This PDF file contains the errata for “JRS Vol. 4 Issue 01 Paper 3359616” for JRS Vol. 4 Issue 01

This PDF file contains the errata for “JRS Vol. 4 Issue 01 Paper 3442413” for JRS Vol. 4 Issue 01

This PDF file contains the errata for “JRS Vol. 4 Issue 01 Paper 3459124” for JRS Vol. 4 Issue 01

This PDF file contains the errata for “JRS Vol. 4 Issue 01 Paper 3491190” for JRS Vol. 4 Issue 01

Hyperspectral remote sensing has been widely utilized in high-resolution climate observation, environment monitoring, resource mapping, etc. However, it brings undesirable difficulties for transmission and storage due to the huge amount of the data. Lossless compression has been demonstrated to be an efficient strategy to solve these problems. In this paper, a novel Band Regrouping based Lossless Compression (BRLlC) algorithm is proposed for lossless compression of hyperspectral images. The affinity propagation clustering algorithm, which can achieve adaptive clustering with high efficiency, is firstly applied to classify all of the hyperspectral bands into several groups based on the inter-band correlation matrix of hyperspectral images. Consequently, hyperspectral bands with high correlation are clustered into one group so that the prediction efficiency in each group can be greatly enhanced. In addition, a linear prediction algorithm based on context prediction is applied to the hyperspectral images in each group followed by arithmetic coding. Experimental results demonstrate that the proposed algorithm outperforms some classic lossless compression algorithms in terms of bit per pixel per band and in terms of processing performance.

Hyperspectral image compression has become increasingly important in data exploitation because of enormous data volumes and high redundancy provided by hundreds of contiguous spectral channels. Since a hyperspectral image can be viewed as a 3-dimensional (3D) image cube, many efforts have been devoted to extending 2D image compression techniques to perform 3D image compression on hyperspectral image cubes. Unfortunately, some major issues generally encountered in hyperspectral data exploitation at low or very low-bit rate compression, for example, subpixels and mixed pixels which do not occur in traditional pure pixel-based image compression are often overlooked in such a 2D-to-3D compression. Accordingly, a direct application of 2D-to-3D compression techniques to hyperspectral image cubes without taking precaution may result in significant loss of crucial spectral information provided by subtle substances such as small objects, anomalies during low bit-rate lossy compression. This paper takes a rather different view by investigating lossy hyperspectral compression from a perspective of exploring spectral information, referred to as exploitation-based lossy compression and further develops spectral/spatial hyperspectral image compression to effectively preserve crucial and vital spectral information of objects which are generally missed by commonly used mean-squared error (MSE) or signal-to-noise ratio (SNR)-based compression techniques when lossy compression is performed at low bit rates. In order to demonstrate advantages of the proposed spectral/spatial compression approach applications of subpixel target detection and mixed pixel analysis are used for experiments for performance evaluation.

In this paper, we propose two compression methods for hyperspectral images with discriminant features enhanced. Generally, when hyperspectral images are compressed with conventional image compression algorithms, which mainly minimize mean squared errors, discriminant features of the original data may not be well preserved since they may not be necessarily large in energy. In this paper, we propose two compression methods that do preserve the discriminant information. In the first method, we enhanced the discriminant features and then compressed the enhanced data using conventional image compression algorithms such as 3D JPEG 2000. In the second method, we applied a feature extraction method and extracted the discriminantly dominant feature vectors. By examining the dominant feature vectors, we determined the discriminant usefulness of each spectral band. Based on these findings, we determined the bit allocation of each spectral band assuming 2D compression methods are used. Experiments show that the proposed methods effectively preserved the discriminant information and yielded improved classification accuracies compared to existing compression algorithms.

In hyperspectral imagery, greedy modular eigenspace (GME) was developed by clustering highly correlated bands into a smaller subset based on the greedy algorithm. Unfortunately, GME is hard to find the optimal set by greedy scheme except by exhaustive iteration. The long execution time has been the major drawback in practice. Accordingly, finding the optimal (or near-optimal) solution is very expensive. Instead of adopting the band-subset-selection paradigm underlying this approach, we introduce a simulated annealing band selection (SABS) approach, which takes sets of non-correlated bands for high-dimensional remote sensing images based on a heuristic optimization algorithm, to overcome this disadvantage. It utilizes the inherent separability of different classes embedded in high-dimensional data sets to reduce dimensionality and formulate the optimal or near-optimal GME feature. Our proposed SABS scheme has a number of merits. Unlike traditional principal component analysis, it avoids the bias problems that arise from transforming the information into linear combinations of bands. SABS can not only speed up the procedure to simultaneously select the most significant features according to the simulated annealing optimization scheme to find GME sets, but also further extend the convergence abilities in the solution space based on simulated annealing method to reach the global optimal or near-optimal solution and escape from local minima. The effectiveness of the proposed SABS is evaluated by NASA MODIS/ASTER (MASTER) airborne simulator data sets and airborne synthetic aperture radar images for land cover classification during the Pacrim II campaign. The performance of our proposed SABS is validated by supervised k-nearest neighbor classifier. The experimental results show that SABS is an effective technique of band subset selection and can be used as an alternative to the existing dimensionality reduction method.

Efficient data transmission is an important part of satellite communication, particularly when large data volumes need to be downlinked to the Earth. One general approach to dealing with this dilemma is data compression, either lossless or lossy. For hyperspectral data, compression is specifically crucial due to its high inter-band spectral correlation, resulting from the use of hundreds of high spectral resolution bands for data collection. This paper develops a new approach, called progressive band selection (PBS), to achieve both data compression and data transmission, in the sense that data can be compressed and transmitted progressively. First, PBS prioritizes each spectral band by assigning a priority score based on its information content measured by a certain criterion. Then, bands are selected progressively according to the priority scores assigned to each spectral band. Consequently, data can be compressed and transmitted in a progressive fashion to meet the application's requirements; this task cannot be accomplished by most data compression techniques. Most importantly, PBS can be implemented in two opposite manners. One is forward progressive band selection (FPBS), which starts with a low number of bands and gradually improves data quality by including more bands progressively, based on their priority scores, until data quality is satisfactory. The other is backward progressive band selection (BPBS), which begins with a high number of spectral bands and progressively removes them in accordance with their priority scores, until data quality falls below a given tolerance level. In order to determine the lower and upper bounds on the number of bands used for FPBS and BPBS, we use a recently developed concept called virtual dimensionality (VD). We demonstrate the utility of PBS in compression and transmission for satellite communication with an experiment in land use and cover classification, which uses a dataset collected by the Hyperion instrument aboard NASA's EO-1 satellite.

The amount of data generated by ultraspectral sounders is so large that considerable savings in data storage and transmission bandwidth can be achieved using data compression. Due to this large amount of data, the data compression time is of utmost importance. Increasing the programmability of the commodity Graphics Processing Units (GPUs) offer potential for considerable increases in computation speeds in applications that are data parallel. In our experiments, we implemented a spectral image data compression method called Linear Prediction with Constant Coefficients (LP-CC) using NVIDIA's CUDA parallel computing architecture. LP-CC compression method represents a current state-of-the-art technique in lossless compression of ultraspectral sounder data. The method showed an average compression ratio of 3.39 when applied to publicly available NASA AIRS data. We achieved a speed-up of 86 compared to a single threaded CPU version. Thus, the commodity GPU was able to significantly decrease the computational time of a compression algorithm based on a constant coefficient linear prediction.

According to the data characteristics of remote sensing stereo image pairs, a novel adaptive compression algorithm based on the combination of feature-based image matching (FBM), area-based image matching (ABM), and region-based disparity estimation is proposed. First, the Scale Invariant Feature Transform (SIFT) and the Sobel operator are carried out for texture classification. Second, an improved ABM is used in the flat area, while the disparity estimation is used in the alpine area. The radiation compensation is applied to further improve the performance. Finally, the residual image and the reference image are compressed by JPEG2000 independently. The new algorithm provides a reasonable prediction in different areas according to the image textures, which improves the precision of the sensed image. The experimental results show that the PSNR of the proposed algorithm can obtain up to about 3dB's gain compared with the traditional algorithm at low or medium bitrates, and the DTM and subjective quality is also obviously enhanced.

A joint exploitation of coding and diversity techniques to achieve efficient, reliable wireless transmission is considered. The system comprises a powerful non-binary low-density parity-check (LDPC) code that will be soft-decoded to supply strong error protection, a quadratic amplitude modulator (QAM) that directly takes in the non-binary LDPC symbols and a modulation diversity operator that will provide power- and bandwidth-efficient diversity gain. By relaxing the rate of the modulation diversity rotation matrices to below 1, we show that a better rate allocation can be arranged between the LDPC codes and the modulation diversity, which brings significant performance gain over previous systems. To facilitate the design and evaluation of the relaxed modulation diversity rotation matrices, based on a set of criteria, three practical design methods are given and their point pairwise error rate are analyzed. With EXIT chart, we investigate the convergence between demodulator and decoder.A rate match method is presented based on EXIT analysis. Through analysis and simulations, we show that our strategies are very effective in combating random fading and strong noise on fading channels.

More than a decade has passed since the Consultative Committee for Space Data Systems (CCSDS) made its recommendation for lossless data compression. The CCSDS standard is commonly used for scientific missions because it is a general-purpose lossless compression technique with a low computational cost which results in acceptable compression ratios. At the core of this compression algorithm it is the Rice coding method. Its performance rapidly degrades in the presence of outliers, as the Rice coder is conceived for noiseless data following geometric distributions. To overcome this problem we present here a new entropy coder, the so-called Prediction Error Coder (PEC), as well as its fully adaptive version (FAPEC) which we show is a reliable alternative to the CCSDS standard. We show that PEC and FAPEC achieve high compression ratios even when a large amount of outliers are present in the data. This is done by testing our compressors with synthetic and real data, comparing the compression ratios and processor requirements with those obtained using the CCSDS standard.

In this paper, we propose an embedded satellite image compression method using Weighted ZeroBlock Coding (WZBC) and optimal sorting. In order to reduce average codeword length, Set Partition Embedded block (SPECK) and Embedded ZeroBlock Coder (EZBC) both encode significant block-sets with fixed-length bits, while WZBC assigns different-length bits to encode block-sets which contain different numbers of significant subblocks. In view of the context correlation among coefficients/blocks, WZBC employs a weight context to optimize the scanning order of the significance testing and the ratedistortion performance. Experimental results show that the proposed WZBC in binary coding mode provides excellent coding performance compared with those of SPECK and Set Partitioning In Hierarchical Trees (SPIHT) which use arithmetic coding, and can even closely approach that of JPEG2000. When arithmetic coding is extensively used, the proposed method has clear advantages.

Our research focuses on reducing complexity of hyperspectral image codecs based on transform and/or subband coding, so they can be on-board a satellite. It is well-known that the Karhunen Loeve transform (KLT) can be sub-optimal for non Gaussian data. However, it is generally recommended as the best calculable coding transform in practice. Now, for a compression scheme compatible with both the JPEG2000 Part2 standard and the CCSDS recommendations for onboard satellite image compression, the concept and computation of optimal spectral transforms (OST), at high bit-rates, were carried out, under low restrictive hypotheses. These linear transforms are optimal for reducing spectral redundancies of multi- or hyper-spectral images, when the spatial redundancies are reduced with a fixed 2-D discrete wavelet transform. The problem of OST is their heavy computational cost. In this paper we present the performances in coding of a quasi-optimal spectral transform, called exogenous OrthOST, obtained by learning an orthogonal OST on a sample of hyperspectral images from the spectrometer MERIS. Moreover, we compute an integer variant of OrthOST for lossless compression. The performances are compared to the ones of the KLT in both lossy and lossless compressions. We observe good performances of the exogenous OrthOST.

The aim of this work is to, within the JPEG2000 framework, enhance the coding performance obtained for images that contain regions without useful information, or without information at all, here named as NODATA regions. In Geographic Information Systems (GIS) and in Remote Sensing (RS), NODATA regions arise due to several factors, such as geometric and radiometric corrections, atmospheric events, the overlapping of successive layers of information, etc. Most coding systems are not devised to consider these regions separately from the rest of the image, sometimes causing a loss in the coding efficiency and in the post-processing applications. We propose two approaches that address this issue; the first technique (Average Data Region, ADR) is carried out as simple pre-processing and the second technique (Shape-Adaptive JPEG2000, SA-JPEG2000) modifies the coding system to avoid the regions without information. Experimental results, performed on data from real applications and different scenarios, suggest that the proposed approaches can achieve, e.g., for SA-JPEG2000, a Signal-to- Noise Ratio improvement of about 8 dB. Moreover, in a post-processing application such as a digital classification, the best classification results are obtained when the proposed approaches SA-JPEG2000 and ADR are applied.

Lossy hyperspectral image compression has received considerable interest in recent years due to the extremely high dimensionality of the data. However, the impact of lossy compression on spectral unmixing techniques has not been widely studied. These techniques characterize mixed pixels (resulting from insufficient spatial resolution) in terms of a suitable combination of spectrally pure substances (called endmembers) weighted by their estimated fractional abundances. This paper focuses on the impact of JPEG2000-based lossy compression of hyperspectral images on the quality of the endmembers extracted by different algorithms. The three considered algorithms are the orthogonal subspace projection (OSP), which uses only spatial information, and the automatic morphological endmember extraction (AMEE) and spatial spectral endmember extraction (SSEE), which integrate both spatial and spectral information in the search for endmembers. The impact of compression on the resulting abundance estimation based on the endmembers derived by different methods is also substantiated. Experimental results are conducted using a hyperspectral data set collected by NASA Jet Propulsion Laboratory over the Cuprite mining district in Nevada. The experimental results are quantitatively analyzed using reference information available from U.S. Geological Survey, resulting in recommendations to specialists interested in applying endmember extraction and unmixing algorithms to compressed hyperspectral data.

This paper reviews the researches and developments in the last decade on near lossless satellite data compression techniques at the Canadian Space Agency (CSA). After briefly describing the two vector quantization based near lossless hyperspectral data compression techniques, it reviews the activities that assessed the near lossless properties of the compression techniques. The assessment results demonstrated that the compression errors introduced by the compression techniques are smaller than the intrinsic noise of the original data. This level of compression errors is considered as near lossless, as it has no impact or minor impact on the afterwards application utilization comparing the original data. This paper summarizes the activities of evaluating how satellite data product level impacts the compression performance for making decision whether or not on-board data processing is required or radiometric conversion should be applied on-board before compression. These evaluations examined the impact of the anomalies in raw hyperspectral data and the impact of on-board pre-processing and radiometric conversion on compression performance. The studies on the effect of spatial and spectral distortion of hyperspectral sensors on compression performance are also reviewed. This paper summarizes a multi-disciplinary user acceptability study that systematically assessed the impacts of the compression techniques on remote sensing products and applications. Eleven user groups covered a wide range of application areas and a variety of hyperspectral sensors participated in the study. This paper reviews the effort to explore the benefits of employing forward error correction to further enhance the resilience to bit-errors of the compressed data. The hardware developments are reported. Two versions of hardware compressor prototypes that implement the CSA near lossless compression techniques for on-board processing have been built.

This PDF file contains the editorial “Guest Editorial: Satellite Data Compression” for JRS Vol. 4 Issue 01

Precise leveling surveys across southwest of Tehran have revealed a significant subsidence due to the overexploitation of groundwater. In order to monitor the temporal evolution of the deformation, Interferometric SAR time series analysis was applied using ENVISAT ASAR images recorded between 2003 and 2005. Only Interferograms with small temporal baselines are processed to decrease the temporal decorrelation effect caused by the agricultural fields. However, the spatial baselines of the processed interferograms are not as small as in the conventional Small Baseline Subset (SBAS) method. Coherence analysis reveals that the spatial decorrelation is insignificant. However, since the constructed interferograms are affected by topographic artifacts caused by the large spatial baselines, a multi-step procedure was used in order to refine the interferometric phase. Smoothed time series analysis was then carried out to retrieve the atmospheric-error free deformation corresponding to every acquisition time. The mean displacement velocity map extracted from the time series results indicates a maximum subsidence rate of 24 cm/yr. Chronological sequence of the computed deformations for several points located in the subsidence area shows the permanent aquifer system compaction at a long-term constant rate on which the seasonal effects are superimposed. Sustained hydraulic head declines reveal a relatively low correlation with InSAR derived information. Comparison of the subsidence rate to soil type profiles in different parts of the subsidence area was then used to interpret the deformation signal.

Earthquakes that have caused large-scale damage in developed areas, such as the 1994 Northridge and 1995 Kobe events, remind us of the importance of making quick damage assessments in order to facilitate the resumption of normal activities and restoration planning. Synthetic aperture radar (SAR) can be used to record physical aspects of the Earth's surface under any weather conditions, making it a powerful tool in the development of an applicable method for assessing damage following natural disasters. Detailed building damage data recorded on the ground following the 1995 Kobe earthquake may provide an invaluable opportunity to investigate the relationship between the backscattering properties and the degree of damage. This paper aims to investigate the differences between the backscattering coefficients and the correlations derived from pre- and post-earthquake SAR intensity images to smoothly detect areas with building damage. This method was then applied to SAR images recorded over the areas affected by the 1999 Kocaeli earthquake in Turkey, the 2001 Gujarat earthquake in India, and the 2003 Boumerdes earthquake in Algeria. The accuracy of the proposed method was examined and confirmed by comparing the results of the SAR analyses with the field survey data.

Building models are built to provide three-dimensional (3-D) spatial information, which is needed in a variety of applications including city planning, construction management, location-based services of urban infrastructures, and the like. However, 3-D building models have to be updated on a timely manner to meet the changing demand. Rather than reconstructing building models for the entire area, it would be more convenient and effective to only update parts of the areas where there were changes. This paper aims at developing a new method, namely double-threshold strategy, to find such changes within 3-D building models in the region of interest with the aid of light detection and ranging (LIDAR) data. The proposed modeling scheme comprises three steps, namely, data pre-processing, change detection in building areas, and validation. In the first step for data pre-processing, data registration was carried out based on multi-source data. The second step for data pre-processing requires using the triangulation of an irregular network of data points collected by Light Detection And Ranging (LIDAR), focusing on those locations containing walls or other above-ground objects that were ever removed. Then, change detection in the building models can be made possible for finding differences in height by comparing the LIDAR point measurements and the estimates of the building models. The results may be further refined using spectral and feature information collected from aerial imagery. A double-threshold strategy was applied to cope with the highly sensitive thresholding often encountered when using the rule-based approach. Finally, ground truth data were used for model validation. Research findings clearly indicate that the double-threshold strategy improves the overall accuracy from 93.1% to 95.9%.

Evapotranspiration (ET) is deemed critical for water resources management. Even in the same climatic and meteorological conditions, actual ET (ET_a) may exhibit remarkable spatial variability across different vegetation covers, agricultural land use practices, and differing types of urban land development. The main objectives of this study are (1) to evaluate the possible closure of the heat balance equation using Oklahoma's unique environmental monitoring network; and (2) to estimate ET_a and determine the variation with regards to varying types of land use and land cover in urban settings. In this study, a Surface-Energy-Balance ET algorithm was implemented to estimate ETa at a higher spatial resolution using Landsat 5 satellite images while the Oklahoma Mesonet observations can be used as our ground truth data. Accuracy of the estimated ET_a was assessed using latent heat flux measurements provided by AmeriFlux towers. The associated bias ratios of daily mean ET_a with respect to both burn and control sites are -0.92%, and -8.86% with a correlation of 0.83 and 0.81, respectively. Additionally, estimated ET_a from a water balance budget analysis and the remotely sensed ET_a are cross-validated with a low bias ratio of 5.2%, and a correlation coefficient of 0.7 at the catchment scale. The lowest ET_a was observed for developed urban areas and highest for open water bodies. The ET_a difference is also demonstrated from two contrasting counties. The results show Garfield County (agricultural) has higher ET_a values than Oklahoma County (urban) for all land cover types except open water bodies.

The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument is considered a very versatile tool in studying environmental changes. The multi-spectral sensor owns a high revisit period, a large scanning area, plus a handful of other advantages. The main purpose of this study is to employ reflectance data retrieved by the MODIS sensor in detecting smoke plumes, estimating their respective intensity and retrieving the AOD (Aerosol Optical Depth). Specifically, in the detection of the smoke plumes, biomass burning cases are studied in delineating the reflective characteristics. Following the detection, the Deep-Blue Aerosol Index (DAI) is utilized to evaluate the intensity. Relevant AOD information is retrieved by analyzing the relationship between the DAI and AOD. Results show a high correlation between the satellite-retrieved AOD and Sun Photometer-observed AOD data, thus demonstrating the feasibility in obtaining the aerosol distribution over highly reflective areas. As the proposed approach in this study is capable of accurately portraying the spatial distribution and intensity of smoke plumes, it can be effectively used in monitoring biomass burning hazards.

Accurately detecting urban expansion with remote sensing techniques is a challenge due to the complexity of urban landscapes. This paper explored methods for detecting urban expansion with multitemporal QuickBird images in Lucas do Rio Verde, Mato Grosso, Brazil. Different techniques, including image differencing, principal component analysis (PCA), and comparison of classified impervious surface images with the matched filtering method, were used to examine urbanization detection. An impervious surface image classified with the hybrid method was used to modify the urbanization detection results. As a comparison, the original multispectral image and segmentation-based mean-spectral images were used during the detection of urbanization. This research indicates that the comparison of classified impervious surface images with matched filtering method provides the best change detection performance, followed by the image differencing method based on segmentation-based mean spectral images. The PCA is not a good method for urban change detection in this study. Shadows and high spectral variation within the impervious surfaces represent major challenges to the detection of urban expansion when high spatial resolution images are used.

In the last decade, various algorithms have been developed for extracting the digital terrain model from LiDAR point clouds. Although most filters perform well in flat and uncomplicated landscapes, landscapes containing steep slopes and discontinuities are still problematic. In this research, we develop a novel bare-earth extraction algorithm consisting of segmentation modeling and surface modeling based on our previous work, forest canopy removal. The proposed segmentation modeling is built on a triangulated irregular network and composed of triangle assimilation, edge clustering, and point classification to achieve better discrimination of objects and preserve terrain discontinuities. The surface modeling is proposed to iteratively correct both Type I and Type II errors through estimating roughness of digital surface/terrain models, detecting bridges and sharp ridges, etc. Finally, we have compared our obtained filtering results with twelve other filters working on the same fifteen study sites provided by the ISPRS. Our average error and kappa index of agreement in the automated process are 4.6% and 84.5%, respectively, which outperform all other twelve proposed filters. Our kappa index, 84.5%, can be interpreted as almost perfect agreement. In addition, applying this work with optimized parameters further improves performance.

This study examined linear spectral unmixing techniques for mapping the variation in crop yield for precision agriculture. Both unconstrained and constrained linear spectral unmixing models were applied to airborne hyperspectral imagery collected from a grain sorghum field and a cotton field. A pair of crop plant and soil spectra derived from each image was used as endmember spectra to generate unconstrained and constrained plant and soil cover abundance fractions. For comparison, the simulated broad-band normalized difference vegetation index (NDVI) and narrow-band NDVI-type indices involving all possible two-band combinations of the 102 bands in the hyperspectral imagery were calculated and related to yield. Statistical results showed that plant abundance fractions provided better correlations with yield than the broad-band NDVI and the majority of the narrow-band NDVIs, indicating that plant abundance maps derived from hyperspectral imagery can be used as relative yield maps to characterize yield variability in grain sorghum field and cotton fields without the need to choose the best NDVI. Moreover, the unconstrained plant abundance provided essentially the same results for yield estimation as the constrained plant abundance either with the abundance sum-to-one constraint only or with both the sum-to-one and non-negativity constraints, indicating that the more computationally complex constrained linear unmixing does not offer any advantage over the simple unconstrained linear unmixing for this application.

In this paper, we propose approaches to improve the pixel-based support vector machine (SVM) classification for urban land use and land cover (LULC) mapping from airborne hyperspectral imagery with high spatial resolution. Class spatial neighborhood relationship is used to correct the misclassified class pairs, such as roof and trail, road and roof. These classes may be difficult to be separated because they may have similar spectral signatures and their spatial features are not distinct enough to help their discrimination. In addition, misclassification incurred from within-class trivial spectral variation can be corrected by using pixel connectivity information in a local window so that spectrally homogeneous regions can be well preserved. Our experimental results demonstrate the efficiency of the proposed approaches in classification accuracy improvement. The overall performance is competitive to the object-based SVM classification.

The purpose of this study is to examine the conversions of forests in Northeast China during 1988-2005 by using a 1-km area percentage data model (1-km APDM) with remote sensing data and to find the spatiotemporal characteristics of land conversions between forests and other land uses/covers and internal conversions between forest cover types. Data were derived from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) images of bands 3, 5, and 4 acquired in 1988, 1995, 2000, and 2005. Research results show that in the period between 1988 and 2005, the forest area in Northeast China underwent dramatic changes, and 4.11 million ha of forest area was aggregately lost because of the conversions of forests to other land uses/covers; at the same time, the forest area also gained 2.00 million ha because of the conversions from other land uses/covers to forests. The results also demonstrate the forest degradation resulting from the conversions between different forest cover types. This research demonstrates the feasibility and importance of using the 1-km APDM at a finer resolution to trace the spatiotemporal patterns of the forest conversions.

This paper presents results of a study investigating the potential for improvement of a physically-based model approach, when the static input data is enhanced by dynamic remote sensing information. The model PROMET (PROcess of Radiation Mass and Energy Transfer), which is normally used to simulate the water and energy fluxes at the landscape level, was applied on a field scale to simulate crop growth and yield. The remote sensing input data was derived from hyperspectral images of the CHRIS (Compact High Resolution Imaging Spectrometer) sensor, which is operated by ESA (European Space Agency). PROMET was set up for a field scale model run for two test fields grown with winter wheat (Triticum aestivum L.) mapping the crop development of the seasons 2004 and 2005. During the model runs, information on the absorptive capacity of the leaves for two canopy layers (sunlit and shaded layer) was updated using remotely sensed chlorophyll measurements. The chlorophyll contents of these two vegetation layers were assessed using angular CHRIS data. Control data were acquired through field measurements, which were conducted throughout the growing periods of both years and also accompanying the satellite overpasses. The stand-alone model was able to reproduce the average development of the crop and yield reasonably well, but the spatial heterogeneity was severely underestimated and yield was overestimated by approximately 20%. The combination of remote sensing data with the model led to an improvement of the spatial heterogeneity of the crop development and yield. The use of ground truth data to improve the modeling accuracy can be made possible.

This PDF file contains the editorial “Guest Editorial: Remote Sensing for Coupled Natural Systems and Built Environments” for JRS Vol. 4 Issue 01

Innovative algorithm development for small-footprint full-waveform lidar data processing extends this technology's capabilities to more complicated acquisition scenarios then previously determined, namely success of surveys over obscured areas. Waveform decomposition and the extraction of waveform metrics provide a straightforward approach to identifying vertical structure within each laser measurement. However, there are some limitations in this approach as faint returns within the waveform go undetected within the classical processing chain. These faint returns are the result of reduced energy levels due to obscurant scattering, attenuation and absorption. Lidar surveys over non-homogeneous wooded regions indicate that there are meaningful ground returns within dense tree coverage if extracted correctly from the data. By using a waveform stacking technique with appropriate waveforms in near geospatial proximity to the original, these faint returns can be augmented and detected during data processing. In comparison to the traditional approach, the waveform stacking technique provides up to a 60% increase in perceived ground returns with the faint signal extraction for the particular datasets analyzed over a broadleaf forest in Mississippi. The enhanced capability in the presence of foliage provides a decrease in operational effort associated with data density, dwell or targeting techniques, in addition to required survey expense.

Municipal wastewater discharged into the ocean through a submerged pipe, or outfall, can rise buoyantly to the sea surface, resulting in a near-field mixing zone and, in the presence of an ambient ocean current, an extended surface plume. In this paper, data from a CASI (Compact Airborne Spectrographic Imager) and an airborne infrared (IR) camera are shown to detect a municipal wastewater discharge off the southeast coast of Florida, U.S.A., through its elevated levels of chromophoric dissolved organic matter plus detrital material (CDOM) and cooler sea surface temperatures. CDOM levels within a ~15-m-diameter surface 'boil' are found to be about twice those in the ambient shelf water, and surface temperatures near the boil are lower by ~0.4°C, comparable to the vertical temperature difference across the ambient water column. The CASI and IR imagery show a nearly identically shaped buoyant plume, consistent with a fully surfacing discharge, but the IR data more accurately delineate the area of most rapid dilution as compared with previous in-situ measurements. The imagery also allows identification of ambient oceanographic processes that affect dispersion and transport in the far field. This includes an alongshore front, which limits offshore dispersion of the discharge, and shoreward-propagating nonlinear internal waves, which may be responsible for an enhanced onshore transport of the discharge.

Laser radar (lidar) provides an excellent tool for characterizing the physical properties of atmospheric aerosols which play a very important role in modifying the radiative budget of the Earth's atmosphere. One of the important issues in lidar research is to derive accurate backscattering or extinction coefficient profiles required for understanding the basic mechanisms in the formation of aerosols and identifying their sources and sinks. Most of the inversion methods used for deriving the aerosol coefficients assume a range independent value for the extinction-to- backscattering ratio [lidar ratio, (LR)]. However, it is known that in a realistic atmosphere the value of LR is range dependent and varies with the physical and chemical properties of the aerosols. In this paper, we use a variant of widely applied Klett's method to obtain the range dependent LR values and derive the aerosol extinction profiles with good accuracy. We present the lidar derived aerosol extinction profiles in the upper troposphere and lower stratosphere corresponding to different seasons of the year of two distinctly different stations in the Indian subcontinent namely Trivandrum (8.33° N, 77° E), Kerala, India, a coastal station and Gadanki (13.5° N, 79.2° E), Tirupati, India an inland station. The range dependent LR is derived corresponding to different seasons of the year at the two stations. The lidar ratio, aerosol extinction coefficient (AEC), aerosol scattering ratio and aerosol optical depth show strong to medium seasonal variation at both the stations. The lidar ratio values at Trivandum vary in the range of 11-38 sr whereas the values range from 20-34 sr at Gadanki. AEC values at the Trivandum station vary from 7.9×10^-6 to 6.9×10^-5 m^-1 and at Gadanki station the variation is from 1.27×10^-5 to 6.9×10^-5 m^-1. It is proposed to use back-trajectory analysis to understand the sources of aerosol at the two stations.

The concentration of suspended particulate matter smaller than 2.5 μm (PM_2.5) was linearly correlated with the column aerosol optical thickness (AOT) based on simultaneous measurements at a NASA/AERONET station at Kinki University Campus, Higashi-Osaka, Japan, between March 2004 and June 2006. The correlation coefficient differed with the aerosol type, being maximal when PM_2.5 values were measured 120 minutes after AOT data for a dust episode, but almost independent of the time difference between measurements for anthropogenic aerosols. The obtained results were validated using data obtained at the Higashi-Osaka and Noto sites. Our results suggest that the PM_2.5 mass concentration can be estimated from the AOT, and vice versa, and hence a distribution map of PM_2.5 can be produced from the satellite-derived AOT map determined from the Aqua/MODIS sensor.

Coherence of interferometric synthetic aperture radar (InSAR) complex image pair is a fundamental observable in interferometric radar measurements, which is usually measured by comparing the radar return across several nearby radar image pixels and has found diverse applications. This paper proposes a coherence estimation method which requires three arbitrary parts of the two complex images to implement interference. The proposed method can also be used to co-register InSAR image pair, which means that the imaginary part of the master image can be left away. In addition, an improved quality-guided phase unwrapping method is forwarded with the quality map generated by the coherence estimation method we have recommended above. A look-up table is adopted to reduce the processing time. The experimental results show that both methods are effective and greatly reduce the phase unwrapping time compared with the existing quality-guided methods.

Main objectives of this paper are to evaluate SeaWiFS, MODIS, and AVHRR satellite imagery performances against in situ data around two Northeast Atlantic seamounts, Sedlo and Seine. The temporal and spatial variability of satellite-derived near-surface chlorophyll a (Chl a) and sea surface temperature (SST) is also analysed. SeaWiFS tends to show good accuracy with the in situ data for Sedlo seamount, while for Seine it tends to slightly overestimate the values. Oppositely, MODIS tends to underestimate Chl a for both seamounts. Match-up SST analyses show that MODIS underestimates the in situ values on Seine seamount. The best correlation was attained with AVHRR on Sedlo. Seasonal variations are clearly pronounced on Sedlo with typical spring and autumn Chl a blooms, while further to the south, on Seine, less intense blooms are registered, as expected. Higher/lower SST values are observed during summer/winter respectively, showing clear seasonal patterns. A time lag of about one month for the maximum SST heating/cooling from Sedlo to Seine is noted.

With the recent increase in the availability of multiple polarization radar data, the need to assess the relative usefulness of these data for land cover/use classification and other applications is important and is the premise of this study. The primary methodology employed is spectral signature extraction and use of Transformed Divergence (TD) separability measures to evaluate the relative utility of multiple radar bands and polarizations (Shuttle Imaging Radar-C), optical imagery (Landsat Thematic Mapper) and texture derived information for a study site in Bangladesh. Variance texture measures at a 7×7 window size are used in an evaluation of improving the overall separability among classes. SIR-C L-band provided slightly better results than C-band but no single polarization provided consistently higher TD values. Texture greatly improved separability for all SIR-C bands but did not assist TM separability. This study also indicated the value of combining TM and SIR-C and particularly the TM original data and SIR-C texture.

Urban environmental conditions are strongly dependent on the land use and land cover properties. Urban and rural areas normally exhibit obvious difference in land surface temperature (LST). The Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua (PM satellite) MYD11A1 temperature products (daily and 1 km spatial resolution) for the period from June 1 to September 30 between 2002 and 2008 were used to screen the existence of urban heat island (UHI) phenomena for the city of San Antonio, TX. 8-day MYD11A2 temperature products between 2002 and 2008 were also retrieved to map the temperature climatology at the 1:30 a.m. for the region. The UHI effect was detected in both satellite surface-temperature and meteorological station air-temperature record. The existence of an UHI of the San Antonio downtown area was clearly shown in about 90% of the available cloud-free (or cloudless) data from June 1-September 30 each year. It is especially prevalent in the night-time imagery due to less cloud contamination. During nighttime, the heat island (HI) is about 4 - 5 °K (6 - 8 °F) higher than the average temperature of the study area and 6 - 7 °K (8 - 12 °F) higher than the rural area. Surprisingly, the HI phenomenon is found not only in the downtown area, but also several other small areas in the northern corner. Finally, the long-term UHI effect of San Antonio and its relationship with normalized difference vegetation index (NDVI) were discussed. USGS rainfall data were also used to discuss the possible connections between the UHI and several local storm events.

Modern biogeographers recognize that humans are seen as constituents of ecosystems, drivers of significant change, and perhaps, the most invasive species on earth. We found it instructive to model humans as invasive organisms with the same environmental factors. We present a preliminary model of the spread of modern humans in the conterminous United States between 1992 and 2001 based on a subset of National Land Cover Data (NLCD), a time series LANDSAT product. We relied on the commonly used Maxent model, a species-environmental matching model, to map urbanization. Results: Urban areas represented 5.1% of the lower 48 states in 2001, an increase of 7.5% (18,112 km²) in the nine year period. At this rate, an area the size of Massachusetts is converted to urban land use every ten years. We used accepted models commonly used for mapping plant and animal distributions and found that climatic and environmental factors can strongly predict our spread (i.e., the conversion of forests, shrub/grass, and wetland areas into urban areas), with a 92.5% success rate (Area Under the Curve). Adding a roads layer in the model improved predictions to a 95.5% success rate. 8.8% of the 1-km² cells in the conterminous U.S. now have a major road in them. In 2001, 0.8% of 1-km² cells in the U.S. had an urbanness value of > 800, (>89% of a 1-km² cell is urban), while we predict that 24.5% of 1-km² cells in the conterminous U.S. will be > 800 eventually. Main conclusion: Humans have a highly predictable pattern of urbanization based on climatic and topographic variables. Conservation strategies may benefit from that predictability.

The National Ecological Observatory Network (NEON) is an ecological observation platform for discovering, understanding and forecasting the impacts of climate change, land use change, and invasive species on continental-scale ecology. NEON will operate for 30 years and gather long-term data on ecological response changes and on feedbacks with the geosphere, hydrosphere, and atmosphere. Local ecological measurements at sites distributed within 20 ecoclimatic domains across the contiguous United States, Alaska, Hawaii, and Puerto Rico will be coordinated with high resolution, regional airborne remote sensing observations. The Airborne Observation Platform (AOP) is an aircraft platform carrying remote sensing instrumentation designed to achieve sub-meter to meter scale ground resolution, bridging scales from organisms and individual stands to satellite-based remote sensing. AOP instrumentation consists of a VIS/SWIR imaging spectrometer, a scanning small-footprint waveform LiDAR for 3-D canopy structure measurements and a high resolution airborne digital camera. AOP data will be openly available to scientists and will provide quantitative information on land use change and changes in ecological structure and chemistry including the presence and effects of invasive species. AOP science objectives, key mission requirements, and development status are presented including an overview of near-term risk-reduction and prototyping activities.

Floodplains in the Sahel region of Africa are of exceptional socio-economical and ecological importance. Due to their large extent and highly dynamic nature, monitoring these ecosystems can only be performed by means of remote sensing. The capability of the Envisat Advanced Synthetic Aperture Radar (ASAR) sensor to capture radar backscattering at various incident angles and with different polarization combinations, provides opportunities for improved wetland mapping and monitoring. However, little is known of the optimal image parameters, i.e. incident angle, polarization combination, and acquisition time. Backscatter σ° signatures of Land Use and Land Cover (LULC) classes in and around the Waza-Logone floodplain (Cameroon) were analyzed to determine these optimal image parameters. Based on Jeffries-Matusita (JM) distances between all LULC classes it was determined that best separation was obtained with images acquired in the middle of the flooding cycle at a steep incident angle. Furthermore, separability of cross-polarized images was higher than for co-polarized images. The combination of two and three ASAR Alternating Polarization images with highest separability were used as input for a LULC classification. Two methods were evaluated: Pixel-based Maximum Likelihood and object-based Nearest Neighbour (NN) classification. Best results were obtained with the object-based approach.

A 2-µm wavelength coherent Doppler lidar for wind measurement has been developed of an unprecedented laser pulse energy of 250-mJ in a rugged package. This high pulse energy is produced by a Ho:Tm:LuLiF laser with an optical amplifier. While the lidar is meant for use as an airborne instrument, ground-based tests were carried out to characterize performance of the lidar. Atmospheric measurements are presented, showing the lidar's capability for wind measurement in the atmospheric boundary layer and free troposphere. Lidar wind measurements are compared to a balloon sonde, showing good agreement between the two sensors.

Estimation of surface radiation budget is a crucial step to analyze the climate effects caused by rapid urbanization. This paper reports a study of the integration of remote sensing images and ancillary data for analyzing the spatial and temporal variations of surface radiation budget in Beijing, China. Landsat-5 Thematic Mapper (TM) images and meteorological data of Beijing metropolitan area acquired in the summer and winter were used to calculate land surface parameters and surface radiation fluxes, including shortwave net radiation, effective radiation and net radiation. Validation with in situ measurements shows that the calculation of net radiation yielded high accuracy. It suggests that the integration of remote sensing and ancillary data provide an applicable and feasible routine for analysis of surface radiation budget in urban environment. In order to understand the spatial patterns of surface radiation budgets, parameters, such as radiation fluxes, albedo and land surface temperature, were analyzed in terms of variations among different land cover types. Results indicate that the city can be characterized as a "basin" of net radiation in the summer, while it is characterized as a "plateau" in the winter. The albedo and land surface temperature were two primary factors contributing to the spatial variations of net radiation, while the solar elevation angle controlled the seasonal variations of the absolute amount.

By definition, HSC (HyperSpectral Camera) images are much richer in spectral data than, say, a COTS (Commercial-Off-The-Shelf) color camera. But data are not information. If we do the task right, useful information can be derived from the data in HSC images. Nature faced essentially the identical problem. The incident light is so complex spectrally that measuring it with high resolution would provide far more data than animals can handle in real time. Nature's solution was to do irreversible POCS (Projections Onto Convex Sets) to achieve huge reductions in data with minimal reduction in information. Thus we can arrange for our manmade systems to do what nature did - project the HSC image onto two or more broad, overlapping curves. The task we have undertaken in the last few years is to develop this idea that we call Artificial Color. What we report here is the use of the measured HSC image data projected onto two or three convex, overlapping, broad curves in analogy with the sensitivity curves of human cone cells. Testing two quite different HSC images in that manner produced the desired result: good discrimination or segmentation that can be done very simply and hence are likely to be doable in real time with specialized computers. Using POCS on the HSC data to reduce the processing complexity produced excellent discrimination in those two cases. For technical reasons discussed here, the figures of merit for the kind of pattern recognition we use is incommensurate with the figures of merit of conventional pattern recognition. We used some force fitting to make a comparison nevertheless, because it shows what is also obvious qualitatively. In our tasks our method works better.

This work describes the design and testing of a highly-tunable differential absorption lidar (DIAL) instrument utilizing an all-semiconductor transmitter. This new DIAL instrument transmitter has a highly-tunable external cavity diode laser (ECDL) as a seed laser source for two cascaded commercial tapered amplifiers. The transmitter has the capability of tuning over a range of ~ 17 nm centered at about 832 nm to selectively probe several water vapor absorption lines. This capability has been requested in other recent DIAL experiments for wavelengths near 830 nm. The transmitter produces pulse energies of approximately 0.25 µJ at a repetition rate of 20 kHz. The linewidth is exceptionally narrow at <0.3 MHz, with frequency stability that has been shown to be +/- 88 MHz and spectral purity of 0.995. Tests of the DIAL instrument to prove the validity of its measurements were undertaken. Preliminary water vapor profiles, taken in Bozeman, Montana, agree to within 5-60% with profiles derived from co-located radiosondes 800 meters above ground altitude. Below 800 meters, the measurements are biased low due to a number of systematic issues that are discussed. The long averaging times required by low-power systems have been shown to lead to biases in data, and indeed, our results showed strong disagreements on nights when the atmosphere was changing rapidly, such as on windy nights or when a storm system was entering the area. Improvements to the system to correct the major systematic biases are described.

We used the radar data from satellite ALOS/PALSAR of Japan and D-InSAR technology to derive the coseismic displacement produced by the Wenchuan, China Ms 8.0 earthquake on 12 May 2008. The result shows that the coseismic displacement primarily concentrated in a near-field range about 100km width on both sides of the Yingxiu-Beichuan fault. The incoherent zone about 250km long and 15~35km wide nearby the fault suffered the largest deformation with surface ruptures. The secondary deformed areas are 70km wide on each side of the incoherent zone, where the displacements exhibit a sunk northern wall with maximum -110~120cm and an uplifted southern wall with maximum 120~130cm, respectively. In the far-field range of the fault, displacements are less than 10cm. Using the offset tracking, we found clear rupture traces and coseismic displacement of 3m along the faults. With a model of four fault sections, we retrieved slip distribution on the faults. The inversion result reveals two slips of 10m at depths 5~20km beneath the Yingxiu-Beichuan fault and one slip of 2.3m at depth 5~20km below the Guanxian-Jiangyou fault, respectively. Thrust faulting dominates the southwestern Yingxiu-Beichuan fault and the entire Guanxian-Jiangyou fault, while right-slip is the primary component along the northeastern Yingxiu-Beichuan fault.

Three-dimensional (3D) FLASH Laser Radar (LADAR) sensors are unique due to the ability to rapidly acquire a series of two dimensional remote scene data (i.e. range images). Principal causes of 3D FLASH LADAR range estimation error include spatial blur, detector blurring, noise, timing jitter, and inter-sample targets. Unlike previous research, this paper accounts for pixel coupling by defining the range image mathematical model as a 2D convolution between the system spatial impulse response and the object (target or remote scene) at a particular point in time. Using this model, improved range estimation is possible by object restoration from the data observations. Object estimation is performed by deriving a blind deconvolution Generalized Expectation Maximization (GEM) algorithm with the range determined from the estimated object by a normalized correlation method. Theoretical derivations and simulation results are verified with experimental data of a bar target taken from a 3D FLASH LADAR system in a laboratory environment. Simulation examples show that the GEM improves range estimation over the unprocessed data and a Wiener filter method by 75% and 26% respectively. In the laboratory experiment, the GEM improves range estimation by 34% and 18%over the unprocessed data and Wiener filter method respectively.

In Part I a set of two layer feed-forward neural networks, trained via back propagation of sensitivities, was applied to a synthetic set of radiances in micro-windows of the near-infrared to make predictions of cloud water (c_w), cloud ice (c_i), effective scattering heights of cloud water and ice, (p_cw and p_ci, respectively) and the column water vapor (w). A threshold test, using 2 g/m^-2 for cloud water and 10 g/m^-2 for cloud ice, was applied to the retrieved values to distinguish clear from cloudy scenes. In that work the discussion was limited to the nadir viewing geometry, and was applied only to land surfaces, excluding desert and snow and ice fields. Part II describes the extension to a set of high resolution radiances, as might be measured by a grating spectrometer from space, in both nadir and sun glint viewing geometries. Furthermore, results are given for all land surface types as well as scenes over ocean. Prior to neural network training, a Principal Component Analysis (PCA) is applied to the high resolution spectra, which consist of three bands centered at 0.76μm (O₂ A-band), 1.61μm (weak CO₂ band) and 2.06μm (strong CO₂ band), each with 1016 channels. Analysis shows that the five leading EOFs together capture 99.9% of the variance in each band, reducing the data size by more than two orders of magnitude. Application of the trained neural networks to an independent data set, generated using CloudSat and Calipso cloud and aerosol profiles, as well as carbon dioxide profiles from a chemical transport model, were used to quantify the skill in the retrieval. The results vary significantly with surface type, viewing mode and cloud properties. Accuracies range from 7% to 100% (typically close to 75%), with confidence levels almost always greater than 90%.

The impact of recent drought and water pollution episodes results in an acute need to project future water availability to assist water managers in water utility infrastructure management within many metropolitan regions. Separate drought and water quality indices previously developed might not be sufficient for the purpose of such an assessment. This paper describes the development of the "Metropolitan Water Availability Index (MWAI)" and its potential applications in assessing the middle-term water availability at the watershed scale in a fast growing metropolitan region - the Manatee County near Tampa Bay, Florida, U.S.A. The MWAI framework is based on a statistical approach that seeks to reflect the continuous spatial and temporal variations of both water quantity and quality using a simple numerical index. Such a trend analysis will surely result in the final MWAI values for regional water management systems within a specified range. By using remote sensing technologies and data processing techniques, continuous monitoring of spatial and temporal distributions of key water availability variables, such as evapotranspiration (ET) and precipitation, is made achievable. These remote sensing technologies can be ground-based (e.g., radar estimates of rainfall), or based on remote sensing data gathered by aircraft or satellites. Using a middle term historical record, the MWAI was applied to the Manatee County water supplies. The findings clearly indicate that only eight out of twelve months in 2008 had positive MWAI values during the year. (Partial Abstract).

Vegetation indices (VIs) are widely used in long-term measurement studies of vegetation changes, including seasonal vegetation activity and interannual vegetation-climate interactions. There is much interest in developing cross-sensor/multi-mission vegetation products that can be extended to future sensors while maintaining continuity with present and past sensors. In this study we investigated multi-sensor spectral bandpass dependencies of the enhanced vegetation index (EVI), a 2-band EVI (EVI2), and the normalized difference vegetation index (NDVI) using spectrally convolved Earth Observing-1 (EO-1) Hyperion satellite images acquired over a range of vegetation conditions. Two types of analysis were carried out, including (1) empirical relationships among sensor reflectances and VIs and (2) decomposition of bandpass contributions to observed cross-sensor VI differences. VI differences were a function of cross-sensor bandpass disparities and the integrative manner in which bandpass differences in red, near-infrared (NIR), and blue reflectances combined to influence a VI. Disparities in blue bandpasses were the primary cause of EVI differences between the Moderate Resolution Imaging Spectroradiometer (MODIS) and other course resolution sensors, including the upcoming Visible Infrared Imager / Radiometer Suite (VIIRS). The highest compatibility was between VIIRS and MODIS EVI2 while AVHRR NDVI and EVI2 were the least compatible to MODIS.

The molecular oxygen absorption band near 0.762 μm can be used for remote sounding of atmospheric pressure profiles. Atmospheric O₂ absorption, atmospheric molecular multi-scattering, extinction of atmospheric aerosols, bi-reflection at the earth surface, the atmospheric refractive effect and the global curvature are taken into account to establish an atmospheric radiation transfer equation. By means of an accurate O₂atmospheric transmittance model and a mean atmospheric transmittance model the remote sounding equation of surface pressure from space suitable to clear sky field of view is derived. At last, a pure physical retrieval method is given and the atmospheric pressure profile can be deduced.

The expressions are derived for both the sensitivity of retrieved surface pressure and that of radiance measurement to atmospheric temperature profile in detail. Calculations indicate that the later should not be regarded as one of bases for channel selection. After removing channels with very large maximal relative fitting errors of atmospheric transmittance model and mean transmittance model, weak absorption channels and channels with higher sensitivity of retrieval surface pressure to atmospheric temperature profile, the remote sounding accuracy of surface pressure with 1000 hPa is improved by about 3.2~21hPa.

It has been shown that the laboratory calibration coefficient of charge-coupled device camera (CCD camera) on China-Brazil Earth Resources Satellite-02B (CBERS-02B) is not fit for the water component retrieval. As a result, the CCD was cross-calibrated by Aqua MODIS with higher calibration accuracy, which was based on the total radiance at the top of atmosphere calculated from normalized water-leaving radiance and aerosol parameters of Aqua MODIS. The analysis about the error related to the parameters used in the cross-calibration showed that the error of the calibration coefficients can be less than 5%, which was mostly determined by the accuracy of aerosol scattering of CCD band 830nm. Using the calibration coefficients, chlorophyll concentration was retrieved from CCD imagery and was compared with that from Aqua MODIS. The comparison showed that the calibration result worked better than the laboratory coefficient.

In this paper, we consider the problem of hyperspectral image denoising. Current denoising is based on multichannel restoration filters assuming the separability of the signal covariance, which describes the between-channel and within-channel relationships. We propose a new algorithm for a spectral band restoration scheme, the adaptive multidimensional Wiener filter, based on a local signal model, without assuming spectral and spatial separability. The proposed filter can be applied as a preprocessing step for detection in hyperspectral imagery. We highlight the target detection improvement when the developed method is used before existing methods the well-known hyperspectral imagery detectors as: AMF (Adaptive Matched Filter), ACE (Adaptive coherence/cosine Estimator) and RX (Reed and Xiaoli algotithm). We demonstrate that integrating a multidimensional restoration leads to significant improvement of the detection probability. The performance of our method is exemplified using real-world HYDICE images.

Remote sensing imagery is effective for monitoring environmental and climatic changes because of the extent of the global coverage and long time scale of the observations. Radiometric calibration of remote sensing sensors is essential for quantitative & qualitative science and applications. Pseudo-invariant ground targets have been extensively used to monitor the long-term radiometric calibration stability of remote sensing sensors. This paper focuses on the use of the Sonoran Desert site to monitor the radiometric stability of the Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. The results are compared with the widely used Libya 4 Desert site in an attempt to evaluate the suitability of the Sonoran Desert site for sensor inter-comparison and calibration stability monitoring. Since the overpass times of ETM+ and MODIS differ by about 30 minutes, the impacts due to different view geometries or test site Bi-directional Reflectance Distribution Function (BRDF) are also presented. In general, the long-term drifts in the visible bands are relatively large compared to the drift in the near-infrared bands of both sensors. The lifetime Top-of-Atmosphere (TOA) reflectance trends from both sensors over 10 years are extremely stable, changing by no more than 0.1% per year (except ETM+ Band 1 and MODIS Band 3) over the two sites used for the study. The use of a semi-empirical BRDF model can reduce the impacts due to view geometries, thus enabling a better estimate of sensor temporal drifts.

Medium resolution (10-100m) optical sensor data such as those from the Landsat, SPOT, ASTER, CBERS and IRS-P6 satellites provide detailed spatial information for studies of ecosystems, vegetation biophysics, and land cover. While Landsat remains a cornerstone of medium resolution remote sensing, the ETM+ scan-line corrector failure in 2003 has highlighted the need for methods to integrate radiometry from multiple international sensors in order to create a consistent, long-term observational record. Such an approach needs to compensate for differing acquisition plans, sensor bandwidths, spatial resolution, and orbit coverage. Different processing approaches used in the calibration and atmosphere correction across sensors make integration even harder. In this paper, we propose a generalized reference-based approach to convert medium resolution satellite digital number (DN) to MODIS-like surface reflectance using MODIS products as a reference data set. This approach does not require explicit calibration and atmospheric correction procedures for individual medium resolution sensors, therefore minimizing the potential impact of those procedures due to among-sensor differences. Therefore, data in MODIS era from different sources such as Landsat TM/ETM+, IRS-P6 AWiFS, and TERRA ASTER can be combined for time-series analysis, biophysical parameter retrievals, and other downstream analysis. Our results from Landsat TM/ETM+ show that this approach can produce surface reflectance with a similar accuracy to physical approaches based on radiative transfer modeling with mean absolute differences of 0.0016 and 0.0105 for red and near infra-red bands respectively. The normalized MODIS-like surface reflectances from multiple sensors and acquisition dates are consistent and comparable both spatially and temporally with known trends in phenology.

Our ability to map coral reef environments using remote sensing has increased through improved access to: satellite images and field survey data at suitable spatial scales, and software enabling the integration of data sources. These data sets can be used to provide validated maps to support science and management decisions. The objective of this paper was to compare two methods for calibrating and validating maps of coral reef benthic communities derived from satellite images captured over a variety of Coral Reefs The two methods for collecting georeferenced benthic field data were: 1), georeferenced photo transects and 2), spot checks. Quickbird imagery was acquired for three Fijian coral reef environments in: Suva, Navakavu and Solo. These environments had variable water clarity and spatial complexity of benthic cover composition. The two field data sets at each reef were each split, and half were used for training data sets for supervised classifications, and the other half for accuracy assessment. This resulted in two maps of benthic communities with associated mapping accuracies, production times and costs for each study-site. Analyses of the spatial patterns in benthic community maps and their Overall and Tau accuracies revealed that for spatially complex habitats, the maps produced from photo transect data were twice as accurate as spot check based maps. In the context of the reefs examined, our results showed that the photo- transect method was a robust procedure which could be used in a range of coral reef environments to map the benthic communities accurately. In contrast, the spot check method is a fast and low cost approach, suitable for mapping benthic communities which have lower spatial complexity. Our findings will enable scientists, technicians and managers to select appropriate methods for collecting field data to integrate with high spatial resolution multi-spectral imagery to create validated coral reef benthic community maps.

Aquatic weeds have caused significant problems in many lakes and river systems worldwide. Weed outbreaks of water hyacinth (Eichhornia crassipes) and para-grass (Urochloa mutica) are common in Australia and their ecological and recreational impacts mostly negative and costly. Remote sensing offers the ability to map and monitor the distribution of aquatic weeds and their early detection. The objective of this project was to develop an efficient method, using remote sensing techniques, to map and monitor the change of dense water weeds in a river system and to identify a suitable spatial scale for this process. Two SPOT (Satellite Pour l'Observation de la Terre) 5 images from May 2006 and May 2007 were used in combination with two mapping approaches on a) multispectral image data with 10 m spatial resolution and b) pan-sharpened multispectral image data with 2.5 m spatial resolution. A scale dependent validation resulted in case a) an overall producer's classification accuracy of 81%. Small outbreaks (~2 m²) alone were 71% accurate with increasing accuracies of >95% for outbreaks larger than 6.25m² (2.5m x 2.5m pixel). Case b) generally had lower accuracies, with accuracies of >95% for outbreaks in the order of 100m² (10m x 10m pixel) and larger. The results suggest that the river infestation by aquatic weeds in a test area of the mid-Brisbane River has increased by a factor of 2 to 3 during the 12-month period. The infested area is estimated to be between 13.6% and 15.9 % of the waterbody in 2007, while 6.2% to 6.8% in 2006. The method applied in this study included geometric and radiometric corrections, along with linear spectral unmixing and spectral angle mapper techniques. This method is applicable to other waterways worldwide and offers the potential for the early detection of infestations of aquatic surface weeds.

Data with 0.4-m spatial resolution acquired ~2 km off the southeast Florida coast using the airborne Portable Hyperspectral Imager for Low-Light Spectroscopy (PHILLS) have been analyzed with the objective of identifying drifting surface macroalgae (Sargassum) through its spectral signature in at-sensor radiance. The observed spectral features of Sargassum include a peak at a wavelength of ~0.570 µm and a photosynthetic 'red edge' between 0.673 and 0.699 µm. Sargassum also exhibits high radiance in the reflected near-infrared but is impacted by the atmospheric absorption bands of water vapor at 0.720 µm and oxygen at 0.756 µm. The spectral signature is clearest and largest in amplitude where the Sargassum occurs as small surface aggregations, or rafts, which tend to lie at the downwind ends of narrow Sargassum windrows. The quantity of floating Sargassum was estimated within a single pixel by linearly mixing a spectrum of Sargassum-free water with varying percentages of a spectrum from a pixel assumed completely filled with floating plants. For our study site about 2.3% of the ocean area is classified as having some Sargassum coverage, with pixels completely filled with Sargassum being rare (only 0.2% of the classified Sargassum pixels) and pixels with the least-resolvable amount of Sargassum (~10% filled) being the most common.

Directional effects in remotely sensed reflectance data can influence the retrieval of plant biophysical and biochemical estimates. Previous studies have demonstrated that directional measurements contain added information that may increase the accuracy of estimated plant structural parameters. Because accurate biochemistry mapping is linked to vegetation structure, also models to estimate canopy nitrogen concentration (C_N) may be improved indirectly from using multiangular data. Hyperspectral imagery with five different viewing zenith angles was acquired by the spaceborne CHRIS sensor over a forest study site in Switzerland. Fifteen canopy reflectance spectra corresponding to subplots of field-sampled trees were extracted from the preprocessed CHRIS images and subsequently two-term models were developed by regressing C_N on four datasets comprising either original or continuum-removed reflectances. Consideration is given to the directional sensitivity of the C_N estimation by generating regression models based on various combinations (n=15) of observation angles. The results of this study show that estimating canopy C_N with only nadir data is not optimal irrespective of spectral data processing. Moreover adding multiangular information improves significantly the regression model fits and thus the retrieval of forest canopy biochemistry. These findings support the potential of multiangular Earth observations also for application-oriented ecological monitoring.

Forest fires are one of the major causes of the deforestation of tropical peat swamps in Malaysia. One way of trying to identify which peat swamp forests are vulnerable to forest fire is to develop a forest fire risk index. The objectives of this study were to develop both a fuel-type map and a forest fire hazard rating assessment for the peat swamp forests. The study was conducted in a peat swamp forest located at Batu Enam, Penor/Kuantan District of Pahang. This area suffered a severe forest fire on 12 March 1998 which degraded the peat swamp area. Digitally processed Landsat Thematic Mapper^TM satellite image were integrated with geographic information layer of fuel type, roads and canal layer to derive a fire hazard rating map of the area. Using the superior spectral and temporal resolution of a Landsat^TM, five fire hazard rating classifications were defined. A forest fire hazard rating map was produced showing that 49% of the area was 'low' fire hazard rating, 23% was 'high', 17% was 'moderate', 10% was 'extreme' and 1% was 'null'. Peat lands within 150 meters of roads and of a canal were identified as having an 'extreme' fire hazard rating. Both the fire hazard rating map and the forest fire hazard rating assessment can be used in future forest fire management planning.

Mapping land cover and vegetation characteristics on a regional scale is critical to soil moisture retrieval using microwave remote sensing. In aircraft-based experiments such as the National Airborne Field Experiment 2006 (NAFE'06), it is challenging to provide accurate high resolution vegetation information, especially on a daily basis. A technique proposed in previous studies was adapted here to the heterogenous conditions encountered in NAFE'06, which included a hydrologically complex landscape consisting of both irrigated and dryland agriculture. Using field vegetation sampling and ground-based reflectance measurements, the knowledge base for relating the Normalized Difference Water Index (NDWI) and the vegetation water content was extended to a greater diversity of agricultural crops, which included dryland and irrigated wheat, alfalfa, and canola. Critical to the generation of vegetation water content maps, the land cover for this region was determined from satellite visible/infrared imagery and ground surveys with an accuracy of 95.5% and a kappa coefficient of 0.95. The vegetation water content was estimated with a root mean square error of 0.33 kg/m². The results of this investigation contribute to a more robust database of global vegetation water content observations and demonstrate that the approach can be applied with high accuracy.

In this study of atmospheric effects on laser ranging and detection (ladar) and radar systems, the parameter space is explored using the Air Force Institute of Technology Center for Directed Energy's (AFIT/CDE) High Energy Laser End-to-End Operational Simulation (HELEEOS) parametric one-on-one engagement level model. The expected performance of ladar systems is assessed at a representative wavelength of 1.557 µm at a number of widely dispersed land and maritime locations worldwide. Radar system performance is assessed at 95 GHz and 250 GHz. Scenarios evaluated include both down looking oblique and vertical engagement geometries over ranges up to 3000 meters in which clear air aerosols and thin layers of fog, locally heavy rain, and low stratus cloud types are expected to occur. Seasonal and boundary layer variations are considered to determine optimum employment techniques to exploit or defeat the environmental conditions. Each atmospheric particulate/obscurant/hydrometeor is evaluated based on its wavelength-dependent forward and off-axis scattering characteristics and absorption effects on system interrogation. Results are presented in the form of worldwide plots of notional signal to noise ratio. The ladar and 95 GHz system types exhibit similar SNR performance for forward oblique clear air operation. 1.557 µm ladar performs well for vertical geometries in the presence of ground fog, but has no near-horizontal performance under such meteorological conditions. It also has no performance if low altitude stratus is present. 95 GHz performs well for both the fog and stratus layer cases, for both vertical and forward oblique geometries. The 250 GHz radar system is heavily impacted by water vapor absorption in all scenarios studied; however it is not as strongly affected by clouds and fog as the 1.557 µm ladar. Locally heavy rain will severely limit ladar system performance at these wavelengths. However, under heavy rain conditions ladar outperforms both radar systems.

The paper presents results of using advanced techniques such as Self-Organizing feature Map (SOM) to incorporate a GIS data layer to compensate for the limited amount of real-time ground-truth data available for land-use and land-cover mapping in wet-season conditions in Bangladesh based on multi-temporal RADARSAT-1 SAR images. The experimental results were compared with those of traditional statistical classifiers such as Maximum Likelihood, Mahalanobis Distance, and Minimum Distance, which are not suitable for incorporating low-level GIS data in the image classification process. The performances of the classifiers were evaluated in terms of the classification accuracy with respect to the collected real-time ground truth data. The SOM neural network provided the highest overall accuracy when a GIS layer of land type classification with respect to the depth and duration of regular flooding was used in the network. Using this method, the overall accuracy was around 15% higher than the previously mentioned traditional classifiers at 79.6% where the training data covered only 0.53% of the total image. It also achieved higher accuracies for more classes in comparison to the other classifiers.

The design of any modern imaging system is the end result of many trade studies, each seeking to optimize image quality within real world constraints such as cost, schedule and overall risk. Image chain analysis - the prediction of image quality from fundamental design parameters - is an important part of this design process. At The Aerospace Corporation we have been using a variety of image chain analysis tools for many years, the Parameterized Image Chain Analysis & Simulation SOftware (PICASSO) among them. In this paper we describe our PICASSO tool, showing how, starting with a high quality input image and hypothetical design descriptions representative of the current state of the art in commercial imaging satellites, PICASSO can generate standard metrics of image quality in support of the decision processes of designers and program managers alike.

By using the O₂ absorption band near 0.762 µm, remote sounding errors of surface pressure caused by 6 scientific and 9 technical factors are estimated one-by-one. Then, the remote sounding accuracy in clear field of view is estimated. If atmospheric aerosols and the bi-reflectance at the earth surface are measured accurately by means of other ways, the remote sounding accuracy for the surface pressure of 1000 hPa is 1.3-4.3 hPa in circumstances of heavy atmospheric aerosols; on the other hand, 0.9-4.7 hPa for larger surface bi-reflectance and 2.8-15 hPa for smaller surface bi-reflectance, respectively, in circumstances of lightest atmospheric aerosols.

In this paper we model sub-pixel image registration for a generic earth-observing satellite system with a focal plane using two offset time delay and integrate (TDI) arrays in the focal plane to improve the achievable ground resolution over the resolution achievable with a single array. The modeling process starts with a high-resolution image as ground truth. The Parameterized Image Chain Analysis & Simulation Software (PICASSO) modeling tool is used to degrade the images to match the optical transfer function, sampling, and noise characteristics of the target system. The model outputs a pair of images with a separation close to the nominal half-pixel separation between the overlapped arrays. A registration estimation algorithm is used to measure the offset for image reconstruction. The two images are aligned and summed on a grid with twice the capture resolution. We compare the resolution in images between the inputs before overlap, the reconstructed image, and a simulation for the image which would have been captured on a focal plane with twice the resolution. We find the performance to always be better than the lower resolution baseline, and to approach the performance of the high-resolution array in the ideal case. We show that the overlapped array imager significantly outperforms both the conventional high- and low-resolution imagers in conditions with high image smear.

Spatio-temporally variable information on total vegetation cover is highly relevant to water quality and land management in river catchments adjacent to the Great Barrier Reef, Australia. A time series of the global Moderate Resolution Imaging Spectroradiometer (MODIS) Fraction of Photosynthetically Active Radiation (FPAR; 2000-2006) and its underlying biome classification (MOD12Q1) were compared to national land cover and regional, remotely sensed products in the dry-tropical Burdekin River. The MOD12Q1 showed reasonable agreement with a classification of major vegetation groups for 94% of the study area. We then compared dry-seasonal, quality controlled MODIS FPAR observations to (i) Landsat-based woody foliage projective cover (wFPC) (2004) and (ii) MODIS bare ground index (BGI) observations (2001-2003). Statistical analysis of the MODIS FPAR revealed a significant sensitivity to Landsat wFPC-based Vegetation Structural Categories (VSC) and VSC-specific temporal variability over the 2004 dry season. The MODIS FPAR relation to 20 coinciding MODIS BGI dry-seasonal observations was significant ( < 0.001) for homogeneous areas of low wFPC. Our results show that the global MODIS FPAR can be used to identify VSC, represent VSC-specific variability of PAR absorption, and indicate that the amount, structure, and optical properties of green and non-green vegetation components contribute to the MODIS FPAR signal.

Unmanned Aircraft Systems (UAS) have great potential for rangeland assessment, monitoring, and numerous other applications in natural resources management. In order for UAS to become a dependable tool for public land management agencies in carrying out their government-mandated responsibilities, it is necessary to integrate UAS into the National Airspace System (NAS), which includes all aircraft, manned or unmanned. To achieve this, Federal Aviation Administration (FAA) regulations have to be followed to assure public safety. UAS operators need to know that FAA safety regulations, which incorporate line-of-sight restrictions, will only allow slow progress towards an operational system, and they must plan accordingly for the extra time necessary to prepare and complete flight missions. By following approved safety procedures, UAS operators can develop a UAS flight team that is capable of accomplishing missions anywhere in the United States while contributing to a totally integrated NAS comprised of all aircraft systems that can be used jointly for natural resources management. At the same time, it is hoped that FAA regulations will change in the future based on the capabilities and experience of the UAS flight team and on the locale in which operations take place, especially over large, remote, and sparsely populated areas.

Most of the recent remote sensing sensors produce higher radiometric resolution than those display devices showing images. Therefore DRC (Dynamic Range Compression) is applied to accommodate original image with large bit-depth to the showing device with less bit-depth. Common stretch methods achieve the DRC with linear line or non-linear curve mapping. The linear stretch loses the details of the highest and lowest luminance area, and the non-linear stretch causes the color distortion issue after the fusion of panchromatic (Pan) and multi-spectrum (MS) image. This paper develops an approach to enhance unapparent features in the Pan image using wavelet fusion method which combine sub-images coming from the same original Pan image. Those sub-image formed by different stretch method for enhancing the radiometric visualization and keeping better correlation with the original color information. According to the experimental result, this approach can apply to not only the airborne but the spaceborne image to produce ideal pan-sharpened product.

A methodology for calibrating multispectral IRS Wide-Field Sensors (WiFS), which cover typically about 700 km swath is suggested. Two available bands B3 and B4 of IRS-1C and -1D WIFS and respective bands from four of IRS-P6 Advanced WiFS (AWiFS) are employed. Data products acquired over the Thar Desert within 5 days (near-synchronous condition) were utilized. Linear regression model is assumed valid to obtain the calibration coefficients (CCs), after compensating for intrinsic variations of these bands due to spectral response functions and solar zenith angles. It is found that IRS-1C and -1D WIFS cameras show a strong linearity relationship, as evident from their data samples with goodness of fit of better than 0.99. The application of the CCs may hence provide the desired integrated information when their data products are used in tandem. Analysis of the IRS-P6 AWIFS and IRS-1D WIFS image pairs, however, shows a large variability in their output, especially for the Band B4 datasets. The CCs derived for these sensors' pair needs to be used cautiously, Variation in their relative spectral responses demands further investigation, probably with inclusion of the in-situ measurements to account for variations of the target reflectance and the atmosphere while attempting cross-calibration analysis.

The instantaneous daytime geographical distribution and radiative effects of high thin clouds (optical thickness < 5) are investigated on the basis of the CloudSat Cloud Profiling Radar (CPR) radiative flux and cloud classification products. The regional features of the fraction and radiative effects of high thin clouds are associated with ITCZ, SPCZ and mid-latitude storm track regions. High thin clouds have positive net cloud-induced radiative effect (CRE) at the top of the atmosphere (TOA) and negative net CRE at the bottom of the atmosphere (BOA). The magnitudes of TOA and BOA CREs depend on cloud optical thickness, cloud fraction and geographical location. The magnitude of the net CRE of high thin clouds increases at both TOA and BOA as cloud optical thickness increases. Net CRE at both TOA and BOA contributes to a positive net CRE in-atmosphere and warms the atmosphere regardless of cloud fraction. The global annual mean of the net CRE multiplied by cloud fraction is 0.49 W/m² at TOA, -0.54 W/m² at BOA and 1.03 W/m² in-atmosphere. The most radiatively effective cloud optical thickness of a high thin cloud is between 1-2 for the TOA and in-atmosphere CREs or 3-4 for the BOA CRE.

The use of remote sensing images collected by space platforms is becoming more and more widespread. The increasing value of space data and its use in critical scenarios call for adoption of proper security measures to protect these data against unauthorized access and fraudulent use. In this paper, based on the characteristics of remote sensing image data and application requirements on secure distribution, a secure distribution method is proposed, including users and regions classification, hierarchical control and keys generation, and multi-level encryption based on regions. The combination of the three parts can make that the same remote sensing images after multi-level encryption processing are distributed to different permission users through multicast, but different permission users can obtain different degree information after decryption through their own decryption keys. It well meets user access control and security needs in the process of high resolution remote sensing image distribution. The experimental results prove the effectiveness of the proposed method which is suitable for practical use in the secure transmission of remote sensing images including confidential information over internet.

Most remote sensing studies of species diversity have been based on the use of passive imagery representing the horizontal dimensions of ecosystems. However, LIDAR (light detection and ranging), provides a means to accurately quantify vertical structure. The goal of this study was to evaluate vascular plant species richness on a coastal barrier island using indicators of community vertical structure derived from airborne, multiple-return LIDAR data. Returns from a 3 m buffer area surrounding each of 90, 15 m vegetation line transects were extracted from LIDAR data of Horn Island, Mississippi, acquired in April, 2004. LIDAR indices did not correlate with richness when data for all habitats were combined. When habitats were considered separately, several LIDAR indices correlated significantly (p 0.05) with richness in marsh, meadow and woodland habitats. Best-fit indices indicated the importance of vegetation height and structural complexity in estimating plant species richness.

The aim of this paper is to analyze the errors in the Digital Elevation Models (DEMs) derived through repeat pass SAR interferometry (InSAR). Out of 29 ASAR images available to us, 8 are selected for this study which has unique data set forming 7 InSAR pairs with single master image. The perpendicular component of baseline (B) varies between 200 to 400 m to generate good quality DEMs. The Temporal baseline (T) varies from 35 days to 525 days to see the effect of temporal decorrelation. It is expected that all the DEMs be similar to each other spatially with in the noise limits. However, they differ very much with one another. The 7 DEMs are compared with the DEM of SRTM for the estimation of errors. The spatial and temporal distribution of errors in the DEM is analyzed by considering several case studies. Spatial and temporal variability of precipitable water vapour is analysed. Precipitable water vapour (PWV) corrections to the DEMs are implemented and found to have no significant effect. The reasons are explained. Temporal decorrelation of phases and soil moisture variations seem to have influence on the accuracy of the derived DEM. It is suggested that installing a number of corner reflectors (CRs) and the use of Permanent Scatter approach may improve the accuracy of the results in desert test sites.

In recent years, the risk of health effects caused by the increased exposure to Ultraviolet-B (UVB) due to stratospheric ozone depletion has received wide attention. In the US, there are two ways to accurately measure the UVB. They include: 1) the National Aeronautical and Space Administration (NASA) Nimbus-7 total ozone mapping spectrometer (TOMS), and 2) the United State Department of Agriculture (USDA) ground-based network. This paper compares these two sensors' data for the ultraviolet index (UVI) nationally and regionally to support possible public health, agricultural, and ecological analyses in the future. The major findings of our study are: 1) although there are discrepancies between these two data sets, the temporal correlation coefficients can be as high as 98%. 2) Both types of data sources depict the macroscopic spatial pattern of the UVI across the continental US.indicating a strong spatial correlation; 3) The two data sources are generally consistent though the UVI of the NASA TOMS data are often about 0.13-1.05 units larger than those of the USDA ground-based measurements; and 4) Varying differences can be seen between the Midwest and two coastal regions. While the level of the UVI on the west coast has shown a decreasing trend in the past few years, its counterpart on the east coast showed an opposite trend in between 2000 and 2005. It is hard to conclude that the changes are due to variations of total ozone concentrations in this study period. The USDA ground-based measurements may be better applied for time series analysis for public health, ecological, and agricultural applications due to their ability to provide intensive calibrated point measurements.

A 1.57-µm laser remote sensor using differential absorption spectrometry is being developed as a candidate for the next space-based mission to observe atmospheric CO₂ and/or other trace gases. The performance of the newly-developed active remote sensor has been evaluated for horizontal measurements and initial vertical measurements have been demonstrated. This study shows the results of in-house and field measurements to evaluate column-averaged CO₂ mixing ratios. The in-house measurements demonstrated the instrumental response showing agreement within a correlation coefficient of 0.998 for a known CO₂ density. Field measurements to evaluate horizontal and vertical column-averaged CO₂ mixing ratio were made with a measured precision of 0.49% and 1.7%, respectively. The horizontal integration range was 2.1 km and the vertical range extended from the surface up to the cloud base at ~3 km with corresponding accumulation time of 25 min. Complementary measurements with a multi-positioned in-situ sensor along the observation path demonstrated that the mean horizontal column-averaged CO₂ density agreed within the difference of 2.8 ppm of the atmospheric CO₂ density.

Lidar measurements of spike returns from clear air are presented. These spikes occur infrequently (approximately one in hundred returns) but provide returns that are significantly stronger (occasionally an order of magnitude larger) than the average aerosol backscatter signal. The spike density is 5.7e-3 spikes m^-3 for backscattering cross sections estimates to be between 0.003 and 0.080 mm² sr^-1. A modified form of the lidar equation which includes returns from large particulates is presented and the probability distribution for the spike magnitudes is derived from five million measurements.

A process is developed to assess the effect of fusing polarimetric and spectral sensing modalities for an urban target detection scenario through simulation with the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. Two novel multimodal fusion algorithms are proposed--one for the pixel level, and another for the decision level. A synthetic urban scene is validated to ensure the presence of enough background clutter. The signal-to-clutter ratios (SCR) of both the simulated spectral and polarimetric data are calculated, and the synthetic spectral SCR is compared to data collected with the Compact Airborne Spectral Sensor (COMPASS). A qualitative examination of the polarimetric background clutter level is also described. The fusion algorithms' performances are evaluated at 355 different sun-target-sensor viewing geometries, and a method to quantify the increase in performance is described. Tasking conditions where target detection performance is enhanced are identified and the decision fusion algorithm is shown to outperform the pixel fusion algorithm. The utility of polarimetric information is shown to vary with the sun-target-sensor geometry, but data fusion consistently enhances spectral target detection performance when the sensor is located in the sun's specular reflection lobe.

Satellite remote sensing technology and the science associated with evaluation of land use and land cover (LULC) in an urban region makes use of the wide range images and algorithms. Improved land management capacity is critically dependent on real-time or near real-time monitoring of land-use/land cover change (LUCC) to the extent to which solutions to a whole host of urban/rural interface development issues may be well managed promptly. Yet previous processing with LULC methods is often time-consuming, laborious, and tedious making the outputs unavailable within the required time window. This paper presents a new image classification approach based on a novel neural computing technique that is applied to identify the LULC patterns in a fast growing urban region with the aid of 2.5-meter resolution SPOT-5 image products. The classifier was constructed based on the partial Lanczos extreme learning machine (PL-ELM), which is a novel machine learning algorithm with fast learning speed and outstanding generalization performance. Since some different classes of LULC may be linked with similar spectral characteristics, texture features and vegetation indexes were extracted and included during the classification process to enhance the discernability. A validation procedure based on ground truth data and comparisons with some classic classifiers prove the credibility of the proposed PL-ELM classification approach in terms of the classification accuracy as well as the processing speed. A case study in Dalian Development Area (DDA) with the aid of the SPOT-5 satellite images collected in the year of 2003 and 2007 and PL-ELM fully supports the monitoring needs and aids in the rapid change detection with respect to both urban expansion and coastal land reclamations.

For sensing systems that characterize the spectro-polarimetric radiance reaching the camera, the origin of the sensed phenomenology is a complex mixture of sources. While some of these sources do not contribute to the polarimetric signature, many do such as the polarization state of the downwelled sky radiance, the target and background p-BRDF(polarimetric bidirectional reflectance distribution function), the polarization state of the upwelled path radiance, and the sensor Mueller matrix transfer function. In this paper we derive portions of the p-BRDF in terms of both the spectral diffuse and polarimetric specular components of the reflectance using an in-scene calibration technique. This process is applied to simulated data, laboratory data, and data from a field collection. Spectra of a car panel for clean and contaminated states derived using laboratory data are injected into a hyperspectral image cube. It is shown how this target can be identified using a target specific tracking vector derived from its polarimetric signature as it moves between spatial locations within a scene.

Mobile laser scanning (MLS), which recently has been developing so quickly as a promising technology for mapping and remote sensing (RS), offers a good means to measure the fundamental geographic data, e.g. culverts, for urban planning and road engineering. This study as the first try presents a new automatic method to detect culverts in MLS point clouds, in which actually only partial characterization of this category of objects can be presented due to the restricted scanning zenith of MLS. The schematic is based on the raster-form of the data, and the digital terrain models (DTMs) with multi-leveled resolutions are first yielded by local minimum filtering. Then, the common layout of the expanded areas containing culverts is generalized as the theoretical basis, and the schematic components are derived to deploy the concrete judgment. The geometry and intensity information about culverts are both utilized to determine the real locations from coarse- to fine-scales. Numerical analysis based on the real-measured MLS data at the Espoonlahti test site has basically validated the proposed approach. Concretely, the statistical errors of the retrieved lengths and widths of the pedestrian culverts are less than 9% and 16% compared to the real ones individually, notwithstanding the inner heights innately in-accessible.

The resilience and vulnerability of terrestrial ecosystem in the Tarim River Basin, Xinjiang is critical in sustainable development of the northwest region in China. To learn more about causes of the ecosystem evolution in this wide region, vegetation dynamics can be a surrogate indicator of environmental responses and human perturbations. This paper aims to use the inter-annual and intra-annual coefficient of variation (CoV) derived by the SPOT-VGT Normalized Difference Vegetation Index (NDVI) as an integrated measure of vegetation dynamics to address the environmental implications in response to climate change. To finally pin down the vegetation dynamics, the intra-annual CoV based on monthly NDVI values and the inter-annual CoV based on seasonally accumulated NDVI values were respectively calculated. Such vegetation dynamics can then be associated with precipitation patterns extracted from the Tropical Rainfall Measuring Mission (TRMM) data and irrigation efforts reflecting the cross-linkages between human society and natural systems. Such a remote sensing analysis enables us to explore the complex vegetation dynamics in terms of distribution and evolution of the collective features of heterogeneity over local soil characteristics, climate change impacts, and anthropogenic activities at differing space and time scales. Findings clearly indicate that the vegetation changes had an obvious trend in some high mountainous areas as a result of climate change whereas the vegetation changes in fluvial plains reflected the increasing evidence of human perturbations due to anthropogenic activities. Some possible environmental implications were finally elaborated from those cross-linkages between economic development and resources depletion in the context of sustainable development.

Climate change is heavily impacted by changing vegetation cover and productivity with large scale monitoring of vegetation only possible with remote sensing techniques. The goal of this effort was to evaluate existing reflectance (R) spectroscopic methods for determining vegetation parameters related to photosynthetic function and carbon (C) dynamics in plants. Since nitrogen (N) is a key constituent of photosynthetic pigments and C fixing enzymes, biological C sequestration is regulated in part by N availability. Spectral R information was obtained from field corn grown at four N application rates (0, 70, 140, 280 kg N/ha). A hierarchy of spectral observations were obtained: leaf and canopy with a spectral radiometer; aircraft with the AISA sensor; and satellite with EO-1 Hyperion. A number of spectral R indices were calculated from these hyperspectral observations and compared to geo-located biophysical measures of plant growth and physiological condition. Top performing indices included the R derivative index D730/D705 and the normalized difference of R750 vs. R705 (ND705), both of which differentiated three of the four N fertilization rates at multiple observation levels and yielded high correlations to these carbon parameters: light use efficiency (LUE); C:N ratio; and crop grain yield. These results advocate the use of hyperspectral sensors for remotely monitoring carbon cycle dynamics in managed terrestrial ecosystems.

Mountain resort development is having increasing effects on ecological functions in the intermountain West. High-resolution remote sensing has the potential to assist in monitoring this development. We evaluated classification of mountain resort development in the Big Sky, Montana, watershed using Quickbird 2.4-m multispectral imagery with an object-oriented classification. Quickbird imagery, however, has limited spectral resolution; we therefore also evaluated the benefits of fusing Quickbird imagery with LiDAR bare ground and surface model data in an object-oriented approach. Classification accuracies with the fused data increased approximately 1% and were not statistically significantly different based on a 1735 point sample. The classified objects, however, demonstrated more spatial coherency, with more realistically defined shapes and edges.

The global network FLUXNET supplies environmental scientists with valuable data on ecosystem exchange processes along with meteorological measurements. Ecosystem characteristics at these sites can be efficiently complemented by remote sensing data from the MODIS sensors on-board the NASA satellites Terra and Aqua. The ORNL DAAC makes resampled MODIS key products as so called 'Land Product Subsets' available. These subsets comprise selected MODIS products in a 7x7 km grid centered on FLUXNET sites. One of these products is the leaf area index (LAI). Despite the frequent application of MODIS LAI data in ecosystem models, there is still no consensus on its usage and the employment of the additionally provided quality criteria (QC). In this study, we analyze the effects of various QC filters, spatial aggregations and sensor choices on magnitude and temporal dynamics of LAI data at six FLUXNET sites. Additionally, we assess the sensitivity of a simple soil-vegetation-atmosphere-transfer (SVAT) model on differently post-processed LAI times series. It is found that it is advantageous to combine the products of both sensors. The consideration of quality assessments is essential, but the QC application is not straightforward for forest sites and the QC choice can have significant effects on the resulting LAI time series with considerable consequences on the outcome of subsequently applied SVAT models.

The addition of polarization diversity for a non-imaging laser speckle system provides improvement for the phase retrieval problem. The polarization diversity of the remote scene provides additional information for successfully recovering a two-dimensional image from noisy autocorrelations obtained from laser speckle patterns or pupil plane images. The proposed system may be used to characterize space-borne objects and debris with an earthbased sensor array. We propose an Expectation-Maximization (EM) algorithm with a simple, statistical-based stopping criteria. Results from both simulation and laboratory experiment are presented.

The optical properties of the cirrus clouds over a tropical inland station Gadanki, Tirupati were studied using a dual polarization lidar. The extinction coefficient, backscatter coefficient, optical depth and linear depolarization of the cirrus clouds are derived using the range dependent lidar ratio. This work reports the results obtained during the period of December 2006 to July 2007 which covers the three prominent seasons of the year in the Indian subcontinent. A variety of ice crystals like hexagonal thin plate, thick plate, columns, dendrites and aggregates were observed within the cloud. The geometrical and optical thicknesses of the clouds show strong seasonal variations. The occurrence frequency of thin cirrus clouds was found to be relatively high as compared to sub-visible and dense clouds. In almost all the cases, the cloud contains smaller ice crystals in the top part, larger crystals in the middle portion and mixed phase in the bottom portion. Compared to the winter and summer seasons the horizontally oriented ice crystals were observed more in monsoon period. The lidar ratio and linear depolarization ratio of the cirrus clouds were in the range of 3-40 sr and 0.1-1.5 respectively. The maximum linear depolarization ratio was observed for the clouds containing randomly oriented ice crystal with temperature below -80°C. The lidar ratio was found to be maximum for the thin plate crystals and minimum for thick clouds with horizontally oriented ice crystals. The extinction and backscattering coefficients of the clouds were in the range of 0.3x10^-4 to 6 x10^-4 m^-1 and 0.12x10^-4 to 3x10-4 m^-1 sr^-1 respectively during the observation period.

The potential to estimate the nutrient status in important agricultural crops such as maize and sugarcane is of significant interest. In South African sugarcane agriculture, just like in global ecosystem, the estimation of Nitrogen (N) and Silicon (Si) is very important. These nutrients are one of the factors influencing the prevalence of the stalk borer Eldana saccharina Walker (Lepidoptera: Pyralidae). Therefore, the researchers aim at estimating leaf N and Si concentration as well as their ratio in sugarcane using hyperspectral remote sensing (spectroradiometry) for monitoring E. saccharina. A hand-held Analytical Spectral Devices (ASD) Field Spec® 3 spectroradiometer was used to take leaf spectral measurements of sugarcane plants from a potted-plant trial taking place under shade house conditions. In this trial, nitrogen and silicon nutrient applications as well as varieties used were known. In addition, watering regimes and artificial infestation of E. saccharina were carefully controlled. The study results indicate that the Red-edge Index (R740/R720) is linearly related to N concentration (R² = 0.81, Root Mean Square Error (RMSE) = 0.103) for N37 with the highest correlation coefficient. For Si, the index (R₇₅₀-R₅₆₀)/(R₇₅₀+R₅₆₀) was linearly related to Si concentration (R² = 0.53, RMSE = 0.118) for N25. Finally, the N:Si ratio was linearly correlated to the index (R₁₀₇₅-R₇₃₀)/(R₁₀₇₅+R₇₃₀) (R² = 0.67, RMSE = 1.508) for N37, hence this index can be used for early detection of E. saccharina damage or for identifying sugarcane that is prone to attack by E. saccharina. It was concluded that hyperspectral remote sensing has potential for use in estimating the N:Si ratio and E. saccharina potential infestations can be monitored rapidly and nondestructively in sugarcane under controlled conditions. It is recommended that an advanced study be conducted in field conditions using airborne and/or spaceborne hyperspectral sensors.

Particulate absorption (aP(λ)) including phytoplankton (aPHY(λ)) and non-algal particles (NAP) (aNAP(λ)) were measured in southeastern Bering Sea during a cruise in July 2008. This study analyzes the aP(λ) properties through in-situ and quasi analytical algorithm (QAA) derived ocean color satellite Medium Resolution Imaging spectrometer (MERIS) and Moderate resolution Imaging Spectroradiometer (MODIS) observations. We found that the aP(λ) and aPHY(λ) correlated well with chlorophyll-a and were lower as a function of chlorophyll-a as compared to low latitudes. The specific phytoplankton absorption (a*PHY(λ)) showed more variability in the blue as compared to the red part of the spectrum indicating pigment packaging and/or change in pigment composition. The remote sensing reflectance (Rrs(λ)) showed significant variability in spectral shape and magnitude which was consistent with the variable total absorption minus pure water absorption (aT-W(λ)) spectra observed in the study area. Simple satellite retrieved Rrs(λ) ratios were related to in-situ aPHY(λ) and aDG(λ) by applying an inverse power fit; Rrs(490)/Rrs(510) gave the best results for aPHY(443) and aDG(443) (R² - 0.80 and 0.75) respectively. The match-ups of in-situ and MERIS retrieved aPHY(λ) and NAP plus colored dissolved organic matter (aDG(λ)) using QAA after log-transformation showed reasonable agreement with R² of 0.71 and 0.61 and RMSE of 0.316 and 0.391 at 443 nm, respectively. Although the QAA derived aPHY(λ) and aDG(λ) from MERIS overestimated and underestimated, respectively the in-situ measurements at all wavelengths, the match-up analysis was encouraging.

A series of trade studies was carried out using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model to assess how varying the spectral signal-to-noise ratio (SNR), spectral ground sample distance (GSD), or target spectrum affected the impact of spectral and polarimetric data fusion via the spectral polarimetric integration (SPI) algorithm for a notional multimodal sensor. When varying the SNR, the impact depended on the constraints placed on the sensor's tasking. When spectral GSD was varied, the benefit of incorporating polarimetric information increased as the GSD increased. However, a threshold GSD was identified beyond which no benefit was observed. Reducing the target/background spectral contrast by changing the target spectrum from a red vehicle to a green vehicle produced variations in the impact due to fusion, although the SPI algorithm produced a general increase in performance in both cases. The trade studies demonstrated that incorporating additional polarimetric information may enable suitable performance with a less capable multispectral sensor. Finally, the SPI decision fusion algorithm was shown to be robust across a range of scenarios possibly encountered in the multimodal sensor design process.

Reflectance-domain methods convert hyperspectral data from radiance to reflectance using an atmospheric compensation model. Material detection and identification are performed by comparing the compensated data to target reflectance spectra. We introduce two radiance-domain approaches, Single atmosphere Adaptive Cosine Estimator (SACE) and Multiple atmosphere ACE (MACE) in which the target reflectance spectra are instead converted into sensor-reaching radiance using physics-based models. For SACE, known illumination and atmospheric conditions are incorporated in a single atmospheric model. For MACE the conditions are unknown so the algorithm uses many atmospheric models to cover the range of environmental variability, and it approximates the result using a subspace model. This approach is sometimes called the invariant method, and requires the choice of a subspace dimension for the model. We compare these two radiance-domain approaches to a Reflectance-domain ACE (RACE) approach on a HYDICE image featuring concealed materials. All three algorithms use the ACE detector, and all three techniques are able to detect most of the hidden materials in the imagery. For MACE we observe a strong dependence on the choice of the material subspace dimension. Increasing this value can lead to a decline in performance.

Many methods for the detection of oil pollution on the sea surface from remotely sensed images have been developed in recent years. However, because of the diverse physical properties of oil on the sea surface in the visible wavelengths, such images are easily affected by the surrounding environment. This is a common difficulty encountered when optical satellite images are used as data sources for observing oil slicks on the sea surface. However, provided the spectral interference generated by the surrounding environment can be regarded as noise and properly modeled, the spectral anomalies caused by an oil slick on normal sea water may be observed after the suppression of this noise. In this study, sea surface oil slicks are extracted by detecting spectral anomalies in multispectral optical satellite images. First, assuming that the sea water and oil slick comprise the dominant background and target anomaly, respectively, an RX algorithm is used to enhance the oil slick anomaly. The oil slick can be distinguished from the sea water background after modeling and suppression of inherent noise. Next, a Gaussian mixture model is used to characterize the statistical distributions of the background and anomaly, respectively. The expectation maximization (EM) algorithm is used to obtain the parameters needed for the Gaussian mixture model. Finally, according to the Bayesian decision rule of minimum error, an optimized threshold can be obtained to extract the oil slick areas from the source image. Furthermore, with the obtained Gaussian distributions and optimized threshold, a theoretical false alarm level can be established to evaluate the quality of the extracted oil slicks. Experimental results show that the proposed method can not only successfully detect oil slicks from multispectral optical satellite images, but also provide a quantitative accuracy evaluation of the detected image.

Argus 1000 is a new generation miniature pollution-monitoring instrument to monitor greenhouse-gas emission from the space. Argus was launched on the CanX-2 micro-satellite April 28, 2008. Operating in the near infrared and in a nadir-viewing mode, Argus provides a capability for the monitoring of Earth-based sources and sinks of anthropogenic pollution. It has 136 near infrared channels in the spectral range of 0.9-1.7 μm with an instantaneous spatial resolution of 1.25 km. With a mass of just 228 g in flight-model configuration, the instrument is a demonstrator for a future micro-satellite network that can supply near-real time monitoring of pollution events in order to facilitate the detection of the sources causing climate change. In this Letter, we describe the instrument, the analysis concept behind Argus 1000 and its in-orbit performance. Recent spectral data taken over Ontario, Canada, are presented.