Remote Sensing

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19 KiB

Open AccessEditorial

Ecological Status and Change by Remote Sensing

by Duccio Rocchini

Remote Sens. 2010, 2(10), 2424-2425; https://doi.org/10.3390/rs2102424 - 19 Oct 2010

Cited by 4 | Viewed by 8354

Abstract

Evaluating ecological patterns and processes is crucial for the conservation of ecosystems [1]. In this view, remote sensing is a powerful tool for monitoring their status and change. This involves several tasks like biodiversity estimate, landscape ecology, and species distribution modeling, to name [...] Read more.

Evaluating ecological patterns and processes is crucial for the conservation of ecosystems [1]. In this view, remote sensing is a powerful tool for monitoring their status and change. This involves several tasks like biodiversity estimate, landscape ecology, and species distribution modeling, to name a few [2]. Due to the difficulties associated with field-based data collection [3], the use of remote sensing for estimating ecological status and change is promising since it provides a synoptic view of an area with a high temporal resolution [4]. Of course in some cases remote sensing should be viewed as a help to plan a field survey rather than a replacement of it. Further, its improper use may lead to pitfalls and misleading results. [...] Full article

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

Research

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724 KiB

Open AccessArticle

Investigation on the Patterns of Global Vegetation Change Using a Satellite-Sensed Vegetation Index

by Ainong Li, Wei Deng, Shunlin Liang and Chengquan Huang

Remote Sens. 2010, 2(6), 1530-1548; https://doi.org/10.3390/rs2061530 - 3 Jun 2010

Cited by 16 | Viewed by 10728

Abstract

The pattern of vegetation change in response to global change still remains a controversial issue. A Normalized Difference Vegetation Index (NDVI) dataset compiled by the Global Inventory Modeling and Mapping Studies (GIMMS) was used for analysis. For the period 1982–2006, GIMMS-NDVI analysis indicated [...] Read more.

The pattern of vegetation change in response to global change still remains a controversial issue. A Normalized Difference Vegetation Index (NDVI) dataset compiled by the Global Inventory Modeling and Mapping Studies (GIMMS) was used for analysis. For the period 1982–2006, GIMMS-NDVI analysis indicated that monthly NDVI changes show homogenous trends in middle and high latitude areas in the northern hemisphere and within, or near, the Tropic of Cancer and Capricorn; with obvious spatio-temporal heterogeneity on a global scale over the past two decades. The former areas featured increasing vegetation activity during growth seasons, and the latter areas experienced an even greater amplitude in places where precipitation is adequate. The discussion suggests that one should be cautious of using the NDVI time-series to analyze local vegetation dynamics because of its coarse resolution and uncertainties. Full article

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

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Figure 1
The GIMMS-NDVI temporal profiles for several typical vegetation classes in the north mid-high latitude area. Noise points, original profiles, and smoothed profiles from the S-G filter are shown as examples. Full article ">Figure 2
Overall data flow and processes of EOF analysis. Full article ">Figure 3
Spectrum of the covariance matrix of monthly global NDVI anomalies, and the accumulated percentage of Eigenvalue. Vertical bars show the standard deviation of the percentage Eigenvalue over 12 months (January-December) by EOF analysis. The Eigenvalue percentage is the ratio of an individual Eigen-value to the sum of all Eigenvalues. Full article ">Figure 4
The spatial patterns of the 10t ten principal components linear composition of monthly global NDVI anomaly transformation during 1982–2006 by EOF method, showing global vegetation total changing patterns and trends. The obvious change tendency is represented by the colors or in black. Full article ">Figure 4 Cont.
The spatial patterns of the 10t ten principal components linear composition of monthly global NDVI anomaly transformation during 1982–2006 by EOF method, showing global vegetation total changing patterns and trends. The obvious change tendency is represented by the colors or in black. Full article ">Figure 4 Cont.
The spatial patterns of the 10t ten principal components linear composition of monthly global NDVI anomaly transformation during 1982–2006 by EOF method, showing global vegetation total changing patterns and trends. The obvious change tendency is represented by the colors or in black. Full article ">Figure 4 Cont.
The spatial patterns of the 10t ten principal components linear composition of monthly global NDVI anomaly transformation during 1982–2006 by EOF method, showing global vegetation total changing patterns and trends. The obvious change tendency is represented by the colors or in black. Full article ">

1034 KiB

Open AccessArticle

Mapping Bush Encroaching Species by Seasonal Differences in Hyperspectral Imagery

by Jens Oldeland, Wouter Dorigo, Dirk Wesuls and Norbert Jürgens

Remote Sens. 2010, 2(6), 1416-1438; https://doi.org/10.3390/rs2061416 - 27 May 2010

Cited by 54 | Viewed by 14040

Abstract

Bush encroachment is a form of land degradation prominent worldwide, but particularly present in semi-arid areas. In this study, we mapped the spatial distribution of the two encroacher species, Acacia mellifera and Acacia reficiens,in Central Namibia, based on their different phenological behavior. [...] Read more.

Bush encroachment is a form of land degradation prominent worldwide, but particularly present in semi-arid areas. In this study, we mapped the spatial distribution of the two encroacher species, Acacia mellifera and Acacia reficiens,in Central Namibia, based on their different phenological behavior. We used constrained principal curves to extract a one dimensional gradient of phenological change from two hyperspectral images taken in different seasons. Field measurements of species composition and cover values were statistically related to bi-temporal differences in hyperspectral vegetation indices in a direct gradient analysis. The extracted gradient reflected the relationship between species composition and cover values, and the phenological pattern as captured by the image data. Cover values of four dominant plant species were mapped and species responses along the phenological gradient were interpreted. Full article

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

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Graphical abstract

Graphical abstract
Full article ">Figure 1
Localization of the study area on the Omatako Farm in Central Namibia on top of a vegetation map by Giess [<a href="#B26-remotesensing-02-01416" class="html-bibr">26</a>]. Full article ">Figure 2
HyMap images and photos from the study area showing the differences between rainy and dry season in the study area. (a) Rainy season image (April, 2004, CIR). (b) Dry season image (October, 2005, CIR). (c) Rainy season vegetation aspect (d) Dry season vegetation aspect. Full article ">Figure 3
The constrained principal curve, shown in red, is fitted to the initial projection of vegetation samples based on a NMDS using Bray Curtis distance explaining 26% of the variation in the species composition. Green lines represent projection-vectors that connect data points with their respective curve locations. The zero marks the starting point of the principal curve, i.e., the left-hand side, while the 2.5 indicates its end, i.e., the right-hand side of the curve. Full article ">Figure 4
Hellinger transformed cover values of four dominant species plotted for each location on the curve. The red line represents the smoothing spline used to fit the individual response shape. Full article ">Figure 5
Predicted image of curve locations. Continuous colors from black to red represent the phenological change gradient from left to the right hand side of the curve. Full article ">Figure 6
Predicted species cover maps of Hellinger-transformed cover values. Included are the positions of the permanent plot monitored by the BIOTA-Africa project, the source of the external validation dataset. Observatory marks the total covered area of 1 km2 by the monitoring design. Images are stretched between minimum and maximum values. Full article ">

1401 KiB

Open AccessArticle

Forecasting Areas Vulnerable to Forest Conversion in the Tam Dao National Park Region, Vietnam

by Duong Dang Khoi and Yuji Murayama

Remote Sens. 2010, 2(5), 1249-1272; https://doi.org/10.3390/rs2051249 - 30 Apr 2010

Cited by 67 | Viewed by 13172

Abstract

Tam Dao National Park (TDNP) is a remaining primary forest that supports some of the highest levels of biodiversity in Vietnam. Forest conversion due to illegal logging and agricultural expansion is a major problem that is hampering biodiversity conservation efforts in the TDNP [...] Read more.

Tam Dao National Park (TDNP) is a remaining primary forest that supports some of the highest levels of biodiversity in Vietnam. Forest conversion due to illegal logging and agricultural expansion is a major problem that is hampering biodiversity conservation efforts in the TDNP region. Yet, areas vulnerable to forest conversion are unknown. In this paper, we predicted areas vulnerable to forest changes in the TDNP region using multi-temporal remote sensing data and a multi-layer perceptron neural network (MLPNN) with a Markov chain model (MLPNN-M). The MLPNN-M model predicted increasing pressure in the remaining primary forest within the park as well as on the secondary forest in the surrounding areas. The primary forest is predicted to decrease from 18.03% in 2007 to 15.10% in 2014 and 12.66% in 2021. Our results can be used to prioritize locations for future biodiversity conservation and forest management efforts. The combined use of remote sensing and spatial modeling techniques provides an effective tool for monitoring the remaining forests in the TDNP region. Full article

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

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6930 KiB

Open AccessArticle

Remote Sensing of Vegetation Structure Using Computer Vision

by Jonathan P. Dandois and Erle C. Ellis

Remote Sens. 2010, 2(4), 1157-1176; https://doi.org/10.3390/rs2041157 - 21 Apr 2010

Cited by 230 | Viewed by 26072

Abstract

High spatial resolution measurements of vegetation structure in three-dimensions (3D) are essential for accurate estimation of vegetation biomass, carbon accounting, forestry, fire hazard evaluation and other land management and scientific applications. Light Detection and Ranging (LiDAR) is the current standard for these measurements [...] Read more.

High spatial resolution measurements of vegetation structure in three-dimensions (3D) are essential for accurate estimation of vegetation biomass, carbon accounting, forestry, fire hazard evaluation and other land management and scientific applications. Light Detection and Ranging (LiDAR) is the current standard for these measurements but requires bulky instruments mounted on commercial aircraft. Here we demonstrate that high spatial resolution 3D measurements of vegetation structure and spectral characteristics can be produced by applying open-source computer vision algorithms to ordinary digital photographs acquired using inexpensive hobbyist aerial platforms. Digital photographs were acquired using a kite aerial platform across two 2.25 ha test sites in Baltimore, MD, USA. An open-source computer vision algorithm generated 3D point cloud datasets with RGB spectral attributes from the photographs and these were geocorrected to a horizontal precision of <1.5 m (root mean square error; RMSE) using ground control points (GCPs) obtained from local orthophotographs and public domain digital terrain models (DTM). Point cloud vertical precisions ranged from 0.6 to 4.3 m RMSE depending on the precision of GCP elevations used for geocorrection. Tree canopy height models (CHMs) generated from both computer vision and LiDAR point clouds across sites adequately predicted field-measured tree heights, though LiDAR showed greater precision (R² > 0.82) than computer vision (R² > 0.64), primarily because of difficulties observing terrain under closed canopy forest. Results confirm that computer vision can support ultra-low-cost, user-deployed high spatial resolution 3D remote sensing of vegetation structure. Full article

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

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590 KiB

Open AccessArticle

Using Spatial Structure Analysis of Hyperspectral Imaging Data and Fourier Transformed Infrared Analysis to Determine Bioactivity of Surface Pesticide Treatment

by Christian Nansen, Noureddine Abidi, Amelia Jorge Sidumo and Ali Hosseini Gharalari

Remote Sens. 2010, 2(4), 908-925; https://doi.org/10.3390/rs2040908 - 26 Mar 2010

Cited by 16 | Viewed by 11624

Abstract

Many food products are subjected to quality control analyses for detection of surface residue/contaminants, and there is a trend of requiring more and more documentation and reporting by farmers regarding their use of pesticides. Recent outbreaks of food borne illnesses have been a [...] Read more.

Many food products are subjected to quality control analyses for detection of surface residue/contaminants, and there is a trend of requiring more and more documentation and reporting by farmers regarding their use of pesticides. Recent outbreaks of food borne illnesses have been a major contributor to this trend. With a growing need for food safety measures and “smart applications” of insecticides, it is important to develop methods for rapid and accurate assessments of surface residues on food and feed items. As a model system, we investigated detection of a miticide applied to maize leaves and its miticidal bioactivity over time, and we compared two types of reflectance data: fourier transformed infrared (FTIR) data and hyperspectral imaging (HI) data. The miticide (bifenazate) was applied at a commercial field rate to maize leaves in the field, with or without application of a surfactant, and with or without application of a simulated “rain event”. In addition, we collected FTIR and HI from untreated control leaves (total of five treatments). Maize leaf data were collected at seven time intervals from 0 to 48 hours after application. FTIR data were analyzed using conventional analysis of variance of miticide-specific vibration peaks. Two unique FTIR vibration peaks were associated with miticide application (1,700 cm^?1 and 763 cm^?1). The integrated intensities of these two peaks, miticide application, surfactant, rain event, time between miticide application, and rain event were used as explanatory variables in a linear multi-regression fit to spider mite mortality. The same linear multi-regression approach was applied to variogram parameters derived from HI data in five selected spectral bands (664, 683, 706, 740, and 747 nm). For each spectral band, we conducted a spatial structure analysis, and the three standard variogram parameters (“sill”, “range”, and “nugget”) were examined as possible “indicators” of miticide bioactivity. We demonstrated that both FTIR peaks and standard variogram parameters could be used to accurately predict spider mite mortality, but linear multi-regression fits based on standard variogram parameters had the highest accuracy and were successfully validated with independent data. Based on experimental manipulation of HI data, the use of spatial structure analysis in classification of HI data was discussed. Full article

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

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Variogram illustrates relationship of distance between paired observations (lag distance) and variance, and variogram analysis is used to determine three parameters (“Nugget”, “Range”, and “Sill”). Full article ">Figure 2
Average miticide bioactivity (spider mite mortality) from different treatments. Statistical analyses of spider mite bioassay results are presented in <a href="#remotesensing-02-00908-t001" class="html-table">Table 1</a>. Full article ">Figure 3
Fourier transformed infrared analysis (FTIR) spectra from maize leaves. Untreated control maize leaf (in green), maize leaf treated with miticide before rain event (blue), and maize leaf treated with miticide after simulated rain (red). Full article ">Figure 4
Average hyperspectral profiles acquired from untreated and miticide-treated maize leaves over time. Full article ">Figure 5
Variogram analysis of reflectance data from single spectral band (683 nm) of an untreated maize leaf were experimentally manipulated in four ways and compared with actual: multiplying all reflectance values with either 1.025 or 1.050 to simulate a 2.5% and 5.0% increase in all reflectance values or multiplying half the reflectance values (random selection) with 1.025 or one-third of the reflectance values (random selection) with 1.050. Full article ">

402 KiB

Open AccessArticle

Land-Cover Phenologies and Their Relation to Climatic Variables in an Anthropogenically Impacted Mediterranean Coastal Area

by Ignacio Melendez-Pastor, Jose Navarro-Pedreño, Magaly Koch, Ignacio Gómez and Encarni I. Hernández

Remote Sens. 2010, 2(3), 697-716; https://doi.org/10.3390/rs2030697 - 2 Mar 2010

Cited by 17 | Viewed by 10496

Abstract

Mediterranean coastal areas are experiencing rapid land cover change caused by human-induced land degradation and extreme climatic events. Vegetation index time series provide a useful way to monitor vegetation phenological variations. This study quantitatively describes Enhanced Vegetation Index (EVI) temporal changes for Mediterranean [...] Read more.

Mediterranean coastal areas are experiencing rapid land cover change caused by human-induced land degradation and extreme climatic events. Vegetation index time series provide a useful way to monitor vegetation phenological variations. This study quantitatively describes Enhanced Vegetation Index (EVI) temporal changes for Mediterranean land-covers from the perspective of vegetation phenology and its relation with climate. A time series from 2001 to 2007 of the MODIS Enhanced Vegetation Index 16-day composite (MOD13Q1) was analyzed to extract anomalies (by calculating z-scores) and frequency domain components (by the Fourier Transform). Vegetation phenology analyses were developed for diverse land-covers for an area in south Alicante (Spain) providing a useful way to analyze and understand the phenology associated to those land-covers. Time series of climatic variables were also analyzed through anomaly detection techniques and the Fourier Transform. Correlations between EVI time series and climatic variables were computed. Temperature, rainfall and radiation were significantly correlated with almost all land-cover classes for the harmonic analysis amplitude term. However, vegetation phenology was not correlated with climatic variables for the harmonic analysis phase term suggesting a delay between climatic variations and vegetation response. Full article

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

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3002 KiB

Open AccessArticle

Effects of Spatial and Spectral Resolutions on Fractal Dimensions in Forested Landscapes

by Mohammad Al-Hamdan, James Cruise, Douglas Rickman and Dale Quattrochi

Remote Sens. 2010, 2(3), 611-640; https://doi.org/10.3390/rs2030611 - 26 Feb 2010

Cited by 21 | Viewed by 12344

Abstract

Recent work has shown that more research is needed in applying fractal analysis to multi-resolution remote sensing data for landscape characterization. The purpose of this study was to closely examine the impacts that spatial and spectral resolutions have on fractal dimensions using real-world [...] Read more.

Recent work has shown that more research is needed in applying fractal analysis to multi-resolution remote sensing data for landscape characterization. The purpose of this study was to closely examine the impacts that spatial and spectral resolutions have on fractal dimensions using real-world multi-resolution remotely sensed data as opposed to the more conventional single resolution and aggregation approach. The study focused on fractal analysis of forested landscapes in the southeastern United States and Central America. Initially, the effects of spatial resolution on the computed fractal dimensions were examined using data from three instruments with different spatial resolutions. Based on the criteria of mean value and variation within the accepted ranges of fractal dimensions, it was determined that 30-m Landsat TM data were best able to capture the complexity of a forested landscape in Central America compared to 4-m IKONOS data and 250-m MODIS data. Also, among the spectral bands of Landsat TM images of four national forests in the southeastern United States, tests showed that the spatial indices of fractal dimensions are much more distinguishable in the visible bands than they are in the near-mid infrared bands. Thus, based solely on the fractal analysis, the fractal dimensions could have relatively higher chances to distinguish forest characteristics (e.g., stand sizes and species) in the Landsat TM visible wavelength bands than in the near-mid infrared bands. This study has focused on a relative comparison between visible and near-mid infrared wavelength bands; however it will be important to study in the future the effect of a combination of those bands such as the Normalized Difference Vegetation Index (NDVI) on fractal dimensions of forested landscapes. Full article

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

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1951 KiB

Open AccessArticle

Assessing Plant Diversity in a Dry Tropical Forest: Comparing the Utility of Landsat and Ikonos Satellite Images

by Harini Nagendra, Duccio Rocchini, Rucha Ghate, Bhawna Sharma and Sajid Pareeth

Remote Sens. 2010, 2(2), 478-496; https://doi.org/10.3390/rs2020478 - 2 Feb 2010

Cited by 105 | Viewed by 15618

Abstract

While high expectations have been raised about the utility of high resolution satellite imagery for biodiversity assessment, there has been almost no empirical assessment of its use, particularly in the biodiverse tropics which represent a very challenging environment for such assessment challenge. This [...] Read more.

While high expectations have been raised about the utility of high resolution satellite imagery for biodiversity assessment, there has been almost no empirical assessment of its use, particularly in the biodiverse tropics which represent a very challenging environment for such assessment challenge. This research evaluates the use of high spatial resolution (IKONOS) and medium spatial resolution (Landsat ETM+) satellite imagery for assessing vegetation diversity in a dry tropical forest in central India. Contrary to expectations, across multiple measures of plant distribution and diversity, the resolution of IKONOS data is too fine for the purpose of plant diversity assessment and Landsat imagery performs better. Full article

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

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353 KiB

Open AccessArticle

Interannual Changes of Fire Activity in the Protected Areas of the SUN Network and Other Parks and Reserves of the West and Central Africa Region Derived from MODIS Observations

by Jean-Marie Grégoire and Dario Simonetti

Remote Sens. 2010, 2(2), 446-463; https://doi.org/10.3390/rs2020446 - 29 Jan 2010

Cited by 18 | Viewed by 10902

Abstract

Time series of fire occurrence, derived from MODIS data, have been used to characterise the spatio-temporal distribution of fire events during the 2004–2009 period in 17 protected areas (PAs) of West and Central Africa, with particular attention to those of the SUN network [...] Read more.

Time series of fire occurrence, derived from MODIS data, have been used to characterise the spatio-temporal distribution of fire events during the 2004–2009 period in 17 protected areas (PAs) of West and Central Africa, with particular attention to those of the SUN network in Senegal, Burkina Faso, Benin and Niger. The temporal distribution of the fire activity and the number of fire occurences are quite different inside the PAs and in their surrounding area. A progressive increase of the length of the burning season is observed in the West Africa PAs. Quantitatively, the general trend over the last five years is an increase of the fire density (+22%) inside the PAs and a decrease (?27%) outside. The results indicate that the capacity of the PAs to maintain the biological diversity of the region is probably decreasing due to the combined effects of the anthropic pressure inside the PAs and of an on-going isolation process. Full article

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

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Graphical abstract

Graphical abstract
Full article ">Figure 1
Location of the three regional windows and of the protected areas (black circles) considered in the analysis. Full article ">Figure 2
Temporal distribution at a weekly time step of the fire activity in the Park W transboundary national park (Benin, Burkina Faso and Niger) for the 2006–2007 dry season. The vertical lines show from left to right when the cumulative number of fire pixels equal 25%, 50% and 75% of the seasonal total. Full article ">Figure 3
Fire distribution in Patako (red), Boulon (green), Park W (orange) and other 11 protected areas of West and Central Africa from 2004 to 2009. Number of weeks from the start of the burning season to reach 25% of the seasonal total (a) and 50% (b). Full article ">Figure 4
Fire density (nb. fire pixels/1,000 ha) in Patako (red), Boulon (green), Park W (orange) and other 11 protected areas of West and Central Africa from 2004 to 2009. Fire density in the regional windows: window 1 (black), 2 (grey) and 3 (striped). Full article ">Figure 5
Specificity Index in the Patako (red), Boulon (green), Park W (orange) and 11 other protected areas of West and Central Africa from 2004 to 2009. Full article ">Figure 6
Fire density (nb. fire events/1,000 ha) inside the SUN test sites (a), in their 25 km buffer zone (b) and in the regional windows (c) from 2004 to 2009. Full article ">

7749 KiB

Open AccessArticle

Phenological Characterization of Desert Sky Island Vegetation Communities with Remotely Sensed and Climate Time Series Data

by Willem J.D. Van Leeuwen, Jennifer E. Davison, Grant M. Casady and Stuart E. Marsh

Remote Sens. 2010, 2(2), 388-415; https://doi.org/10.3390/rs2020388 - 27 Jan 2010

Cited by 47 | Viewed by 14334

Abstract

Climate change and variability are expected to impact the synchronicity and interactions between the Sonoran Desert and the forested sky islands which represent steep biological and environmental gradients. The main objectives were to examine how well satellite greenness time series data and derived [...] Read more.

Climate change and variability are expected to impact the synchronicity and interactions between the Sonoran Desert and the forested sky islands which represent steep biological and environmental gradients. The main objectives were to examine how well satellite greenness time series data and derived phenological metrics (e.g., season start, peak greenness) can characterize specific vegetation communities across an elevation gradient, and to examine the interactions between climate and phenological metrics for each vegetation community. We found that representative vegetation types (11), varying between desert scrub, mesquite, grassland, mixed oak, juniper and pine, often had unique seasonal and interannual phenological trajectories and spatial patterns. Satellite derived land surface phenometrics (11) for each of the vegetation communities along the cline showed numerous distinct significant relationships in response to temperature (4) and precipitation (7) metrics. Satellite-derived sky island vegetation phenology can help assess and monitor vegetation dynamics and provide unique indicators of climate variability and patterns of change. Full article

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

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1757 KiB

Open AccessArticle

Estimating Daily Land Surface Temperatures in Mountainous Environments by Reconstructed MODIS LST Data

by Markus Neteler

Remote Sens. 2010, 2(1), 333-351; https://doi.org/10.3390/rs1020333 - 18 Jan 2010

Cited by 304 | Viewed by 24364

Abstract

Continuous monitoring of extreme environments, such as the European Alps, is hampered by the sparse and/or irregular distribution of meteorological stations, the difficulties in performing ground surveys and the complexity of interpolating existing station data. Remotely sensed Land Surface Temperature (LST) is therefore [...] Read more.

Continuous monitoring of extreme environments, such as the European Alps, is hampered by the sparse and/or irregular distribution of meteorological stations, the difficulties in performing ground surveys and the complexity of interpolating existing station data. Remotely sensed Land Surface Temperature (LST) is therefore of major interest for a variety of environmental and ecological applications. But while MODIS LST data from the Terra and Aqua satellites are aimed at closing the gap between data demand and availability, clouds and other atmospheric disturbances often obscure parts or even the entirety of these satellite images. A novel algorithm is presented in this paper, which is able to reconstruct incomplete MODIS LST maps. All nine years of the available daily LST data (2000–2008) have been processed, allowing the original LST map resolution of 1,000 m to be improved to 200 m, which means the resulting LST maps can be applied at a regional level. Extracted time series and aggregated data are shown as examples and are compared to meteorological station time series as an indication of the quality obtained. Full article

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

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758 KiB

Open AccessArticle

Accessibility, Demography and Protection: Drivers of Forest Stability and Change at Multiple Scales in the Cauvery Basin, India

by Nikhil Lele, Harini Nagendra and Jane Southworth

Remote Sens. 2010, 2(1), 306-332; https://doi.org/10.3390/rs2010306 - 12 Jan 2010

Cited by 16 | Viewed by 13819

Abstract

The Cauvery basin of Karnataka State encompasses a range of land cover types, from dense forest areas and plantations in the Western Ghats hills, to fertile agricultural lands in the river valley. Recent demographic changes, rapid economic development and urbanization have led to [...] Read more.

The Cauvery basin of Karnataka State encompasses a range of land cover types, from dense forest areas and plantations in the Western Ghats hills, to fertile agricultural lands in the river valley. Recent demographic changes, rapid economic development and urbanization have led to the conversion of vast stretches of forested land into plantations and permanent agriculture. We examine the human drivers of forest cover change between 2001 and 2006, using MODIS 250 m data at multiple spatial scales of nested administrative units i.e., districts and taluks. Population density does not emerge as a major driver of forest distribution or deforestation. Protected areas and landscape accessibility play a major role in driving the distribution of stable forest cover at different spatial scales. The availability of forested land for further clearing emerges as a major factor impacting the distribution of deforestation, with new deforestation taking place in regions with challenging topography. This research highlights the importance of using a regional approach to study land cover change, and indicates that the drivers of forest change may be very different in long settled landscapes, for which little is known in comparison to frontier forests. Full article

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

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893 KiB

Open AccessArticle

Spatial Enhancement of MODIS-based Images of Leaf Area Index: Application to the Boreal Forest Region of Northern Alberta, Canada

by Quazi K. Hassan and Charles P.-A. Bourque

Remote Sens. 2010, 2(1), 278-289; https://doi.org/10.3390/rs2010278 - 8 Jan 2010

Cited by 20 | Viewed by 11927

Abstract

Leaf area index (LAI) is one of the most commonly used ecological variables in describing forests. Since 2000, 1-km resolution Moderate Resolution Imaging Spectroradiometer (MODIS)-based 8-day composites of LAI have been operationally available from the National Aeronautics and Space Administration (NASA), USA, at [...] Read more.

Leaf area index (LAI) is one of the most commonly used ecological variables in describing forests. Since 2000, 1-km resolution Moderate Resolution Imaging Spectroradiometer (MODIS)-based 8-day composites of LAI have been operationally available from the National Aeronautics and Space Administration (NASA), USA, at no cost to the user. In this paper, we present a simple protocol to enhance the spatial resolution of NASA-produced LAI composites to 250-m resolution. This is done by fusing MODIS-based estimates of enhanced vegetation index (EVI), consisting of 16-day 250-m resolution composites (also from NASA), with estimates of LAI. We apply the protocol to derive 250-m resolution maps of LAI for the boreal forest region of northern Alberta, Canada. Data fusion was possible in this study because of the inherent linear correlation that exists between EVI and LAI for the April to October growing period of 2005–2008, producing r²-values of 0.85–0.95 and p-values < 0.0001. Comparison of MODIS-based LAI with field-based measurements using the Tracing Radiation and Architecture of Canopies (TRAC) sensor and LAI-2000 Plant Canopy Analyzer showed reasonable agreement across values; statistical comparison of LAI data points produced an r²-value of 0.71 and a p-value < 0.0001. Seventy one percent of MODIS-based LAI were within ±20% of field estimates. Full article

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

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Figure 1
Extent of study area. A landcover map, derived from an annual composite of 2004 MODIS images, appears in the background. The black polygon outlines the Province of Alberta. The red box identifies the Athabasca Oil Sands Region, an area where field-based estimates of LAI were acquired to validate estimates of LAI generated from MODIS data. Full article ">Figure 2
Scatterplot of cell-to-cell comparison of growing season (2006) averages for LAI and EVI for needleleaf forests; F-statistic = 2,120.31 and p < 0.0001. Full article ">Figure 3
Temporal behaviour of LAI and EVI for the April–October period (DOY 97–297) of (a) 2005, (b) 2006, (c) 2007 and (d) 2008, and for (e) all years (2005–2008). Full article ">Figure 4
Data comparison of LAI and EVI for the April–October period (DOY 97–297) of (a) 2005 (F-statistics = 87.69), (b) 2006 (F-statistics = 225.08), (c) 2007 (F-statistics = 63.68) and (d) 2008 (F-statistics = 119.65), and for (e) all years (2005–2008; F-statistics = 416.98). For all cases considered, the p-values were <0.0001. Full article ">Figure 5
Comparison of LAI values at 1-km resolution without data fusion (left panel) and at 250-m resolution with data fusion (right panel) for various landcover types identified in <a href="#remotesensing-02-00278-f001" class="html-fig">Figure 1</a>, including (a) needleleaf forests (comprising 71.4% of the study area), (b) grasses/cereal crops (15.3%), (c) shrubs (3.7%), (d) broadleaf crops (1.8%), (e) savannah (1.6%), and (f) broadleaf forests (0.9%). Full article ">Figure 6
Study-area-wide comparison of LAI from the original 1-km and enhanced 250-m image for an 8-day period in June 2009 (DOY 169–176); F-statistic=10164 and p < 0.0001. Full article ">Figure 7
Spatial distribution of LAI for an 8-day period in June 2009 (DOY 169–176) at 250-m resolution for the Athabasca Oil Sands Region (red box in <a href="#remotesensing-02-00278-f001" class="html-fig">Figure 1</a>). Open circles in the centre, along the road network (green lines) and elsewhere in the image, denote the sites where field-based measurements of LAI were acquired. Full article ">Figure 8
Comparison of 72 field-based measurements with their corresponding MODIS-based LAI for June 2009 at 250-m resolution; F-statistic = 171.23 and p-value < 0.0001. Full article ">

2144 KiB

Open AccessArticle

Individual Tree Species Classification by Illuminated—Shaded Area Separation

by Eetu Puttonen, Paula Litkey and Juha Hyyppä

Remote Sens. 2010, 2(1), 19-35; https://doi.org/10.3390/rs2010019 - 28 Dec 2009

Cited by 44 | Viewed by 12720

Abstract

A new method, called Illumination Dependent Colour Channels (IDCC), is presented to improve individual tree species classification. The method is based on tree crown division into illuminated and shaded parts on a digital aerial image. Colour values of both sides of the tree [...] Read more.

A new method, called Illumination Dependent Colour Channels (IDCC), is presented to improve individual tree species classification. The method is based on tree crown division into illuminated and shaded parts on a digital aerial image. Colour values of both sides of the tree crown are then used in species classification. Tree crown division is achieved by comparing the projected location of an aerial image pixel with its neighbours on a Canopy Height Model (CHM), which is calculated from a synchronized LIDAR point cloud. The sun position together with the mapping aircraft position are also utilised in illumination status detection. The new method was tested on a dataset of 295 trees and the classification results were compared with ones measured with two other feature extraction methods. The results of the developed method gave a clear improvement in overall tree species classification accuracy. Full article

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

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1249 KiB

Open AccessArticle

Using Urban Landscape Trajectories to Develop a Multi-Temporal Land Cover Database to Support Ecological Modeling

by Jeffrey Hepinstall-Cymerman, Stefan Coe and Marina Alberti

Remote Sens. 2009, 1(4), 1353-1379; https://doi.org/10.3390/rs1041353 - 22 Dec 2009

Cited by 13 | Viewed by 14735

Abstract

Urbanization and the resulting changes in land cover have myriad impacts on ecological systems. Monitoring these changes across large spatial extents and long time spans requires synoptic remotely sensed data with an appropriate temporal sequence. We developed a multi-temporal land cover dataset for [...] Read more.

Urbanization and the resulting changes in land cover have myriad impacts on ecological systems. Monitoring these changes across large spatial extents and long time spans requires synoptic remotely sensed data with an appropriate temporal sequence. We developed a multi-temporal land cover dataset for a six-county area surrounding the Seattle, Washington State, USA, metropolitan region. Land cover maps for 1986, 1991, 1995, 1999, and 2002 were developed from Landsat TM images through a combination of spectral unmixing, image segmentation, multi-season imagery, and supervised classification approaches to differentiate an initial nine land cover classes. We then used ancillary GIS layers and temporal information to define trajectories of land cover change through multiple updating and backdating rules and refined our land cover classification for each date into 14 classes. We compared the accuracy of the initial approach with the landscape trajectory modifications and determined that the use of landscape trajectory rules increased our ability to differentiate several classes including bare soil (separated into cleared for development, agriculture, and clearcut forest) and three intensities of urban. Using the temporal dataset, we found that between 1986 and 2002, urban land cover increased from 8 to 18% of our study area, while lowland deciduous and mixed forests decreased from 21 to 14%, and grass and agriculture decreased from 11 to 8%. The intensity of urban land cover increased with 252 km² in Heavy Urban in 1986 increasing to 629 km² by 2002. The ecological systems that are present in this region were likely significantly altered by these changes in land cover. Our results suggest that multi-temporal (i.e., multiple years and multiple seasons within years) Landsat data are an economical means to quantify land cover and land cover change across large and highly heterogeneous urbanizing landscapes. Our data, and similar temporal land cover change products, have been used in ecological modeling of past, present, and likely future changes in ecological systems (e.g., avian biodiversity, water quality). Such data are important inputs for ecological modelers, policy makers, and urban planners to manage and plan for future landscape change. Full article

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

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593 KiB

Open AccessArticle

Improving Landsat and IRS Image Classification: Evaluation of Unsupervised and Supervised Classification through Band Ratios and DEM in a Mountainous Landscape in Nepal

by Krishna Bahadur K.C.

Remote Sens. 2009, 1(4), 1257-1272; https://doi.org/10.3390/rs1041257 - 8 Dec 2009

Cited by 63 | Viewed by 18677

Abstract

Modification of the original bands and integration of ancillary data in digital image classification has been shown to improve land use land cover classification accuracy. There are not many studies demonstrating such techniques in the context of the mountains of Nepal. The objective [...] Read more.

Modification of the original bands and integration of ancillary data in digital image classification has been shown to improve land use land cover classification accuracy. There are not many studies demonstrating such techniques in the context of the mountains of Nepal. The objective of this study was to explore and evaluate the use of modified band and ancillary data in Landsat and IRS image classification, and to produce a land use land cover map of the Galaudu watershed of Nepal. Classification of land uses were explored using supervised and unsupervised classification for 12 feature sets containing the LandsatMSS, TM and IRS original bands, ratios, normalized difference vegetation index, principal components and a digital elevation model. Overall, the supervised classification method produced higher accuracy than the unsupervised approach. The result from the combination of bands ration 4/3, 5/4 and 5/7 ranked the highest in terms of accuracy (82.86%), while the combination of bands 2, 3 and 4 ranked the lowest (45.29%). Inclusion of DEM as a component band shows promising results. Full article

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

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324 KiB

Open AccessArticle

Evaluating the Effects of Environmental Changes on the Gross Primary Production of Italian Forests

by Fabio Maselli, Marco Moriondo, Marta Chiesi, Gherardo Chirici, Nicola Puletti, Anna Barbati and Piermaria Corona

Remote Sens. 2009, 1(4), 1108-1124; https://doi.org/10.3390/rs1041108 - 19 Nov 2009

Cited by 11 | Viewed by 14488

Abstract

A ten-year data-set descriptive of Italian forest gross primary production (GPP) has been recently constructed by the application of Modified C-Fix, a parametric model driven by remote sensing and ancillary data. That data-set is currently being used to develop multivariate regression models which [...] Read more.

A ten-year data-set descriptive of Italian forest gross primary production (GPP) has been recently constructed by the application of Modified C-Fix, a parametric model driven by remote sensing and ancillary data. That data-set is currently being used to develop multivariate regression models which link the inter-year GPP variations of five forest types (white fir, beech, chestnut, deciduous and evergreen oaks) to seasonal values of temperature and precipitation. The five models obtained, which explain from 52% to 88% of the inter-year GPP variability, are then applied to predict the effects of expected environmental changes (+2 °C and increased CO₂ concentration). The results show a variable response of forest GPP to the simulated climate change, depending on the main ecosystem features. In contrast, the effects of increasing CO₂concentration are always positive and similar to those given by a combination of the two environmental factors. These findings are analyzed with reference to previous studies on the subject, particularly concerning Mediterranean environments. The analysis confirms the plausibility of the scenarios obtained, which can cast light on the important issue of forest carbon pool variations under expected global changes. Full article

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

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213 KiB

Open AccessArticle

A Simple Method to Determine the Timing of Snow Melt by Remote Sensing with Application to the CO₂ Balances of Northern Mire and Heath Ecosystems

by Janne Rinne, Mika Aurela and Terhikki Manninen

Remote Sens. 2009, 1(4), 1097-1107; https://doi.org/10.3390/rs1041097 - 19 Nov 2009

Cited by 8 | Viewed by 11544

Abstract

The timing of the disappearance of the snow cover in spring, or snow melt day (SMD), is a key parameter controlling the carbon dioxide balance between the northern mire and heath ecosystems and the atmosphere. We present a simple method for the determination [...] Read more.

The timing of the disappearance of the snow cover in spring, or snow melt day (SMD), is a key parameter controlling the carbon dioxide balance between the northern mire and heath ecosystems and the atmosphere. We present a simple method for the determination of the SMD using a satellite-based surface albedo product (SAL). The method is based on the local change of albedo from higher wintertime values towards the lower summertime values. The satellite SMD timing correlates well with the SMD determined from snow depth measurements at Finnish weather stations (r = 0.86, slope 1.05). In 50% of the cases the error was 3.4 days or less and bias less than half a day. This would lead to a moderate uncertainty in the annual CO₂ balance of mire and heath ecosystems, if the published SMD—CO₂ balance relations are valid. However, due to the limited data sets available a systematic validation is left for the future. Full article

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

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Figure 1
Time series of albedo values in Southern Finland in 2006. The coordinates of the selected SAL grid square are 61°48’N, 24°11’E. Siikaneva (61°50’N, 24°12’E) and Hyytiälä (61°51’N, 24°17’E) are surface observation sites within the selected SAL grid square that are located in an open wetland and a Scots pine forest, respectively. Full article ">Figure 2
Snow melt day in Northern Europe as derived using the SAF surface albedo product in 2005 (Panel A) and 2006 (Panel B). Full article ">Figure 3
(A) Snow melt day (SMD) as derived using the SAL surface albedo product plotted against that derived from surface snow depth measurements. The grey dots are observations from the year 2005 and the black ones from the year 2006. The solid line indicates the 1:1 relation; (B) Cumulative distribution of the absolute differences between SAL and snow-depth-derived SMD. Full article ">Figure 4
The difference between snow-depth-derived SMD and SAL-derived SMD at Finnish weather stations in the year 2005 against that in the year 2006. Full article ">Figure 5
Time series of albedo at the Kaamanen mire measured locally (black solid line) and remotely (circles) in 2006. The error bars mark the periods from which the remotely- measured albedo are determined. Full article ">

1755 KiB

Open AccessCommunication

Investigating the Impacts of Landuse-landcover (LULC) Change in the Pearl River Delta Region on Water Quality in the Pearl River Estuary and Hong Kong’s Coast

by Yuanzhi Zhang, Yufei Wang, Yunpeng Wang and Hongyan Xi

Remote Sens. 2009, 1(4), 1055-1064; https://doi.org/10.3390/rs1041055 - 17 Nov 2009

Cited by 26 | Viewed by 14617

Abstract

Water quality information in the coastal region of Hong Kong and the Pearl River Estuary (PRE) is of great concern to the local community. Due to great landuse-landcover (LULC) changes with rapid industrialization and urbanization in the Pearl River Delta (PRD) region, water [...] Read more.

Water quality information in the coastal region of Hong Kong and the Pearl River Estuary (PRE) is of great concern to the local community. Due to great landuse-landcover (LULC) changes with rapid industrialization and urbanization in the Pearl River Delta (PRD) region, water quality in the PRE has worsened during the last 20 years. Frequent red tide and harmful algal blooms have occurred in the estuary and its adjacent coastal waters since the 1980s and have caused important economic losses, also possibly threatening to the coastal environment, fishery, and public health in Hong Kong. In addition, recent literature shows that water nutrients in Victoria Harbor of Hong Kong have been proven to be strongly influenced by both the Pearl River and sewage effluent in the wet season (May to September), but it is still unclear how the PRE diluted water intrudes into Victoria Harbor. Due to the cloudy and rainy conditions in the wet season in Hong Kong, ASAR images will be used to monitor the PRE river plumes and track the intruding routes of PRE water nutrients. In this paper, we first review LULC change in the PRD and then show our preliminary results to analyze water quality spatial and temporal information from remote observations with different sensors in the coastal region and estuary. The study will also emphasizes on time series of analysis of LULC trends related to annual sediment yields and critical source areas of erosion for the PRD region since the 1980s. Full article

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

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1218 KiB

Open AccessArticle

Analysis of Land Use/Cover Changes and Animal Population Dynamics in a Wildlife Sanctuary in East Africa

by Charles Ndegwa Mundia and Yuji Murayama

Remote Sens. 2009, 1(4), 952-970; https://doi.org/10.3390/rs1040952 - 11 Nov 2009

Cited by 36 | Viewed by 19997

Abstract

Changes in wildlife conservation areas have serious implications for ecological systems and the distribution of wildlife species. Using the Masai Mara ecosystem as an example, we analyzed long-term land use/cover changes and wildlife population dynamics. Multitemporal satellite images, together with physical and social [...] Read more.

Changes in wildlife conservation areas have serious implications for ecological systems and the distribution of wildlife species. Using the Masai Mara ecosystem as an example, we analyzed long-term land use/cover changes and wildlife population dynamics. Multitemporal satellite images, together with physical and social economic data were employed in a post classification analysis with GIS to analyze outcomes of different land use practices and policies. The results show rapid land use/cover conversions and a drastic decline for a wide range of wildlife species. Integration of land use/cover monitoring data and wildlife resources data can allow for the analysis of changes, and can be used to project trends to provide knowledge about potential land use/cover change scenarios and ecological impacts. Full article

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

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970 KiB

Open AccessArticle

Regional Assessment of Aspen Change and Spatial Variability on Decadal Time Scales

by Temuulen Tsagaan Sankey

Remote Sens. 2009, 1(4), 896-914; https://doi.org/10.3390/rs1040896 - 10 Nov 2009

Cited by 11 | Viewed by 11934

Abstract

Quaking aspen (Populus tremuloides) is commonly believed to be declining throughout western North America. Using a historical vegetation map and Landsat TM5 imagery, this study detects changes in regional aspen cover over two different time periods of 85 and 18 years [...] Read more.

Quaking aspen (Populus tremuloides) is commonly believed to be declining throughout western North America. Using a historical vegetation map and Landsat TM5 imagery, this study detects changes in regional aspen cover over two different time periods of 85 and 18 years and examines aspen change patterns with biophysical variables in the Targhee National Forest of eastern Idaho, USA. A subpixel classification approach was successfully used to classify aspen. The results indicate greater spatial variability in regional aspen change patterns than indicated by local-scale studies. The observed spatial variability appears to be an inherent pattern in regional aspen dynamics, which interacts with biophysical variables, but persists over time. Full article

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

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Figure 1
Study area and 300 randomly-generated sample polygon locations in the Targhee National Forest in Idaho, USA. Full article ">Figure 2
Mean spectral reflectance of aspen and other dominant vegetation cover types within the study area in green (G = 0.52–0.60 μm), red (R = 0.63–0.69 μm), near infrared (NIR = 0.76–0.90 μm), and middle infrared (Mid IR = 1.55–1.75 μm) portions of the electromagnetic spectrum in the fall (F) and summer (S) seasons. Error bars are standard errors. Full article ">Figure 3
Aspen presence and absence classification of 2005 Landsat TM5 multitemporal composite using Mixture Tuned Matched Filtering (MTMF) technique with a regression approach. The exponential regression model was fitted to the MTMF-produced matched filtering scores and infeasibility values (R2 = 0.57, p < 0.0001). Pixels that fell under the regression curve (solid grey line) that had matched filtering scores of 0.5–1 (dashed grey lines) and infeasibility values of <5 (dotted grey line) were classified as aspen presence. All other pixels were classified as aspen absence. Full article ">Figure 4
The Mixture Tuned Matched Filtering (MTMF) classification images and final aspen map for 2005. The image of matched filtering scores (a) estimates the abundance of target cover within each pixel, while the image of infeasibility values (b) indicates the relative accuracy of the matched filtering score in each pixel. Aspen presence and absence map (c) is produced after the regression integration of the two images. Full article ">Figure 5
Examples of local-scale aspen changes between 1920 and 2005. Simple image differencing was performed using 1920 (a) and 2005 (b) aspen presence and absence maps, which resulted in three different classes: aspen decrease, no-change, and aspen increase. Full article ">Figure 6
Aspen change patterns in the 1920–2005 time period. Aspen increase (positive grey bars) and aspen decrease (negative black bars) were simultaneously analyzed as two response variables in a MANOVA model. Grazing, forest harvest, and vegetation cover types were significant predictor variables in aspen increase (p < 0.05), while all predictor variables were significant in aspen decrease (p < 0.05). (a) Aspen changes patterns and grazing; (b) Aspen change patterns and forest harvest; (c) Aspen change patterns and vegetation cover type (LP pine = Lodgepole pine); (d) Aspen change patterns and forest stand age. Full article ">Figure 7
Aspen change patterns in the 1987–2005 time period. Aspen increase (positive grey bars) and aspen decrease (negative black bars) were simultaneously analyzed as two response variables in a MANOVA model. Grazing and vegetation cover types were statistically significant predictor variables in aspen increase (p < 0.05), while forest harvest and stand age were significant in aspen decrease (p < 0.05). (a) Aspen changes patterns and grazing; (b) Aspen change patterns and forest harvest; (c) Aspen change patterns and vegetation cover type (LP pine = Lodgepole pine); (d) Aspen change patterns and forest stand age. Full article ">

5234 KiB

Open AccessArticle

On the Suitability of MODIS Time Series Metrics to Map Vegetation Types in Dry Savanna Ecosystems: A Case Study in the Kalahari of NE Namibia

by Christian Hüttich, Ursula Gessner, Martin Herold, Ben J. Strohbach, Michael Schmidt, Manfred Keil and Stefan Dech

Remote Sens. 2009, 1(4), 620-643; https://doi.org/10.3390/rs1040620 - 30 Sep 2009

Cited by 68 | Viewed by 18325

Abstract

The characterization and evaluation of the recent status of biodiversity in Southern Africa’s Savannas is a major prerequisite for suitable and sustainable land management and conservation purposes. This paper presents an integrated concept for vegetation type mapping in a dry savanna ecosystem based [...] Read more.

The characterization and evaluation of the recent status of biodiversity in Southern Africa’s Savannas is a major prerequisite for suitable and sustainable land management and conservation purposes. This paper presents an integrated concept for vegetation type mapping in a dry savanna ecosystem based on local scale in-situ botanical survey data with high resolution (Landsat) and coarse resolution (MODIS) satellite time series. In this context, a semi-automated training database generation procedure using object-oriented image segmentation techniques is introduced. A tree-based Random Forest classifier was used for mapping vegetation type associations in the Kalahari of NE Namibia based on inter-annual intensity- and phenology-related time series metrics. The utilization of long-term inter-annual temporal metrics delivered the best classification accuracies (Kappa = 0.93) compared with classifications based on seasonal feature sets. The relationship between annual classification accuracies and bi-annual precipitation sums was conducted using data from the Tropical Rainfall Measuring Mission (TRMM). Increased error rates occurred in years with high rainfall rates compared to dry rainy seasons. The variable importance was analyzed and showed high-rank positions for features of the Enhanced Vegetation Index (EVI) and the blue and middle infrared bands, indicating that soil reflectance was crucial information for an accurate spectral discrimination of Kalahari vegetation types. Time series features related to reflectance intensity obtained increased rank-positions compared to phenology-related metrics. Full article

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

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Full article ">Figure 1
Overview of the study region in communal areas in the Namibian Eastern Kalahari showing the main savanna vegetation types after Giess [<a href="#B4-remotesensing-01-00620" class="html-bibr">4</a>] overlain with the distribution of botanical field samples [<a href="#B40-remotesensing-01-00620" class="html-bibr">40</a>]. Full article ">Figure 2
Examples for the generation of training data from in-situ to MODIS 232 m pixel size. Botanical field plots were intersected with homogeneous segments retrieved from Landsat imagery [2a]. The training data on the 232 m MODIS pixel is visualized in [2b], displayed on the MODIS image of the 81th day of the year (DOY) 2004. Note the detailed description of the vegetation type legend in <a href="#remotesensing-01-00620-t001" class="html-table">Table 1</a>. Full article ">Figure 3
Flowchart of the extraction of intensity-related temporal segment metrics and phenological time series metrics derived from the TIMESAT software [<a href="#B52-remotesensing-01-00620" class="html-bibr">52</a>]. Note the resulting feature sets for the classification in the grey boxes. Full article ">Figure 4
EVI time series of the year 2003–2004 averaged for the vegetation type classes with 4a moderately closed shrub- and bushland vegetation cover and 4b semi-open shrub- and bushland vegetation after Edwards [<a href="#B41-remotesensing-01-00620" class="html-bibr">41</a>] and graminoid crops and pans shown in the MODIS (MOD13Q1, 232 m) Enhanced Vegetation Index (EVI) smothed with a Savitzky-Golay filter. See <a href="#remotesensing-01-00620-t001" class="html-table">Table 1</a> for class labels. Full article ">Figure 5
Vegetation type classification derived from inter-annual MODIS time series metrics (2001–2007) based on Random Forest classification. The vegetation type map is shown in 5a. 5b and 5c show the classification result for examples of the Omatako River region (Box B) and an Omarumba valley cut deep into Kalahari sands (Box C), 5b and 5c are compared to Landsat-TM images (RGB-4-3-2). Full article ">Figure 6
Relationship between the out-of-bag (OOB) error rates for seasonal vegetation type classifications and cumulative bi-annual rainfall (mean and standard deviation). Note the increasing OOB error rates with increasing precipitation. Full article ">Figure 7
Variable importance visualizing the 15 top-ranked MODIS time series metrics for mapping Kalahari vegetation types based on the seasonal feature set 2003–2004 as decrease of OOB error. Full article ">

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Open AccessArticle

Mapping Invasive Tamarisk (Tamarix): A Comparison of Single-Scene and Time-Series Analyses of Remotely Sensed Data

by Paul H. Evangelista, Thomas J. Stohlgren, Jeffrey T. Morisette and Sunil Kumar

Remote Sens. 2009, 1(3), 519-533; https://doi.org/10.3390/rs1030519 - 31 Aug 2009

Cited by 101 | Viewed by 17989

Abstract

In this study, we tested the Maximum Entropy model (Maxent) for its application and performance in remotely sensing invasive Tamarix sp. Six Landsat 7 ETM+ satellite scenes and a suite of vegetation indices at different times of the growing season were selected for [...] Read more.

In this study, we tested the Maximum Entropy model (Maxent) for its application and performance in remotely sensing invasive Tamarix sp. Six Landsat 7 ETM+ satellite scenes and a suite of vegetation indices at different times of the growing season were selected for our study area along the Arkansas River in Colorado. Satellite scenes were selected for April, May, June, August, September, and October and tested in single-scene and time-series analyses. The best model was a time-series analysis fit with all spectral variables, which had an AUC = 0.96, overall accuracy = 0.90, and Kappa = 0.79. The top predictor variables were June tasselled cap wetness, September tasselled cap wetness, and October band 3. A second time-series analysis, where the variables that were highly correlated and demonstrated low predictive strengths were removed, was the second best model. The third best model was the October single-scene analysis. Our results may prove to be an effective approach for mapping Tamarix sp., which has been a challenge for resource managers. Of equal importance is the positive performance of the Maxent model in handling remotely sensed datasets. Full article

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

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595 KiB

Open AccessArticle

Potential Species Distribution of Balsam Fir Based on the Integration of Biophysical Variables Derived with Remote Sensing and Process-Based Methods

by Quazi K. Hassan and Charles P.-A. Bourque

Remote Sens. 2009, 1(3), 393-407; https://doi.org/10.3390/rs1030393 - 17 Aug 2009

Cited by 18 | Viewed by 14408

Abstract

In this paper we present a framework for modelling potential species distribution (PSD) of balsam fir [bF; Abies balsamea (L.) Mill.] as a function of landscape-level descriptions of: (i) growing degree days (GDD: a temperature related index), (ii) land-surface wetness, (iii) incident photosynthetically [...] Read more.

In this paper we present a framework for modelling potential species distribution (PSD) of balsam fir [bF; Abies balsamea (L.) Mill.] as a function of landscape-level descriptions of: (i) growing degree days (GDD: a temperature related index), (ii) land-surface wetness, (iii) incident photosynthetically active radiation (PAR), and (iv) tree habitat suitability. GDD and land-surface wetness are derived primarily from remote sensing data acquired with the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the Terra satellite. PAR is calculated with an existing spatial model of solar radiation. Raster-based calculations of habitat suitability and PSD are obtained by multiplying normalized values of species environmental-response functions (one for each environmental variable) parameterized for balsam fir. As a demonstration of the procedure, we apply the calculations to a high bF-content area in northwest New Brunswick, Canada, at 250-m resolution. Location of medium-to-high habitat suitability values (i.e., >0.50) and actual forests, with >50% bF, matched on average 92% of the time. Full article

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

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Full article ">Figure 1
Location of the study area: (a) in northwest New Brunswick, Canada; (b) extent of high bF-content stands (with >50% bF, by volume) from an existing digital forest-cover map (28120 stands, in all). Forest cover in ecoregion 3a is not available for this study. Full article ">Figure 2
Relation between daily mean <math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mi>T</mi> <mo>¯</mo> </mover> <mi>a</mi> </msub> </mrow> </semantics> </math> and TS for the January-October period of 2004 collected over a predominantly bF forest near the southern limit of the study area. Full article ">Figure 3
Interpretative diagram for TVWI determination (after [<a href="#B22-remotesensing-01-00393" class="html-bibr">22</a>]). Full article ">Figure 4
Conceptual framework for modelling and validating PSD. Full article ">Figure 5
Spatial and frequency distribution of GDD [(a) and (b)]; TVWI [(c) and (d)]; and nPAR [(e) and (f)]. Full article ">Figure 6
Spatial distribution of modelled HS and PSD for bF (a); outlined areas A-C are identified to assist with discussion on PSD characteristics in the main body of the paper; bF-dominated stands (light gray polygons; >50% bF) are overlain a portion of the study area (b). Full article ">

274 KiB

Open AccessArticle

Remote Sensing and Mapping of Tamarisk along the Colorado River, USA: A Comparative Use of Summer-Acquired Hyperion, Thematic Mapper and QuickBird Data

by Gregory A. Carter, Kelly L. Lucas, Gabriel A. Blossom, Cheryl L. Lassitter, Dan M. Holiday, David S. Mooneyhan, Danielle R. Fastring, Tracy R. Holcombe and Jerry A. Griffith

Remote Sens. 2009, 1(3), 318-329; https://doi.org/10.3390/rs1030318 - 31 Jul 2009

Cited by 72 | Viewed by 14847

Abstract

Tamarisk (Tamarix spp., saltcedar) is a well-known invasive phreatophyte introduced from Asia to North America in the 1800s. This report compares the efficacy of Landsat 5 Thematic Mapper (TM5), QuickBird (QB) and EO-1 Hyperion data in discriminating tamarisk populations near De [...] Read more.

Tamarisk (Tamarix spp., saltcedar) is a well-known invasive phreatophyte introduced from Asia to North America in the 1800s. This report compares the efficacy of Landsat 5 Thematic Mapper (TM5), QuickBird (QB) and EO-1 Hyperion data in discriminating tamarisk populations near De Beque, Colorado, USA. As a result of highly correlated reflectance among the spectral bands provided by each sensor, relatively standard image analysis methods were employed. Multispectral data at high spatial resolution (QB, 2.5 m Ground Spatial Distance or GSD) proved more effective in tamarisk delineation than either multispectral (TM5) or hyperspectral (Hyperion) data at moderate spatial resolution (30 m GSD). Full article

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

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