-italic">R<sub>rscorr</sub></span> values are close to zero at all depths for the Estuarine, Coastal and Lagoonal scenarios.</p> Full article ">Figure 8
<p>Endmember and average spectra for the “light” MODIS cluster and the light spectral signature from <span class="html-italic">McKinna et al.</span> [<a href="#B17-remotesensing-07-15852" class="html-bibr">17</a>].</p> Full article ">Figure 9
<p>Endmember and average spectra for the “dark” MODIS and SeaWiFS cluster and the dark spectral signature from <span class="html-italic">McKinna et al.</span> [<a href="#B17-remotesensing-07-15852" class="html-bibr">17</a>].</p> Full article ">Figure 10
<p>Endmember and average spectra for the “light” SeaWiFS cluster and the light spectral signature from <span class="html-italic">McKinna et al.</span> [<a href="#B17-remotesensing-07-15852" class="html-bibr">17</a>].</p> Full article ">
3005 KiB  
Article
A Hybrid Model for Mapping Relative Differences in Belowground Biomass and Root: Shoot Ratios Using Spectral Reflectance, Foliar N and Plant Biophysical Data within Coastal Marsh
by Jessica L. O’Connell, Kristin B. Byrd and Maggi Kelly
Remote Sens. 2015, 7(12), 16480-16503; https://doi.org/10.3390/rs71215837 - 5 Dec 2015
Cited by 23 | Viewed by 8543
Abstract
Broad-scale estimates of belowground biomass are needed to understand wetland resiliency and C and N cycling, but these estimates are difficult to obtain because root:shoot ratios vary considerably both within and between species. We used remotely-sensed estimates of two aboveground plant characteristics, aboveground [...] Read more.
Broad-scale estimates of belowground biomass are needed to understand wetland resiliency and C and N cycling, but these estimates are difficult to obtain because root:shoot ratios vary considerably both within and between species. We used remotely-sensed estimates of two aboveground plant characteristics, aboveground biomass and % foliar N to explore biomass allocation in low diversity freshwater impounded peatlands (Sacramento-San Joaquin River Delta, CA, USA). We developed a hybrid modeling approach to relate remotely-sensed estimates of % foliar N (a surrogate for environmental N and plant available nutrients) and aboveground biomass to field-measured belowground biomass for species specific and mixed species models. We estimated up to 90% of variation in foliar N concentration using partial least squares (PLS) regression of full-spectrum field spectrometer reflectance data. Landsat 7 reflectance data explained up to 70% of % foliar N and 67% of aboveground biomass. Spectrally estimated foliar N or aboveground biomass had negative relationships with belowground biomass and root:shoot ratio in both Schoenoplectus acutus and Typha, consistent with a balanced growth model, which suggests plants only allocate growth belowground when additional nutrients are necessary to support shoot development. Hybrid models explained up to 76% of variation in belowground biomass and 86% of variation in root:shoot ratio. Our modeling approach provides a method for developing maps of spatial variation in wetland belowground biomass. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
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Full article ">Figure 1
<p>The Sacramento-San Joaquin Delta, CA, USA, showing locations of studied wetland impoundments. Insets show biomass and foliar N plots within study sites, superimposed over a Landsat 7 false color (Near infrared, Red and Green bands shown as RGB channels). Symbols indicate average water depth (cm) and relative percent cover of <span class="html-italic">Typha</span> spp. <span class="html-italic">vs. S. acutus</span> as measured overtime during our study.</p> Full article ">Figure 2
<p>Model building process for estimating belowground biomass (BG) and root:shoot ratio (RS) from spectral reflectance (λ) estimates of aboveground biomass (AG) and foliar nitrogen (N). Foliar N and aboveground biomass models were built separately for flooded and non-flooded plots and the best estimates for each plot were joined back into a single dataset. Prediction of belowground biomass or root:shoot ratio was conducted at the plot scale only. * indicates spectrally derived estimates and quantities without asterisks are field derived estimates. Model numbers refer to those listed in <a href="#remotesensing-07-15837-t002" class="html-table">Table 2</a>.</p> Full article ">Figure 3
<p>Spectral signature of <span class="html-italic">Typha</span> spp. and <span class="html-italic">S. acutus</span> (all water depths) (<b>A</b>) and of flooded and unflooded plots (both species combined) (<b>B</b>) from freshwater marsh impoundments in the Sacramento-San Joaquin Delta, CA, USA. Gaps indicate areas of noise in the spectra that were removed during pre-processing.</p> Full article ">Figure 4
<p>Loadings values of % foliar N from PLS regression of hyperspectral data for (<b>A</b>) <span class="html-italic">Typha</span> spp. and (<b>B</b>) <span class="html-italic">S. acutus</span> (model set S1). Gaps indicate spectra removed during pre-processing. The mid-point of Landsat 7 bands are superimposed and labeled with band number (b1-7). (<b>C</b>–<b>F</b>) Measured <span class="html-italic">vs.</span> predicted values for training and testing data for <span class="html-italic">Typha</span> and <span class="html-italic">S. acutus</span>.</p> Full article ">Figure 5
<p>Measured <span class="html-italic">vs.</span> % foliar N developed from PLS regression of Landsat 7 for training (<b>A</b>,<b>B</b>) and testing (<b>C</b>,<b>D</b>) data for <span class="html-italic">Typha</span> spp. and for <span class="html-italic">S. acutus</span> (model set S2). Percent foliar N is in g N per 100 g dry leaf tissue.</p> Full article ">Figure 6
<p>(<b>Left</b>) <span class="html-italic">Typha</span> spp. best belowground biomass (g·m<sup>−2</sup>) model (model H4b, % foliar N (<b>A</b>) is the predictor). (<b>Right</b>) <span class="html-italic">S. acutus</span> best belowground biomass (g·m<sup>−2</sup>) model (model H2c, aboveground biomass (<b>B</b>) is the predictor). (<b>C</b>,<b>D</b>) the measured <span class="html-italic">vs.</span> predicted belowground biomass outcome from the best models for <span class="html-italic">Typha</span> spp. and <span class="html-italic">S. acutus</span> respectively. RMSEP is root mean squared error of prediction in g·m<sup>−2</sup>, while nRMSEP is normalized RMSEP.</p> Full article ">Figure 7
<p>Best root:shoot ratio models for <span class="html-italic">Typha</span> (model H4b) (<b>A</b>) and <span class="html-italic">S. acutus</span> (model H6c) (<b>B</b>). The measured <span class="html-italic">vs.</span> predicted outcomes from the best models for <span class="html-italic">Typha</span> (<b>C</b>) and <span class="html-italic">S. acutus</span> (<b>D</b>). RMSEP is root mean squared error of prediction, nRMSEP is normalized RMSEP.</p> Full article ">
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