This PDF file contains the errata for “JRS Vol. 5 Issue 1 Paper 3628662” for JRS Vol. 5 Issue 1

Computationally efficient processing of hyperspectral image cubes can be greatly beneficial in many application domains, including environmental modeling, risk/hazard prevention and response, and defense/security. As individual cluster computers often cannot satisfy the computational demands of emerging problems in hyperspectral imaging, there is a growing need for distributed supercomputing using multicluster systems. A well-known manner of obtaining speedups in hyperspectral imaging is to apply data parallel approaches, in which commonly used data structures (e.g., the image cubes) are being scattered among the available compute nodes. Such approaches work well for individual compute clusters, but-due to the inherently large wide-area communication overheads-these are generally not applied in distributed multi-cluster systems. Given the nature of many algorithmic approaches in hyperspectral imaging, however, and due to the increasing availability of high-bandwidth optical networks, wide-area data parallel execution may well be a feasible acceleration approach. This paper discusses the wide-area data parallel execution of two realistic and state-of-the-art algorithms for endmember extraction in hyperspectral unmixing applications: automatic morphological endmember extraction and orthogonal subspace projection. It presents experimental results obtained on a real-world multicluster system, and provides a feasibility analysis of the applied parallelization approaches. The two parallel algorithms evaluated in this work had been developed before for single-cluster execution, and were not changed.Because no further implementation efforts were required, the proposed methodology is easy to apply to already available algorithms, thus reducing complexity and enhancing standardization.

The micro-Doppler (m-D) effect describes the subtle micromotion features of a radar target and provides a new approach for feature extraction and auto radar target recognition. However, the microwave radar cannot provide enough resolution to detect the m-D effect of small targets and long distance targets. In order to obtain high range resolution for the extraction of subtle m-D signatures, inverse synthetic aperture imaging LADAR (ISAIL) is used here. Because the ISAIL uses a frequency modulation continuous wave signal, the m-D effect of ISAIL is different from the microwave radar. In this paper, the m-D effect of ISAIL is analyzed. The features of the m-D signatures in ISAIL are extracted by an improved Hough transform method associated with erosion and dilation operations in binary mathematical morphology. The simulations are given to validate the theoretical analyses and the proposed m-D extraction method. The experiment results show that the ISAIL can offer sufficient information of micromotions when the feature of motions is tiny.

The radiative transfer for television operational vertical sounder (RTTOV) is a widely-used radiative transfer model (RTM) for calculation of radiances for satellite infrared and microwave sensors, including the 8461-channel infrared atmospheric sounding interferometer (IASI) and the 15-band Advanced Microwave Sounding Unit-A (AMSU-A). In the era of hyperspectral sounders with thousands of spectral channels, the computation of the RTM becomes more time-consuming. The RTM performance in operational numerical weather prediction systems still limits the number of used channels in hyperspectral sounders to only a few hundred. To take full advantage of such high-resolution infrared observations, a computationally efficient radiative transfer model is needed to facilitate satellite data assimilation. In this paper, we develop the parallel implementation of the RTTOV-7 IASI and AMSU-A RTMs to run the predictor module on CPUs in pipeline with the transmittance and radiance modules on NVIDIA many-core graphics processing units (GPUs). We show that concurrent execution of RTTOV-7 IASI RTM on CPU and GPU, in addition to asynchronous data transfer from CPU to GPU, allows the GPU accelerated code running on the 240-core NVIDIA Tesla C1060 to reach a speedup of 461× and 1793× for 1- and 4-GPU configurations, respectively. To compute one day's amount of 1,296,000 IASI spectra, the CPU code running on the host AMD Phenom II X4 940 CPU core with 3.0 GHz will take 2.8 days. Thus, GPU acceleration reduced running time to 8.75 and 2.25 min on 1- and 4-GPU configurations, respectively. Speedup for the RTTOV AMSU-A RTM varied from 29× to 75× for 1 and 4 GPUs, respectively.To further boost the speedup of a multispectral RTM, we developed a novel pure-GPU version of the RTTOV AMSU-A RTM where the predictor module also runs on GPUs to achieve a 96% reduction in the host-to-device data transfer. The speedups for the pure-GPU AMSU-A RTM are significantly increased to 56× and 125× for 1- and 4-GPU configurations, respectively.

Ultraspectral sounders revolutionize the way remote sensing data are collected, retrieved, and assimilated to provide better weather, climate, and environmental prediction and monitoring. This unprecedented amount of ultraspectral data increases the burden of data bandwidth and the chance of transmission noise and error contamination. It is desired that source coding of ultraspectral data also have some error-resilient capability, in addition to the error-correcting channel coding. Earlier, we developed three-dimensional wavelet reversible variable-length coding (3DWT-RVLC) for lossless compression of ultraspectral sounder data, which has significantly better error resilience than JPEG2000 Part 2 at only a small reduction in compression gain. The reversible variable-length codes allow instantaneous decoding in both directions, which affords better detection of bit errors due to synchronization losses over a noisy channel. To explore the feasibility of 3DWT-RVLC for real-time ultraspectral data processing, we implement a memory-limited digital signal processor (DSP) version of 3DWT-RVLC. Compression experiments on 10 ultraspectral test granules obtained from the NASA Atmospheric Infrared Sounder show that the memory-limited DSP-based 3DWT-RVLC is able to perform high-speed data processing at only a small reduction in compression ratio as compared to the original 3DWT-RVLC.

We present a parallel image classification approach, referred to as the parallel positive Boolean function (PPBF), to multisource remote sensing images. PPBF is originally from the positive Boolean function (PBF) classifier scheme. The PBF multiclassifier is developed from a stack filter to classify specific classes of land covers. In order to enhance the efficiency of PBF, we propose PPBF to reduce the execution time using parallel computing techniques. PPBF fully utilizes the significant parallelism embedded in PBF to create a set of PBF stack filters on each parallel node based on different classes of land uses. It is implemented by combining the message-passing interface library and the open multiprocessing (OpenMP) application programing interface in a hybrid mode. The experimental results demonstrate that PPBF significantly reduces the computational loads of PBF classification.

An introduction to the special section by the guest editors.

In recent years, the urban impervious surface has been recognized as a key quantifiable indicator in assessing urbanization impacts on environmental and ecological conditions. A surge of research interests has resulted in the estimation of urban impervious surface using remote sensing studies. The objective of this paper is to examine and compare the effectiveness of two algorithms for extracting impervious surfaces from Landsat TM imagery; the multilayer perceptron neural network (MLPNN) and the support vector machine (SVM). An accuracy assessment was performed using the high-resolution WorldView images. The root mean square error (RMSE), the mean absolute error (MAE), and the coefficient of determination (R²) were calculated to validate the classification performance and accuracies of MLPNN and SVM. For the MLPNN model, the RMSE, MAE, and R² were 17.18%, 11.10%, and 0.8474, respectively. The SVM yielded a result with an RMSE of 13.75%, an MAE of 8.92%, and an R² of 0.9032. The results indicated that SVM performance was superior to that of MLPNN in impervious surface classification. To further evaluate the performance of MLPNN and SVM in handling the mixed-pixels, an accuracy assessment was also conducted for the selected test areas, including commercial, residential, and rural areas. Our results suggested that SVM had better capability in handling the mixed-pixel problem than MLPNN. The superior performance of SVM over MLPNN is mainly attributed to the SVM's capability of deriving the global optimum and handling the over-fitting problem by suitable parameter selection. Overall, SVM provides an efficient and useful method for estimating the impervious surface.

In many modern applications, methods and algorithms used for image processing require a priori knowledge or estimates of noise type and its characteristics. Noise type and basic parameters can be sometimes known in advance or determined in an interactive manner. However, it occurs more and more often that they should be estimated in a blind manner. The results of noise-type blind determination can be false, and the estimates of noise parameters are characterized by certain accuracy. Such false decisions and estimation errors have an impact on performance of image-processing techniques that is based on the obtained information. We address some issues of such a negative influence. Possible structures of automatic procedures are presented and discussed for several typical applications of image processing as remote sensing data preprocessing and compression.

The problem of hyperspectral remote sensing images classification is revisited by posterior probability support vector machines (PPSVMs). To address the multiclass classification problem, PPSVMs are extended using binary tree structure and boosting with the Fisher ratio as class separability measure. The class pair with larger Fisher ratio separability measure is separated at upper nodes of the binary tree to optimize the structure of the tree and improve the classification accuracy. Two approaches are proposed to select the class pair and construct the binary tree. One is the so-called some-against-rest binary tree of PPSVMs (SBT), in which some classes are separated from the remaining classes at each node considering the Fisher ratio separability measure. For the other approach, named one-against-rest binary tree of PPSVMs (OBT), only one class is separated from the remaining classes at each node. Both approaches need only to train n - 1 (n is the number of classes) binary PPSVM classifiers, while the average convergence performance of SBT and OBT are O(log2n) and O[(n! − 1)/n], respectively. Experimental results show that both approaches obtain classification accuracy if not higher, at least comparable to other multiclass approaches, while using significantly fewer support vectors and reduced testing time.

Waveform lidar imagery was acquired on September 26, 1999 over the Bartlett Experimental Forest (BEF) in New Hampshire (USA) using NASA's Laser Vegetation Imaging Sensor (LVIS). This flight occurred 20 months after an ice storm damaged millions of hectares of forestland in northeastern North America. Lidar measurements of the amplitude and intensity of ground energy returns appeared to readily detect areas of moderate to severe ice storm damage associated with the worst damage. Southern through eastern aspects on side slopes were particularly susceptible to higher levels of damage, in large part overlapping tracts of forest that had suffered the highest levels of wind damage from the 1938 hurricane and containing the highest levels of sugar maple basal area and biomass. The levels of sugar maple abundance were determined through analysis of the 1997 Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) high resolution spectral imagery and inventory of USFS Northern Research Station field plots. We found a relationship between field measurements of stem volume losses and the LVIS metric of mean canopy height (r² = 0.66; root mean square errors = 5.7 m³/ha, p < 0.0001) in areas that had been subjected to moderate-to-severe ice storm damage, accurately documenting the short-term outcome of a single disturbance event.

In this contribution, we present a simple method based on Albedo-VI (vegetation index) triangular space to determine the Priestley-Taylor parameter for estimating evaporative fraction (EF) and evapotranspiration (ET) in arid and semi-arid regions. We apply this method to MODIS and observation data acquired during the Heihe river basin field experiment from July 1 to September 30, 2008. Results show that the decreasing trend of the estimated EF from MODIS data is consistent with that of precipitation during the period of day 183 to 274, 2008. The bias of estimated daily ET deviating from the corresponding ground-measured ET is −8.66 W/m² and the root-mean-square error is 21.55 W/m², indicating the Albedo-VI triangular method has a potential in ET estimation as a simple satellite-based method independent of surface ancillary data.

Lake Chagan represents a complex situation of major optical constituents and emergent spectral signals for remote sensing analysis of water quality in the Songnen Plain. As such it provides a good test of the combined radiometric correction methods developed for optical remote sensing data to monitor water quality. Landsat thematic mapper (TM) data and in situ water samples collected concurrently with satellite overpass were used for the analysis, in which four important water quality parameters are considered: chlorophyll-a, turbidity, total dissolved organic matter, and total phosphorus in surface water. Both empirical regressions and neural networks were established to analyze the relationship between the concentrations of these four water parameters and the satellite radiance signals. It is found that the neural network model performed at better accuracy than empirical regressions with TM visible and near-infrared bands as spectral variables. The relative root mean square error (RMSE) for the neural network was < 10%, while the RMSE for the regressions was less than 25% in general. Future work is needed on establishing the dynamic characteristic of Chagan Lake water quality with TM or other optical remote sensing data. The algorithms developed in this study need to be further tested and refined with multidate imagery data.

Due to the increasing development of image spectroscopy techniques, airborne and spaceborne hyperspectral images have in recent years become readily available for use in geological applications. One of the prominent advantages of imaging spectroscopy is its high spectral resolution, producing detailed spectral information in each pixel. The current study aims at exploring the feasibility of the Earth-Observing-1 Hyperion imaging spectrometer to map the geology arena over the Dana Geology National Park, Jordan. After overcoming the common preprocessing difficulties (e.g., smile effect), a classification scheme of two levels was applied. The first level resulted in a stratigraphic classification product of eleven classes and the second level in a lithologic classification product of six classes. The overall accuracy of the stratigraphic product was 57%, while that of the lithologic product was 79%. Mismatches in classification were mostly related to terrestrial cover of the lower topography formation by rock and sand debris. In addition, low accuracy values can be attributed to Hyperion's high sensitivity, leading to recognition of different mineral compositions as different classes within a rock formation, while the conventional geology-stratigraphic map generalizes these different classes into one formation. The methods practiced in the current research can advance the Hyperion's classification capabilities and therefore can be applied in different geological settings and additional disciplines such as penology, agriculture, ecology, forestry, urban, and other environmental studies.

Airborne light detection and ranging (LIDAR) technology now makes it possible to sample the Earth's surface with point spacings well below 1 m. It is, however, time consuming, costly, and technically challenging to directly use very high resolution LIDAR data for hydraulic modeling because of the computational requirements associated with solving fluid dynamics equations over complex boundary conditions in large data sets. For high relief terrain and urban areas, using coarse digital elevation models (DEMs) can cause significant degradation in hydraulic modeling, particularly when artificial obstructions, such as buildings, mask spatial correlations between terrain points. In this paper we present a strategy to reduce the computational complexity in the estimation of surface water discharge through a decomposition of the DEM data, wherein features have different characteristic spatial frequencies. Though the optimal DEM scale for a particular application will ultimately be decided by the user's tolerance for error, we present guidelines to choose a proper scale by balancing computer memory usage and accuracy. We also suggest a method to parameterize man-made structures, such as buildings in hydraulic modeling, to efficiently and accurately account for their effects on surface water discharge.

The information on the mangrove forests for the Pacific region is scarce or outdated. A regional assessment based on a consistent methodology and data sources was needed to understand their true extent. Our investigation offers a regionally consistent, high resolution (30 m), and the most comprehensive mapping of mangrove forests on the islands of American Samoa, Fiji, French Polynesia, Guam, Hawaii, Kiribati, Marshall Islands, Micronesia, Nauru, New Caledonia, Northern Mariana Islands, Palau, Papua New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu, Vanuatu, and Wallis and Futuna Islands for the year 2000. We employed a hybrid supervised and unsupervised image classification technique on a total of 128 Landsat scenes gathered between 1999 and 2004, and validated the results using existing geographic information science (GIS) datasets, high resolution imagery, and published literature. We also draw a comparative analysis with the mangrove forests inventory published by the Food and Agriculture Association (FAO) of the United Nations. Our estimate shows a total of 623755 hectares of mangrove forests in the Pacific region; an increase of 18% from FAO's estimates. Although mangrove forests are disproportionately distributed toward a few larger islands on the western Pacific, they are also significant in many smaller islands.

The digital elevation model (DEM) forms fundamental topographical data for many scientific and engineering purposes including hydrology, geology, and civil applications. Using active and passive satellite remote sensing, it is an efficient and cost-effective approach to acquire up-to-date, accurate land cover and topographic information. One of the most important applications of interferometric synthetic aperture radar (SAR) technology is the determination of three-dimensional topographical information. Interferometric synthetic aperture radar (InSAR) DEM generation uses the measurement of the phase difference between two complex radar signals, or the difference in distance between satellite-borne SAR sensors and ground targets from two imaging passes. The parameters of the final DEM are related to the information contained within the master image. In addition, InSAR DEMs generated with different master images have varying grid sizes and location despite using the same coordinate systems. Consequently, the InSAR method can generate a multiplicity of DEMs using different combinations of the SAR image pairs. The authors propose a method that exploits the information contained in the area of overlap between different master and perpendicular baseline InSAR DEMs.

Wetlands are valuable ecosystems that benefit society. However, throughout history wetlands have been converted to other land uses. For this reason, timely wetland maps are necessary for developing strategies to protect wetland habitat. The goal of this research was to develop a time-efficient, automated, low-cost method to map wetlands in a semi-arid landscape that could be scaled up for use at a county or state level, and could lay the groundwork for expanding to forested areas. Therefore, it was critical that the research project contain two components: accurate automated feature extraction and the use of low-cost imagery. For that reason, we tested the effectiveness of geographic object-based image analysis (GEOBIA) to delineate and classify wetlands using freely available true color aerial photographs provided through the National Agriculture Inventory Program. The GEOBIA method produced an overall accuracy of 89% (khat = 0.81), despite the absence of infrared spectral data. GEOBIA provides the automation that can save significant resources when scaled up while still providing sufficient spatial resolution and accuracy to be useful to state and local resource managers and policymakers.

Mapping of vegetation using remote sensing in mountainous areas is considerably hampered by topographic effects on the spectral response pattern. A variety of topographic normalization techniques have been proposed to correct these illumination effects due to topography. The purpose of this study was to compare six different topographic normalization methods (Cosine correction, Minnaert correction, C-correction, Sun-canopy-sensor correction, two-stage topographic normalization, and slope matching technique) for their effectiveness in enhancing vegetation classification in mountainous environments. Since most of the vegetation classes in the rugged terrain of the Lancang Watershed (China) did not feature a normal distribution, artificial neural networks (ANNs) were employed as a classifier. Comparing the ANN classifications, none of the topographic correction methods could significantly improve ETM+ image classification overall accuracy. Nevertheless, at the class level, the accuracy of pine forest could be increased by using topographically corrected images. On the contrary, oak forest and mixed forest accuracies were significantly decreased by using corrected images. The results also showed that none of the topographic normalization strategies was satisfactorily able to correct for the topographic effects in severely shadowed areas.

Laser detection and ranging (LADAR) systems can be used to provide 2-D and 3-D images of scenes. Generally, 2-D images possess superior spatial resolution but without range data due to the density of their focal plane arrays. A 3-D LADAR system can produce range-to-target data at each pixel but lacks the 2-D system's superior spatial resolution. We develop an algorithm using an expectation maximization approach to estimate both 3-D LADAR range and the bias associated with a 3-D LADAR system. The algorithm we develop demonstrates both spatial and range resolution improvement over standard interpolation techniques using both real and simulated 3-D and 2-D LADAR data.

Honey mesquite (Prosopis glandulosa Torr.) invasion can negatively impact grazing capacity, spatial livestock distribution, and forage production in Chihuahuan Desert rangelands. High spatial resolution remote sensing data can be used to develop maps of shrub encroachment for arid rangelands. The objective of this study was to map changes in honey mesquite abundance and to evaluate honey mesquite impacts on perennial grass production at the Chihuahuan Desert Rangeland Research Center in south-central New Mexico using high resolution satellite imagery. The project employed QuickBird ortho-ready satellite imagery with spatial resolution of 2.4 m in multispectral bands and panchromatic resolution of 0.6 m for the study area on May 19, 2009. We used a maximum likelihood supervised classification algorithm to distinguish honey mesquite from other land cover categories. We then measured grass production (kg/ha) in May, 2009 on 10 permanent, evenly spaced key areas in each pasture. We identified 12×60 m plots from the classified map and used these to calculate honey mesquite canopy cover on the 40 transects across the study area. Areas classified as dominated by honey mesquite estimated from image analyses encompassed 143, 50, 92, and 136 hectares in pastures 1, 4, 14, and 15, respectively. Regression analyses showed that increasing levels of honey mesquite canopy cover corresponded to lower perennial grass forage production (r² = 0.73, n = 40). Our findings indicate that classification of high-resolution satellite imagery is a very useful tool for mapping invasive shrubs and determining their influence on forage production in desert landscapes.

This paper addresses bathymetry estimation from high resolution multispectral satellite images by proposing an accurate supervised method, based on a neuro-fuzzy approach. The method is applied to two Quickbird images of the same area, acquired in different years and meteorological conditions, and is validated using truth data. Performance is studied in different realistic situations of in situ data availability. The method allows to achieve a mean standard deviation of 36.7 cm for estimated water depths in the range [−18, −1] m. When only data collected along a closed path are used as a training set, a mean STD of 45 cm is obtained. The effect of both meteorological conditions and training set size reduction on the overall performance is also investigated.

In this paper, we present the results of a net primary production (NPP) modeling of teak (Tectona grandis Lin F.), an important species in tropical deciduous forests. The biome-biogeochemical cycles or Biome-BGC model was calibrated to estimate net NPP through the inverse modeling approach. A genetic algorithm (GA) was linked with Biome-BGC to determine the optimal ecophysiological model parameters. The Biome-BGC was calibrated by adjusting the ecophysiological model parameters to fit the simulated LAI to the satellite LAI (SPOT-Vegetation), and the best fitness confirmed the high accuracy of generated ecophysioligical parameter from GA. The modeled NPP, using optimized parameters from GA as input data, was evaluated using daily NPP derived by the MODIS satellite and the annual field data in northern Thailand. The results showed that NPP obtained using the optimized ecophysiological parameters were more accurate than those obtained using default literature parameterization. This improvement occurred mainly because the model's optimized parameters reduced the bias by reducing systematic underestimation in the model. These Biome-BGC results can be effectively applied in teak forests in tropical areas. The study proposes a more effective method of using GA to determine ecophysiological parameters at the site level and represents a first step toward the analysis of the carbon budget of teak plantations at the regional scale.

In this study, the maximum likelihood supervised classification and the post-classification comparison change detection are applied in order to monitor the wetlands by assessing and quantifying the wetland cover changes in the Nile swamps of southern Sudan, called the Sudd, by using the ERDAS IMAGINE software. Three multispectral satellite imageries, acquired in the wet season from 1986 to 2006 by Landsat TM and Landsat ETM+ images, are classified into five main land cover classes namely water, vegetation, urban, wetland-vegetation, and wetland-no vegetation, by using the maximum likelihood supervised classification. A pixel-by-pixel comparison was then performed over the classified thematic map images. The post-classification change detection results show a 3.69% decrease in the wetland-vegetation areas and a 2.68% decrease in the wetland-no vegetation areas within the period 1986 to 1999. In addition, a noticeable increase is observed in the wetland-vegetation areas within the period 1999 to 2006 in the Sudd area as 14.95% of the land cover classes' areas, excluding the wetland-vegetation areas are changed into wetland-vegetation areas while there was a decrease of 5.18% in the wetland-no vegetation areas within the period 1999 to 2006. The objective of this paper is to introduce precedence in studying the wetland cover changes over the Sudd area which can help the output planners develop water resources management projects leading to enhance the life conditions in the Sudd region.

The differential interferometric synthetic aperture radar (SAR)(DInSAR) technique has been applied to the earth surface deformation monitoring in many areas. In this paper, the DInSAR technique is used to process the spaceborne SAR data including C band ENVISAT ASAR, L band JERS SAR, and ALOS PALSAR data to derive the temporal land subsidence information in the Fengfeng coal mine area, Hebei province in China. Since JERS and ALOS do not have precise orbit, an orbit adjustment must be accomplished before the DInSAR interferogram was formed. Twenty-three differential interferograms are derived to show the temporal change of the land subsidence range and position. At the acquisition time of ENVISAT ASAR, the leveling in the Dashucun coal mine in Fengfeng area was carried, the historical excavation data in 8 coal mines in Fengfeng area from 1992 to 2007 were collected as well. In our analysis, the DInSAR results are compared with leveling data and historical excavation data. The comparison results show the DInSAR subsidence results are consistent with the leveling results and the historical excavation data, and the L band DInSAR shows more advantages than C band in the coal mining induced subsidence monitoring in a rural area. The feasibility and limitations in coal mining induced subsidence monitoring with DInSAR are analyzed, and the possibility of underground mining activity monitoring by spaceborne InSAR data is evaluated.The experimental results show that both C and L band can accomplish monitoring mining area subsidence, but C band has more restricted conditions of its perpendicular baseline. In order to get a satisfactory outcome in mining area subsidence by the DInSAR method, the time series of SAR images of every visit and SAR deformation interferograms should be archived.

Many places in the world show spectacular landscape changes caused by increasingly rapid alterations of natural phenomena observed in the last 3 to 4 decades. More and more studies reveal the consequences of global climate change strengthened by anthropogenic effects. The cause of rapid changes in the landscape is often due to the alteration of the natural water-cycle. Using moderate resolution imaging spectoradiometer vegetation indices, this study analyzed the relationship between biomass and precipitation, being one of the most important climate elements, over a Hungarian landscape that has been highly affected by the process of groundwater-table sinking in the last decades. Research proved that the reasons for this decrease are mainly the precipitation shortage due to climate change and to a much smaller extent, anthropogenic effects. In the forests of the study area, the annual distribution of precipitation proved to be an important factor, and the biomass produced by forests is influenced by the precipitation over a shorter interval-compared to less sensitive landscapes. Under increasing aridification, further degradation of vegetation can be expected as has already been observed during drier periods in the case of tree species with high water demand.

True-color imagery, which is formed via a weighted combination of red, green, and blue (RGB) spectral information, has important operational applications for qualitative environmental characterization, including the detection of smoke plumes, volcanic ash, and other aerosols that are not as easily discerned in conventional visible or infrared imagery, but may be more readily characterized via color properties. Despite its universal popularity, true-color is currently unavailable from geostationary satellites, and the next-generation GOES-R advanced baseline imager (ABI) will fall one band (green; 0.55 μm) short of doing so. However, approximations exist, and a process for simulating true-color imagery representative of capabilities anticipated from the ABI is presented and assessed here. High-resolution atmospheric model simulations are used to produce the ABI reflective band imagery required for true-color imagery. Those simulations are then rendered at ABI spatial (0.5-km visible) and temporal (5 min) resolution, to provide realistic data, long before the anticipated 2015 launch of GOES-R. An additional analysis, a color-space transformation, is used to assess the true-color (RGB) ABI images. The resulting hue images verify the less-green bias in the synthetic-green band and synthetic-RGB images created on ABI simulated data. Assessing the deficiencies in the RGB process will hopefully lead to an improved and standard means for generating an RGB product from the ABI data stream. Finally, as one of the many product applications of true-color imagery, an example of synthetic true-color imagery with added smoke is presented. The incorporation of aerosol properties into simulated imagery may help reveal the limits of detectability for atmospheric aerosols with future ABI.

A LIDAR system to receive Mie backscattered photons has been developed at the Manora peak, Nainital, India and it is the first of its kind in the central Himalayan region. The system is sensitive to receive backscattered photons from heights up to ∼20 km (above ground level). The atmospheric extinction profiles using Mie LIDAR have been estimated for the first time at this site in January (winter) and March (spring) seasons in three campaigns and maximum values are found to be ∼0.01, 0.03, and 0.08 km⁻¹, respectively. The aerosol optical depth (AOD) values are found to be ∼0.051, 0.098, and 0.233 in three campaigns, respectively, showing enhancement from January (winter) to March (spring) indicating a seasonal variation. AOD values of LIDAR, aerosol robotic network, and moderate resolution imaging spectroradiometer were found within the standard deviations. The aerosol loading at the site has increased during the last decade as evident from previous studies.

This paper reports a two-dimensional time-domain inverse scattering algorithm based upon the finite-difference time domain method (FDTD) for determining the shape of a perfectly conducting cylinder. FDTD is used to solve the scattering electromagnetic wave of a perfectly conducting cylinder. The inverse problem is resolved by an optimization approach and the global searching scheme asynchronous particle swarm optimization is then employed to search the parameter space. By properly processing the scattered field, some electromagnetic properties can be reconstructed. A set of representative numerical results is presented to demonstrate that the proposed approach is able to efficiently reconstruct the electromagnetic properties of metallic scatterer even when the initial guess is far away from the exact one. In addition, the effects of Gaussian noises on imaging reconstruction are also investigated.

To reach the goals laid out by the U.S. Government for displacing fossil fuels with biofuels, high-biomass sorghum is well-suited to achieving this goal because it requires less water per unit dry biomass and can produce very high biomass yields. In order to make biofuels economically competitive with fossil fuels it is essential to maximize production efficiency throughout the system. The goal of this study was to use remote sensing technologies to optimize the yield and harvest logistics of high-biomass sorghum with respect to production costs based on spatial variability within and among fields. Specific objectives were to compare yield to aerial multispectral imagery and develop predictive relationships. A 19.2-ha high-biomass sorghum field was selected as a study site and aerial multispectral images were acquired with a four-camera imaging system on July 17, 2009. Sorghum plant samples were collected at predetermined geographic coordinates to determine biomass yield. Aerial images were processed to find relationships between image reflectance and yield of the biomass sorghum. Results showed that sorghum biomass yield in early August was closely related (R² = 0.76) to spectral reflectance. However, in the late season the correlations between the biomass yield and spectral reflectance were not as positive as in the early season. The eventual outcome of this work could lead to predicted-yield maps based on remotely sensed images, which could be used in developing field management practices to optimize yield and harvest logistics.

Different soil moisture estimation models have been developed based on using optical, radar, or a combination of optical and radar data. However, it is not clear which of these models is more suitable to estimate soil moisture in vegetated areas. Soil moisture is estimated in sparse vegetated areas using both optical and synthetic aperture radar (SAR) images. Also a hybrid model that is based on a combination of SAR and optical derived indices is used to decrease the effects of vegetation cover on SAR backscatter coefficients. The results show that the SAR model is more accurate than the optical model. However, after using the hybrid model and removing vegetation cover effects from radar backscattering coefficient, the accuracies improve. This shows that the hybrid model is the most accurate model and can be used as a suitable model to estimate soil moisture.

Rice is the most important economic crop in Vietnam's Mekong Delta (MD). It is the main source of employment and income for rural people in this region. Yearly estimates of rice growing areas and delineation of spatial distribution of rice crops are needed to devise agricultural economic plans and to ensure security of the food supply. The main objective of this study is to map rice cropping systems with respect to monitoring agricultural practices in the MD using time-series moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) 250-m data. These time-series NDVI data were derived from the 8-day MODIS 250-m data acquired in 2008. Various spatial and nonspatial data were also used for accuracy verification. The method used in this study consists of the following three main steps: 1. filtering noise from the time-series NDVI data using wavelet transformation (Coiflet 4); 2. classification of rice cropping systems using parametric and nonparametric classification algorithms: the maximum likelihood classifier (MLC) and support vector machines (SVMs); and 3. verification of classification results using ground truth data and government rice statistics. Good results can be found using wavelet transformation for cleaning rice signatures. The results of classification accuracy assessment showed that the SVMs outperformed the MLC. The overall accuracy and Kappa coefficient achieved by the SVMs were 89.7% and 0.86, respectively, while those achieved by the MLC were 76.2% and 0.68, respectively. Comparison of the MODIS-derived areas obtained by the SVMs with the government rice statistics at the provincial level also demonstrated that the results achieved by the SVMs (R² = 0.95) were better than the MLC (R² = 0.91). This study demonstrates the effectiveness of using a nonparametric classification algorithm (SVMs) and time-series MODIS NVDI data for rice crop mapping in the Vietnamese MD.

This paper aims to model leaf area index (LAI) and crop height to spectral vegetation indices (VI), such as normalized difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI), and weighted difference vegetation index (WDVI). The intended purpose is to create empirical statistical models to support evapotranspiration algorithms applied under the current conditions in the island of Cyprus. Indeed, a traditionally agricultural area was selected in the Mandria Village in the Paphos District area in Cyprus, where one of the island's main exported crops, potatoes, are cultivated. A GER-1500 field spectroradiometer was used in this study in order to retrieve the necessary spectrum data of the different crops for estimating the VI's. A field campaign was undertaken with spectral measurements of LAI and crop height using the Sun-Scan canopy analyzer, acquired simultaneously with the spectroradiometric measurements between March and April of 2008 and 2009. Regarding the measurements, the phenological cycle of potatoes was followed. Several regression models have been applied to relate LAI/crop height and the three indices. It was found that the best fitted vegetation index to both LAI and crop height was WDVI. When LAI was regressed against WDVI for potatoes, the determination coefficient (R²) was 0.72, while for crop height R² reached 0.78. Two Landsat TM-5 images acquired simultaneously during the spectroradiometric and LAI and crop height measurements are used to validate the proposed regression model. From the whole analysis it was found that the modeled results are very close to real values. This fact enables the specific empirical models to be used in the future for hydrological purposes.

This paper proposes a new algorithm for automatic digital terrain model (DTM) generation from high resolution CARTOSAT-1 satellite images. It consists of two major steps: generation of digital surface models (DSM) from stereo scenes and hierarchical image filtering for DTM generation. Automatic georeferencing, dense stereo matching, and interpolation into a regular grid yields a DSM. In the second step, the DSM pixels are classified into ground and nonground regions using an algorithm motivated from gray-scale image reconstruction to suppress unwanted elevated pixels. Nonground regions, i.e., 3D objects as well as outliers are iteratively separated from the ground regions. The generated DTM is qualitatively and quantitatively evaluated. Height profiles and comparisons between the generated DSM, derived DTM, and ground truth data are presented. The evaluation indicates that almost all nonground objects regardless of their size are eliminated and appropriate results are archived in hilly as well as smooth residential areas.

Rice is the most important food crop in Taiwan. During recent decades, rice production in Taiwan has sharply declined because of industrialization and urbanization. Monitoring the areas of rice cultivation thus becomes important due to the official initiatives to ensure food supply. This study aims to develop a remote sensing classification approach for mapping double-cropped irrigated rice fields in Taiwan using time-series SPOT (Satellite Pour l'Observation de la Terre) data. Three study sites with different farming conditions in Taitung, Chiayi, and Taoyuan counties were chosen to test the new classification method. Data processing steps include: 1. filtering time-series SPOT-based normalized difference vegetation index (NDVI) using empirical mode decomposition (EMD) and wavelet transform, 2. classifying double-cropped irrigated rice fields using statistical methods (i.e., correlation analysis and sign-test statistics), and 3. assessing classification accuracy. The comparisons between the classification maps and ground-truth maps in 2005 indicated that classification using the EMD-based filtered NDVI time-series data yielded more accurate results than did the wavelet transform-based filtered data.

Increased use of polarization in optical remote sensing provides motivation for a study of instruments and methods that can be used to test and validate polarized atmospheric radiative transfer codes and simulation tools. An example comparison of measured skylight polarization and calculations from a preliminary version of the polarized MODTRAN radiative transfer code (MODTRAN-P) for cloud-free conditions is presented. The study combines data from an all-sky polarization imager at 452, 491, 532, 632, and 701 nm, a solar radiometer, a zenith-viewing aerosol and cloud lidar, a weather station, and radiosonde profiles of atmospheric temperature and pressure to compare measurements and model calculations of the maximum degree of linear polarization for cloud-free atmospheres. Comparisons for conditions ranging from extremely clear to thick forest fire smoke indicate that the additional data most needed for constraining calculations are aerosol size distributions. Nevertheless, comparisons made with standard aerosol models in version 2.1-alpha of MODTRAN-P with an unpolarized multiple-scattering algorithm illustrate the methodology and provide quantitative information about the range of conditions for which a single-scattering radiative transfer code is useful for predicting skylight polarization. This approach is also warranted because many users simulate atmospheres with the MODTRAN standard aerosol models. The agreement of model calculations with measurements is high for low aerosol optical depth and degrades with increasing optical depth. Agreement between measurements and model results is best for the longer wavelengths.

A new approach for determining the forest leaf area index (LAI) from a geometric-optical model inversion using multisensor observations is developed. For improving the LAI estimate for the forested area on rugged terrain, a priori information on tree height and the spectra of four scene components of a geometric-optical mutual shadowing (GOMS) model are extracted from airborne light-detection and ranging (LiDAR) data and optical remote sensing data with high spatial resolution, respectively. The slope and aspect of the study area are derived from digital elevation model data. These extracted parameters are applied in an inversion to improve the estimates of forest canopy structural parameters in a GOMS model. For the field investigation, a bidirectional reflectance factor data set of needle forest pixels is collected by combining moderate-resolution-imaging-spectroradiometer (MODIS) and multiangle-imaging-spectroradiometer (MISR) multiangular remote sensing observations. Then, forest canopy parameters are inverted based on the GOMS model. Finally, the LAI of the forest canopy of each pixel is estimated from the retrieved structural parameters and validated by field measurements. The results indicate that the accuracy of forest canopy LAI estimates can be improved by combining observations of passive multiangle and active remote sensors.

This paper proposes an experimental methodology toward describing and quantifying coral reef bleaching using very high spatial resolution optical satellite imagery. Sea surface temperature-based bleaching alerts issued by NOAA's Coral Reef Watch triggered image acquisition and served as an indication for high bleaching probability. Images of suspected coral reef bleaching events and reference images of the same reefs during previous unbleached conditions were coregistered and radiometrically normalized for change detection. An experimental methodology was developed to describe the severity and extent of the bleaching. The methodology hinges on the creation of the Coral Bleaching Index (CBI), constructed from change detected in the green, blue, and red wavelength bands. Results are provided in the form of colorized difference images showing areas of observed bleaching in gold, as well as CBI images, visualizing varying bleaching intensities. Comparison of the CBI with available field validation data yielded a correlation, however additional reference data would be needed for more detailed quality assessment. This technique is seen as a step toward the routine detection and long-term monitoring of coral reef bleaching from space and serves as a proposed tool for detecting bleaching in remote areas where observers cannot be deployed.

Lake level elevation and variation are important indicators of regional and global climate and environmental change. Lake Qinghai, the largest saline lake in China, located in the joint area of the East Asian monsoon, Indian summer monsoon, and Westerly jet stream, is particularly sensitive to climate change. This study examines the lake's water level and temporal change using the ice, cloud, and land elevation satellite (ICESat) altimetry data and gauge measurements. Results show that the mean water level from ICESat rose 0.67 m from 2003 to 2009 with an increase rate of 0.11 m/yr and that the ICESat data correlates well (r² = 0.90, root mean square difference 0.08 m) with gauge measurements. Envisat altimetry data show a similar change rate of 0.10 m/yr, but with ∼0.52 m higher, primarily due to different referencing systems. Detailed examination of three sets of crossover ICESat tracks reveals that the lake level increase from 2004 to 2006 was 3 times that from 2006 to 2008, with the largest water level increase of 0.58 m from Feb. 2005 to Feb. 2006. Combined analyses with insitu precipitation, evaporation, and runoff measurements from 1956 to 2009 show that an overall decreasing trend of lake level (−0.07 m/yr) correlated with an overall increasing trend (+0.03°C/yr) of temperature, with three major interannual peaks of lake level increases. The longest period of lake level increase from 2004 to 2009 could partly be due to accelerated glacier/perennial snow cover melt in the region during recent decades. Future missions of ICESat type, with possible increased repeatability, would be an invaluable asset for continuously monitoring lake level and change worldwide, besides its primary applications to polar regions.

Next generation imaging spectrometers with higher signal-to-noise ratio and broader swath-width bring new perspectives for crop classification over large areas. Here, we used Hyperion/Earth Observing-One data collected over Brazilian soybean fields to evaluate the performance of four classification techniques (maximum likelihood - ML; spectral angle mapper - SAM; spectral information divergence - SID; support vector machine - SVM) to discriminate five soybean varieties. The spectral resolution influence on classifying them was analyzed by simulating the spectral bands of seven multispectral sensors using Hyperion data. Before classification, the Waikato environment for knowledge analysis was used for feature selection. Results showed the importance of the green, red-edge, near-infrared, and shortwave infrared to discriminate the soybean varieties. Because the soybean variety Monsoy 8411 was sensed by Hyperion in a later reproductive stage, it was more easily discriminated than the other varieties. The best classification techniques were ML and SVM with overall accuracy of 89.80% and 81.76%, respectively. The accuracy of spectral matching techniques was lower (70.84% for SAM and 72.20% for SID). When ML was applied to the simulated spectral resolution of the multispectral sensors, moderate resolution imaging spectroradiometer and enhanced thematic mapper plus presented the highest accuracy, whereas advanced very high resolution radiometer showed the lowest one.

Coastal seabed mapping is essential for a variety of nearshore management related activities including sustainable resource management, ecological protection, and environmental change detection in coastal sites...In this study, a soft seabed classification (SSC) algorithm is developed using unsupervised classification with fuzzy clustering to produce classification products accounting for a sub-footprint habitat mixture. With this approach, each shot is assigned to multiple seabed classes with a percentage cover measuring the extent to which each seabed class is present in the ALB footprint. This has the added benefit of generating smooth spatial ecological transitions of the seabed instead of sharp boundaries between classes or clusters. Furthermore, due to the multivariate nature of the SSC output (i.e., percentage cover for each seabed class for a given shot), a recently developed selforganizing map neural network-based approach to geo-visualization of seabed classification results was used to visualize seabed habitat diversity. An ALB dataset of an area approximately20000 m² collected from Quebec, Canada was used. Cross-validation of the SSC approach yields percentage cover accuracy of approximately 71.7% with 16 seabed classes for a real ALB dataset, while dominant seabed class prediction based on hardening of percentage coverpredictions yielded 66% accuracy for 4 seabed classes.

The Songnen Plain is an important commodity grain product base in China for which a spatiotemporal pattern of actual evapotranspiration (ET_a) would provide critical important information to evaluate crop growth status and water use efficiency. ET_a over the Songnen Plain in the 2008 growing season (from May to September) was mapped using the moderate resolution imaging spectroradiometer time-series products based on the surface energy balance algorithm for land model and the Penman-Monteith equation. The estimated ET_a was validated using eddy covariance surface data. The calculated and observed ETa values were highly consistent with a total difference of 18.26% in the whole growing season. Therefore, the ET_a retrieval method based on remote sensing technology could satisfy the requirements for regional ET_a estimation over the Songnen Plain. The total ET_a over the Songnen Plain in the 2008 growing season ranged from 182.7 to 1002.4 mm, and the average value for the whole study area was 591.1 ± 122.2 mm (standard deviation). ETa exhibited obvious spatial variation, gradually increasing from low values in the southwest to higher values in the east and northeast. Monthly ETa varied with meteorological conditions, land covers, root-zone soil moisture, and vegetation phenology. Higher monthly ET_a values appeared in June, July, and August with a maximum value of 139.5 mm observed in July. The average monthly ET_a for water-body, woodland, and wetland was much higher than cropland and grassland during the growing season. Grassland obtained the lowest monthly ET_a due to the scarcity of rainfall and lower groundwater level.