Abstract unavailable.

Abstract unavailable.

This article was originally published in Vol. 6 of the Journal of Applied Remote Sensing on 12 December 2012 with an incorrect citation identifier (CID) of 063614 and on 17 December 2012 with an incorrect CID of 061706. The paper was removed and republished online with the correct CID of 061710 on 3 January 2013.

Comparing numerical simulation results with accelerograph readings is essential in earthquake investigations and discoveries. We provide a case study on the magnitude 7.6 Taiwan Chi-Chi earthquake in 1999. More than 400 seismic sensor stations recorded this event, and the readings from this event increased global strong-motion records fivefold so that the accuracy of the earthquake simulation was enhanced significantly. Direct volume rendering is used to depict the space-time relationships of numerical results and seismic readings. When earthquake simulation data are volume rendered, it reveals the sequence of seismic wave initiation, propagation, attenuation, and energy releasing events of fault ruptures so that the direction of seismic wave propagation can be observed. Both accelerograph readings and earthquake simulation data are used to generate a sequence of ground-motion maps. Stacking these maps up in sequence forms a volume data. Visual analysis of the time-varying component reveals hidden features for better comparison and evaluation. Earthquake scientists are able to obtain insights and evaluate their simulation criteria from volume rendering.

A typical LIDAR (light detection and ranging) scan contains hundreds of millions of points. As such, the visualization of LIDAR point clouds poses a significant challenge in data analysis. We propose to visualize and process LIDAR point clouds on a large display wall with an array of monitors. This provides researchers with a high-resolution display environment for looking at and studying large data sets. High-resolution large displays offer both global perspectives and local details of point clouds, which is essential in the process of data exploration. The ability to explore, conceptualize, and correlate spatial and temporal changes of topographical records is required for the development of new analytical models that capture the mechanisms contributing toward cliff erosion. Large displays driven by high-performance parallel visualization cluster allow researchers to fully interact with LIDAR point clouds of slopes in Houshanyue mountain and cliff failures observed in Solana Beach in California. In our study, cases studies of visualization based approaches were conducted using large displays in digital immersive environments. Visual analytics techniques such as delineation, segmentation, and classification of features, change detection, and annotation were used to perform erosion assessment. The results showed that the researchers can observe the temporal change of a failure mass effectively in high-resolution large display environments.

Anomaly detection is an important task for hyperspectral data exploitation. A standard approach for anomaly detection in the literature is the method developed by Reed and Yu, also called RX algorithm. It implements the Mahalanobis distance, which has been widely used in hyperspectral imaging applications. A variation of this algorithm, known as kernel RX (KRX), consists of applying the same concept to a sliding window centered around each image pixel. KRX is computationally very expensive because, for every image pixel, a covariance matrix and its inverse has to be calculated. We develop an efficient implementation of the kernel RX algorithm. Our proposed approach makes use of linear algebra libraries and further develops a parallel implementation optimized for multi-core platforms, which is a well known, inexpensive and widely available high performance computing technology. Experimental results for two hyperspectral data sets are provided. The first one was collected by NASA's airborne visible infra-red imaging spectrometer (AVIRIS) system over the World Trade Center (WTC) in New York, five days after the terrorist attacks, and the second one was collected by the hyperspectral digital image collection experiment (HYDICE). Our anomaly detection accuracy, evaluated using receiver operating characteristics (ROC) curves, indicates that KRX can significantly outperform the classic RX while achieving close to linear speedup in state-of-the-art multi-core platforms.

Band selection is a common technique to reduce the dimensionality of hyperspectral imagery. When the desired object information is known, the reduction process can be achieved by selecting the bands that contain the most object information. It is expected that these selected bands can offer an overall satisfactory detection and classification performance. In this paper, we propose a new particle swarm optimization (PSO) based supervised band-selection algorithm that uses the known class signatures only without examining the original bands or the need of class training samples. Thus, this method requires much less computing time than other traditional methods. However, the PSO process itself may introduce additional computation cost. To tackle this problem, we propose parallel implementations via emerging general-purpose graphics processing units that can provide satisfactory results in speedup when compared to the cluster-based parallel implementation.

In our previous work, we proposed a joint optical flow and principal component analysis (PCA) approach to improve the performance of optical flow based detection, where PCA is applied on the calculated two-dimensional optical flow image, and motion detection is accomplished by a metric derived from the two eigenvalues. To reduce the computational time when processing airborne videos, parallel computing using graphic processing unit (GPU) is implemented on NVIDIA GeForce GTX480. Experimental results demonstrate that our approach can efficiently improve detection performance even with dynamic background, and processing time can be greatly reduced with parallel computing on GPU.

Recent advances in hyperspectral imaging sensors allow the acquisition of images of a scene at hundreds of contiguous narrow spectral bands. Target detection algorithms try to exploit this high-resolution spectral information to detect target materials present in a scene, but this process may be computationally intensive due to the large data volumes generated by the hyperspectral sensors, typically hundreds of megabytes. Previous works have shown that hyperspectral data processing can significantly benefit from the parallel computing resources of graphics processing units (GPUs), due to their highly parallel structure and the high computational capabilities that can be achieved at relative low costs. We studied the parallel implementation of three target detection algorithms (RX algorithm, matched filter, and adaptive matched subspace detector) for hyperspectral images in order to identify the aspects in the structure of these algorithms that can exploit the CUDA™ architecture of NVIDIA® GPUs. A data set was generated using a SOC-700 hyperspectral imager to evaluate the performance and detection accuracy of the parallel implementations on a NVIDIA® Tesla™ C1060 graphics card, achieving real-time performance in the GPU implementations based on global statistics.

Hyperspectral image compression has received considerable interest in recent years due to the enormous data volumes collected by imaging spectrometers for Earth Observation. JPEG2000 is an important technique for data compression, which has been successfully used in the context of hyperspectral image compression, either in lossless and lossy fashion. Due to the increasing spatial, spectral, and temporal resolution of remotely sensed hyperspectral data sets, fast (on-board) compression of hyperspectral data is becoming an important and challenging objective, with the potential to reduce the limitations in the downlink connection between the Earth Observation platform and the receiving ground stations on Earth. For this purpose, implementation of hyperspectral image compression algorithms on specialized hardware devices are currently being investigated. We have developed an implementation of the JPEG2000 compression standard in commodity graphics processing units (GPUs). These hardware accelerators are characterized by their low cost and weight and can bridge the gap toward on-board processing of remotely sensed hyperspectral data. Specifically, we develop GPU implementations of the lossless and lossy modes of JPEG2000. For the lossy mode, we investigate the utility of the compressed hyperspectral images for different compression ratios, using a standard technique for hyperspectral data exploitation such as spectral unmixing. Our study reveals that GPUs represent a source of computational power that is both accessible and applicable to obtaining compression results in valid response times in information extraction applications from remotely sensed hyperspectral imagery.

With recent technical advances in graphic processing units (GPUs), GPUs have outperformed CPUs in terms of compute capability and memory bandwidth. Many successful GPU applications to high performance computing have been reported. JPEG-LS is an ISO/IEC standard for lossless image compression which utilizes adaptive context modeling and run-length coding to improve compression ratio. However, adaptive context modeling causes data dependency among adjacent pixels and the run-length coding has to be performed in a sequential way. Hence, using JPEG-LS to compress large-volume hyperspectral image data is quite time-consuming. We implement an efficient parallel JPEG-LS encoder for lossless hyperspectral compression on a NVIDIA GPU using the computer unified device architecture (CUDA) programming technology. We use the block parallel strategy, as well as such CUDA techniques as coalesced global memory access, parallel prefix sum, and asynchronous data transfer. We also show the relation between GPU speedup and AVIRIS block size, as well as the relation between compression ratio and AVIRIS block size. When AVIRIS images are divided into blocks, each with 64×64 pixels, we gain the best GPU performance with 26.3x speedup over its original CPU code.

Accurate estimation of evapotranspiration (ET) has long been an important issue in hydrology. Many experimental observations indicate advection has a great impact on ET in arid and semiarid areas. However, most of the remote sensing models only focus on the vertical energy balance and deviate from reality. A revised Penman equation has been derived to estimate actual ET under normal conditions in order to account for advection. The parameter of the water availability for ET is introduced and the T s /f (surface temperature and vegetation fraction) space is used to characterize this parameter in the revised formula. The estimates were validated using the observations measured with eddy covariance systems at the Yingke station. In 22 of all 24 days, the difference between the observations and the estimates was smaller than 70  W/m 2 . The correlation coefficient is 0.91 and the RMSE is 48.38  W/m 2 . This finding reveals that this approach is capable of providing reliable results. In addition, when considering advection, the potential ET can be 83.23  W/m 2 larger than the available energy. This finding indicates that the advection effect needs to be considered in remote sensing models in order to derive more reliable regional ET.

TerraSAR-X add-on for digital elevation measurement (TanDEM-X) is a German Earth observation mission collecting a total of two global coverages of very high resolution (VHR) synthetic aperture radar (SAR) X-band data with a spatial resolution of around three meters in the years 2011 and 2012. With these, the TanDEM-X mission (TDM) will provide a unique data set which is complementary to existing global coverages based on medium (MR) or high resolution (HR) optical imagery. The capabilities of the TDM in terms of supporting the analysis and monitoring of global human settlement patterns are explored and demonstrated. The basic methodology for a fully-operational detection and delineation of built-up areas from VHR SAR data is presented along with a description of the resulting geo-information product-the urban footprint (UF) mask-and the operational processing environment for the UF production. Moreover, potential follow-on analyses based on the intermediate products generated in the context of the UF analysis are introduced and discussed. The results of the study indicate the high potential of the TDM with respect to an analysis of urbanization patterns, peri-urbanization, spatio-temporal dynamics of settlement development as well as population estimation, vulnerability assessment and modeling of global change.

Geo-visualization is significantly changing the way we view spatial data and discover information. On the one hand, a large number of spatial data are generated every day. On the other hand, these data are not well utilized due to the lack of free and easily used data-visualization tools. This becomes even worse when most of the spatial data remains in the form of plain text such as log files. This paper describes a way of visualizing massive plain-text spatial data at no cost by utilizing Google Earth and NASA World Wind. We illustrate our methods by visualizing over 170,000 global download requests for satellite images maintained by the Earth Resources Observation and Science (EROS) Center of U.S. Geological Survey (USGS). Our visualization results identify the most popular satellite images around the world and discover the global user download patterns. The benefits of this research are: 1. assisting in improving the satellite image downloading services provided by USGS, and 2. providing a proxy for analyzing the "hot spot" areas of research. Most importantly, our methods demonstrate an easy way to geo-visualize massive textual spatial data, which is highly applicable to mining spatially referenced data and information on a wide variety of research domains (e.g., hydrology, agriculture, atmospheric science, natural hazard, and global climate change).

Knowledge of the air and land surface temperature and their temporal and spatial variations within a city environment is of prime importance to the study of urban climate and human-environment interactions and to monitoring environmental changes due to urbanization. We present a number of air and land surface temperature products that have been produced, archived, evaluated, and analyzed for 10 European cities within the framework of the European Space Agency-funded "Urban Heat Islands and Urban Thermography" project. We evaluate in what way these products are suited to explore the urban thermal dynamics and how products with different temporal and spatial resolution can provide a complementary view, both for thermal patterns as well as heat waves. Level of confidence was evaluated through quantitative, qualitative, and user-based analyses.

Fuel moisture content (FMC), an important variable for predicting the occurrence and spread of wildfire, is the ratio of foliar water content and foliar dry matter content. One approach for the remote sensing of FMC has been to estimate the change in canopy water content over time by using a liquid-water spectral index. Recently, the normalized dry matter index (NDMI) was developed for the remote sensing of dry matter content using high-spectral-resolution data. The ratio of a spectral water index and a dry matter index corresponds to the ratio of foliar water and dry matter contents; therefore, we hypothesized that FMC may be remotely sensed with a spectral water index divided by NDMI. For leaf-scale simulations using the PROSPECT (leaf optical properties spectra) model, all water index/NDMI ratios were significantly related to FMC with a second-order polynomial regression. For canopy-scale simulations using the SAIL (scattering by arbitrarily inclined leaves) model, two water index/NDMI ratios, with numerators of the normalized difference infrared index (NDII) and the normalized difference water index (NDWI), predicted FMC with R2 values of 0.900 and 0.864, respectively. Leaves from three species were dried or stacked to vary FMC; measured NDII/NDMI was best related to FMC. Whereas the planned NASA mission Hyperspectral Infrared Imager (HyspIRI) will have high spectral resolution and very high signal-to-noise properties, the planned 19-day repeat frequency will not be sufficient for monitoring FMC with NDII/NDMI. Because increased fire frequency is expected with climatic change, operational assessment of FMC at large scales may require polar-orbiting environmental sensors with narrow bands to calculate NDMI.

A comparative analysis of land use/land cover (LULC) classification results in the Brazilian Amazon based on four classification algorithms and four remote sensing datasets was conducted in order to better understand the selection of a classification algorithm suitable for a specific remote sensing data. It is shown that maximum likelihood classifier (MLC) provided reasonably good classification accuracy when Landsat Thematic Mapper (TM) or the TM and Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) data-fusion images were used, but nonparametric algorithms such as classification tree analysis for TM multispectral bands and K-nearest neighbor for the combination of TM and PALSAR data provided better classification than MLC. Individual PALSAR dataset is not suitable for detailed LULC classification and has much poorer classification accuracy (47.6% to 59.4%) than Landsat TM image (79.7% to 84.9%). However, integration of TM and PALSAR data through the wavelet-merging technique improved classification accuracy. It is implied that the importance of selecting a suitable classification algorithm for a specific dataset by considering such factors as overall classification accuracy and time and labor involved in a classification procedure. Important information for guiding the selection of remote sensing dataset and associated classification algorithms for LULC classification in the moist tropical regions is also provided.

Applications of the intensity-hue-saturation (IHS) based image fusion techniques in resource inventory and environmental monitoring are usually hampered by considerable spectral distortion in the spatially enhanced image. The image transform model needs to be regulated via a proper design of the weight structure and controlling parameters to achieve a better spectral fidelity. Use of localized weight estimation and an output constraint to modify the generalized intensity-hue-saturation transform (GIHS) for the purpose of rectifying digital numbers of the fused image back to their original counterparts are proposed. The weight localization was achieved via land cover classification of the multispectral data, and the spectral constraint was constructed using a ratio between individual spectral bands stratified with each land cover type and the modified image intensity value. This method was compared both spatially and spectrally with the traditional IHS and the GIHS that has a weight structure induced from the sensor's spectral response characteristics. Experiments with WorldView-2 multispectral and panchromatic data indicated that the new image fusion approach achieved the highest level of spectral fidelity with enhancement of spatial details comparable to the other IHS-based methods.

The gradients of elevations and latitudes in the Appalachian Mountains provide a unique regional perspective on landscape variations in the eastern United States and southeastern Canada. We reveal patterns and trends of landscape dynamics, land surface phenology, and ecosystem production along the Appalachian Mountains using time series data from Global Inventory Modeling and Mapping Studies and Advanced Very High Resolution Radiometer Global Production Efficiency Model datasets. We analyze the spatial and temporal patterns of the normalized difference vegetation index (NDVI), length of growing season (LOS), and net primary production (NPP) of selected ecoregions along the Appalachian Mountains regions. We compare the results in different spatial contexts, including North America and the Appalachian Trail corridor area. To reveal latitudinal variations, we analyze data and compare the results between the 30°-to-40°N and the 40°-to-50°N latitudes. The result reveal significant decreases in annual peak NDVI in the Appalachian Mountains regions. The trend for the Appalachian Mountains regions was a −0.0018 (R2=0.55, P<0.0001) NDVI unit decrease per year during 25 years from 1982 to 2006. The LOS was prolonged by 0.3  days per year−1 during the 25-year percent. The NPP increased by 2.68  g Cm−2 yr−2 from 1981 to 2000.

Accelerated urbanization creates challenges of water shortages, air pollution, and reductions in green space. To address these issues, methods for assessing urban expansion with the goal of achieving reasonable urban growth should be explored. In this study, an improved slope, land use, exclusion, urban, transportation, hillshade (SLEUTH) cellular automata model is developed and applied to the city of Tangshan, China, for urban expansion research. There are three modifications intended to improve SLEUTH: first, the utilization of ant colony optimization to calibrate SLEUTH to simplify the calibration procedures and improve their efficiency; second, the introduction of subregional calibration to replace calibration of the entire study area; and third, the incorporation of social and economic data to adjust the self-modification rule of SLEUTH. The first two modifications improve the calibration accuracy and efficiency compared with the original SLEUTH. The third modification fails to improve SLEUTH, and further experiments are needed. Using the improvements to the SLEUTH model, forecasts of urban growth are performed for every year up to 2020 for the city of Tangshan under two scenarios: an inertia trend scenario and a policy-adjusted scenario.

Satellite radar altimeter measurements of lake and reservoir water levels complement in situ observations by providing stage information for ungauged basins and by filling data gaps in existing gauge records. Such additional measurements assist both research and operational programs. However, for a particular lake or reservoir, altimetric products offered to end-users may differ due to choice of employed instrument, processing technique, and applied geophysical corrections. To explore these differences, particularly with their potential impact on climate-based research, an intercomparison of three web-based water-level products (produced by Laboratoire d’Etudes en Géophysique et Océanographie Spatiale, National Aeronautics and Space Administration/United States Department of Agriculture, and European Space Agency/De Montfort University) has been undertaken based on 18 lakes and reservoirs. The products are well correlated with each other (r=0.87 to 0.99) and where in situ data are available are quite well correlated with the gauge measurements (r=0.73 to 0.99). Despite variations in data processing, the poorest root-mean-square differences between any altimeter product and gauge data (∼0.20 to 1.41 m) occur for the narrow reservoirs and smaller lakes. The largest discrepancies between the altimeter products occur for the lakes that freeze (Lake Athabasca and Woods). The current altimeter products provide acceptable accuracy, long-term trends and seasonality for climate applications. We discuss the merits of each product system, but recommend further validations and the provision of ice-detection flags.

The 21st century will experience widespread global changes due to climatic variations, shifting demographics, population migration, and economic development. These environmental changes are expected to have significant impacts on the quantity and quality of water, air, and land resources across the globe. The complexity of global environmental changes will pose significant challenges to the scientific community. Remote sensing, with its capability for handling spatial and temporal information of the earth's systems, has been extensively used to understand the problems of global environmental changes and their potential solutions. This special section focuses on advances in remote sensing for monitoring global environmental changes, and is comprised of ten full-length papers selected from a number of areas of expertise. Such a collection improves our understanding of global environmental changes through various types of assessment in which remote sensing technologies have demonstrated their application potential in dealing with different sustainability issues. This special section elucidates three important themes related to remote sensing for addressing global environmental changes: (1) land use and land cover changes; (2) environmental monitoring to address physical and ecological variations; and (3) cyber-enabled assessment of environmental changes via data quality improvement and visualization.

The 21st century will experience widespread global changes due to climatic variations, shifting demographics, population migration, and economic development. These environmental changes are expected to have significant impacts on the quantity and quality of water, air, and land resources across the globe. The complexity of global environmental changes will pose significant challenges to the scientific community. Remote sensing, with its capability for handling spatial and temporal information of the earth’s systems, has been extensively used to understand the problems of global environmental changes and their potential solutions. This special section focuses on advances in remote sensing for monitoring global environmental changes, and is comprised of ten full-length papers selected from a number of areas of expertise. Such a collection improves our understanding of global environmental changes through various types of assessment in which remote sensing technologies have demonstrated their application potential in dealing with different sustainability issues. This special section elucidates three important themes related to remote sensing for addressing global environmental changes: (1) land use and land cover changes; (2) environmental monitoring to address physical and ecological variations; and (3) cyber-enabled assessment of environmental changes via data quality improvement and visualization.

We summarize the capacity of high spatial resolution (<1  m) digital aerial imagery to support forest health monitoring. We review the current use of digital aerial imagery in the context of the recent mountain pine beetle epidemic in western Canada. Supported by this review, we posit that high spatial resolution digital aerial imagery can play at least two critical roles in forest health monitoring. First, the capacity to characterize damage at the individual tree level directly supports a broad range of forest health information needs (e.g., tree-level attributes for estimating the population at risk and for inputs to models, estimates of mortality, rates of population growth). Second, the level of detail afforded by the digital high spatial resolution aerial imagery provides critical calibration and validation data for lower spatial resolution remotely sensed imagery (e.g., QuickBird, Landsat) for large-area detection and mapping of forest damage and can be used in a double sampling scheme as a bridge between detailed field measures and landscape-level estimates of mortality. In an era with increasing numbers of commercially deployed sensors capable of acquiring high spatial resolution satellite imagery, the flexibility and cost-effectiveness of aerial image options should not be disregarded. Moreover, experiences with airborne imagery can continue to inform applications using high spatial resolution satellite imagery for forest health information needs.

Mangrove forests are being removed or degraded at an alarming rate, even though they play a vital role in the sustainability of tropical coastal communities. Many of these forests are identified as degraded based on observable changes in their leaves (e.g., density, size, color, etc.). Of these, color can be considered one of the most important indicators of degradation because changes in the spectral response may be indicative of changes in the leaf pigment content. In this investigation, hyperspectral laboratory techniques were applied to examine potential relationships between the mangrove leaf spectral response and three leaf pigments: chlorophyll a, chlorophyll b, and total carotenoid content. Using an ASD spectroradiometer, the spectral reflectance of leaf samples were collected from poor condition, dwarf and healthy black (Avicennia germinans) and from healthy and poor condition red (Rhizophora mangle) mangroves located in a degraded mangrove system of the Mexican Pacific. A subset of 150 representational leaves was then used for pigment content analysis. The results indicate significant relationships between the spectral response and the levels of chlorophyll a, b, and total carotenoid content contained in the leaves. In particular, wavebands at the red edge position were shown to be the best predictors of the pigment contents. The results also indicate that vegetation indices do not necessarily improve the ability to predict these constituents. Finally, the red edge position was found to be significantly different between the healthy and poor condition mangroves (P = 0), with the healthy mangroves having longer wavelengths associated with the red edge position.

We describe crown-extraction (CE) filtering to accurately determine tree apex positions for various coniferous species using an airborne light detection and ranging-derived digital canopy height model (DCHM). This method uses a square mask, with a frame at the edges, that overlaps pixels within the DCHM image; when no pixels touch the frame, the pixel at the center is extracted as a tree-crown pixel. The apex of each tree is determined by choosing the pixel with maximum height from the pixels in the crown. We compared the performance of this method and of two other methods (local-maximum filtering and canopy-segmentation method) for several species. The CE filtering had the most accurate results for most tree species with appropriate mask size selection. The mean omission, commission, and total errors for all tree species were 8.1%, 1.6%, and 9.7%, respectively, for CE filtering. Comparing mask sizes and canopy diameters estimated from the DCHM for each species revealed that the smallest canopy diameter of each species was close to the most appropriate mask size for that species in CE filtering. We also confirmed that the smoothing process used in the DCHM has little effect on the accuracy of CE filtering.

Substantial uncertainties exist in the current knowledge of aerosol-cloud-precipitation relationships and stem from the complicated interactions among the atmospheric constituents. We use a straightforward statistical method, the regression analysis technique, to examine the aerosol-cloud-precipitation relationships from satellite observational data sets, including the aqua moderate resolution imaging spectroradiometer (MODIS) aerosol and cloud products and the tropical rainfall measuring mission (TRMM) precipitation rate. Furthermore, the conventional MODIS aerosol product is combined with the Deep Blue algorithm product to reconstruct a complete global map of aerosol optical depth. Numerical simulations using the latest version of the community earth system model (CESM) are also carried out. Globally, distinct statistically significant relationships between aerosol optical depth, cloud fraction, and precipitation rate are obtained over both land and ocean. Signals agreeing with the first and second indirect effects of aerosols are detected, but other factors are likely contributors. The modeling results are found to generally agree with satellite observations, but the model usually overestimates the aerosol-cloud-precipitation relationship. An increasing trend in cloud fraction with the increase of aerosol optical depth (AOD) over ocean regions is found in the observations, while the reverse is true in the model simulation. It is mostly consistent that the model and observation both show a negative relationship between AOD and precipitation rate over land and a positive relationship over ocean.

Forty-three digital elevation models (DEMs) of the Managesh oil field, Kuwait desert study area, are derived from 28 advanced synthetic aperture radar (ASAR) images using radar interferometry (InSAR) technique. Weighted-average technique is used to reduce the noise in the integrated DEM. The final DEM is compared with DEM of Shuttle Radar Topography Mission (SRTM) and found to have a general agreement. The standard deviation (STD) of individual DEMs with reference to DEM of SRTM is in the range 10 to 40 m. The results indicate that the 90 m spatial resolution DEM of SRTM is noisier over the Managesh oil field as it shows drastic changes in elevations of neighboring pixels which is not expected for a plain desert like the Managesh oil field. Using mean filter, the noise level is estimated as one meter. The reasons for high noise in the DEM of SRTM may be due to uneven distribution of soil moisture leading to uneven penetration of microwaves. The results are in confirmation with the earlier investigators as explained in the text.

Content security protection methods should be adopted to guarantee the security of highly sensitive remote sensing images during their transmission and usage. The joint fingerprinting and decryption (JFD) framework, which integrates encryption and fingerprinting, can provide comprehensive, effective content security protection for remote sensing information. However, several challenges need to be overcome. In order to solve the contradictory relationship between encryption security and fingerprinted image quality in JFD, a selective content encryption strategy is proposed to enhance encryption security and a new method for choosing fingerprint embedding area is proposed to reduce partial decryption's influence on image quality. Furthermore, a new method for generating and controlling fingerprints is introduced to JFD to eliminate its vulnerability to collusion attacks. The experiment results show that the proposed techniques are highly effective. Because of its high security, good fingerprinted image quality and high data processing and transmission speed, the new JFD scheme is an effective content security protection approach for remote sensing images, especially suitable for massive remote sensing images.

An object-oriented, multiscale feature extraction approach is proposed for the land-cover classification of high spatial resolution images. The approach provides more discriminative features by considering the spatial context information from different segmentation levels. It consists of three successive substeps: segmentation by mean-shift algorithm, an iteratively merging process controlled by merging cost function and range-of-scale parameter, and feature extraction from linked multilevel image partitions. The mean-shift method is to get boundary-preserved and spectrally homogeneous over-segmentation regions. Then, a family of nested image partitions is constructed by a merging procedure. Meanwhile, every region of the finest scale is linked to image objects of its superlevels. Finally, every region in the finest scale is treated as a basic analysis unit, and the feature vectors are created by stacking statistics from the region and their superlevels. A support vector machine is used as a classifier and the method on two widely used high spatial resolution data sets over Pavia City, Italy, are evaluated. Compared with results reported in many papers, the result indicates superior accuracy.

Due to cloud coverage and obstruction, it is difficult to obtain useful images during the critical periods of monitoring vegetation using medium-resolution spatial satellites such as Landsat and Satellite Pour l'Observation de la Terre (SPOT), especially in pluvial regions. Although high temporal resolution sensors, such as the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS), can provide high-frequency data, the coarse ground resolutions of these sensors make them unsuitable to quantify the vegetation growth processes at fine scales. This paper introduces a new data fusion model for blending observations of high temporal resolution sensors (e.g., MODIS) and moderate spatial resolution satellites (e.g., Landsat) to produce synthetic imagery with both high-spatial and temporal resolutions. By detecting temporal change information from MODIS daily surface reflectance images, our algorithm produced high-resolution temporal synthetic Landsat data based on a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) image at the beginning time (T1). The algorithm was then tested over a 185×185  km2 area located in East China. The results showed that the algorithm can produce high-resolution temporal synthetic Landsat data that were similar to the actual observations with a high correlation coefficient (r) of 0.98 between synthetic imageries and the actual observations.

We present two methods to improve three hyperspectral stochastic algorithms for target detection; the algorithms are the constrained energy minimization, the generalized likelihood ratio test, and the adaptive coherence estimator. The original algorithms rely solely on spectral information and do not use spatial information; this usage is normally justified in subpixel target detection, since the target size is smaller than the size of a pixel. However, we found that since the background (and the false alarms) may be spatially correlated and the point spread function can distribute the energy of a point target between several neighboring pixels, the implementation of spatial filtering algorithms considerably improved target detection. Our first improvement used the local spatial mean and covariance matrices, which take into account the spatial local mean instead of the global mean. While this concept has been found in the literature, the effect of its implementation in both the estimated mean and the covariance matrix is examined quantitatively here. The second was based on the fact that the effect of a target of physical subpixel size will extend to a cluster of pixels. We tested our algorithms by using the data set and scoring methodology of the Rochester Institute of Technology Target Detection Blind Test project. The results showed that both spatial methods independently improved the basic spectral algorithms mentioned above, and when the two methods were used together, the results were even better.

The HJ-1 A and HJ-1 B satellites were launched on September 6, 2008 from China. The radiometric normalization of charge coupled device (CCD) images is still challenging work. In this paper, an automatic algorithm for relative radiometric normalization between HJ-1A/B CCD images and Landsat TM is presented. This method directly normalizes the digital numbers (DN) of HJ-1A/B CCD images, band by band, to surface reflectance. A united linear relationship between the DN of the target images and the surface reflectance of the referenced images was derived, and the applicable conditions are described here. The iteratively reweighted modification of the multivariate alteration detection (IR-MAD) transformation was used to automatically select pseudoinvariant features (PIFs). This procedure is simple, fast, and completely automatic. The algorithm was applied to normalize three subregions of different HJ-1A/B CCD images. The results show that the retrieval quality of the surface reflectance does meet the requirements of quantitative remote sensing.

Here, we demonstrate and characterize a high-energy, eye-safe, spectrally narrow, and frequency-agile near-IR optical parametric oscillator (OPO). The injection-seeded, noncritical phase-matched (NCPM) potassium titanyle arsenate (KTA) and ring-cavity OPO was pumped in single longitudinal mode (SLM) at 7 ns full width at half maximum FWHM and 30 Hz, neodymium-doped yttrium aluminum garnet (Nd:YAG), generating 243 mJ per pulse OPO signal output with a conversion efficiency of 27%, spectral linewidth of 157 MHz, and approximately M2 of 29. Also, we demonstrate a nonmechanical method to switch the frequency of the OPO at a rate of 2 Hz from 1535.036 to 1535.195 nm, which represents the on/off resonances of carbon dioxide, respectively. However, the switching rate can be extended into the MHz range and is limited by the electronics driving the diode laser. Given the performance results of our frequency-agile OPO, this transmitter has great potential as a source in DIAL applications.

The U.S. Agency for International Development (USAID)'s Famine Early Warning System Network (FEWS NET) provides monitoring and early warning support to decision makers responsible for responding to food insecurity emergencies on three continents. FEWS NET uses satellite remote sensing and ground observations of rainfall and vegetation in order to provide information on drought, floods, and other extreme weather events to decision makers. Previous research has presented results from a professional review questionnaire with FEWS NET expert end-users whose focus was to elicit Earth observation requirements. The review provided FEWS NET operational requirements and assessed the usefulness of additional remote sensing data. We analyzed 1342 food security update reports from FEWS NET. The reports consider the biophysical, socioeconomic, and contextual influences on the food security in 17 countries in Africa from 2000 to 2009. The objective was to evaluate the use of remote sensing information in comparison with other important factors in the evaluation of food security crises. The results show that all 17 countries use rainfall information, agricultural production statistics, food prices, and food access parameters in their analysis of food security problems. The reports display large-scale patterns that are strongly related to history of the FEWS NET program in each country. We found that rainfall data were used 84% of the time, remote sensing of vegetation 28% of the time, and gridded crop models 10% of the time, reflecting the length of use of each product in the regions. More investment is needed in training personnel on remote sensing products to improve use of data products throughout the FEWS NET system.

The spatial resolution of Landsat imagery has proven to be well suited for the analysis of vegetation patterns and dynamics at regional scale; however, the low temporal frequency is often a limitation for the quantification of vegetation dynamics. The spatial and temporal adaptive reflectance fusion model (STARFM) combines moderate resolution imaging spectrometer (MODIS) and Landsat thematic mapper/enhanced thematic mapper plus (TM/ETM+) imagery to a high spatiotemporal resolution dataset. A time series of 333 STARFM images was generated between February 2000 and September 2007 (8-day interval) at Landsat spatial and spectral resolution for a 12×10  km heterogeneous test area within the North Queensland Savannas. Time series of observed Landsat and predicted STARFM images correlated high for each spectral band (0.89 to 0.99). The STARFM algorithm was tested in a regionalization study where sudden change events were analyzed for a pallustrine wetland. A MODIS subpixel analysis showed a very close relationship between STARFM normalized difference vegetation index (NDVI) data and MODIS NDVI data (root mean square error of 0.027). A phenological description of the major vegetation classes within the region revealed distinct differences and lag times within the ecosystem. The 2004 dry season NDVI minimum-map correlated highly with the validated 2004 foliage projective cover product (r2 = 0.92) from the Queensland Department of Environment and Resource Management.

The planetary boundary layer (PBL) structures on four light wind days at Suzhou city, China were analyzed with both micropulse lidar (MPL) and passive microwave radiometer (MWR). The planetary boundary layer height (PBLH) was estimated with the first-derivation method from the lidar observations. The lidar-observed PBLH and microwave radiometer observations were used to estimate the lapse rate in a simple encroachment model. Under light wind conditions in this area, the nocturnal PBLH is about 300 to 400 m. The maximum of PBLH was about 800 to 1000 m. The daytime PBL development could be divided into three periods: growing, maintenance, and decay. The PBLH is nearly linear in the growing period with a growth ratio of about 110 to 120  m/h. The encroachment model lapse rate was calculated from the MPL and MWR observations. The lapse rate γ is estimated in the four days and the average is about 0.0010  ?startKend?/m. The vertical structure of air temperature and humidity were also analyzed with the microwave radiometer observations. Diurnal variations of relative humidity in the boundary layer partly explain the light fog appearing at night during this period.

Hyperspectral imaging is broadly used in the identification of hydroxylated and hydrated silicate minerals, especially at the wavelengths of 1.0 to 2.5 µm. The vibrations of the hydroxyl (OH) molecule, water molecule (H−O−H), and metal−OH bonds, or their combinations, produce prominent absorption features at ∼ 1.40, ∼ 1.91, and 2.20 to 2.40 μm wavelengths. In remote sensing applications, minerals showing one or two of these absorption features are usually categorized as hydroxylated and/or hydrated, and minerals showing all three absorption features are usually categorized as hydrated phyllosilicates. We examine the USGS spectral library of common silicate minerals and find that some showing these absorption features are not hydroxylated and/or hydrated but with slight alteration or impurities. This implies that the identification of hydroxylated and/or hydrated silicate minerals may not be merely based on these absorption features. Rather, incorporation of geological and environmental background is also important. We further calculate the absorption depths and silicate mineral ratios in these absorption features as references for discrimination. This study provides a cautionary perspective on the identification of hydrated phyllosilicate minerals with respect to mineralogical interpretations of Mars and other planets.

An unsupervised neuro-fuzzy system, Gaussian fuzzy self-organizing map (GFSOM), is proposed for hyperspectral image classification. This algorithm operates by integrating an unsupervised neural network with a Gaussian function-based fuzzy system. We also explore the potential for hyperspectral image analysis of three other artificial intelligence (AI)-based unsupervised techniques popular for multispectral image analysis: self-organizing map (SOM), fuzzy c-mean (FCM), and descending fuzzy learning vector quantization (DFLVQ). To apply these methods effectively and efficiently to hyperspectral imagery, an optimal learning sample selection strategy and a prototype initialization system are developed. An experimental study on classifying an EO-1/Hyperion hyperspectral image illustrates that GFSOM achieves the best accuracy, since it can model both the central tendency characteristics of input samples and capture the dispersion characteristics of data within a cluster. By adopting the system initialization approach developed here, all the AI-based techniques have the capability to classify hyperspectral images and can deliver acceptable accuracy, which could consequently accelerate their transitions from the multispectral to the hyperspectral field.

The observation of spatially distributed soil moisture fields is an essential component for a large range of hydrological, climate, and agricultural applications. While direct measurements are expensive and limited to small spatial domains, the inversion of airborne and satellite L-band radiometer data has shown the potential to provide spatial estimates of near surface soil moisture from the local up to the global scale. When using L-band radiometer observations for soil moisture retrieval, a major limitation is the attenuation of the microwave signal by the vegetation, hampering the signal inversion and thereby making spatially distributed plant information necessary. Usually vegetation types are considered with a vegetation type specific global parameterization, e.g., for leaf area index (LAI). Within this study we evaluate and address the effect of spatially varying LAI on high spatial resolution (pixel size 50 m) airborne L-band brightness temperature of crop canopies that are usually regarded homogeneous. To account for within field variations of LAI we used airborne imaging spectrometer data (pixel size 1.5 m) to empirically create maps of LAI using spectral greenness vegetation indices. We found clear (R2<0.90) functional relationships between spatially varying L-band brightness temperature and LAI variations within crop canopies that in literature are usually assumed homogeneous. Very good (R2 = 0.93) near surface soil moisture estimates were achieved using multi-variate regression and adding plant specific spectral information to the independent variable set for final soil moisture retrieval. The study shows that a multi-sensor campaign using airborne L-band radiometer and imaging spectrometers provide a powerful data set for monitoring patterns of near surface soil moisture and vegetation canopy at the field scale with high accuracy.

We develop a robust framework for the registration of light detection and ranging (LiDAR) images with 2-D visual images using a method based on intensity gradients. Our proposed algorithm consists of two steps. In the first step, we extract lines from the digital surface model (DSM) given by the LiDAR image, then we use intensity gradients to register the extracted lines from the LiDAR image onto the visual image to roughly estimate the extrinsic parameters of the calibrated camera. In our approach, we overcome some of the limitations of 3-D reconstruction methods based on the matching of features between the two images. Our algorithm achieves an accuracy for the camera pose recovery of about 98% for the synthetic images tested, and an accuracy of about 95% for the real-world images we tested, which were from the downtown New Orleans area.

The estimation of surface soil moisture status and evapotranspiration from optical remote sensing using the vegetation index-surface temperature (VI-TS) relationship is severely hampered in regions with strong topography, due to the influence of altitude and terrain orientation on surface temperature. In our study, a new empirical approach to normalize surface temperature for terrain elevation-a stratified linear regression model-is presented and is applied on moderate-resolution imaging spectroradiometer (MODIS) data over Calabria, Italy. The method incorporates remotely sensed land surface temperature, a vegetation index, and a digital elevation model. The influence of the newly developed normalization on the VI-TS relationship and on a soil dryness index is compared to the influence of two existing normalization methods: one using a standard lapse rate of 0.65 K per 100 m and one using a lapse rate derived through simple linear regression between elevation and surface temperature. Stratified linear regression adequately corrects surface temperature while the two other normalization techniques seem to overestimate the actual temperature lapse rate during certain periods of the year. Comparison of a soil dryness index derived using the three different normalization methods with limited in situ soil moisture data results in a slightly stronger correlation for the stratified linear regression model than for the two other normalization methods. VI-TS-based soil wetness estimation in mountainous terrains remains, however, limited by other spatially varying factors, including terrain orientation and atmospheric conditions.

Accurate estimation of chlorophyll-a (chl-a), a proxy of the eutrophication risk, is necessary in coastal areas for the assessment of water quality in accordance with European Directives. Local parameterization of remote sensing algorithms is useful to cope with the variability and specificity of optically-active in-water constituents. Using the Bay of Biscay coastal waters, affected by Basque river runoffs, as a case study, the objectives of this investigation are to: 1. develop an empirical algorithm to estimate water surface chl-a for the optically-complex Basque coastal waters; 2. explore the influence of suspended matter, phytoplankton species, and pigment content on the algorithm developed for medium resolution imaging spectrometer instrument (MERIS) imagery; 3. compare the local algorithm to three ocean color algorithms (OC4v6, Gitelson's algorithm, and the OC5); and 4. apply the local algorithm to the MERIS images. For this purpose, two surveys were undertaken within the study area, the Batel-1 survey in 2007, and the Batel-2, in 2009. The empirical algorithm was developed with remote sensing reflectances (Rrs), undertaken with a TriOS field spectrometer, and chl-a measured in situ from the Batel-2 survey. The algorithm was not affected by different concentrations of suspended matter in surface waters, within the range from 0.0 to 6.6  g·m−3. There was no significant effect of 23 accessory pigments found in the area on the algorithm. Eighty-four Rrs and chl-a measurements from the Batel-1 survey were used to validate the local algorithm and to compare it with output of the other algorithms. The local algorithm provided the lowest root-mean-square difference (RMS = 1.7  mg·m−3), the best correlation with the observed data (R = 0.8), together with the best slope-intercept combination between predicted and observed chl-a (slope = 0.5, intercept = 0.6).

A generalized sensor reduction technique is developed for a sensor network used in surveillance and target tracking operations. Reducing the number of sensors in the network leads to addressing immediate threats more quickly and lowering costs for acquiring and processing data. The methods in this work use Bellman optimality principles to estimate possible paths of an agent given an assumed environment model. These paths are then used to determine causal relationships between states in a surveillance field. By using this approach, a capture set is defined where the final states of trajectories are known using information from sensors located in other states. Sensors can then be removed from the network based on this capture set. This method is applied to a crowded hallway surveillance scenario where an agent may choose between two possible exits. The sensor network in this scenario determines if a target deviates from the crowd and moves toward an alternate exit. A proximity sensor grid is placed above the crowd to record the number of people that pass through the hallway. Our result shows that the Bellman optimal approximation of the capture set for the alternate exit identifies the region of the surveillance field where sensors are needed, allowing the others to be removed. Using the reduced sensor network, results are given that show the probability of a deviating agent becoming more distinct with respect to normal motion of the crowd. Therefore, we conclude that by incorporating a dynamic model of the agents' motion into the sensor network, sensors can be reduced, and increased detectability is noticed when sensors are removed early in a trajectory of interest.

A novel shadow detection method for color remotely sensed images that satisfies requirements for both high accuracy and wide adaptability in applications is presented. This method builds on previously reported work investigating the shadow properties in both red/green/blue (RGB) and hue saturation value (HSV) color spaces. The method integrates several shadow features for modeling and uses a region growing (RG) algorithm and a perception machine (PM) of a neural network (NN) to identify shadows. To ensure efficiency of the parameters, first the proposed method uses a small number of shadow samples manually obtained from an input image to automatically estimate the necessary parameters. Then, the method uses the estimated threshold to binarize the hue map of the input image for obtaining possible shadow seeds and applies the RG algorithm to produce a candidate shadow map from the intensity channel. Subsequently, all of the hue, saturation, and intensity maps from the candidate shadow map are filtered with a corresponding band-pass filter, and the filtered results are input into the PM algorithm for the final shadow segmentation. Experiments indicate that the proposed algorithm has better performance in multiple cases, providing a new and practical shadow detection method.

Gross primary production (GPP) is a key indicator of ecosystem performance, and helps in many decision-making processes related to environment. We used the Eddy covariancelight use efficiency (EC-LUE) model for estimating GPP in the Great Plains, United States in order to evaluate the performance of this model. We developed a novel algorithm for computing the photosynthetically active radiation (PAR) based on net radiation. A strong correlation (R2 = 0.94,N = 24) was found between daily PAR and Landsat-based mid-day instantaneous net radiation. Though the Moderate Resolution Spectroradiometer (MODIS) based instantaneous net radiation was in better agreement (R2 = 0.98,N = 24) with the daily measured PAR, there was no statistical significant difference between Landsat based PAR and MODIS based PAR. The EC-LUE model validation also confirms the need to consider biological attributes (C3 versus C4 plants) for potential light use efficiency. A universal potential light use efficiency is unable to capture the spatial variation of GPP. It is necessary to use C3 and C4 based land use/land cover map for using EC-LUE model for estimating spatiotemporal distribution of GPP.

Information on the distribution and transport of upper tropospheric humidity helps our understanding of atmospheric circulation. However, efforts to develop a dataset that is not diurnally biased-that is, one that is based on geostationary data-have been hindered by a lack of intercalibration of the historical geostationary satellite water vapor channels. Toward this end, the high-resolution infrared radiation sounder (HIRS) intersatellite bias is determined by using collocated observations of geostationary and HIRS to compute monthly biases between HIRS instruments on different satellites. The resulting HIRS intersatellite bias then allows the construction of a temporally consistent HIRS record, from which geostationary satellite water vapor channels can be intercalibrated. This intercalibration will allow a uniform analysis of upper tropospheric water vapor.

In this study, new add-on bands in a multispectral dataset of WorldView-2, DigitalGlobe's second next-generation satellite, have been evaluated. For extraction of a specific agriculture crop at a time, WorldView-2 multispectral single, as well as two-date data sets, were used. For this purpose, a class-based sensor independent spectral band ratio normalized difference vegetation index (NDVI) (CBSI-NDVI) and its possibilistice fuzzy classification approach was used. Different agriculture crops selected for the study were sugarcane, late wheat, cauliflower, berseem (fodder), early wheat and ratoon. It is found that bands four and eight with temporal data are good for extracting sugarcane, while bands four, eight and five, seven with temporal data are suitable for late wheat and bands four and eight work well for cauliflower. Similarly, bands five, seven and five, eight with temporal data are good for extracting berseem (fodder), bands four, eight work for early wheat with temporal data and for ratoon four, six single date or four, six and four, eight temporal data. This suitability of bands has been observed with respect to a maximum membership value difference, as well as maximum entropy difference, between the two closest agriculture crops. Thus, it can be concluded that existing bands five, seven and new bands four, six, eight in WorldView-2 are important for identifying and mapping crops mentioned in this study. This indicates new bands, especially four, six, eight introduced in WorldView-2, are more effective than existing bands in QuickBird for mapping specific crops.

Backscattering characteristics of L-band synthetic aperture radar (SAR) polarization data over industrial plantation forests of the fast-growing Acacia mangium in Sumatra, Indonesia, were investigated by combined analysis of microwave and optical reflectance imagery acquired by the Advanced Land Observation Satellite (ALOS) and ground observation data of forest stand characteristics. Prior to the satellite data analysis, growth curves of the planted trees are analyzed using ground observation data. The results show that tree growth fits a sigmoidal curve with a maximum growth rate between 1 and 2 years of age, which gradually declines after that. Our statistical analyses using satellite and ground observation data provide the following findings: 1. The regression between SAR backscattering intensity and forest stand characteristics fits negative quadratic curves for the cross-polarization HV, which show higher correlation than those obtained for the co-polarizations (HH, VV); 2. A decreasing trend in optical normalized difference vegetation index (NDVI) values with age prevails for the trees 2 years and older; 3. A significant correlation is obtained between SAR backscattering and NDVI for the cross-polarization. These findings suggest that the L-band SAR cross-polarization is strongly affected by the acacia tree foliage.

Most paddy rice fields in Asia are comprised of small parcels of land, and the weather conditions during the growing season are usually cloudy. This study develops a geographic information system (GIS) object-based post classification (GOBPC) that combines low-cost remotely sensed and GIS data to precisely map paddy rice fields in the intensively cultivated but fragmented growing areas which are characteristic of Asia. FORMOSAT-2 multispectral images have an 8-meter resolution and a one-day recurrence, making them ideal for mapping such areas. Multitemporal images are examined to distinguish the different growth characteristics between paddy rice and other types of ground cover. The pixel-based hybrid classification technique is used with both the unsupervised and supervised approach to distinguish the paddy rice fields from their surroundings. In addition to the pixel-based approach, we also use GOBPC to deal with over-fragmented parcels of land and to reduce the incidence of misclassification caused by speckle or mixed pixels (mixels) in the images. A comparison is made with the pixel-based technique. The Kappa index of agreement obtained with the GOBPC reaches 0.095 to 0.291, and there is a statistically significant improvement in the user and producer accuracy for all the classes (z>1.96) with McNemar's test in the four study areas. The proposed GOBPC approach is shown to be useful in highly fragmented rice growing areas and may have the potential for other agricultural applications.

In the southwest of China, it is anticipated that synthetic aperture radar (SAR) will become an important tool for forest inventory because of its all-weather capabilities. The Zhazuo area in Guizhou Province of southwest China, with a typical Karst landform, was selected as the test site. Six RADARSAT-2 polarimetric images were acquired in order to analyze polarimetric backscattering behavior and temporal variation of forest and deforested area. Polarimetric decomposition was conducted, and Pauli and Freeman-Durden decomposition were demonstrated to be more suitable for identifying forest and deforestation respectively. Finally, a scheme for multitemporal polarimetric SAR data fusion was proposed, which could greatly improve image quality and make forest identification more efficient. Support vector machine classification showed that the overall accuracy for forest identification was 87.63%, and the accuracy could be enhanced to 91.49% after gamma filtering.

Leaf area index (LAI) is a key variable for modeling the interaction between vegetation and the atmosphere. We collected field LAI measurements over 12 sites in 2005 in the Lys valley, Northern Italy, to calibrate regressive models using normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and wide dynamic range vegetation index (WDRVI) products derived from 250-m moderate resolution imaging spectroradiomete (MODIS) imagery. Field data were compared to the 1 km MODIS leaf area index-fraction of photosynthetically active radiation (LAI/fAPAR) product to show that regressive techniques are better suited for local applications. We investigated these LAI-vegetation index (VI) regressive models to 1. test the sensitivity of the model to forest type and phenology, 2. identify the most suitable VI for LAI retrieval, and 3. verify the feasibility of using a linear model. Results show that in our experimental conditions the LAI-VI relationship is primarily influenced by phenology and that the leaf constant period (maximum LAI) is significantly different compared to the other phenological phases. Among the indices, EVI yielded the poorest performance (R2<0.41) and the linear regressive models for NDVI and WDRVI derived by pooling together data from different phenological phases show a good correlation with field data (R2>0.65); the use of a logarithmic model does not improve the performance. The LAI-WDRVI and LAI-NDVI models were inverted and applied to 2004 MODIS data and model performance was assessed by comparing predicted and measured LAI. Results show that WDRVI performs best in a linear regressive model, yielding a relative root mean square error <23%.

The East China Sea (ECS) is threatened by frequent Skeletonema costatum (S. costatum) blooms every year, which can cause severe environmental harm, as well as considerable economic losses. Remote sensing is an efficient tool for monitoring these harmful algal blooms (HABs) and studying concerned marine conditions. This study investigated two intensive S. costatum HABs in the ECS by analysis of water distribution and spatial-temporal pattern of four oceanographic parameters derived from moderate resolution imaging spectroradiometer (MODIS) and QuickSCAT satellite data using multiple remote sensing approaches (composite imagery interpretation, classification, and parameters retrieval). Results show that high chlorophyll- a (Chl-a) concentrations and net primary production (NPP) decrease from the HAB areas toward the open sea. A peak of Chl-a (>10 mgm−3) and NPP (>5000 mg · C · m−2 · d−1) are considered indicators of large-scale S. costatum blooms in the ECS. Low sea surface temperature (SST; approximately 23°C) are observed in S. costatum HAB areas. In early stages, winds in terms of direction and speed can bring nutrients to facilitate the formation of S. costatum blooms, but then sharply change into unfavorable conditions to cause the final disappearance of HABs. This study also explored multiple oceanographic explanations in the ECS from biochemical, meteorological, physical, and geological perspectives for a better understanding of such S. costatum HABs mechanisms.

The aim of this study was to evaluate the potential of MODIS normalized difference vegetation index hypertemporal data analysis for assessing Mediterranean pasture conditions in North Western Sardinia (Italy). During the seasons 2006 to 2007 and 2007 to 2008, field observations were carried out to classify 67 pasture sites in three condition classes based on expert knowledge. The local net primary productivity scaling (LNS) method was applied, and its potential for discriminating the pasture condition classes was evaluated by logistic regression models (LRM). Yearly and average LNS maps were generated for the period 2000 to 2008, and analyzed to identify areas that exhibited persistently low LNS values (hotspots). The LNS method proved useful to discriminate pastures in different conditions (LRM bootstrapped Nagelkerke pseudo R2 ¼ 0.52). The analysis of persistence of low LNS values allow identifying regional hotspots of degradation. A qualitative evaluation of the main hotspots on aerial photographs revealed that approximately 62% of the hotspots were clearly characterized by pasture degradation patterns, whereas the remaining were associated to highly fragmented landscapes or to errors in the land cover map. This result emphasizes the importance of using multiscale approaches by integrating the LNS regional assessment with high spatial resolution remote sensing data analysis.

A strong earthquake with magnitude 7.7 that shook the Indian Province of Gujarat on the morning of January 26, 2001 caused wide spread destruction and casualties. Earthquakeinduced ground failures, including liquefaction and lateral spreading, were observed in many areas. Optical remote sensing offers an excellent opportunity to understand the post-earthquake effects both qualitatively and quantitatively. The impact of using conventional indices from Landsat-7 temporal images for the liquefaction is empirically investigated and compared with class-based sensor independent (CBSI) indices, while applying possibilistic fuzzy classification as a soft computing approach via supervised classification. Five spectral indices, namely simple ratio (SR), normalized difference vegetation index (NDVI), transformed normalized difference vegetation index (TNDVI), soil-adjusted vegetation index (SAVI), and modified normalized difference water index (MNDWI) are investigated to identify liquefaction using temporal multi-spectral images. A soft-computing based fuzzy algorithm, which is independent of statistical distribution data assumption, is used to extract a single land cover class from remote sensing multi-spectral images. The result indicates that appropriately used indices can incorporate temporal variations, while extracting liquefaction with soft computing techniques for coarser spatial resolution with temporal remote sensing data. It is found that CBSI-NDVI with temporal data was good for extraction liquefaction while CBSI-TNDVI with temporal data was good for extraction water bodies.

Observations from satellite remote sensing provide important tools for regional and global monitoring of terrestrial ecosystems. This article evaluated a recently developed vegetation index (VI) model that estimates gross primary production (GPP) in an open grassland using flux measurements and 250-m moderate-resolution imaging spectroradiometer (MODIS) images. Two VIs, the two-band enhanced vegetation index (EVI2) and the normalized difference vegetation index (NDVI), were tested as indicators of both fraction of absorbed photosynthetic active radiation (f APAR) and light use efficiency (LUE). The standard MODIS GPP product was moderately correlated with flux-measured GPP as indicated by the coefficients of determination (R2) of 0.56 and a root mean square error (RMSE) of 15.4 gCm−2 8d−1. In comparison, results suggested that the VI model can provide improved estimates of GPP, with R2 of 0.80, 0.82, and 0.81 for EVI2 × EVI2 × photosynthetically active radiation (PAR), NDVI × EVI2 × PAR, and NDVI × NDVI × PAR, respectively. The underlying reason was that both EVI2 and NDVI were potential proxies of f APAR (R2 of 0.43 and 0.53, respectively) and LUE (R2 of 0.52 and 0.46, respectively). The primary analysis will be helpful for spatial improvements of future GPP models.

The quantitative description of forest canopy structure is significant for the investigation of a forest, which serves as an important component of the terrestrial ecosystem. Light detection and ranging (LIDAR), as a new technical means that can acquire high-precision vertical information, plays a crucial role in forest monitoring and management. Choosing Dayekou forest experimental area in the Heihe watershed as a study area, we separated the ground points from the vegetation points using the skewness-change algorithm based on the intensity information from airborne LIDAR data. After that, digital terrain model (DTM) and digital surface model (DSM) were generated, respectively, based on which the canopy height model (CHM) was acquired. Finally, using the variational window, the local maximum filter method was used to extract individual tree heights and crown widths from CHM. The determination coefficients of tree heights and crown widths were 0.8568 and 0.3923, respectively. The validation results indicated that the tree heights could be effectively extracted from intensity information of airborne LIDAR, while the accuracy of extracted crown widths needed to be improved. In the future work, aerial photos and other high-resolution images would be combined to improve the accuracy.

Terrain slope of Greenland is usually calculated from surface elevation on a large scale. Novel physical models were established to estimate the terrain slope within laser footprints on a smaller scale. Based on the time-stamped waveforms of ice, clouds, and land elevation satellite/geoscience laser altimeter system (ICESat/GLAS), the physical models for calculating the slopes were established in two cases: zero attitude angles and non-zero attitude angles, respectively. The expressions of waveform slopes are related not only to the waveform width and the mean pulse delay of the time-stamped waveform, both in nanoseconds, but also to the attitude of the satellite and the laser divergence angle. In order to calculate the waveform width and the mean pulse delay, a non-Gaussian mathematical function was proposed to curve-fit the waveforms. The slopes estimated from the waveforms were compared with the slopes calculated from surface elevations. Results show that the two methods generate almost identical slope estimations for the same terrain. Calculation results also indicate that the method for slope estimation proposed in this paper performs better for extreme sloping terrain than for gentle sloping terrain.

Using remote-sensing technologies, this study sought to provide an up-to-date map of C3/C4 distribution representative of temperate grassland in northern China. Several studies focused on the central grasslands of North America have demonstrated that C4 species coverage can be discriminated from C3 species by using time-series vegetation index data based on their phenological differences. Considering that the hydrothermal patterns and C4 percentage of grass flora in the study area and North America are different, we first examined temporal features of C3/C4 communities by using multitemporal moderate resolution imaging spectroradiometer normalized difference vegetation index data throughout the 2010 growing season. It was found that the asynchronous seasonality exhibited by communities with varied C3/C4 compositions also existed in our study region. Based on this asynchrony and separation rate, a hierarchical decision tree was developed to classify four grassland types with varied C3/C4 compositions. As a result, a classification map of the mixed C3/C4 grassland was generated with an overall accuracy of 87.3% and a kappa coefficient of 0.83. The geographic distribution of C3 and C4 species showed that the study area was dominated by C3 grasses, but C4-dominated grasslands accounted for 39% of the land cover. Thus C4 species also made an important contribution to grassland biomass, especially in dry and low-lying saline-alkaline habitats. The results also indicated that the trajectory-based methods for C3/C4 mapping rooted in asynchronous seasonality worked effectively in the climate regimes of both northern China and North America.

The author presents a technique to calculate instrument stellar magnitudes for silicon photosensors used in modern star trackers as well as estimates the error of the said technique. The technique implies calculation of instrument stellar magnitudes as specifically selected functions of color indices B-V, V-J of the Tycho-2 and two micron all sky survey (2MASS) catalogs. This function is a sum of basis functions with coefficients determined based on star trackers' response. The coefficients are calculated individually for each star tracker response. Calculation of the coefficients of the function is done using the least squares method for color indices of artificial stars. Spectra of artificial stars are created on the basis of reddening of typical subclasses spectra without interstellar extinction, such spectra taken from the Pickles catalog. Thickness of interstellar medium for reddening is selected basing on random law. Validation of accuracy of the proposed methodology is performed by calculating star brightness for standard photometric bands R and I and subsequent comparison of the obtained results with actual data. Such check indicates that the root-mean-square deviation of error is not over 0.08 m. Due to usage of data related to star brightness in J band of 2MASS catalog, the number of stars in the guide star catalog is increased by 30% as compared with using data only from Tycho and HIPPARCOS catalogs.

The problem of atmospheric intervention has received considerable attention from researchers in remote sensing who have developed a range of methods, either simple or sophisticated. The sophisticated methods require auxiliary information about the state of the atmosphere which is obtained either from standard databases or from simultaneous in-situ field measurements or by iterative techniques. It has been found that the darkest pixel atmospheric correction (DP) is one of the most effective atmospheric correction methods especially for visible spectral bands. The DP is the simplest and fully image-based correction method. The integrated use of the DP basic theory and the radiative transfer equation is implemented in this study. Indeed, this leads to the development of the proposed 'image-based atmospheric correction algorithm.' The proposed algorithm retrieves the aerosol optical thickness (AOT) only for areas with urban and maritime aerosols. The effectiveness of this algorithm is assessed by comparing the AOT values retrieved from the proposed 'image-based atmospheric correction algorithm' after applied to Landsat TM/ETM+ images with those measured in-situ both from MICROTOPS II hand-held sun photometer and the CIMEL sun photometer (AERONET). It has been found that the AOT values retrieved from the proposed algorithm were very close with those measured from the CIMEL sun photometer for the Limassol area in Cyprus.