[go: up one dir, main page]
Next Issue
Volume 5, March
Previous Issue
Volume 5, January
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Volume 5
Issue 2
remotesensing-logo

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Remote Sens., Volume 5, Issue 2 (February 2013) – 24 articles , Pages 454-1000

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
2487 KiB  
Article
Length of Growing Period over Africa: Variability and Trends from 30 Years of NDVI Time Series
by Anton Vrieling, Jan De Leeuw and Mohammed Y. Said
Remote Sens. 2013, 5(2), 982-1000; https://doi.org/10.3390/rs5020982 - 22 Feb 2013
Cited by 143 | Viewed by 17675
Abstract
The spatial distribution of crops and farming systems in Africa is determined by the duration of the period during which crop and livestock water requirements are met. The length of growing period (LGP) is normally assessed from weather station data—scarce in large parts [...] Read more.
The spatial distribution of crops and farming systems in Africa is determined by the duration of the period during which crop and livestock water requirements are met. The length of growing period (LGP) is normally assessed from weather station data—scarce in large parts of Africa—or coarse-resolution rainfall estimates derived from weather satellites. In this study, we analyzed LGP and its variability based on the 1981–2011 GIMMS NDVI3g dataset. We applied a variable threshold method in combination with a searching algorithm to determine start- and end-of-season. We obtained reliable LGP estimates for arid, semi-arid and sub-humid climates that are consistent in space and time. This approach effectively mapped bimodality for clearly separated wet seasons in the Horn of Africa. Due to cloud contamination, the identified bimodality along the Guinea coast was judged to be less certain. High LGP variability is dominant in arid and semi-arid areas, and is indicative of crop failure risk. Significant negative trends in LGP were found for the northern part of the Sahel, for parts of Tanzania and northern Mozambique, and for the short rains of eastern Kenya. Positive trends occurred across western Africa, in southern Africa, and in eastern Kenya for the long rains. Our LGP analysis provides useful information for the mapping of farming systems, and to study the effects of climate variability and other drivers of change on vegetation and crop suitability. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
►▼ Show Figures


<p>(<b>a</b>) Average start of season (for the first season in the calendar year) derived from 1981–2011 NDVI3g data using the adaptive threshold method. (<b>b</b>) Average end of season. (<b>d,e</b>) Start and end of season for areas with a second season. (<b>c,f</b>) Number of years with valid phenology retrievals for both seasons.</p> Full article ">
<p>(<b>a</b>) Average length of growing period (in days) for the first season in calendar year. (<b>b</b>) Average length of growing period for areas with a second season. Note that cloud-contaminated areas along the Guinea Coast that were identified as bimodal (<a href="#f1-remotesensing-05-00982" class="html-fig">Figure 1</a>) are masked out.</p> Full article ">
<p>Coefficient of variation (CV) of the length of growing period between 1981 and 2011: (<b>a</b>) for unimodal areas and the first season in calendar year of bimodal areas, (<b>b</b>) for the second season of bimodal areas.</p> Full article ">
<p>Bivariate maps showing simultaneously the length of growing period (LGP) and its coefficient of variance (CV) as calculated from the 1981–2011 time series: (<b>a</b>) for unimodal areas and the first season in calendar year of bimodal areas, (<b>b</b>) for the second season of bimodal areas.</p> Full article ">
<p>Trends in LGP as determined from the 1981–2011 time series through Spearman’s rank correlation. The classes indicate the sign of the relationship (green is increasing LGP, purple is decreasing LGP) and its significance: (<b>a</b>) for unimodal areas and the first season in calendar year of bimodal areas, (<b>b</b>) for the second season of bimodal areas.</p> Full article ">
<p>Annual rate of change in LGP (days/yr) determined through linear regression from the 1981–2011 time. We only display rates of change for pixels where the Spearman’s rank correlation gave significant trends (p &lt; 0.10, see <a href="#f5-remotesensing-05-00982" class="html-fig">Figure 5</a>): (<b>a</b>) for unimodal areas and the first season in calendar year of bimodal areas, (<b>b</b>) for the second season of bimodal areas.</p> Full article ">
2268 KiB  
Review
Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs
by Clement Atzberger
Remote Sens. 2013, 5(2), 949-981; https://doi.org/10.3390/rs5020949 - 22 Feb 2013
Cited by 723 | Viewed by 48396 | Correction
Abstract
Many remote sensing applications are devoted to the agricultural sector. Representative case studies are presented in the special issue “Advances in Remote Sensing of Agriculture”. To complement the examples published within the special issue, a few main applications with regional to global focus [...] Read more.
Many remote sensing applications are devoted to the agricultural sector. Representative case studies are presented in the special issue “Advances in Remote Sensing of Agriculture”. To complement the examples published within the special issue, a few main applications with regional to global focus were selected for this review, where remote sensing contributions are traditionally strong. The selected applications are put in the context of the global challenges the agricultural sector is facing: minimizing the environmental impact, while increasing production and productivity. Five different applications have been selected, which are illustrated and described: (1) biomass and yield estimation, (2) vegetation vigor and drought stress monitoring, (3) assessment of crop phenological development, (4) crop acreage estimation and cropland mapping and (5) mapping of disturbances and land use/land cover (LULC) changes. Many other applications exist, such as precision agriculture and irrigation management (see other special issues of this journal), but were not included to keep the paper concise. The paper starts with an overview of the main agricultural challenges. This section is followed by a brief overview of existing operational monitoring systems. Finally, in the main part of the paper, the mentioned applications are described and illustrated. The review concludes with some key recommendations. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
►▼ Show Figures


<p>Annual dependence of per capita demand for crop calories on per capita real Gross Domestic Product (GDP) for each of the economic groups A–G [<a href="#b3-remotesensing-05-00949" class="html-bibr">3</a>].</p> Full article ">
<p>(<b>A</b>) Global population, (<b>B</b>) per capita Gross Domestic Product (GDP), (<b>C</b>) per capita demand for crop calories and (<b>D</b>) global demand for crop calories in 2005 (black) and projected 2050 increases (white; percent increases above bars). Nations were assigned to economic groups A–G based on their rankings per capita GDP (average for 2000–2007). Group A had the highest and group G had the lowest per capita GDP ((A–C): [<a href="#b3-remotesensing-05-00949" class="html-bibr">3</a>]; (D): own calculations).</p> Full article ">
<p>Environmental (bottom) and food security goals (top). The signs after the different items indicate if an increase is necessary (+), respectively, or a reduction (−) (adapted from [<a href="#b2-remotesensing-05-00949" class="html-bibr">2</a>]).</p> Full article ">
<p>Average yield gaps for major cereal crops, maize, wheat and rice [<a href="#b4-remotesensing-05-00949" class="html-bibr">4</a>].</p> Full article ">
<p>(<b>A</b>) Additional calories that could be produced by closing current yield gaps of crops; (<b>B</b>) increased food supply (in calories) by shifting crops to 100% human food and away from current mix of uses; and (<b>C</b>) fraction of cropland that is allocated in 2000 to growing food crops (crops that are directly consumed by people) <span class="html-italic">versus</span> all other crop uses, including animal feed and bioenergy crops [<a href="#b2-remotesensing-05-00949" class="html-bibr">2</a>]</p> Full article ">
<p>Management factors limiting yield-gap closure to 75% of attainable yields for maize [<a href="#b4-remotesensing-05-00949" class="html-bibr">4</a>].</p> Full article ">
<p>Illustration of the yield-masking approach involving a data set of 11 years [<a href="#b7-remotesensing-05-00949" class="html-bibr">7</a>].</p> Full article ">
<p>Application of the combined drought index (CDI) for the Greater Horn of Africa [<a href="#b97-remotesensing-05-00949" class="html-bibr">97</a>]. (<b>A</b>) Maps depicting the situation in 2010–2011. (<b>B</b>) Time profile of CDI from 1999 to 2012 for Belet Weyne (Somalia).</p> Full article ">
<p>Example maps showing satellite-derived phenological indicators: (<b>top</b>) average start of season (SOS) and peak of season (POS) in Brazil (from SPOT Vegetation satellite data 2000–2009); (<b>bottom</b>) Moderate Resolution Imaging Spectroradiometer (MODIS)-derived start of season (SOS) in 2011 in Austria (unpublished).</p> Full article ">
1479 KiB  
Article
Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011
by Zaichun Zhu, Jian Bi, Yaozhong Pan, Sangram Ganguly, Alessandro Anav, Liang Xu, Arindam Samanta, Shilong Piao, Ramakrishna R. Nemani and Ranga B. Myneni
Remote Sens. 2013, 5(2), 927-948; https://doi.org/10.3390/rs5020927 - 22 Feb 2013
Cited by 763 | Viewed by 42163
Abstract
Long-term global data sets of vegetation Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) are critical to monitoring global vegetation dynamics and for modeling exchanges of energy, mass and momentum between the land surface and planetary boundary [...] Read more.
Long-term global data sets of vegetation Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) are critical to monitoring global vegetation dynamics and for modeling exchanges of energy, mass and momentum between the land surface and planetary boundary layer. LAI and FPAR are also state variables in hydrological, ecological, biogeochemical and crop-yield models. The generation, evaluation and an example case study documenting the utility of 30-year long data sets of LAI and FPAR are described in this article. A neural network algorithm was first developed between the new improved third generation Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) and best-quality Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LAI and FPAR products for the overlapping period 2000–2009. The trained neural network algorithm was then used to generate corresponding LAI3g and FPAR3g data sets with the following attributes: 15-day temporal frequency, 1/12 degree spatial resolution and temporal span of July 1981 to December 2011. The quality of these data sets for scientific research in other disciplines was assessed through (a) comparisons with field measurements scaled to the spatial resolution of the data products, (b) comparisons with broadly-used existing alternate satellite data-based products, (c) comparisons to plant growth limiting climatic variables in the northern latitudes and tropical regions, and (d) correlations of dominant modes of interannual variability with large-scale circulation anomalies such as the EI Niño-Southern Oscillation and Arctic Oscillation. These assessment efforts yielded results that attested to the suitability of these data sets for research use in other disciplines. The utility of these data sets is documented by comparing the seasonal profiles of LAI3g with profiles from 18 state-of-the-art Earth System Models: the models consistently overestimated the satellite-based estimates of leaf area and simulated delayed peak seasonal values in the northern latitudes, a result that is consistent with previous evaluations of similar models with ground-based data. The LAI3g and FPAR3g data sets can be obtained freely from the NASA Earth Exchange (NEX) website. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
►▼ Show Figures


<p>Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g products. (<b>a</b>) Thirty year average annual mean LAI3g. (<b>b</b>) Thirty year average annual mean FPAR3g. (<b>c</b>) Time series of LAI3g anomalies for different latitudinal bands. (<b>d</b>) Time series of LAI3g anomalies for different vegetation types. The background shading in (c) and (d) shows the occurrence and intensity of EI Niño-Southern Oscillation (ENSO) events as defined by the Multivariate ENSO Index. The black dashed lines indicate transition times for the various National Oceanic and Atmospheric Administration (NOAA) satellites (N07 to N18). The two major volcanic eruptions (El Chichón and Mount Pinatubo) and the two recent Amazonian droughts are depicted by the orange and purple dashed lines, respectively.</p> Full article ">
<p>Comparison of LAI3g with scaled field measurements from six biomes representative of the global land cover classes. A total of 45 field data sets from 29 sites listed in Table A4 of [<a href="#b40-remotesensing-05-00927" class="html-bibr">40</a>] were used (details of field data handling to derive LAI values comparable to satellite retrievals of LAI can be found in [<a href="#b36-remotesensing-05-00927" class="html-bibr">36</a>]).</p> Full article ">
<p>Comparison of monthly LAI values from CYCLOPES and LAI3g data sets for four broad vegetation classes (forests, herbaceous vegetation, other woody vegetation and cropland/natural vegetation mosaics) for the period 1999 to 2007. These classes are groups of International Geosphere Biosphere Programme (IGBP) land cover types as per <a href="#SD1" class="html-supplementary-material">Table S5</a>.</p> Full article ">
<p>Comparison of monthly LAI values from CYCLOPES and LAI3g data sets for four broad vegetation classes (forests, herbaceous vegetation, other woody vegetation and cropland/natural vegetation mosaics) for the period 1999 to 2007. These classes are groups of International Geosphere Biosphere Programme (IGBP) land cover types as per <a href="#SD1" class="html-supplementary-material">Table S5</a>.</p> Full article ">
<p>Density scatter plots of monthly LAI3g and CYCLOPES LAI for 323 BELMANIP sites for the time period from 1999 to 2007. The plots show correlation between the two products for four broad groups of vegetation which are grouping of the IGBP land covers (<a href="#SD1" class="html-supplementary-material">Table S5</a>). The black dash line is the 1:1 line. The solid black lines are regression lines derived from the scatter plot.</p> Full article ">
<p>Statistical evaluation of LAI3g with temperature in the northern latitudes and precipitation in the tropical regions. (<b>a</b>) Statistical analyses between approximate growing season (May to September) averages of LAI3g and surface temperature in the northern latitudes (50°N–90°N) for the overlapping period of the two data sets (1982 to 2009). The inset in (a) shows temporal variations of standardized anomalies of growing season averages of LAI3g and temperature. (<b>b</b>) Correlation between annual mean LAI3g and annual total precipitation in the tropical latitudes (23°S–23°N). Similar plots for FPAR3g are shown in <a href="#SD1" class="html-supplementary-material">Figure S6</a>.</p> Full article ">
<p>Correlations between the standardized time series of the first and second canonical factors (CF1 and CF2) of land surface temperature, precipitation and LAI3g with NINO3 and AO indices in the northern (10°N to 90°N) and tropical/extra-tropical regions (40°S to 40°N) for the period 1982 to 2009. The standardized September through November average NINO3 index time series of the preceding year and the January through March average AO index are shown in these plots as black dash lines.</p> Full article ">
<p>Comparison of 1982 to 2005 average seasonal cycle between LAI simulated by 18 Earth System Models (ESMs) and LAI3g. The shaded area shows the standard variation for the 18 ESMs. This analysis is based on the assumption that ESM LAI is defined with respect to the vegetated area of the model grid cell. In the southern hemisphere, dates for regions south of 23°S are shifted by 6 months.</p> Full article ">
562 KiB  
Article
Estimating Winter Annual Biomass in the Sonoran and Mojave Deserts with Satellite- and Ground-Based Observations
by Grant M. Casady, Willem J.D. Van Leeuwen and Bradley C. Reed
Remote Sens. 2013, 5(2), 909-926; https://doi.org/10.3390/rs5020909 - 22 Feb 2013
Cited by 20 | Viewed by 6816
Abstract
Winter annual plants in southwestern North America influence fire regimes, provide forage, and help prevent erosion. Exotic annuals may also threaten native species. Monitoring winter annuals is difficult because of their ephemeral nature, making the development of a satellite monitoring tool valuable. We [...] Read more.
Winter annual plants in southwestern North America influence fire regimes, provide forage, and help prevent erosion. Exotic annuals may also threaten native species. Monitoring winter annuals is difficult because of their ephemeral nature, making the development of a satellite monitoring tool valuable. We mapped winter annual aboveground biomass in the Desert Southwest from satellite observations, evaluating 18 algorithms using time-series vegetation indices (VI). Field-based biomass estimates were used to calibrate and evaluate each algorithm. Winter annual biomass was best estimated by calculating a base VI across the period of record and subtracting it from the peak VI for each winter season (R2 = 0.92). The normalized difference vegetation index (NDVI) derived from 8-day reflectance data provided the best estimate of winter annual biomass. It is important to account for the timing of peak vegetation when relating field-based estimates to satellite VI data, since post-peak field estimates may indicate senescent biomass which is inaccurately represented by VI-based estimates. Images generated from the best-performing algorithm show both spatial and temporal variation in winter annual biomass. Efforts to manage this variable resource would be enhanced by a tool that allows the monitoring of changes in winter annual resources over time. Full article
►▼ Show Figures


<p>Map of the five study areas (shown as diamonds) used to take field based estimates of winter annual biomass. Study areas consisted of Joshua Tree National Park in the Mojave Desert, Sonoran Desert National Monument, Ironwood Forest National Monument, and Catalina State Park in the Sonoran Desert, and Agua Fria National Monument along the ecotone between the Sonoran Desert and the Mogollon Rim.</p> Full article ">
<p>A portion of the smoothed EVI time-series (2007–2008) is shown to illustrate the three methods investigated for the determination of peak winter annual production in the winter of 2008–2009 for a single pixel. All three methods identify the peak of the VI in the 2008–2009 winter season (grayed area) and subtract it from a base value, as shown by the labeled brackets for each of the tree techniques. For VI<sub>0</sub> the base value is 0, making the VI<sub>0</sub> value equal to the smoothed EVI. For VI<sub>10</sub> the baseline is the tenth percentile for the entire period of record from 2000 to 2009, shown as a straight dashed line, the VI<sub>10</sub> measure therefore being equal to the difference between the smoothed EVI and the value of the tenth percentile for that pixel over the period of record. For VI<sub>MED</sub> the baseline is represented by the repeating dotted line, and is the calculated as the median value for each period across all years, and VI<sub>MED</sub> calculated as the difference between the current period and the median of the period for all years.</p> Full article ">
<p>RMSE values are shown for the 18 models tested using (<b>A</b>) all study areas and (<b>B</b>) excluding the Agua Fria study area. Data are grouped by VI data type, and within each data type the techniques are ordered such that the first three bars in each group (white) indicate methods without adjusting for post-peak data collection, and the next three (gray) indicate models after adjusting for post-peak collection dates. N = 51.</p> Full article ">
<p>Plots of biomass against VI data after differencing from baseline vegetation indices using three different approaches. In all cases, calculations are made after accounting for post-peak field collection data by using peak VI in place of the corresponding field data collection dates. Trend lines indicate the least squared estimate of the best linear fit to the data. Plots on the left side show relationships including all field data (N = 51), and plots on the right show relationships after excluding Agua Fria data (N = 40).</p> Full article ">
<p>Joshua Tree National Park peak winter annual above ground biomass, estimated as a function of 8-day MODIS NDVI over the 10th percentile from winter 2000–2001 through winter 2008–2009. The red line indicates the boundary of Joshua Tree National Park. Differences in both spatial and temporal distribution of winter annual biomass illustrate the value of using satellite-based maps of winter annual biomass. Black or grayscale areas represent less than 50 kg/ha above ground biomass.</p> Full article ">
3031 KiB  
Article
Relationship between Hyperspectral Measurements and Mangrove Leaf Nitrogen Concentrations
by Chunhua Zhang, John M. Kovacs, Mark P. Wachowiak and Francisco Flores-Verdugo
Remote Sens. 2013, 5(2), 891-908; https://doi.org/10.3390/rs5020891 - 22 Feb 2013
Cited by 37 | Viewed by 8531
Abstract
The use of spectral response curves for estimating nitrogen (N) leaf concentrations generally has been found to be a challenging task for a variety of plant species. In this investigation, leaf N concentration and corresponding laboratory hyperspectral data were examined for two species [...] Read more.
The use of spectral response curves for estimating nitrogen (N) leaf concentrations generally has been found to be a challenging task for a variety of plant species. In this investigation, leaf N concentration and corresponding laboratory hyperspectral data were examined for two species of mangrove (Avicennia germinans, Rhizophora mangle) representing a variety of conditions (healthy, poor condition, dwarf) of a degraded mangrove forest located in the Mexican Pacific. This is the first time leaf nitrogen content has been examined using close range hyperspectral remote sensing of a degraded mangrove forest. Simple comparisons between individual wavebands and N concentrations were examined, as well as two models employed to predict N concentrations based on multiple wavebands. For one model, an Artificial Neural Network (ANN) was developed based on known N absorption bands. For comparative purposes, a second model, based on the well-known Stepwise Multiple Linear Regression (SMLR) approach, was employed using the entire dataset. For both models, the input data included continuum removed reflectance, band depth at the centre of the absorption feature (BNC), and log (1/BNC). Weak to moderate correlations were found between N concentration and single band spectral responses. The results also indicate that ANNs were more predictive for N concentration than was SMLR, and had consistently higher r2 values. The highest r2 value (0.91) was observed in the prediction of black mangrove (A. germinans) leaf N concentration using the BNC transformation. It is thus suggested that artificial neural networks could be used in a complementary manner with other techniques to assess mangrove health, thereby improving environmental monitoring in coastal wetlands, which is of prime importance to local communities. In addition, it is recommended that the BNC transformation be used on the input for such N concentration prediction models. Full article
►▼ Show Figures


<p>Location of the mangrove sampling area within the Mexican Pacific (Enhanced Near Infrared, Red, Green of Advanced Land Observing Satellite date Advanced Visible and Near Infrared Radiometer (ALOS AVNIR)-2 dated 28 March 2010).</p> Full article ">
<p>Types of mangrove sampled. From left to right: (<b>a</b>) healthy black mangrove (<span class="html-italic">Avicennia germinans</span>), (<b>b</b>) dwarf black mangrove, (<b>c</b>) poor condition black mangrove, (<b>d</b>) healthy red mangrove (<span class="html-italic">Rhizophora mangle</span>), (<b>e</b>) poor condition red mangrove.</p> Full article ">
<p>Original reflectance spectrum and continuum lines for healthy black mangroves.</p> Full article ">
<p>Continuum removed (<b>a</b>) mean canopy reflectance spectra for <span class="html-italic">Avicennia germinans</span> of three conditions (Tall: <span class="html-italic">n</span> = 30; Dwarf: <span class="html-italic">n</span> = 30; Poor: <span class="html-italic">n</span> = 30); (<b>b</b>) log transformed.</p> Full article ">
<p>Mean spectral curves recorded for two mangrove species located near Mazatlan, Mexico.</p> Full article ">
<p>The first derivative reflectance of mean mangrove spectral response. The highest derivative value indicates the position of the red edge inflection point.</p> Full article ">
<p>Correlations between mangrove leaf N concentration and leaf reflectance.</p> Full article ">
<p>Artificial Neural Network (ANN) testing results for red mangrove reflectance. Dashed line indicates measured concentration = predicted concentration (<span class="html-italic">i.e.</span>, r<sup>2</sup> = 1) (<b>a</b>) Continuum-removed (CR), (<b>b</b>) log(1/CR), (<b>c</b>) BNC, (<b>d</b>) log(1/BNC).</p> Full article ">
<p>Artificial Neural Network (ANN) testing results for black mangrove reflectance. Dashed line indicates measured concentration = predicted concentration (<span class="html-italic">i.e.</span>, r<sup>2</sup> = 1) (<b>a</b>) Continuum-removed (CR), (<b>b</b>) log(1/CR), (<b>c</b>) BNC, (<b>d</b>) log(1/BNC).</p> Full article ">
<p>Artificial Neural Network (ANN) testing results for black mangrove reflectance. Dashed line indicates measured concentration = predicted concentration (<span class="html-italic">i.e.</span>, r<sup>2</sup> = 1) (<b>a</b>) Continuum-removed (CR), (<b>b</b>) log(1/CR), (<b>c</b>) BNC, (<b>d</b>) log(1/BNC).</p> Full article ">
1620 KiB  
Article
Use of Satellite Radar Bistatic Measurements for Crop Monitoring: A Simulation Study on Corn Fields
by Leila Guerriero, Nazzareno Pierdicca, Luca Pulvirenti and Paolo Ferrazzoli
Remote Sens. 2013, 5(2), 864-890; https://doi.org/10.3390/rs5020864 - 20 Feb 2013
Cited by 38 | Viewed by 7838
Abstract
This paper presents a theoretical study of microwave remote sensing of vegetated surfaces. The purpose of this study is to find out if satellite bistatic radar systems can provide a performance, in terms of sensitivity to vegetation geophysical parameters, equal to or greater [...] Read more.
This paper presents a theoretical study of microwave remote sensing of vegetated surfaces. The purpose of this study is to find out if satellite bistatic radar systems can provide a performance, in terms of sensitivity to vegetation geophysical parameters, equal to or greater than the performance of monostatic systems. Up to now, no suitable bistatic data collected over land surfaces are available from satellite, so that the electromagnetic model developed at Tor Vergata University has been used to perform simulations of the scattering coefficient of corn, over a wide range of observation angles at L- and C-band. According to the electromagnetic model, the most promising configuration is the one which measures the VV or HH bistatic scattering coefficient on the plane that lies at the azimuth angle orthogonal with respect to the incidence plane. At this scattering angle, the soil contribution is minimized, and the effects of vegetation growth are highlighted. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
►▼ Show Figures


<p>Geometric elements that identify the transmitter-target-receiver (Tx-TG-Rx) bistatic configuration.</p> Full article ">
<p>Plant Water Content <span class="html-italic">vs.</span> Plant Height measured on two agricultural sites. Diamonds: Central Plain (CH) and Loamy (B). Regression lines are also reported.</p> Full article ">
<p>Bistatic scattering coefficient <span class="html-italic">σ</span><sup>0</sup> (dB) of corn plants with 50 cm and 150 cm height (left and right column, respectively), at L-band, <span class="html-italic">θ<sub>i</sub></span> = 20°, soil roughness <span class="html-italic">σ<sub>z</sub></span> = 0.5 cm (height standard deviation), <span class="html-italic">SM</span> = 10%. From top to bottom: VV, HV, HH polarizations.</p> Full article ">
<p>Bistatic scattering coefficient <span class="html-italic">σ</span><sup>0</sup> (dB) of corn plants with 50 cm and 150 cm height (left and right column, respectively), at C-band, <span class="html-italic">θ<sub>i</sub></span> = 20°, soil roughness <span class="html-italic">σ<sub>z</sub></span> = 0.5 cm (height standard deviation), <span class="html-italic">SM</span> = 10%. From top to bottom: VV, HV, HH polarizations.</p> Full article ">
<p>Sensitivity to corn plant height [dB/(10 cm)] at L-band, <span class="html-italic">θ<sub>i</sub></span> = 20°, soil roughness <span class="html-italic">σ<sub>z</sub></span> = 1.5 cm, <span class="html-italic">SM</span> = 25%. On top: VV (left), HV (right); bottom: HH.</p> Full article ">
<p>Sensitivity to corn plant height [dB/(10 cm)]. Same parameters as in <a href="#f5-remotesensing-05-00864" class="html-fig">Figure 5</a>, but for a denser vegetation cover. On top: VV (left), HV (right); bottom: HH.</p> Full article ">
<p>The scattering components along the scattering cone with <span class="html-italic">θ<sub>s</sub></span> = <span class="html-italic">θ<sub>i</sub></span> = 20°. L-band, <span class="html-italic">σ</span><sub>z</sub> = 1.5 cm, m<sub>g</sub> = 25%. Corn height = 50 cm, vegetation cover = 40% (left column) and 150 cm, vegetation cover = 100% (right column). VV polarization (top row), HV polarization (middle row) and HH polarization (bottom row).</p> Full article ">
<p>Square root of Cramér–Rao Lower Bound (<span class="html-italic">CRLB)</span> [10 cm] for a corn field. L-band, <span class="html-italic">θ<sub>i</sub></span> = 20°, <span class="html-italic">σ<sub>z</sub></span> = 1.5 cm. Available measurements are: bistatic <span class="html-italic">σ</span><sup>0</sup><span class="html-italic"><sub>VV</sub></span> and bistatic <span class="html-italic">σ</span><sup>0</sup><span class="html-italic"><sub>HV</sub></span> (upper left), bistatic <span class="html-italic">σ</span><sup>0</sup><span class="html-italic"><sub>HH</sub></span> and bistatic <span class="html-italic">σ</span><sup>0</sup><span class="html-italic"><sub>VH</sub></span> (upper right) and bistatic <span class="html-italic">σ</span><sup>0</sup><span class="html-italic"><sub>HH</sub></span> and bistatic <span class="html-italic">σ</span><sup>0</sup><span class="html-italic"><sub>VV</sub></span> (bottom).</p> Full article ">
<p>Square root of <span class="html-italic">CRLB</span> [10 cm] for a corn field. L-band, <span class="html-italic">θ<sub>i</sub></span> = 20°, <span class="html-italic">σ<sub>z</sub></span> = 1.5 cm. Available measurements, first row: bistatic <span class="html-italic">σ</span><sup>0</sup><span class="html-italic"><sub>VV</sub></span> and monostatic <span class="html-italic">σ</span><sup>0</sup><span class="html-italic"><sub>VV</sub></span> on the left, bistatic <span class="html-italic">σ</span><sup>0</sup><span class="html-italic"><sub>VV</sub></span> and monostatic <span class="html-italic">σ</span><sup>0</sup><span class="html-italic"><sub>HV</sub></span> on the right; second row: bistatic <span class="html-italic">σ</span><sup>0</sup><span class="html-italic"><sub>HV</sub></span> and monostatic <span class="html-italic">σ</span><sup>0</sup><span class="html-italic"><sub>HV</sub></span> on the left, bistatic <span class="html-italic">σ</span><sup>0</sup><span class="html-italic"><sub>HV</sub></span> and monostatic <span class="html-italic">σ</span><sup>0</sup><span class="html-italic"><sub>VV</sub></span> (or <span class="html-italic">σ</span><sup>0</sup><span class="html-italic"><sub>HH</sub></span>) on the right; third row: bistatic <span class="html-italic">σ</span><sup>0</sup><span class="html-italic"><sub>HH</sub></span> and monostatic <span class="html-italic">σ</span><sup>0</sup><span class="html-italic"><sub>HH</sub></span> on the left and bistatic <span class="html-italic">σ</span><sup>0</sup><span class="html-italic"><sub>HH</sub></span> and monostatic <span class="html-italic">σ</span><sup>0</sup><span class="html-italic"><sub>VH</sub></span> on the right.</p> Full article ">
128 KiB  
Editorial
Remote Sensing Best Paper Award 2013
by Prasad Thenkabail
Remote Sens. 2013, 5(2), 862-863; https://doi.org/10.3390/rs5020862 - 20 Feb 2013
Viewed by 9057
Abstract
Remote Sensing has started to institute a “Best Paper” award to recognize the most outstanding papers in the area of remote sensing techniques, design and applications published in Remote Sensing. We are pleased to announce the first “Remote Sensing Best Paper [...] Read more.
Remote Sensing has started to institute a “Best Paper” award to recognize the most outstanding papers in the area of remote sensing techniques, design and applications published in Remote Sensing. We are pleased to announce the first “Remote Sensing Best Paper Award” for 2013. Nominations were selected by the Editor-in-Chief and selected editorial board members from among all the papers published in 2009. Reviews and research papers were evaluated separately. [...] Full article
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">
757 KiB  
Article
Assessing Performance of NDVI and NDVI3g in Monitoring Leaf Unfolding Dates of the Deciduous Broadleaf Forest in Northern China
by Xiangzhong Luo, Xiaoqiu Chen, Lin Xu, Ranga Myneni and Zaichun Zhu
Remote Sens. 2013, 5(2), 845-861; https://doi.org/10.3390/rs5020845 - 18 Feb 2013
Cited by 35 | Viewed by 9324
Abstract
Using estimated leaf unfolding data and two types of Normalized Difference Vegetation Index (NDVI and NDVI3g) data generated from the Advanced Very High Resolution Radiometer (AVHRR) in the deciduous broadleaf forest of northern China during 1986 to 2006, we analyzed spatial, temporal and [...] Read more.
Using estimated leaf unfolding data and two types of Normalized Difference Vegetation Index (NDVI and NDVI3g) data generated from the Advanced Very High Resolution Radiometer (AVHRR) in the deciduous broadleaf forest of northern China during 1986 to 2006, we analyzed spatial, temporal and spatiotemporal relationships and differences between ground-based growing season beginning (BGS) and NDVI (NDVI3g)-retrieved start of season (SOS and SOS3g), and compared effectiveness of NDVI and NDVI3g in monitoring BGS. Results show that the spatial series of SOS (SOS3g) correlates positively with the spatial series of BGS at all pixels in each year (P < 0.001). Meanwhile, the time series of SOS (SOS3g) correlates positively with the time series of BGS at more than 65% of all pixels during the study period (P < 0.05). Furthermore, when pooling SOS (SOS3g) time series and BGS time series from all pixels, a significant positive correlation (P < 0.001) was also detectable between the spatiotemporal series of SOS (SOS3g) and BGS. In addition, the spatial, temporal and spatiotemporal differences between SOS (SOS3g) and BGS are at acceptable levels overall. Generally speaking, SOS3g is more consistent and accurate than SOS in capturing BGS, which suggests that NDVI3g data might be more sensitive than NDVI data in monitoring vegetation leaf unfolding. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
►▼ Show Figures


<p>Distribution of the temperate deciduous broadleaf forest areas in northern China.</p> Full article ">
<p>Distribution of phenological stations and time series length of leaf unfolding data for the four tree species.</p> Full article ">
<p>Flow-diagram of daily mean temperature-based spatial LU simulation and extrapolation.</p> Full article ">
<p>Spatial pattern of mean BGS (growing season beginning), SOS (start of season) and SOS3g over 1986 to 2006 in the deciduous broadleaf forest of northern China. (<b>a</b>) BGS; (<b>b</b>) SOS; (<b>c</b>) SOS3g.</p> Full article ">
<p>Spatial patterns of temporal correlation coefficients and mean differences between SOS (SOS3g) and BGS at each pixel from 1986 to 2006. (<b>a</b>) correlation coefficients between SOS and BGS; (<b>b</b>) correlation coefficients between SOS3g and BGS; (<b>c</b>) ME between SOS and BGS; (<b>d</b>) ME between SOS3g and BGS; (<b>e</b>) MAE between SOS and BGS; (<b>f</b>) MAE between SOS3g and BGS.</p> Full article ">
<p>Spatiotemporal correlation coefficients and differences between SOS (SOS3g) and BGS at each pixel in each year. (<b>a</b>) between SOS and BGS; (<b>b</b>) between SOS3g and BGS.</p> Full article ">
<p>(<b>a</b>) Interannual variations of regional mean AVHRR NDVI and AVHRR NDVI3g and (<b>b</b>) relationships between regional mean AVHRR NDVI and AVHRR NDVI3g from 1986 to 2006.</p> Full article ">
<p>Relationship between SOS (SOS3g)-BGS spatial correlation coefficient and the mean range of leaf unfolding dates of the four tree species at all pixels from 1986 to 2006. (<b>a</b>) between SOS-BGS spatial correlation coefficients and the mean range of leaf unfolding dates; (<b>b</b>) between SOS3g-BGS spatial correlation coefficients and the mean range of leaf unfolding dates.</p> Full article ">
748 KiB  
Article
The Impact of Potential Land Cover Misclassification on MODIS Leaf Area Index (LAI) Estimation: A Statistical Perspective
by Hongliang Fang, Wenjuan Li and Ranga B. Myneni
Remote Sens. 2013, 5(2), 830-844; https://doi.org/10.3390/rs5020830 - 15 Feb 2013
Cited by 47 | Viewed by 8266
Abstract
Understanding the impact of vegetation mixture and misclassification on leaf area index (LAI) estimation is crucial for algorithm development and the application community. Using the MODIS standard land cover and LAI products, global LAI climatologies and statistics were obtained for both pure and [...] Read more.
Understanding the impact of vegetation mixture and misclassification on leaf area index (LAI) estimation is crucial for algorithm development and the application community. Using the MODIS standard land cover and LAI products, global LAI climatologies and statistics were obtained for both pure and mixed pixels to evaluate the effects of biome mixture on LAI estimation. Misclassification between crops and shrubs does not generally translate into large LAI errors (<0.37 or 27.0%), partly due to their relatively lower LAI values. Biome misclassification generally leads to an LAI overestimation for savanna, but an underestimation for forests. The largest errors caused by misclassification are also found for savanna (0.51), followed by evergreen needleleaf forests (0.44) and broadleaf forests (~0.31). Comparison with MODIS uncertainty indicators show that biome misclassification is a major factor contributing to LAI uncertainties for savanna, while for forests, the main uncertainties may be introduced by algorithm deficits, especially in summer. The LAI climatologies for pure pixels are recommended for land surface modeling studies. Future studies should focus on improving the biome classification for savanna systems and refinement of the retrieval algorithms for forest biomes. Full article
►▼ Show Figures


<p>Global distribution of the primary biome types based on the MODIS Leaf Area Index (LAI)/Fraction of Absorbed Photosynthetically Active Radiation (FPAR) (Type 3) classification system. Data from the MODIS (MCD12Q1 C5) land cover product in 2003 (1 km).</p> Full article ">
<p>Global distribution of the secondary biome types based on the MODIS LAI/FPAR (Type 3) classification system. Data converted from the MODIS (MCD12Q1 C5) International Geosphere Biosphere Program (IGBP) (Type 1) classification system (1 km, 2003). Pink pixels show the primary biome types with high confidence. White areas are IGBP classes (e.g., mixed forest) with no equivalent MODIS LAI/FPAR classes.</p> Full article ">
<p>LAI climatologies for both pure and mixed pixels from 2003 to 2009. Each panel is for a primary biome type, and the temporal profiles indicate the secondary biome types in the legend. The solid dark lines are for pure pixels (same primary and secondary types), while the dashed dark lines are the average of all mixed pixels.</p> Full article ">
<p>Comparison of misclassification induced LAI errors (misclassification induced errors (MIEs) in absolute values, solid red) with theoretical uncertainties reported in quantitative quality indicators (QQIs) (dashed blue). Data calculated globally from 2003 to 2009.</p> Full article ">
1495 KiB  
Article
Trends and Variability of AVHRR-Derived NPP in India
by Govindasamy Bala, Jaideep Joshi, Rajiv K. Chaturvedi, Hosahalli V. Gangamani, Hirofumi Hashimoto and Rama Nemani
Remote Sens. 2013, 5(2), 810-829; https://doi.org/10.3390/rs5020810 - 15 Feb 2013
Cited by 56 | Viewed by 10748
Abstract
In this paper, we estimate the trends and variability in Advanced Very High Resolution Radiometer (AVHRR)-derived terrestrial net primary productivity (NPP) over India for the period 1982–2006. We find an increasing trend of 3.9% per decade (r = 0.78, R2 = 0.61) [...] Read more.
In this paper, we estimate the trends and variability in Advanced Very High Resolution Radiometer (AVHRR)-derived terrestrial net primary productivity (NPP) over India for the period 1982–2006. We find an increasing trend of 3.9% per decade (r = 0.78, R2 = 0.61) during the analysis period. A multivariate linear regression of NPP with temperature, precipitation, atmospheric CO2 concentration, soil water and surface solar radiation (r = 0.80, R2 = 0.65) indicates that the increasing trend is partly driven by increasing atmospheric CO2 concentration and the consequent CO2 fertilization of the ecosystems. However, human interventions may have also played a key role in the NPP increase: non-forest NPP growth is largely driven by increases in irrigated area and fertilizer use, while forest NPP is influenced by plantation and forest conservation programs. A similar multivariate regression of interannual NPP anomalies with temperature, precipitation, soil water, solar radiation and CO2 anomalies suggests that the interannual variability in NPP is primarily driven by precipitation and temperature variability. Mean seasonal NPP is largest during post-monsoon and lowest during the pre-monsoon period, thereby indicating the importance of soil moisture for vegetation productivity. Full article
►▼ Show Figures


<p>Spatial pattern of (<b>a</b>) annual mean, (<b>b</b>) standard deviation, (<b>c</b>) temporal evolution of domain-mean NPP, (<b>d</b>) coefficient of variation of NPP, (<b>e</b>) spatial pattern of the annual NPP trends, and (<b>f</b>) percentage decadal trends of NPP from 1982 to 2006.</p> Full article ">
<p>Temporal evolutions of domain-averaged NPP and (<b>a</b>) temperature, (<b>b</b>) and precipitation, (<b>c</b>) global-mean atmospheric CO<sub>2</sub>, (<b>d</b>) downward solar radiation at the surface and (<b>e</b>) soil moisture in the top 3.4 m soil for 1982–2006. Panels (<b>f</b>–<b>j</b>) are similar to (a–e) except the variables are now interannual anomalies (<span class="html-italic">i.e.</span>, mean and trends are removed from the original data). In each panel, the correlation (r) between the two time series is also shown.</p> Full article ">
<p>Scatter plots between the annual-mean observed NPP over India and (<b>a</b>) temperature, (<b>b</b>) precipitation, (<b>c</b>) CO<sub>2</sub> concentration, (<b>d</b>) multivariate NPP using precipitation and temperature as independent variables and (<b>e</b>) multivariate NPP using precipitation, temperature and CO<sub>2</sub> as independent variables. Panels (<b>f</b>–<b>j</b>) are similar to (a–e) except that the variables NPP, temperature, precipitation and CO<sub>2</sub> are interannual anomalies (the linear trends are removed from the original data). In each panel, R<sup>2</sup> (coefficient of determination) values for the regression model is also shown.</p> Full article ">
<p>Scatter plots between the annual-mean observed NPP over India and (<b>a</b>) temperature, (<b>b</b>) precipitation, (<b>c</b>) CO<sub>2</sub> concentration, (<b>d</b>) multivariate NPP using precipitation and temperature as independent variables and (<b>e</b>) multivariate NPP using precipitation, temperature and CO<sub>2</sub> as independent variables. Panels (<b>f</b>–<b>j</b>) are similar to (a–e) except that the variables NPP, temperature, precipitation and CO<sub>2</sub> are interannual anomalies (the linear trends are removed from the original data). In each panel, R<sup>2</sup> (coefficient of determination) values for the regression model is also shown.</p> Full article ">
<p>Correlation of NPP with (<b>a</b>) temperature and (<b>b</b>) precipitation. Correlation of interannual NPP anomaly (trend and mean removed) with (<b>c</b>) temperature anomaly and (<b>d</b>) precipitation anomaly.</p> Full article ">
<p>Temporal evolutions of interannual NPP anomaly over India and (<b>a</b>) CO<sub>2</sub> growth rate anomaly, (<b>b</b>) Nino-3 SST anomaly, and (<b>c</b>) Multivariate ENSO Index (MEI). The right panels show the corresponding scatter plots. The coefficient of correlation (r) and R<sup>2</sup> values are also shown.</p> Full article ">
<p>Temporal evolutions of domain-averaged seasonal mean NPP for pre-monsoon (MAM), southwest summer monsoon (JJA), post-monsoon (SON) and northeast winter monsoon (DJF) seasons (<b>top left</b>), mean seasonal cycle of NPP, surface temperature, precipitation, solar radiation and soil moisture (<b>top right</b>), and the multivariate NPP estimated using precipitation and temperature as independent variables (blue), soil water and surface solar radiation as independent variables (green), precipitation, temperature and soil water as independent variables (black) and precipitation, temperature, soil water and solar radiation as independent variables (orange; <b>bottom</b>). The correlation coefficient (r) between seasonal NPP anomaly and independent variables are shown in the top right panel. R<sup>2</sup> values for the regression between seasonal cycle of NPP and the various multivariate NPP are shown in bottom panel.</p> Full article ">
<p>Temporal evolutions of domain-averaged seasonal mean NPP for pre-monsoon (MAM), southwest summer monsoon (JJA), post-monsoon (SON) and northeast winter monsoon (DJF) seasons (<b>top left</b>), mean seasonal cycle of NPP, surface temperature, precipitation, solar radiation and soil moisture (<b>top right</b>), and the multivariate NPP estimated using precipitation and temperature as independent variables (blue), soil water and surface solar radiation as independent variables (green), precipitation, temperature and soil water as independent variables (black) and precipitation, temperature, soil water and solar radiation as independent variables (orange; <b>bottom</b>). The correlation coefficient (r) between seasonal NPP anomaly and independent variables are shown in the top right panel. R<sup>2</sup> values for the regression between seasonal cycle of NPP and the various multivariate NPP are shown in bottom panel.</p> Full article ">
<p>Spatial pattern of decadal trends in NPP for (<b>a</b>) forest areas and (<b>b</b>) non-forest areas. Temporal evolution of domain averaged NPP in (<b>c</b>) forest and (<b>d</b>) non-forest areas. Correlation of forest NPP with cumulative afforestation (<b>e</b>), correlation of non-forest NPP with irrigation area (<b>f</b>) and correlation of non-forest NPP with fertilizer consumption in India (<b>g</b>).</p> Full article ">
11 KiB  
New Book Received
GPS/GNSS Antennas. By B. Rama Rao, W. Kunysz, R. Fante and K. McDonald, Artech House, 2012; 420 Pages. Price £109.00, ISBN 978-1-59693-150-3
by Shu-Kun Lin
Remote Sens. 2013, 5(2), 808-809; https://doi.org/10.3390/rs5020808 - 5 Feb 2013
Cited by 2 | Viewed by 8113
Abstract
This practical resource provides a current and comprehensive treatment of GPS/GNSS antennas, taking into account modernized systems and new and developing applications. The book presents a number of key applications, describing corresponding receiver architectures and antenna details. You find important discussions on antenna [...] Read more.
This practical resource provides a current and comprehensive treatment of GPS/GNSS antennas, taking into account modernized systems and new and developing applications. The book presents a number of key applications, describing corresponding receiver architectures and antenna details. You find important discussions on antenna characteristics, including theory of operation, gain, bandwidth, polarization, phase center, mutual coupling effects, and integration with active components. Full article
10295 KiB  
Review
Recent Trend and Advance of Synthetic Aperture Radar with Selected Topics
by Kazuo Ouchi
Remote Sens. 2013, 5(2), 716-807; https://doi.org/10.3390/rs5020716 - 5 Feb 2013
Cited by 244 | Viewed by 30926
Abstract
The present article is an introductory paper in this special issue on synthetic aperture radar (SAR). A short review is presented on the recent trend and development of SAR and related techniques with selected topics, including the fields of applications, specifications of airborne [...] Read more.
The present article is an introductory paper in this special issue on synthetic aperture radar (SAR). A short review is presented on the recent trend and development of SAR and related techniques with selected topics, including the fields of applications, specifications of airborne and spaceborne SARs, and information contents in and interpretations of amplitude data, interferometric SAR (InSAR) data, and polarimetric SAR (PolSAR) data. The review is by no means extensive, and as such only brief summaries of of each selected topics and key references are provided. For further details, the readers are recommended to read the literature given in the references theirin. Full article
(This article belongs to the Special Issue Remote Sensing by Synthetic Aperture Radar Technology)
►▼ Show Figures


<p>Band designation of microwave spectrum used for SAR.</p> Full article ">
<p>One-way transmission rate (%) of microwave through vapor clouds, ice clouds, and rain as a function of frequency (and wavelength). For lower frequencies at L- and P-band, the transmission rate is almost 100% (the figure was produced by the author based on [<a href="#b7-remotesensing-05-00716" class="html-bibr">7</a>]).</p> Full article ">
<p>Illustration of SEASAT, ALOS, RADARSAT-2, SAR-Lupe, and TerraSAR-X satellites.</p> Full article ">
<p>Illustration of SRTM, AIRSAR, MQ-1 Predator UAV carrying Lynx SAR, and Pi-SAR.</p> Full article ">
<p>Different beam modes. From left to right: strip (map), squint strip (map), wide-swath scan, and spotlight modes.</p> Full article ">
<p>The geometry and terms of the SAR systems with the parameters used in this article. <span class="html-italic">H<sub>SAR</sub></span> is the height of the SAR platform, <span class="html-italic">c</span> is the velocity of the microwave, and <span class="html-italic">r</span>(<span class="html-italic">t</span>) and <span class="html-italic">R</span> are respectively the slant-range distances at the azimuth time <span class="html-italic">t</span> and when the antenna is nearest to the target at the origin of the ground coordinate system (<span class="html-italic">x, y</span>).</p> Full article ">
<p>JERS-1 SAR DInSAR phase image showing the crust movement caused by the 1995 Great Hanshin-Awaji Earthquake, Japan. The image center is approximately at (N: 34.55°, E: 135.02°). (Courtesy of Professor H. Ohkura, Hiroshima Institute of Technology, Japan).</p> Full article ">
<p>ALOS-PALSAR L-band SAR image around Mt. Fuji, where the scene center is approximately at (N: 35.39°, E: 138.92°). The image amplitude differs depending on the normalized backscatter radar cross section (NRCS) of the surface, where smooth surfaces of lakes and bare soils have very small amplitude, vegetation fields have slightly larger amplitude, rather large amplitude can be seen in forests, and cities with buildings appear to have very large amplitude. The effects of geometrical distortion can be seen in mountain areas of high relief. (Courtesy of JAXA/EORC).</p> Full article ">
<p>ERS-1 C-band SAR image of the English Channel, where the scene center is approximately at (N: 50.57°, W: 1.17°). Several oceanic features can be seen, including ocean waves, oil slick, warm water mass, front, wind-sheltered calm sea, rough sea associated with shallow bottom topography and numerous ships. (Courtesy of ESA).</p> Full article ">
4084 KiB  
Article
Flood Mapping and Flood Dynamics of the Mekong Delta: ENVISAT-ASAR-WSM Based Time Series Analyses
by Claudia Kuenzer, Huadong Guo, Juliane Huth, Patrick Leinenkugel, Xinwu Li and Stefan Dech
Remote Sens. 2013, 5(2), 687-715; https://doi.org/10.3390/rs5020687 - 5 Feb 2013
Cited by 205 | Viewed by 24275
Abstract
Satellite remote sensing is a valuable tool for monitoring flooding. Microwave sensors are especially appropriate instruments, as they allow the differentiation of inundated from non-inundated areas, regardless of levels of solar illumination or frequency of cloud cover in regions experiencing substantial rainy seasons. [...] Read more.
Satellite remote sensing is a valuable tool for monitoring flooding. Microwave sensors are especially appropriate instruments, as they allow the differentiation of inundated from non-inundated areas, regardless of levels of solar illumination or frequency of cloud cover in regions experiencing substantial rainy seasons. In the current study we present the longest synthetic aperture radar-based time series of flood and inundation information derived for the Mekong Delta that has been analyzed for this region so far. We employed overall 60 Envisat ASAR Wide Swath Mode data sets at a spatial resolution of 150 meters acquired during the years 2007–2011 to facilitate a thorough understanding of the flood regime in the Mekong Delta. The Mekong Delta in southern Vietnam comprises 13 provinces and is home to 18 million inhabitants. Extreme dry seasons from late December to May and wet seasons from June to December characterize people’s rural life. In this study, we show which areas of the delta are frequently affected by floods and which regions remain dry all year round. Furthermore, we present which areas are flooded at which frequency and elucidate the patterns of flood progression over the course of the rainy season. In this context, we also examine the impact of dykes on floodwater emergence and assess the relationship between retrieved flood occurrence patterns and land use. In addition, the advantages and shortcomings of ENVISAT ASAR-WSM based flood mapping are discussed. The results contribute to a comprehensive understanding of Mekong Delta flood dynamics in an environment where the flow regime is influenced by the Mekong River, overland water-flow, anthropogenic floodwater control, as well as the tides. Full article
►▼ Show Figures


<p>The study area: the Mekong Delta and its 13 Vietnamese provinces in southern Vietnam. Here the Mekong splits into nine branches before reaching the South China Sea.</p> Full article ">
<p>Typical flood regime depictions of the Mekong Delta, Vietnam. <b>Upper left</b>: Small canal with permanent water used for transport. <b>Upper centre</b>: Banks of canals are usually vegetated, and water may occur beneath vegetation. <b>Upper right</b>: Typical small dyke, which borders a large flooded area. <b>Middle left</b>: Overland flow. Large areas are flooded. <b>Middle centre</b>: At this stage flooded fields are used for fishing. <b>Middle right</b>: pumping excess water from field into the canal at a higher level. <b>Lower left</b>: Floodwaters have mostly retreated from this agricultural field. <b>Lower centre</b>: Large rice fields have been freshly planted. <b>Lower right</b>: The rice is heading, fields are slightly irrigated. (All photographs taken by C. Kuenzer in the Mekong Delta, November 2011).</p> Full article ">
<p>Processing chain of the histogram based approach for water mask derivation (modified from Gstaiger <span class="html-italic">et al.</span>[<a href="#b13-remotesensing-05-00687" class="html-bibr">13</a>]).</p> Full article ">
<p>Original ENVISAT ASAR WSM radar image for 2007-06-14 (<b>left</b>), and water mask derived for 2007-06-14 (<b>centre</b>). The differing extent of flooding between the start of the rainy season (centre) and the flood peak around the end of the rainy season (<b>right</b>) is evident. Extent: UL: 12°N, 104°15′E, LR: 8°30′N, 106°50′E.</p> Full article ">
<p>Derivation of flood occurrence (summed) for individual years or all years, considering the largest common coverage (LCC) of all scenes. Data processing procedure; exemplary binary water mask for one time step; creation of the largest common coverage based on radar scene footprints. Extent of lower left image: UL: 12°N, 104°15′E, LR: 8°30′N, 106°50′E.</p> Full article ">
<p>Floodwater distribution during the 2007 rainy season, depicted only for the largest common coverage. Each subset contains the exact date and flood percentage. Image extent: LL: 8°46′50″N, 104°35′05″E, UR: 10°55′50″N, 106°29′26″E.</p> Full article ">
<p>Added flood occurrence (sum) images for 2007, 2008, 2009, and 2010. According to the largest common coverage, LCC, the subsets differ in extent, but are displayed at the same scale. The profile presents inundation frequency along the profile line depicted in the upper left figure of 2007.</p> Full article ">
<p>Flood dynamics in the Mekong Delta for the 2007 rainy season, based on 15 Envisat ASAR WSM derived water masks. Areas which are nearly always flooded are shown in reddish tones. The two zooms ((<b>a</b>) and (<b>b</b>)) depict flooded fields which are separated by dykes (elevated roads, pathways).</p> Full article ">
<p>Inundation in the Mekong Delta from 2007 to 2011 derived from all available Envisat ASAR WSM data. (<b>a</b>) hills in the Tri Ton District north of Chau Lang, (<b>b</b>) Tram Chim National Park, (<b>c</b>) well-dyked agricultural area north of Hon Dat, (<b>d</b>) the Can Tho Cuu Long Rice Research Institute test field, (<b>e</b>) the tip of Ca Mau province with dense mangroves, and (<b>f</b>) fruit tree orchards.</p> Full article ">
1887 KiB  
Article
Assessing Land Degradation/Recovery in the African Sahel from Long-Term Earth Observation Based Primary Productivity and Precipitation Relationships
by Rasmus Fensholt, Kjeld Rasmussen, Per Kaspersen, Silvia Huber, Stephanie Horion and Else Swinnen
Remote Sens. 2013, 5(2), 664-686; https://doi.org/10.3390/rs5020664 - 4 Feb 2013
Cited by 161 | Viewed by 15500
Abstract
The ‘rain use efficiency’ (RUE) may be defined as the ratio of above-ground net primary productivity (ANPP) to annual precipitation, and it is claimed to be a conservative property of the vegetation cover in drylands, if the vegetation cover is not subject to [...] Read more.
The ‘rain use efficiency’ (RUE) may be defined as the ratio of above-ground net primary productivity (ANPP) to annual precipitation, and it is claimed to be a conservative property of the vegetation cover in drylands, if the vegetation cover is not subject to non-precipitation related land degradation. Consequently, RUE may be regarded as means of normalizing ANPP for the impact of annual precipitation, and as an indicator of non-precipitation related land degradation. Large scale and long term identification and monitoring of land degradation in drylands, such as the Sahel, can only be achieved by use of Earth Observation (EO) data. This paper demonstrates that the use of the standard EO-based proxy for ANPP, summed normalized difference vegetation index (NDVI) (National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies 3rd generation (GIMMS3g)) over the year (ΣNDVI), and the blended EO/rain gauge based data-set for annual precipitation (Climate Prediction Center Merged Analysis of Precipitation, CMAP) results in RUE-estimates which are highly correlated with precipitation, rendering RUE useless as a means of normalizing for the impact of annual precipitation on ANPP. By replacing ΣNDVI by a ‘small NDVI integral’, covering only the rainy season and counting only the increase of NDVI relative to some reference level, this problem is solved. Using this approach, RUE is calculated for the period 1982–2010. The result is that positive RUE-trends dominate in most of the Sahel, indicating that non-precipitation related land degradation is not a widespread phenomenon. Furthermore, it is argued that two preconditions need to be fulfilled in order to obtain meaningful results from the RUE temporal trend analysis: First, there must be a significant positive linear correlation between annual precipitation and the ANPP proxy applied. Second, there must be a near-zero correlation between RUE and annual precipitation. Thirty-seven percent of the pixels in Sahel satisfy these requirements and the paper points to a range of different reasons why this may be the case. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
►▼ Show Figures


<p>(<b>A</b>) Correlation between different estimates of vegetation productivity and precipitation. (<b>B</b>) The corresponding correlation between rain use efficiency (RUE) (based on different estimates of vegetation productivity) and (<b>C</b>) precipitation and trends of RUE over time.</p> Full article ">
<p>Sahel delineation (150–700 mm/year precipitation isohyets) and annual average precipitation (Climate Prediction Center Merged Analysis of Precipitation, CMAP 1982–2010).</p> Full article ">
<p>Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI)/Système Pour l’Observation de la Terre (SPOT) VEGETATION (VGT) net primary productivity (NPP) per-pixel correlation based on monthly observations (1999–2010).</p> Full article ">
<p>Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) linear trend 1982–2010 based on (<b>A</b>) annual GIMMS NDVI sums and (<b>B</b>) growing season NDVI integral.</p> Full article ">
<p>Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI)/Global Precipitation Climatology Project (GPCP) precipitation correlation 1982–2010 based (<b>A</b>) annual GIMMS NDVI sums and (<b>B</b>) growing season NDVI integral.</p> Full article ">
<p>Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI)/Global Precipitation Climatology Project (GPCP) precipitation correlation 1982–2010 based (<b>A</b>) annual GIMMS NDVI sums and (<b>B</b>) growing season NDVI integral.</p> Full article ">
<p>Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI)/Climate Prediction Center Merged Analysis of Precipitation (CMAP) linear regression offset values (1982–2010). (<b>A</b>) Advanced Very High Resolution Radiometer (AVHRR) GIMMS NDVI annual sum (<b>B</b>) AVHRR GIMMS growing season NDVI integral. Note the unevenly distributed color ramp for better illustration of offset value variability around zero.</p> Full article ">
<p>Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI)/Climate Prediction Center Merged Analysis of Precipitation (CMAP) linear regression offset values (1982–2010). (<b>A</b>) Advanced Very High Resolution Radiometer (AVHRR) GIMMS NDVI annual sum (<b>B</b>) AVHRR GIMMS growing season NDVI integral. Note the unevenly distributed color ramp for better illustration of offset value variability around zero.</p> Full article ">
<p>RUE linear trends 1982–2010 based on (<b>A</b>) Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI), (<b>B</b>) net primary productivity (NPP), (<b>C</b>) growing season NDVI integration using a per-pixel relative fraction of the annual NDVI maximum and (<b>D</b>) growing season NDVI integration using a constant region specific threshold of NDVI. All productivity estimates are divided by Climate Prediction Center Merged Analysis of Precipitation (CMAP) precipitation to obtain rain use efficiency (RUE).</p> Full article ">
<p>Correlation between RUE and precipitation 1982–2010 with RUE calculation based on (<b>A</b>) Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI), (<b>B</b>) net primary productivity (NPP), (<b>C</b>) growing season NDVI integration using a per-pixel relative fraction of the annual NDVI maximum and (<b>D</b>) growing season NDVI integration using a constant region specific threshold of NDVI.</p> Full article ">
<p>Rain use efficiency (RUE) trends 1982–2010 based on small integral (relative values) Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI)/Climate Prediction Center Merged Analysis of Precipitation (CMAP) precipitation correlation.</p> Full article ">
1795 KiB  
Article
Mapping Tropical Rainforest Canopy Disturbances in 3D by COSMO-SkyMed Spotlight InSAR-Stereo Data to Detect Areas of Forest Degradation
by Janik Deutscher, Roland Perko, Karlheinz Gutjahr, Manuela Hirschmugl and Mathias Schardt
Remote Sens. 2013, 5(2), 648-663; https://doi.org/10.3390/rs5020648 - 4 Feb 2013
Cited by 35 | Viewed by 10872
Abstract
Assessment of forest degradation has been emphasized as an important issue for emission calculations, but remote sensing based detecting of forest degradation is still in an early phase of development. The use of optical imagery for degradation assessment in the tropics is limited [...] Read more.
Assessment of forest degradation has been emphasized as an important issue for emission calculations, but remote sensing based detecting of forest degradation is still in an early phase of development. The use of optical imagery for degradation assessment in the tropics is limited due to frequent cloud cover. Recent studies based on radar data often focus on classification approaches of 2D backscatter. In this study, we describe a method to detect areas affected by forest degradation from digital surface models derived from COSMO-SkyMed X-band Spotlight InSAR-Stereo Data. Two test sites with recent logging activities were chosen in Cameroon and in the Republic of Congo. Using the full resolution COSMO-SkyMed digital surface model and a 90-m resolution Shuttle Radar Topography Mission model or a mean filtered digital surface model we calculate difference models to detect canopy disturbances. The extracted disturbance gaps are aggregated to potential degradation areas and then evaluated with respect to reference areas extracted from RapidEye and Quickbird optical imagery. Results show overall accuracies above 75% for assessing degradation areas with the presented methods. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Forestry)
►▼ Show Figures


<p>Location of the two test sites in Cameroon (Pallisco concession area) and Republic of Congo with a Congo Basin vegetation types map [<a href="#b30-remotesensing-05-00648" class="html-bibr">30</a>] as background. The red squares represent the test sites. The Pallisco site is characterized by dense moist forest, while the RoC test site is more complex, including areas of edaphic forest.</p> Full article ">
<p>Outline of the Pallisco test area (blue) superimposed on RapidEye data from December 2011; green: extent of QuickBird subscene.</p> Full article ">
<p>Combined InSAR Stereo processing chain.</p> Full article ">
<p>Illustration of the <span class="html-italic">Height Variance Approach</span>.</p> Full article ">
<p>Difference model from CSK 3D generated with the <span class="html-italic">SRTM Difference Approach</span>. Red polygon shows the reference degradation area obtained from RapidEye and Quickbird data.</p> Full article ">
<p>3D profile over a logging road and forest disturbance. The black line represents the SRTM model, the red line represents the COSMO-SkyMed model. Blue line (and red cross in imagery) show a disturbance in the forest canopy (gap). Background image is RapidEye.</p> Full article ">
<p>Areas of high negative difference values as indicators for degradation (yellow) and result of aggregation (yellow hatch). Background image is RapidEye.</p> Full article ">
398 KiB  
Article
Sparse Frequency Diverse MIMO Radar Imaging for Off-Grid Target Based on Adaptive Iterative MAP
by Xuezhi He, Changchang Liu, Bo Liu and Dongjin Wang
Remote Sens. 2013, 5(2), 631-647; https://doi.org/10.3390/rs5020631 - 4 Feb 2013
Cited by 39 | Viewed by 7117
Abstract
The frequency diverse multiple-input-multiple-output (FD-MIMO) radar synthesizes a wideband waveform by transmitting and receiving multiple frequency signals simultaneously. For FD-MIMO radar imaging, conventional imaging methods based on Matched Filter (MF) cannot enjoy good imaging performance owing to the few and incomplete wavenumber-domain coverage. [...] Read more.
The frequency diverse multiple-input-multiple-output (FD-MIMO) radar synthesizes a wideband waveform by transmitting and receiving multiple frequency signals simultaneously. For FD-MIMO radar imaging, conventional imaging methods based on Matched Filter (MF) cannot enjoy good imaging performance owing to the few and incomplete wavenumber-domain coverage. Higher resolution and better imaging performance can be obtained by exploiting the sparsity of the target. However, good sparse recovery performance is based on the assumption that the scatterers of the target are positioned at the pre-discretized grid locations; otherwise, the performance would significantly degrade. Here, we propose a novel approach of sparse adaptive calibration recovery via iterative maximum a posteriori (SACR-iMAP) for the general off-grid FD-MIMO radar imaging. SACR-iMAP contains three loop stages: sparse recovery, off-grid errors calibration and parameter update. The convergence and the initialization of the method are also discussed. Numerical simulations are carried out to verify the effectiveness of the proposed method. Full article
►▼ Show Figures


<p>Imaging scenario for multiple-input-multiple-output with frequency diversity (FD-MIMO) radar.</p> Full article ">
<p>The original target.</p> Full article ">
<p>Imaging results of off-grid target by (<b>a</b>) MF; (<b>b</b>) OMP; (<b>c</b>) FOCUSS; (<b>d</b>) S-TLS; (<b>e</b>) TLS-FOCUSS; and (<b>f</b>) SACR-iMAP.</p> Full article ">
<p>Cost function <span class="html-italic">F versus</span> the iteration index.</p> Full article ">
<p>Normalized mean square error (NMSE) <span class="html-italic">versus</span> signal-to-noise ratio (SNR) obtained from 30 Monte-Carlo trials. (<b>Left</b>) NMSE of the target recovery <span class="html-italic">versus</span> SNR. (<b>Right</b>) NMSE of the off-grid error recovery <span class="html-italic">versus</span> SNR.</p> Full article ">
<p>NMSE <span class="html-italic">versus</span> the discretized grid interval obtained from 30 Monte-Carlo trials. (<b>Left</b>) NMSE of the target recovery <span class="html-italic">versus</span> grid interval. (<b>Right</b>) NMSE of the off-grid recovery error <span class="html-italic">versus</span> grid interval.</p> Full article ">
557 KiB  
Article
Remote Distinction of A Noxious Weed (Musk Thistle: CarduusNutans) Using Airborne Hyperspectral Imagery and the Support Vector Machine Classifier
by Mustafa Mirik, R. James Ansley, Karl Steddom, David C. Jones, Charles M. Rush, Gerald J. Michels, Jr. and Norman C. Elliott
Remote Sens. 2013, 5(2), 612-630; https://doi.org/10.3390/rs5020612 - 29 Jan 2013
Cited by 50 | Viewed by 8923
Abstract
Remote detection of non-native invasive plant species using geospatial imagery may significantly improve monitoring, planning and management practices by eliminating shortfalls, such as observer bias and accessibility involved in ground-based surveys. The use of remote sensing for accurate mapping invasion extent and pattern [...] Read more.
Remote detection of non-native invasive plant species using geospatial imagery may significantly improve monitoring, planning and management practices by eliminating shortfalls, such as observer bias and accessibility involved in ground-based surveys. The use of remote sensing for accurate mapping invasion extent and pattern offers several advantages, including repeatability, large area coverage, complete instead of sub-sampled assessments and greater cost-effectiveness over ground-based methods. It is critical for locating, early mapping and controlling small infestations before they reach economically prohibitive or ecologically significant levels over larger land areas. This study was designed to explore the ability of hyperspectral imagery for mapping infestation of musk thistle (Carduus nutans) on a native grassland during the preflowering stage in mid-April and during the peak flowering stage in mid-June using the support vector machine classifier and to assess and compare the resulting mapping accuracy for these two distinctive phenological stages. Accuracy assessment revealed that the overall accuracies were 79% and 91% for the classified images at preflowering and peak flowering stages, respectively. These results indicate that repeated detection of the infestation extent, as well as infestation severity or intensity, of this noxious weed in a spatial and temporal context is possible using hyperspectral remote sensing imagery. Full article
►▼ Show Figures


<p>Location of study site in Parmer County (<b>a</b>) in TX (<b>b</b>), USA.</p> Full article ">
<p>Spectral profile of training samples for musk thistle (N = 200), tansy mustard (N = 91), brome grass (N = 30), wheat (N = 200), senescent grass (N = 35) and bare ground (N = 200) in April (<b>a</b>) and musk thistle (N = 200), Russian thistle (N = 72), Johnsongrass (N = 84), senescent grass (N = 35) and bare ground (N = 200) in June (<b>b</b>) 2006.</p> Full article ">
<p>Color infrared hyperspectral imagery acquired in June 15, 2006 (<b>a</b>), classified image (<b>b</b>) and views of musk thistle infestation (<b>c</b>,<b>d</b>) at the study site. The entire area is native grassland, with the exception of a wheat field on the far right, right of the main road.</p> Full article ">
<p>Color infrared hyperspectral imagery acquired in June 15, 2006 (<b>a</b>), classified image (<b>b</b>) and views of musk thistle infestation (<b>c</b>,<b>d</b>) at the study site. The entire area is native grassland, with the exception of a wheat field on the far right, right of the main road.</p> Full article ">
1140 KiB  
Article
A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data
by Bin Wu, Bailang Yu, Wenhui Yue, Song Shu, Wenqi Tan, Chunling Hu, Yan Huang, Jianping Wu and Hongxing Liu
Remote Sens. 2013, 5(2), 584-611; https://doi.org/10.3390/rs5020584 - 28 Jan 2013
Cited by 209 | Viewed by 15721
Abstract
As an important component of urban vegetation, street trees play an important role in maintenance of environmental quality, aesthetic beauty of urban landscape, and social service for inhabitants. Acquiring accurate and up-to-date inventory information for street trees is required for urban horticultural planning, [...] Read more.
As an important component of urban vegetation, street trees play an important role in maintenance of environmental quality, aesthetic beauty of urban landscape, and social service for inhabitants. Acquiring accurate and up-to-date inventory information for street trees is required for urban horticultural planning, and municipal urban forest management. This paper presents a new Voxel-based Marked Neighborhood Searching (VMNS) method for efficiently identifying street trees and deriving their morphological parameters from Mobile Laser Scanning (MLS) point cloud data. The VMNS method consists of six technical components: voxelization, calculating values of voxels, searching and marking neighborhoods, extracting potential trees, deriving morphological parameters, and eliminating pole-like objects other than trees. The method is validated and evaluated through two case studies. The evaluation results show that the completeness and correctness of our method for street tree detection are over 98%. The derived morphological parameters, including tree height, crown diameter, diameter at breast height (DBH), and crown base height (CBH), are in a good agreement with the field measurements. Our method provides an effective tool for extracting various morphological parameters for individual street trees from MLS point cloud data. Full article
(This article belongs to the Special Issue Advances in Mobile Laser Scanning and Mobile Mapping)
►▼ Show Figures


<p>The Mobile Laser Scanning (MLS) System of East China Normal University (ECNU-MLS). (<b>a</b>) The MLS van, (<b>b</b>) top view of the ECNU-MLS configuration, and (<b>c</b>) side view of ECNU-MLS configuration.</p> Full article ">
<p>Flow chart of the VMNS method.</p> Full article ">
<p>Coordinate system of voxel grid. (<b>a</b>) a voxel, (<b>b</b>) voxel grid used in this study.</p> Full article ">
<p>Three basic types of neighbors of a voxel in a voxel grid: (<b>a</b>) face neighbor, (<b>b</b>) edge neighbor, and (<b>c</b>) vertex neighbor.</p> Full article ">
<p>Three types of voxel neighborhoods used in our method: (<b>a</b>) top face neighbor (TFN), (<b>b</b>) bottom face neighbor (BFN), (<b>c</b>) 8-neighbors in the same layer, and (<b>d</b>) 8-neighbors in the same layer neglected of voxel height.</p> Full article ">
<p>Seed voxel selection from layer <span class="html-italic">L<sub>5</sub></span> above the ground in the voxel grid. (<b>a</b>) Hypothetic <span class="html-italic">L<sub>5</sub></span> (the 6th layer in the voxel grid). (<b>b</b>) Foreground voxel identification, grouping, and marking. (<b>c</b>) Removing the voxel grid based on number and compactness index of voxels. (<b>d</b>) Seed voxel groups and the location of potential tree trunks.</p> Full article ">
<p>Top-down radius-constrained searching and marking for the 5th layer above the ground in the voxel grid. (<b>a</b>) Hypothetic voxel distribution in layer <span class="html-italic">L<sub>4</sub></span> (the 5th layer in the voxel grid). (<b>b</b>) Marking the foreground voxels in the bottom face neighbors of the seed voxels in layer <span class="html-italic">L<sub>5</sub></span>. (<b>c</b>) Setting searching range and the candidate voxels. (<b>d</b>) Neighborhood searching and marking.</p> Full article ">
<p>Bottom-up neighborhood competing searching and marking in the 7th layer above the ground in the voxel grid system. (<b>a</b>) Hypothetic foreground voxel distribution in layer <span class="html-italic">L<sub>6</sub></span> (the 7th layer in the voxel grid). (<b>b</b>) Marking the foreground voxels and removing the background voxels in the top face neighbors of the seed voxels in <span class="html-italic">L<sub>5</sub></span>. (<b>c</b>) Grouping neighboring voxels.</p> Full article ">
<p>Bottom-up neighborhood competing searching and marking for the 8th layer above the ground in the voxel grid. (<b>a</b>) Hypothetic foreground voxel distribution in layer <span class="html-italic">L<sub>7</sub></span> (the 8th layer in the voxel grid). (<b>b</b>) Marking the foreground voxels in the top face neighbors of the seed voxels in <span class="html-italic">L<sub>6</sub></span> and detect their surrounding 8-neighbors voxels. (<b>c</b>) An example of competing neighbors searching and marking. (<b>d</b>) Grouped foreground voxels located in the 8-neighbors of seeds.</p> Full article ">
1761 KiB  
Article
An Object-Based Approach for Mapping Shrub and Tree Cover on Grassland Habitats by Use of LiDAR and CIR Orthoimages
by Thomas Hellesen and Leena Matikainen
Remote Sens. 2013, 5(2), 558-583; https://doi.org/10.3390/rs5020558 - 28 Jan 2013
Cited by 94 | Viewed by 10108
Abstract
Due to the abandonment of former agricultural management practices such as mowing and grazing, an increasing amount of grassland is no longer being managed. This has resulted in increasing shrub encroachment, which poses a threat to a number of species. Monitoring is an [...] Read more.
Due to the abandonment of former agricultural management practices such as mowing and grazing, an increasing amount of grassland is no longer being managed. This has resulted in increasing shrub encroachment, which poses a threat to a number of species. Monitoring is an important means of acquiring information about the condition of the grasslands. Though the use of traditional remote sensing is an effective means of mapping and monitoring land cover, the mapping of small shrubs and trees based only on spectral information is challenged by the fact that shrubs and trees often spectrally resemble grassland and thus cannot be safely distinguished and classified. With the aid of LiDAR-derived information, such as elevation, the classification of spectrally similar objects can be improved. In this study, we applied high point density LiDAR data and colour-infrared orthoimages for the classification of shrubs and trees in a study area in Denmark. The classification result was compared to a classification based only on colour-infrared orthoimages. The overall accuracy increased significantly with the use of LiDAR and, for shrubs and trees specifically, producer’s accuracy increased from 81.2% to 93.7%, and user’s accuracy from 52.9% to 89.7%. Object-based image analysis was applied in combination with a CART classifier. The potential of using the applied approach for mapping and monitoring of large areas is discussed. Full article
►▼ Show Figures


<p>The study area is located in the northwestern part of Denmark and covers 14 km<sup>2</sup>. The two photographs to the right illustrate typical examples of shrub-encroached grassland from the study area. The applied coordinate system is the ETRS 1989 and the map projection is UTM zone 32.</p> Full article ">
<p>The study area divided into subareas (tiles).</p> Full article ">
<p>Overview of the methodology.</p> Full article ">
<p>nDSM of the study area with a zoomed-in extract showing shrubs, trees and two buildings.</p> Full article ">
<p>The excerpt shows the segmentation result on the CIR orthoimages. The nDSM was applied for segmentation in this case using a scale of 10. Objects selected for training (only elevated training data applied) are also shown.</p> Full article ">
<p>The excerpt shows the segmentation result based on the CIR orthoimages using a scale of 92. Objects selected for training (all training data applied) are also shown.</p> Full article ">
<p>Tile 4 and the location of points used for selecting training data (segments) applied as input for the CART analysis. For the CART analysis related to the classification based on the nDSM and the CIR orthoimages, only the elevated training data were used (shrubs and trees and buildings), whereas all training data were applied in the CART analysis related to the classification based only on the CIR orthoimages.</p> Full article ">
<p>The grid consisting of 260 × 240 m cells of which 20 were selected randomly for reference data sampling. Tile 4 is used for training and is therefore not included.</p> Full article ">
<p>Result of the classification tree analysis used for classification of the CIR orthoimages with all the training data applied.</p> Full article ">
1233 KiB  
Article
Remote Sensing Based Yield Estimation in a Stochastic Framework — Case Study of Durum Wheat in Tunisia
by Michele Meroni, Eduardo Marinho, Nabil Sghaier, Michel M. Verstrate and Olivier Leo
Remote Sens. 2013, 5(2), 539-557; https://doi.org/10.3390/rs5020539 - 28 Jan 2013
Cited by 57 | Viewed by 9879
Abstract
Multitemporal optical remote sensing constitutes a useful, cost efficient method for crop status monitoring over large areas. Modelers interested in yield monitoring can rely on past and recent observations of crop reflectance to estimate aboveground biomass and infer the likely yield. Therefore, in [...] Read more.
Multitemporal optical remote sensing constitutes a useful, cost efficient method for crop status monitoring over large areas. Modelers interested in yield monitoring can rely on past and recent observations of crop reflectance to estimate aboveground biomass and infer the likely yield. Therefore, in a framework constrained by information availability, remote sensing data to yield conversion parameters are to be estimated. Statistical models are suitable for this purpose, given their ability to deal with statistical errors. This paper explores the performance in yield estimation of various remote sensing indicators based on varying degrees of bio-physical insight, in interaction with statistical methods (linear regressions) that rely on different hypotheses. Performances in estimating the temporal and spatial variability of yield, and implications of data scarcity in both dimensions are investigated. Jackknifed results (leave one year out) are presented for the case of wheat yield regional estimation in Tunisia using the SPOT-VEGETATION instrument. Best performances, up to 0.8 of R2, are achieved using the most physiologically sound remote sensing indicator, in conjunction with statistical specifications allowing for parsimonious spatial adjustment of the parameters. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
►▼ Show Figures


<p>Location of the study area and cereal area cover fraction. The map of the whole country is reported in the upper right corner.</p> Full article ">
<p>Box-and-whisker plot showing wheat yield for years 1999–2011. Medians, quartiles, and extreme values are given. Department on the x-axis are ordered from North to South.</p> Full article ">
<p>Example of computation of CUM<sub>FAPAR</sub> for the season of 2010–2011, and a pixel located in Beja governorate (36.4776°N, 9.2991°E). Dots refer to actual FAPAR observations. The blue line and area represent the fitted PDHT model and the cumulative FAPAR value, respectively. Black vertical dashed lines indicate <span class="html-italic">start_dek</span> and <span class="html-italic">end_dek</span>.</p> Full article ">
<p>Average CUM<sub>APAR</sub> during the period 1999–2011 for cereal crop areas.</p> Full article ">
<p>Modeled <span class="html-italic">vs.</span> observed yield scatterplot. Modeled points are jackknifed predictions obtained with the FE model using CUM<sub>APAR</sub>. The 1:1 line is drawn for reference.</p> Full article ">
<p>Jackknifed Root Mean Square Error (RMSE) of different modeling solutions as a function of the number of available years of data (the parameters of each model have been estimated with the years available less one). Only models using FAPAR<sup>12</sup> and CUM<sub>APAR</sub> are showed.</p> Full article ">
<p>Jackknifed Root Mean Square Error (RMSE) of three modeling solutions (P-OLS, FE, and G-OLS) as a function of the number of governorates and the number of years of data (fou (<b>a</b>), nine (<b>b</b>) and thirteen (<b>c</b>)). Only models using CUM<sub>APAR</sub> are showed.</p> Full article ">
704 KiB  
Article
Compact Multipurpose Mobile Laser Scanning System — Initial Tests and Results
by Craig Glennie, Benjamin Brooks, Todd Ericksen, Darren Hauser, Kenneth Hudnut, James Foster and Jon Avery
Remote Sens. 2013, 5(2), 521-538; https://doi.org/10.3390/rs5020521 - 25 Jan 2013
Cited by 53 | Viewed by 11009
Abstract
We describe a prototype compact mobile laser scanning system that may be operated from a backpack or unmanned aerial vehicle. The system is small, self-contained, relatively inexpensive, and easy to deploy. A description of system components is presented, along with the initial calibration [...] Read more.
We describe a prototype compact mobile laser scanning system that may be operated from a backpack or unmanned aerial vehicle. The system is small, self-contained, relatively inexpensive, and easy to deploy. A description of system components is presented, along with the initial calibration of the multi-sensor platform. The first field tests of the system, both in backpack mode and mounted on a helium balloon for real-world applications are presented. For both field tests, the acquired kinematic LiDAR data are compared with highly accurate static terrestrial laser scanning point clouds. These initial results show that the vertical accuracy of the point cloud for the prototype system is approximately 4 cm (1σ) in balloon mode, and 3 cm (1σ) in backpack mode while horizontal accuracy was approximately 17 cm (1σ) for the balloon tests. Results from selected study areas on the Sacramento River Delta and San Andreas Fault in California demonstrate system performance, deployment agility and flexibility, and potential for operational production of high density and highly accurate point cloud data. Cost and production rate trade-offs place this system in the niche between existing airborne and tripod mounted LiDAR systems. Full article
►▼ Show Figures


<p>Backpack and balloon (14′ diameter) deployment of LiDAR/Imagery Acquisition System.</p> Full article ">
<p>Close-up images of current multipurpose mobile laser scanning system.</p> Full article ">
<p>System deployment for boresight and lever-arm calibration.</p> Full article ">
<p>Balloon trajectory (red) during Sherman Island, CA Test. Cyan squares show locations of terrestrial laser scans used for comparison to Balloon LiDAR. Background imagery is from the USGS National Map Viewer [<a href="#b27-remotesensing-05-00521" class="html-bibr">27</a>], UTM Zone 10.</p> Full article ">
<p>Balloon trajectory (red) during Carrizo Plain, CA Test. Approximate location of San Andreas Fault is shown by yellow arrows. Cyan squares show locations of terrestrial laser scans used for comparison to Balloon LiDAR. Background imagery is from the USGS National Map Viewer [<a href="#b27-remotesensing-05-00521" class="html-bibr">27</a>], UTM Zone 11.</p> Full article ">
<p>Comparison of TLS scan (yellow) and balloon LiDAR data (red) on a bridge piling, Sherman Island, CA, USA.</p> Full article ">
<p>Balloon LiDAR compared to the B4 airborne LiDAR on the Carrizo Plain. (<b>a</b>) Raw point density for the B4 (<b>left</b>) and balloon LiDAR (<b>right</b>) datasets (pts/m<sup>2</sup>). (<b>b</b>) Digital terrain model derived from the B4 (<b>left</b>) and ballon LiDAR (<b>right</b>) datasets (m).</p> Full article ">
<p>Colored Coded DEM created from balloon LiDAR Data, Sherman Island, CA, USA.</p> Full article ">
1578 KiB  
Article
Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data
by Pasi Raumonen, Mikko Kaasalainen, Markku Åkerblom, Sanna Kaasalainen, Harri Kaartinen, Mikko Vastaranta, Markus Holopainen, Mathias Disney and Philip Lewis
Remote Sens. 2013, 5(2), 491-520; https://doi.org/10.3390/rs5020491 - 25 Jan 2013
Cited by 562 | Viewed by 30245
Abstract
This paper presents a new method for constructing quickly and automatically precision tree models from point clouds of the trunk and branches obtained by terrestrial laser scanning. The input of the method is a point cloud of a single tree scanned from multiple [...] Read more.
This paper presents a new method for constructing quickly and automatically precision tree models from point clouds of the trunk and branches obtained by terrestrial laser scanning. The input of the method is a point cloud of a single tree scanned from multiple positions. The surface of the visible parts of the tree is robustly reconstructed by making a flexible cylinder model of the tree. The thorough quantitative model records also the topological branching structure. In this paper, every major step of the whole model reconstruction process, from the input to the finished model, is presented in detail. The model is constructed by a local approach in which the point cloud is covered with small sets corresponding to connected surface patches in the tree surface. The neighbor-relations and geometrical properties of these cover sets are used to reconstruct the details of the tree and, step by step, the whole tree. The point cloud and the sets are segmented into branches, after which the branches are modeled as collections of cylinders. From the model, the branching structure and size properties, such as volume and branch size distributions, for the whole tree or some of its parts, can be approximated. The approach is validated using both measured and modeled terrestrial laser scanner data from real trees and detailed 3D models. The results show that the method allows an easy extraction of various tree attributes from terrestrial or mobile laser scanning point clouds. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Forestry)
►▼ Show Figures


<p>Segmented point cloud (<b>left</b>) and the final cylinder model (<b>right</b>) of the artificial Scots pine.</p> Full article ">
<p>Point clouds and their final cylinder models. The point cloud (<b>top left</b>) and the cylinder model with some 7,070 cylinders (<b>top right</b>) of the spruce. The point cloud (<b>bottom left</b>) and the cylinder model with about 6,820 cylinders (<b>bottom right</b>) of the maple.</p> Full article ">
<p>Determination of tree components and their bases. Left: The initial classification of trunk points (red). Middle: Part of the trunk point set (red) and its base (blue). The green denotes sets not part of the tree. Right: Final classification of components. The component starting from the base (red), the other tree components (cyan), and the points not part of the tree (green). The units of the axes in the left and right figure are meters.</p> Full article ">
<p>The main steps of the method.</p> Full article ">
<p>A cover which is a partition. Different colors denote different cover sets.</p> Full article ">
<p>Comparison of the covers of a branch. The minimum diameters (d) of the cover sets are 2 cm (<b>left</b>) and 10 cm (<b>right</b>). The smaller cover sets can capture much more detail.</p> Full article ">
<p>Examples of segmented tree parts. (<b>Left</b>) A segmented branch originating from the trunk of an maple. (<b>Right</b>) Close-up of a segmented Norway spruce.</p> Full article ">
<p>A schematic picture of the bifurcation recognition process. The cut region (red) and its extension, the study region (light red), move along the tree component (brown) and construct the segment (green). When the cut and study regions move ahead through a bifurcation, such as a branch, the regions will no longer be connected, and the part of the cut region belonging to the branch becomes a new branch base.</p> Full article ">
<p>The segmenting process.</p> Full article ">
25762 KiB  
Article
Parameterization of High Resolution Vegetation Characteristics using Remote Sensing Products for the Nakdong River Watershed, Korea
by Hyun Il Choi
Remote Sens. 2013, 5(2), 473-490; https://doi.org/10.3390/rs5020473 - 24 Jan 2013
Cited by 7 | Viewed by 6585
Abstract
Mesoscale regional climate models (RCMs), the primary tool for climate predictions, have recently increased in sophistication and are being run at increasingly higher resolutions to be also used in climate impact studies on ecosystems, particularly in agricultural crops. As satellite remote sensing observations [...] Read more.
Mesoscale regional climate models (RCMs), the primary tool for climate predictions, have recently increased in sophistication and are being run at increasingly higher resolutions to be also used in climate impact studies on ecosystems, particularly in agricultural crops. As satellite remote sensing observations of the earth terrestrial surface become available for assimilation in RCMs, it is possible to incorporate complex land surface processes, such as dynamics of state variables for hydrologic, agricultural and ecologic systems at the smaller scales. This study focuses on parameterization of vegetation characteristics specifically designed for high resolution RCM applications using various remote sensing products, such as Advanced Very High Resolution Radiometer (AVHRR), Système Pour l’Observation de la Terre-VEGETATION (SPOT-VGT) and Moderate Resolution Imaging Spectroradiometer (MODIS). The primary vegetative parameters, such as land surface characteristics (LCC), fractional vegetation cover (FVC), leaf area index (LAI) and surface albedo localization factors (SALF), are currently presented over the Nakdong River Watershed domain, Korea, based on 1-km remote sensing satellite data by using the Geographic Information System (GIS) software application tools. For future high resolution RCM modeling efforts on climate-crop interactions, this study has constructed the deriving parameters, such as FVC and SALF, following the existing methods and proposed the new interpolation methods to fill missing data with combining the regression equation and the time series trend function for time-variant parameters, such as LAI and NDVI data at 1-km scale. Full article
►▼ Show Figures


<p>(<b>a</b>) Location map of the Korean Peninsular overlaid with latitude and longitude lines and (<b>b</b>) the Nakdong River Watershed domain overlaid with the 203 × 268 dimensional 1-km spacing grids.</p> Full article ">
<p>Coverage ratio of distribution areas for the existing eleven USGS Land Coverage Category (LCC) types over the Nakdong River Watershed domain.</p> Full article ">
<p>The geographic distribution of the 1-km USGS LCC types over the Nakdong River Watershed domain.</p> Full article ">
<p>The geographic distribution of the 1-km FVC values derived from the average annual maximum SPOT-VGT NDVI over the Nakdong River Watershed domain.</p> Full article ">
<p>The annual cycle of Leaf Area Index (LAI) and NDVI climatologies for the ten LCC types over the Nakdong River Watershed domain.</p> Full article ">
<p>The annual cycle of Leaf Area Index (LAI) and NDVI climatologies for the ten LCC types over the Nakdong River Watershed domain.</p> Full article ">
<p>The scatter plots (blue spots) with the regression curves (red lines) for the LAI-NDVI relationship for the ten LCC types over the Nakdong River Watershed domain</p> Full article ">
<p>The scatter plots (blue spots) with the regression curves (red lines) for the LAI-NDVI relationship for the ten LCC types over the Nakdong River Watershed domain</p> Full article ">
<p>The geographic distributions of mean LAI values based on 2001–2010 climatology data in (<b>a</b>) January, (<b>b</b>) April, (<b>c</b>) July and (<b>d</b>) October over the Nakdong River Watershed domain.</p> Full article ">
<p>The geographic distributions of <span class="html-italic">SALF<sub>λ,η</sub></span> values over the Nakdong River Watershed domain for (<b>a</b>) direct beam visible band, (<b>b</b>) direct beam in the near infrared band, (<b>c</b>) diffuse radiation in the visible band and (<b>d</b>) diffuse radiation in the near infrared band.</p> Full article ">
1148 KiB  
Article
Remote Sensing-Based Fractal Analysis and Scale Dependence Associated with Forest Fragmentation in an Amazon Tri‑National Frontier
by Jing Sun and Jane Southworth
Remote Sens. 2013, 5(2), 454-472; https://doi.org/10.3390/rs5020454 - 24 Jan 2013
Cited by 45 | Viewed by 8684
Abstract
In the Amazon, the development and paving of roads connects regions and peoples, and over time can form dense and recursive networks, which often serve as nodes for continued development. These developed areas exhibit robust fractal structures that could potentially link their spatial [...] Read more.
In the Amazon, the development and paving of roads connects regions and peoples, and over time can form dense and recursive networks, which often serve as nodes for continued development. These developed areas exhibit robust fractal structures that could potentially link their spatial patterns with deforestation processes. Fractal dimension is commonly used to describe the growth trajectory of such fractal structures and their spatial-filling capacities. Focusing on a tri-national frontier region, we applied a box-counting method to calculate the fractal dimension of the developed areas in the Peruvian state of Madre de Dios, Acre in Brazil, and the department of Pando in Bolivia, from 1986 through 2010. The results indicate that development has expanded in all three regions with declining forest cover over time, but with different patterns and rates in each country. Such differences were summarized within a proposed framework to indicate deforestation progress/level, which can be used to understand and regulate deforestation and its evolution in time. In addition, the role and influence of scale was also assessed, and we found local fractal dimensions are not invariant at different spatial scales and thus concluded such scale-dependent features of fragmentation patterns are here mainly shaped by the road paving. Full article
►▼ Show Figures


<p>Map of the MAP region with major roads superimposed. This region encompasses tri-national frontier regions of the Peruvian state of Madre de Dios (capital city: Puerto Maldonado), the Brazilian state of Acre (capital city: Rio Branco), and the Department of Pando (capital city: Cobija), Bolivia.</p> Full article ">
<p>Forest/non-forest maps showing fragmentation dynamics in the MAP region for the years (<b>a</b>) 1986, (<b>b</b>) 1991, (<b>c</b>) 1996, (<b>d</b>) 2000, (<b>e</b>) 2005, and (<b>f</b>) 2010, interpreted from eight mosaicked Landsat images (path/row: 1/67, 1/68, 2/67, 2/68, 2/69, 3/67, 3/68, 3/69).</p> Full article ">
<p>AOI box locations illustrated on the forest/non-forest map of year 2010. The ten yellow boxes delineate the areas of interest (AOI) across the study region. They are selected specifically to vary in location, intensity of deforestation and size to reflect the uses of fractal analyses in land change research. The boxes correspond to the regions around Rio Branco, Acre (A1), Xapuri, Acre (A2), Assis Brasil, Acre (A3), and Control for Acre (A4); Cobija, Pando (P1), Santa Elena, Pando (P2), and Control for Pando (P3); Puerto Maldonado, Madre de Dios (M1), Iberia, Madre de Dios (M2), and Control for Madre de Dios (M3).</p> Full article ">
<p>Deforestation process highlighted in AOI boxes for the year of (<b>a</b>) 1986, (<b>b</b>) 1991, (<b>c</b>) 1996, (<b>d</b>) 2000, (<b>e</b>) 2005, and (<b>f</b>) 2010. The boxes correspond to the regions around Rio Branco, Acre (A1), Xapuri, Acre (A2), Assis Brasil, Acre (A3), and Control for Acre (A4); Cobija, Pando (P1), Santa Elena, Pando (P2), and Control for Pando (P3); Puerto Maldonado, Madre de Dios (M1), Iberia, Madre de Dios (M2), and Control for Madre de Dios (M3).</p> Full article ">
<p>Comparison of fractal dimensions over time and across the different AOI boxes, with proposed deforestation levels labelled on the secondary vertical axis.</p> Full article ">
<p>Example of log-log plots of scaling relations of developed areas in AOI box 1 in Acre, Brazila. (<b>a</b>) 1986, (<b>b</b>) 1991, (<b>c</b>) 1996, (<b>d</b>) 2000, (<b>e</b>) 2001, (f) 2005, (<b>g</b>) 2010.</p> Full article ">
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-24
Previous Issue
Next Issue
Back to TopTop