Remote Sens., Volume 4, Issue 12 (December 2012) – 18 articles , Pages 3638-4021

Most plant species are non-randomly distributed across environmental gradients in light, water, and nutrients. In tropical forests, these gradients result from biophysical processes related to the structure of the canopy and terrain, but how does species richness in tropical forests vary over such gradients, and can remote sensing capture this variation? Using airborne lidar, we tested the extent to which variation in tree species richness is statistically explained by lidar-measured structural variation in canopy height and terrain in the extensively studied, stem-mapped 50-ha plot on Barro Colorado Island (BCI), Panama. We detected differences in species richness associated with variation in canopy height and topography across spatial scales ranging from 0.01-ha to 1.0-ha. However, species richness was most strongly associated with structural variation at the 1.0-ha scale. We developed a predictive generalized least squares model of species richness at the 1.0-ha scale (R² = 0.479, RMSE = 8.3 species) using the mean and standard deviation of canopy height, mean elevation, and terrain curvature. The model demonstrates that lidar-derived measures of forest and terrain structure can capture a significant fraction of observed variation in tree species richness in tropical forests on local-scales. Full article

A new instrument has been setup at the Centre de Recherche Public-Gabriel Lippmann to measure spectral emissivity values of typical earth surface samples in the 8 to 12 μm range at a spectral resolution of up to 0.25 cm⁻¹. The instrument is based on a Hyper-Cam-LW built by Telops with a modified fore-optic for vertical measurements at ground level and a platform for airborne acquisitions. A processing chain has been developed to convert calibrated radiances into emissivity spectra. Repeat measurements taken on samples of sandstone show a high repeatability of the system with a wavelength dependent standard deviation of less than 0.01 (1.25% of the mean emissivity). Evaluation of retrieved emissivity spectra indicates good agreement with reference measurements. The new instrument facilitates the assessment of the spatial variability of emissivity spectra of material surfaces—at present still largely unknown—at various scales from ground and airborne platforms and thus will provide new opportunities in environmental remote sensing. Full article

The expansion of irrigated agriculture during the Soviet Union (SU) era made Central Asia a leading cotton production region in the world. However, the successor states of the SU in Central Asia face on-going environmental damages and soil degradation that are endangering the sustainability of agricultural production. With Landsat MSS and TM data from 1972/73, 1977, 1987, 1998, and 2000 the expansion and densification of the irrigated cropland could be reconstructed in the Kashkadarya Province of Uzbekistan, Central Asia. Classification trees were generated by interpreting multitemporal normalized difference vegetation index data and crop phenological knowledge. Assessments based on image-derived validation samples showed good accuracy. Official statistics were found to be of limited use for analyzing the plausibility of the results, because they hardly represent the area that is cropped in the very dry study region. The cropping area increased from 134,800 ha in 1972/73 to 470,000 ha in 2009. Overlaying a historical soil map illustrated that initially sierozems were preferred for irrigated agriculture, but later the less favorable solonchaks and solonetzs were also explored, illustrating the strategy of agricultural expansion in the Aral Sea Basin. Winter wheat cultivation doubled between 1987 and 1998 to approximately 211,000 ha demonstrating its growing relevance for modern Uzbekistan. The spatial-temporal approach used enhances the understanding of natural conditions before irrigation is employed and supports decision-making for investments in irrigation infrastructure and land cultivation throughout the Landsat era. Full article

As a result of the warming observed at high latitudes, there is significant potential for the balance of ecosystem processes to change, i.e., the balance between carbon sequestration and respiration may be altered, giving rise to the release of soil carbon through elevated ecosystem respiration. Gross ecosystem productivity and ecosystem respiration vary in relation to the pattern of vegetation community type and associated biophysical traits (e.g., percent cover, biomass, chlorophyll concentration, etc.). In an arctic environment where vegetation is highly variable across the landscape, the use of high spatial resolution imagery can assist in discerning complex patterns of vegetation and biophysical variables. The research presented here examines the relationship between ecological and spectral variables in order to generate an ecologically meaningful vegetation classification from high spatial resolution remote sensing data. Our methodology integrates ordination and image classifications techniques for two non-overlapping Arctic sites across a 5° latitudinal gradient (approximately 70° to 75°N). Ordination techniques were applied to determine the arrangement of sample sites, in relation to environmental variables, followed by cluster analysis to create ecological classes. The derived classes were then used to classify high spatial resolution IKONOS multispectral data. The results demonstrate moderate levels of success. Classifications had overall accuracies between 69%–79% and Kappa values of 0.54–0.69. Vegetation classes were generally distinct at each site with the exception of sedge wetlands. Based on the results presented here, the combination of ecological and remote sensing techniques can produce classifications that have ecological meaning and are spectrally separable in an arctic environment. These classification schemes are critical for modeling ecosystem processes. Full article

An approach based on the nearest neighbors techniques is presented for producing thematic maps of forest cover (forest/non-forest) and total stand volume for the Terai region in southern Nepal. To create the forest cover map, we used a combination of Landsat TM satellite data and visual interpretation data, i.e., a sample grid of visual interpretation plots for which we obtained the land use classification according to the FAO standard. These visual interpretation plots together with the field plots for volume mapping originate from an operative forest inventory project, i.e., the Forest Resource Assessment of Nepal (FRA Nepal) project. The field plots were also used in checking the classification accuracy. MODIS satellite data were used as a reference in a local correction approach conducted for the relative calibration of Landsat TM images. This study applied a non-parametric k-nearest neighbor technique (k-NN) to the forest cover and volume mapping. A tree height prediction approach based on a nonlinear, mixed-effects (NLME) modeling procedure is presented in the Appendix. The MODIS image data performed well as reference data for the calibration approach applied to make the Landsat image mosaic. The agreement between the forest cover map and the field observed values of forest cover was substantial in Western Terai (KHAT 0.745) and strong in Eastern Terai (KHAT 0.825). The forest cover and volume maps that were estimated using the k-NN method and the inventory data from the FRA Nepal project are already appropriate and valuable data for research purposes and for the planning of forthcoming forest inventories. Adaptation of the methods and techniques was carried out using Open Source software tools. Full article

Several studies in the past have examined the spectral capability of multispectral and hyperspectral imagery for the identification of crop marks, while recent studies have applied different vegetation indices in order to support remote sensing archaeological applications. However, the use of vegetation indices for the detection of crop marks lacks in accuracy assessment and critical evaluation. In this study, 71 vegetation indices were indexed, from the relevant bibliography, and evaluated for their potential to detect such crop marks. During this study, several ground spectroradiometric campaigns took place, in a controlled archaeological environment in Cyprus, cultivated with barley crops, during a complete phenological cycle (2011–2012). All vegetation indices, both broadband and narrowband, were evaluated for their separability performance, and the results were presented through tables and diagrams. In the end, the use of more than one vegetation index is suggested in order to enhance the final results. In fact, several not widely used vegetation indices are suggested and evaluated using both Landsat TM and EO-1 Hyperion images. Full article

Recent climatic patterns indicate that extreme weather events will increase in frequency and magnitude. Remote sensing offers unique advantages for large-scale monitoring. In this research, Landsat 5 remotely sensed imagery was used to assess flooding caused by Hurricane Katrina, one of the worst natural disasters in the US over the past decades. The objective of our work is to assess whether decisions associated with the classification process, such as location of reference data and algorithm choice, affected flooding results and subsequent analysis using census data. Maximum Likelihood (ML) and Back Propagation Neural Network (NN) were the tested algorithms, the former reflecting a simple and popular classifier, and the latter an advanced but complex method. Flooding estimations were almost identical within the reference sample area, 124.4 km² for the ML classifier and 123.7 km² for the NN classifier. However, large discrepancies were found outside the reference sample area with the ML predicting 462.5 km² and the NN identifying 797.2 km² as flooded, almost twice the amount. Further investigation took place to evaluate the influence of the classification method to a social study, namely the racial characteristics of flooded areas. Using Census 2000 data, our study area was segmented in census tracts. Results indicated a strong positive correlation between concentration of African Americans and proportional residential flooding. Pairwise T-Tests also verified that flooding among different African American concentrations was statistically different. There were no significant differences between the ML and NN methods in the results interpretation, which is mostly attributed to the significant geographic overlap between reference sample area and the examined census tracts. This study suggests that emergency responders should exercise significant caution in their decision making when using classification products from undersampled geographic areas in terms of classification reference data. Full article

Maximal light use efficiency (LUE) is an important ecological index of a vegetation essential attribute, and a key parameter of the LUE-based model for estimating large-scale vegetation productivity by remote sensing technology. However, although currently used in different models there still exists extensive controversy. This paper takes the Zoige Plateau in China as a case area to develop a new approach for estimating the maximal LUEs for different vegetation. Based on an existing land cover map and MODIS NDVI product, the linear unmixing method with a moving window was adopted to estimate the time-series NDVI for different end members in a MODIS NDVI pixel; then Particle Swarm Optimizer (PSO) was applied to search for the optimization of LUE retrievals through the CASA (Carnegie-Ames-Stanford Approach) model combined with time-series NDVI and ground measurements. The derived maximal LUEs present significant differences among various vegetation types. These are 0.669 gC·MJ⁻¹, 0.450 gC·MJ⁻¹ and 0.126 gC·MJ⁻¹ for the xerophilous grasslands with high, moderate and low vegetation fraction respectively, 0.192 gC·MJ⁻¹ for the hygrophilous grasslands, and 0.125 gC·MJ⁻¹ for the helobious grasslands. The field validation shows that the estimated net primary productivity (NPP) by the derived maximal LUE is closely related to the ground references, with R² of 0.8698 and root-mean-square error (RMSE) of 59.37 gC·m⁻²·a⁻¹. This indicates that the default set in the CASA model is not suitable for NPP estimation for the regional mountain area. The derived maximal LUEs can significantly improve the capability of NPP mapping, and open up the perspective for long-term monitoring of vegetation ecological health and ecosystem productivity by combining the LUE-based model with remote sensing observations. Full article

Models and observations show that the Arctic is experiencing the most rapid changes in global near-surface air temperature. We developed novel EASE-grid Level 3 (L3) land surface temperature (LST) products from Level 2 (L2) AATSR and MODIS data to provide weekly, monthly and annual LST means over the pan-Arctic region at various grid resolutions (1–25 km) for the past decade (2000–2010). In this paper, we provide: (1) a review of previous validation of MODIS/AATSR L2; (2) a description of the processing chain of L3 products; (3) an assessment of the 25 km products uncertainty, and; (4) a quantification of the bias introduced by over-representing clear-sky days in MODIS L3 products. In addition, we generated uncertainty maps by comparing L3 products with LST from passive microwave sensors (AMSR-E and SSM/I) and the North American Regional Reanalysis (NARR). Results show a close correspondence between MODIS and AATSR monthly products with a mean-difference (MD) of −1.1 K. Comparing L3 products with NARR indicates a close agreement in summer and a systematic bias in winter, which is entirely negative with respect to MODIS L3 (MD: −3.6, Min: −6.8, Max: −1 K). Comparing monthly averaged MODIS L3 to NARR clear-sky to quantify over-representing clear-sky days indicates a decrease of winter and an increase of summer difference compared to NARR all-sky. Finally, we provide suggestions to improve LST retrieval over Arctic regions. Full article

The coloration of tropical reef corals is mainly due to their association with photosynthetic dinoflagellates commonly known as zooxanthellae. Combining High Performance Liquid Chromatography (HPLC), spectroscopy and derivative analysis we provide a novel approach to discriminate between the Caribbean shallow-water corals Acropora cervicornis and Porites porites based on their associated pigments. To the best of our knowledge, this is the first time that the total array of pigments found within the coral holobiont is reported. A total of 20 different pigments were identified including chlorophylls, carotenes and xanthophylls. Of these, eleven pigments were common to both species, eight were present only in A. cervicornis, and three were present only in P. porites. Given that these corals are living in similar physical conditions, we hypothesize that this pigment composition difference is likely a consequence of harboring different zooxanthellae clades with a possible influence of endolithic green or brown algae. We tested the effect of this difference in pigments on the reflectance spectra of both species. An important outcome was the correlation of total pigment concentration with coral reflectance spectra up to a 97% confidence level. Derivative analysis of the reflectance curves showed particular differences between species at wavelengths where several chlorophylls, carotenes and xanthophylls absorb. Within species variability of spectral features was not significant while interspecies variability was highly significant. We recognize that the detection of such differences with actual airborne or satellite remote sensors is extremely difficult. Nonetheless, based on our results, the combination of these techniques (HPLC, spectroscopy and derivative analysis) can be used as a robust approach for the development of a site specific spectral library for the identification of shallow-water coral species. Studies (Torres-Pérez, NASA Postdoctoral Program) are currently underway to further apply this approach to other Caribbean benthic coral reef features. The data will be used with planned and future airborne and satellite studies of the site and for algorithm development to advance the use of future airborne and satellite instrument capabilities (NASA PRISM and HyspIRI) for discrimination of coral reef benthic composition. Full article

Estimation of actual evapotranspiration (ET_a) based on remotely sensed imagery is very valuable in agricultural regions where ET_a rates can vary greatly from field to field. This research utilizes the image processing model METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) to estimate late season, post-harvest ET_a rates from fields with a cover crop planted after a cash crop (in this case, a rye/radish/pea mixture planted after spring wheat). Remotely sensed ET_oF (unit-less fraction of grass-based reference ET, ET_o) maps were generated using Erdas Imagine software for a 260 km² area in northeastern South Dakota, USA. Meteorological information was obtained from a Bowen-Ratio Energy Balance System (BREBS) located within the image. Nine image dates were used for the growing season, from May through October. Five of those nine were captured during the cover crop season. METRIC was found to successfully differentiate between fields with and without cover crops. In a blind comparison, METRIC compared favorably with the estimated ET_a rates found using the BREBS (ET_λE), with a difference in total estimated ET_a for the cover crop season of 7%. Full article

Detecting changes to the transparency of the water column is critical for understanding the responses of marine organisms, such as corals, to light availability. Long-term patterns in water transparency determine geographical and depth distributions, while acute reductions cause short-term stress, potentially mortality and may increase the organisms’ vulnerability to other environmental stressors. Here, we investigated the optimal, operational algorithm for light attenuation through the water column across the scale of the Great Barrier Reef (GBR), Australia. We implemented and tested a quasi-analytical algorithm to determine the photic depth in GBR waters and matched regional Secchi depth (Z_SD) data to MODIS-Aqua (2002–2010) and SeaWiFS (1997–2010) satellite data. The results of the in situ Z_SD/satellite data matchup showed a simple bias offset between the in situ and satellite retrievals. Using a Type II linear regression of log-transformed satellite and in situ data, we estimated Z_SD and implemented the validated Z_SD algorithm to generate a decadal satellite time series (2002–2012) for the GBR. Water clarity varied significantly in space and time. Seasonal effects were distinct, with lower values during the austral summer, most likely due to river runoff and increased vertical mixing, and a decline in water clarity between 2008–2012, reflecting a prevailing La Niña weather pattern. The decline in water clarity was most pronounced in the inshore area, where a significant decrease in mean inner shelf Z_SD of 2.1 m (from 8.3 m to 6.2 m) occurred over the decade. Empirical Orthogonal Function Analysis determined the dominance of Mode 1 (51.3%), with the greatest variation in water clarity along the mid-shelf, reflecting the strong influence of oceanic intrusions on the spatio-temporal patterns of water clarity. The newly developed photic depth product has many potential applications for the GBR from water quality monitoring to analyses of ecosystem responses to changes in water clarity. Full article

The leaf area index (LAI) is a key characteristic of forest ecosystems. Estimations of LAI from satellite images generally rely on spectral vegetation indices (SVIs) or radiative transfer model (RTM) inversions. We have developed a new and precise method suitable for practical application, consisting of building a species-specific SVI that is best-suited to both sensor and vegetation characteristics. Such an SVI requires calibration on a large number of representative vegetation conditions. We developed a two-step approach: (1) estimation of LAI on a subset of satellite data through RTM inversion; and (2) the calibration of a vegetation index on these estimated LAI. We applied this methodology to Eucalyptus plantations which have highly variable LAI in time and space. Previous results showed that an RTM inversion of Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared and red reflectance allowed good retrieval performance (R² = 0.80, RMSE = 0.41), but was computationally difficult. Here, the RTM results were used to calibrate a dedicated vegetation index (called “EucVI”) which gave similar LAI retrieval results but in a simpler way. The R² of the regression between measured and EucVI-simulated LAI values on a validation dataset was 0.68, and the RMSE was 0.49. The additional use of stand age and day of year in the SVI equation slightly increased the performance of the index (R² = 0.77 and RMSE = 0.41). This simple index opens the way to an easily applicable retrieval of Eucalyptus LAI from MODIS data, which could be used in an operational way. Full article

Drained thermokarst lake basins accumulate significant amounts of soil organic carbon in the form of peat, which is of interest to understanding carbon cycling and climate change feedbacks associated with thermokarst in the Arctic. Remote sensing is a tool useful for understanding temporal and spatial dynamics of drained basins. In this study, we tested the application of high-resolution X-band Synthetic Aperture Radar (SAR) data of the German TerraSAR-X satellite from the 2009 growing season (July–September) for characterizing drained thermokarst lake basins of various age in the ice-rich permafrost region of the northern Seward Peninsula, Alaska. To enhance interpretation of patterns identified in X-band SAR for these basins, we also analyzed the Normalized Difference Vegetation Index (NDVI) calculated from a Landsat-5 Thematic Mapper image acquired on July 2009 and compared both X-band SAR and NDVI data with observations of basin age. We found significant logarithmic relationships between (a) TerraSAR-X backscatter and basin age from 0 to 10,000 years, (b) Landat-5 TM NDVI and basin age from 0 to 10,000 years, and (c) TerraSAR-X backscatter and basin age from 50 to 10,000 years. NDVI was a better indicator of basin age over a period of 0–10,000 years. However, TerraSAR-X data performed much better for discriminating radiocarbon-dated basins (50–10,000 years old). No clear relationships were found for either backscatter or NDVI and basin age from 0 to 50 years. We attribute the decreasing trend of backscatter and NDVI with increasing basin age to post-drainage changes in the basin surface. Such changes include succession in vegetation, soils, hydrology, and renewed permafrost aggradation, ground ice accumulation and localized frost heave. Results of this study show the potential application of X-band SAR data in combination with NDVI data to map long-term succession dynamics of drained thermokarst lake basins. Full article

In order to monitor dryness stress under controlled conditions, we set up an experiment with beech seedlings in plant pots and built a platform for observing the seedlings with field imaging spectroscopy. This serves as a preparation for multi-temporal hyperspectral air- and space-borne data expected to be available in coming years. Half of the trees were watered throughout the year; the other half were cut off from water supply for a five-week period in late summer. Plant health and soil, as well as leaf water status, were monitored. Moreover, hyperspectral images of the trees were acquired four times during the experiment. Results show that the experimental imaging setup is well suited for recording hyperspectral images of objects, like the beech pots, under natural illumination conditions. The high spatial resolution makes it feasible to discern between background, soil, wood, green leaves and brown leaves. Furthermore, it could be shown that dryness stress is detectable from an early stage even in the limited spectral range considered. The decline of leaf chlorophyll over time was also well monitored using imaging spectroscopy data. Full article

To estimate global gross primary production (GPP), which is an important parameter for studies of vegetation productivity and the carbon cycle, satellite data are useful. In 2014, the Japan Aerospace Exploration Agency (JAXA) plans to launch the Global Change Observation Mission-Climate (GCOM-C) satellite carrying the second-generation global imager (SGLI). The data obtained will be used to estimate global GPP. The rate of photosynthesis depends on photosynthesis reduction and photosynthetic capacity, which is the maximum photosynthetic velocity at light saturation under adequate environmental conditions. Photosynthesis reduction is influenced by weather conditions, and photosynthetic capacity is influenced by chlorophyll and RuBisCo content. To develop the GPP estimation algorithm, we focus on photosynthetic capacity because chlorophyll content can be detected by optical sensors. We hypothesized that the maximum rate of low-stress GPP (called “GPP capacity”) is mainly dependent on the chlorophyll content that can be detected by a vegetation index (VI). The objective of this study was to select an appropriate VI with which to estimate global GPP capacity with the GCOM-C/SGLI. We analyzed reflectance data to select the VI that has the best linear correlation with chlorophyll content at the leaf scale and with GPP capacity at canopy and satellite scales. At the satellite scale, flux data of seven dominant plant functional types and reflectance data obtained by the Moderate-resolution Imaging Spectroradiometer (MODIS) were used because SGLI data were not available. The results indicated that the green chlorophyll index, CI_green(ρ_NIR/ρ_green-1), had a strong linear correlation with chlorophyll content at the leaf scale (R² = 0.87, p < 0.001) and with GPP capacity at the canopy (R² = 0.78, p < 0.001) and satellite scales (R² = 0.72, p < 0.01). Therefore, CI_green is a robust and suitable vegetation index for estimating global GPP capacity. Full article

Spaceborne chlorophyll indices based on red fluorescence (wavelength = 680 nm) and water leaving radiance (L_w) in the visible spectrum (i.e., 400–700 nm) were evaluated in the St Lawrence Estuary (SLE) during September of 2011. Relationships between chlorophyll concentration (chl) and fluorescence were constructed based on fluorescence line height (FLH) measurements derived from a compact laser-based spectrofluorometer developed by ENEA (CASPER) and using spectral bands corresponding to the satellite sensor MERIS (MEdium Resolution Imaging Spectrometer). Chlorophyll concentration as estimated from CASPER (chl^CASPER) was relatively high NE of the MTZ (upper Estuary), and nearby areas influenced by fronts or freshwater plumes derived from secondary rivers (lower estuary). These findings agree with historical shipboard measurements. In general, global chl products calculated from L_w had large biases (up to 27-fold overestimation and 50-fold underestimation) with respect to chl^CASPER values. This was attributed to the smaller interference of detritus (mineral + organic non-living particulates) and chromophoric dissolved organic matter on chl^CASPER estimates. We encourage the use of spectrofluorometry for developing and validating remote sensing models of chl in SLE waters and other coastal environments characterized by relatively low to moderate (<10 g·m⁻³) concentrations of detritus. Full article

We examine the impact of atmospheric correction, specifically aerosol model selection, on retrieval of bio-optical properties from satellite ocean color imagery. Uncertainties in retrievals of bio-optical properties (such as chlorophyll, absorption, and backscattering coefficients) from satellite ocean color imagery are related to a variety of factors, including errors associated with sensor calibration, atmospheric correction, and the bio-optical inversion algorithms. In many cases, selection of an inappropriate or erroneous aerosol model during atmospheric correction can dominate the errors in the satellite estimation of the normalized water-leaving radiances (_nL_w), especially over turbid, coastal waters. These errors affect the downstream bio-optical properties. Here, we focus on the impact of aerosol model selection on the _nL_w radiance estimates by comparing Aerosol Robotic Network-Ocean Color (AERONET-OC) measurements of _nL_w and aerosol optical depth (AOD) to satellite-derived values from Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS). We also apply noise to the satellite top-of-atmosphere (TOA) radiance values in the two near-infrared (NIR) wavelengths used for atmospheric correction, to assess the effect on aerosol model selection and _nL_w retrievals. In general, for the data sets examined, we found that as little as 1% uncertainty (noise) in the NIR TOA radiances can lead to the selection of a different pair of bounding aerosol models, thus changing _nL_w retrievals. We also compare aerosol size fraction retrieved from AERONET and size fraction represented by aerosol models selected during atmospheric correction. Full article