Target detection is the front-end stage in any automatic target recognition system for synthetic aperture radar (SAR) imagery (SAR-ATR). The efficacy of the detector directly impacts the succeeding stages in the SAR-ATR processing chain. There are numerous methods reported in the literature for implementing the detector. We offer an umbrella under which the various research activities in the field are broadly probed and taxonomized. First, a taxonomy for the various detection methods is proposed. Second, the underlying assumptions for different implementation strategies are overviewed. Third, a tabular comparison between careful selections of representative examples is introduced. Finally, a novel discussion is presented, wherein the issues covered include suitability of SAR data models, understanding the multiplicative SAR data models, and two unique perspectives on constant false alarm rate (CFAR) detection: signal processing and pattern recognition. From a signal processing perspective, CFAR is shown to be a finite impulse response band-pass filter. From a statistical pattern recognition perspective, CFAR is shown to be a suboptimal one-class classifier: a Euclidian distance classifier and a quadratic discriminant with a missing term for one-parameter and two-parameter CFAR, respectively. We make a contribution toward enabling an objective design and implementation for target detection in SAR imagery.

We propose a new algorithm for ship detection in synthetic aperture radar (SAR) images based on the human visual attention system. The human visual attention system identifies the prominent objects in images or scenes so that these objects can be more noticeable. Since the ships in a SAR image of the ocean are prominent objects, they can easily be identified through the human visual attention system. Thus, for detection of ships in the SAR images, the present study (through its application) has modeled the human visual attention system in the detection stage. In this way, not only can the targets be precisely detected, but also the falsely detected pixels are significantly reduced. Compared to most existing algorithms in the literature, our proposed algorithm can be used for both homogeneous and nonhomogeneous images. Accordingly, its performance is independent of the image type (homogeneous or nonhomogeneous) and the computation time significantly decreases. Experimental results have shown the efficiency of the proposed algorithm for various SAR images from ERS-1, ERS-2, and ALOS PALSAR data.

Remote sensing classification of tidal flat wetlands is important for obtaining high-precision information on wetland features. In this study, Thematic Mapper (TM) images of the Yancheng National Reserves, Jiangsu Province, China, for the years of 1996, 2002, 2006, and 2010 were considered. First, the optimum combination of bands was chosen. Second, vegetation and nonvegetation regions of interest were established to investigate the spectral reflectance characteristics of the different ground objects. Then we used the knowledge-based decision tree method on different features, such as the normalized difference vegetation index and the spectral reflectance. In particular, the ancillary information is helpful to distinguish the vegetation classes. The results demonstrate that the classification system has advantages in identifying the types of vegetation in ecotones, and it is 4 percentage points higher than the maximum likelihood method in classification accuracy. This study is useful to discriminate vegetation, and it provides an important reference for the effective extraction of tidal flat land-cover information from TM images.

Analyzing spatiotemporal characteristics of land-cover (LC) change is important for assessing environmental consequences of urban growth and supporting land management and planning. Less attention, however, has been given to the comparison between land-cover change patterns in developing and developed countries. In this study, Xuzhou city and Dortmund city region were selected as study areas. Multitemporal Landsat images were classified by using the integration method of maximum likelihood classifier, subpixel classifier, and multiple normalized difference vegetation index values based on Vegetation-Impervious Surface-Soil model. Urban growth patterns and processes of the two study areas were investigated and compared through land-cover change detection, buffer analysis, and jaggedness degree. The results indicated that the urban area in Xuzhou city increased more than threefold dramatically from 128.5 to 418.3  km 2 , and the increased sprawling development trend was observed during the study period, while Dortmund city region showed a slight increase from 498 to 715.5  km 2 in urban areas with an increasingly compact development trend. The results revealed a notable difference of spatiotemporal land-cover pattern dynamics between the two study areas as well as confirmed the effectiveness of the combined method of remote sensing and spatial analysis that can be used to support land management and policy decisions.

Recent evolutions and current status of glacial lakes in the central Chinese Himalayas were analyzed using Landsat satellite imagery acquired in 1990, 2000, and 2010. The datasets show that there are 604 glacial lakes with a total area of 85.17  km 2 in the central Chinese Himalayas in 2010, in which moraine-dammed lakes are the most represented typology (199 lakes, 54.92  km 2 ) in terms of area. From 1990 to 2010, the expansion rate of total glacial lake area was 0.57  km 2 /year in the central Chinese Himalayas and was significantly higher than in the Nepal-Bhutan and Western India-Pakistan-Afghanistan Himalayas (−0.08 to 0.45  km 2 /year ) between 1990 and 2009. Of all glacial lakes, moraine-dammed lakes experienced a rapid increase in size at a rate of 0.45  km 2 /year from 1990 to 2010, while the area of other types of glacial lakes grew more slowly with an expansion rate that did not exceed 0.05  km 2 /year (valley lakes at a rate of 0.003  km 2 /year and glacial erosion lakes at a rate of 0.006  km 2 /year ). In addition, 23 potentially dangerous glacial lakes (PDGLs) are identified and their area increased by 77.46% between 1990 and 2010 and the increase rate is higher than non-PDGLs (39%) in the same period.

We compared four different image classification methods to improve the accuracy of cryospheric land cover mapping from very high-resolution WorldView-2 (WV-2) satellite images. We used four pixel-by-pixel classification methods and then integrated the classified images using a winner-takes-all (WTA) approach. The images on which we performed the classification techniques were made up of eight-band multispectral images and panchromatic WV-2 images fused using the hyperspherical color sharpening method. We used four distinctly different methods to classify the WV-2 PAN-sharpened data: a support vector machine (SVM), a maximum likelihood classifier (MXL), a neural network classifier (NNC), and a spectral angle mapper (SAM). Three classes of land cover—land mass/rocks, water/lakes, and snow/ice—were classified using identical training samples. The final thematic land cover map of Larsemann Hills, east Antarctica, was integrated using ensemble classification based on a majority voting–coupled WTA method. Results indicate that the WTA integration method and the SVM classification method were more accurate than the MXL, NNC, and SAM classification methods. The overall accuracy of the WTA method was 97.23% (96.47% with the SVM classifier) with a 0.96 kappa coefficient (0.95 with the SVM classifier). The accuracy of the other classifiers were 93.73 to 95.55% with kappa coefficients of 0.91 to 0.93. This work demonstrates the strengths of different classifiers to extract land cover information from multispectral data collected in cryospheric regions.

The performance of automatic target recognition in synthetic aperture radar images is greatly influenced by preprocessing, viz, despeckling and decluttering. In this work, a particle swarm optimization (PSO)-based adaptive wavelet packet transform is introduced for despeckling and decluttering of military targets including tanks, bulldozers, trucks, cars, cannons, and armored personnel carriers. The proposed method consists of two stages. The first stage removes speckle, and the second stage removes clutter with the aid of PSO to optimize the objective criteria, such as equivalent number of looks and signal to clutter ratio, respectively. The purpose of these methods is to enhance the target feature suitable for further processing. The proposed work has been tested on the moving and stationary target acquisition and recognition database and shows a remarkable performance over existing methods.

Geostationary Operational Environmental Satellite (GOES)-14 imager was operated by National Oceanic and Atmospheric Administration (NOAA) in an experimental rapid scan 1-min mode that emulates the high-temporal resolution sampling of the Advanced Baseline Imager (ABI) on the next generation GOES-R series. Imagery with a refresh rate of 1 min of many phenomena were acquired, including clouds, convection, fires, smoke, and hurricanes, including 6 days of Hurricane Sandy through landfall. NOAA had never before operated a GOES in a nearly continuous 1-min mode for such an extended period of time, thereby making these unique datasets to explore the future capabilities possible with GOES-R. The next generation GOES-R imager will be able to routinely take mesoscale (1000  km×1000  km) images every 30 s (or two separate locations every minute). These images can be acquired even while scanning continental United States and full disk images. These high time-resolution images from the GOES-14 imager are being used to prepare for the GOES-R era and its advanced imager. This includes both the imagery and quantitative derived products such as cloud-top cooling. Several animations are included to showcase the rapid change of the many phenomena observed during super rapid scan operations for GOES-R (SRSOR).

This paper proposes a new spatial scale conversion method, which validates moderate resolution imaging spectroradiometer (MODIS) leaf area index (LAI) product when geometry information from the MODIS 1B product and classification result is combined. The in situ LAI data, Landsat Thematic Mapper (TM), and MODIS 1B product were utilized in this research. An object-oriented method was used to classify TM imaging, where each class was computed using an empirical model to achieve LAI respectively. The 30-m TM LAI image was aggregated into the MODIS 1B product based on the geometry information of MODIS 1B. The simulated MODIS 1B image was then converted into a MODIS LAI product and compared with the simulated LAI map pixel by pixel. The results showed a lower root mean square error and higher normalization of the absolute error with the new method. In addition, the field LAI was not significantly correlated with MODIS LAI, but it did show a strong correlation with TM LAI. The new method achieved a higher correlate coefficient with the MODIS product than the conventional methods. Using this validation method based on classification and image simulation can improve the accuracy of product certification.

Sparsity preserving projections (SPP) has drawn more and more attention recently. However, the SPP only focuses on the sparse structure but ignores the discriminant information of labeled samples. We proposed a new sparse manifold learning method, called sparse discriminant embedding (SDE), for hyperspectral image (HSI) classification. The SDE utilizes the merits of both sparsity property and manifold structure. It not only preserves the sparse reconstructive relations but also explicitly boosts the discriminant manifold structure of the data, and the discriminating power of the SDE is further improved than the SPP. Experiments on the Flightline C1, Washington DC Mall, and Botswana HSI datasets are performed to demonstrate the effectiveness of the proposed SDE method.

Chlorophyll-a concentration (Chla) is a key indicator of water quality, and accurate estimates of Chla using remote sensing data remain challenging in turbid waters. Previous research has demonstrated the feasibility of retrieving Chla in vegetation using spectral index, which may be the potential reference for Chla inversion in turbid waters. In this study, 106 hyperspectral indices, including vegetation, fluorescence, and trilateral indices, as well as combinations thereof, are calculated based on the in situ spectra data of 2004 to 2011 in Taihu Lake, China, to explore their potential use in turbid waters. The results show that the normal chlorophyll index (NCI) (R690/R550−R675/R700)/(R690/R550+R675/R700) is optimal for Chla estimation, with a determination coefficient (R 2 ) of 0.92 and a root mean square error (RMSE) of 14.36  mg/m 3 for the data from July to August 2004, when Chla ranged from 7 to 192  mg/m 3 . Validation using the datasets of 2005, 2010, and 2011 shows that after reparameterization, the NCI model yields low RMSEs and is more robust than the three- and four-band algorithms. The results indicate that the NCI model can satisfactorily estimate Chla in multiple datasets without the need of additional band tuning.

The HJ-1A and HJ-1B satellites were launched successfully on September 6, 2008. For effective monitoring of the environmental and natural disasters, both HJ-1A and HJ-1B carry a charge-coupled device (CCD) sensor, with each CCD sensor containing two cameras, which results in a ground swath of about 700 km for each satellite. The CCD can make cross-track multiple view angle measurements with a field of view of >40  deg . The Earth’s surface can be covered completely within 48 h in four spectral bands from 0.43 to 0.90 μm. We have presented a method of extracting the hemispherical-directional reflectance factor (HDRF) from CCD imagery and normalizing HDRF to a standard geometric situation. After geometric correction and registration, radiometric calibration, and correction for atmospheric effects, multitemporal HDRFs were obtained for the flat land surface located in Northern China with different land cover types. The angular observations were extracted from a series of overpasses of the CCD aboard HJ-1A and HJ-1B. We then inverted the HDRFs by the semiempirical kernel-driven bidirectional reflectance distribution function (BRDF) model and normalized the HDRFs to nadir-viewing direction. This study shows the significance of directional effects in the HJ-1A and HJ-1B CCD data and the feasibility of normalizing HDRFs’ CCD data when the angular effects must be taken into account.

We developed a unique methodology that spectrally translates the enhanced vegetation index (EVI) across sensors for data continuity based on vegetation isoline equations and derived a moderate resolution imaging spectroradiometer (MODIS)-compatible EVI for the visible/infrared imager/radiometer suite (VIIRS) sensor. The derived equation had four coefficients that were a function of soil, canopy, and atmosphere, e.g., soil line slope, leaf area index (LAI), and aerosol optical thickness (AOT). The PROSAIL canopy reflectance and 6S atmospheric models were employed to numerically characterize the MODIS-compatible VIIRS EVI. MODIS-compatible VIIRS EVI values only differed from those of MODIS EVI by, at most, 0.002 EVI units, whereas VIIRS and MODIS EVI values differed by 0.018 EVI units. The derived coefficients were sensitive mainly to LAI and AOT for the full- and a partial-covered canopy, respectively. The MODIS-compatible EVI resulted in a reasonable level of accuracy when the coefficients were fixed at values found via optimization for model-simulated and actual sensor data (83 and 41% reduction in the root mean square error, respectively), demonstrating the potential practical utility of the derived equation. The developed methodology can be used to obtain a spectrally compatible EVI for any pair of sensors in the data continuity context.

We propose a trimmed strategy for affine registration of point sets using the Lie group parameterization. All affine transformations form an affine Lie group, thus finding an optimal transformation in registration is reduced to finding an optimal element in the affine group. Given two point sets (with outliers) and an initial element in the transformation group, we seek the optimal group element iteratively by minimizing an energy functional. This is conducted by sequentially finding the closest correspondence of two point sets, estimating the overlap rate of two sets, and finding the optimal affine transformation via the exponential map of the affine group. This method improves the trimmed iterative closest point algorithm (TrICP) in two aspects: (1) We use the Lie group parameterization to implement TrICP. (2) We also extend TrICP to the case of affine transformations. The performance of the proposed algorithm is demonstrated by using the LiDAR data acquired in the Mount St. Helens area. Both visual inspections and evaluation index (root mean trimmed squared distance) indicate that our algorithm performs consistently better than TrICP and other related algorithms, especially in the presence of outliers and missing points.

Topographic features of lunar craters have been found from the brightness temperature (TB) observed by the multichannel (3.0, 7.8, 19.35, and 37 GHz) microwave radiometer (MRM) aboard Chang’E-1 (CE-1) in a single track view. As the topographic effect is more obvious at 37 GHz, 37 GHz TB has been focused on in this work. The variation of 37 GHz daytime (nighttime) TB along the profile of a crater is found to show an oscillatory behavior. The amplitude of daytime TB is significantly affected by the observation time and the shape of the crater, whose diameter is bigger than the spatial resolution of MRM onboard CE-1. The large and typical diurnal TB difference (nighttime TB minus daytime TB) at 37 GHz over selected young craters due to the large rock abundance in craters, have been discussed and compared with the altitude profile.

Lake-level change can be an important indicator for the water balance in the Qinghai–Tibetan Plateau (QTP). However, it is not feasible to perform continuous in-situ measurements for a large number of lakes because of the remoteness and harsh climatic conditions on this plateau. Satellite altimetry has been successfully used for monitoring water-level changes of inland lakes in recent years. In this study, water-level changes between 2002 and 2012 of 51 lakes on the QTP were monitored using multialtimeter data from Envisat/RA-2, Cryosat-2/Siral, Jason-1/Poseidon-2, and Jason-2/Poseidon-3. The water levels of 42 of the lakes have a mean rising trend of 0.275  m/a , whereas the water levels of nine lakes have a mean decreasing trend of −0.144  m/a . Overall, the water level of these lakes had a mean increasing trend of 0.201   m/a in the past 10 years. For the lakes distributed over the entire plateau, it was found that the lake levels in different basins had different change characteristics: the lake levels in the southern plateau show a decreasing trend, whereas lake levels in the northern plateau show an increasing trend. In the central plateau, the water levels of most lakes also show an increasing trend but the water levels of a small number of the lakes show a decreasing trend. In addition, the winter or summer linear trends for the levels of individual lakes appear to be opposite to the trends observed for the yearly average. The combined use of data from several different altimeters makes the temporal resolution of lake-level change measurements higher than for those results derived using only one kind of altimeter data. However, the number of lakes monitored in this study is affected by the footprints of satellites orbits on the Earth’s surface. After taking into account anomalous lake levels, systematic elevation differences, and periodic changes in the water level, the biggest root-mean-square-error for the lakes monitored in this study is <40  cm/a showing that the results have a high degree of accuracy. Lake-level change is mainly related to rising temperatures, increasing precipitation, and decreasing evaporation. In particular, rising temperatures accelerate the melting of glaciers and perennial snow cover and trigger permafrost degradation, leading to an increase in the water level of most lakes across the plateau.

The red-edge position (REP) was extracted from reflectance spectral data at canopy and leaf scale using six different methods in the temperate typical steppe of Inner Mongolia with relatively high species richness. The results suggested that the REPs varied with the extraction methods, sampling sites, plant species, and estimation scales. At the canopy scale, chlorophyll content (CC) was estimated with the linear extrapolation method, and the polynomial fitting technique had coefficients of determination (R 2 >0.4 ). The chlorophyll estimates at Leymus chinensis- and Stipa grandis-dominated sites were slightly better than those from the large sampling sites with multiple dominant plant species. At the leaf scale, the linear extrapolation method and the polynomial fitting technique presented high coefficients of determination (R 2 >0.6 ). CC estimated at L. chinensis-dominated sites was substantially higher than at S. grandis-dominated sites as well as the large sampling site. The results using the maximum first-derivative method and Lagrangian interpolation techniques revealed a discontinuity, whereas the REPs, as extracted by the linear interpolation method, were shifted toward longer wavelengths. The linear interpolation and inverted Gaussian method were easily saturated. The results obtained with the polynomial fitting technique and the linear extrapolation method had higher sensitivity and accuracy for estimation of CC.

Diverse parameters that are decomposed from quad polarimetric synthetic aperture radar (PolSAR) imagery become the important basis in the target recognition and classification. The selection of effective parameters is a very important research topic. This work aims to explore the algorithm of parameter selection based on the parametric statistics and multidimensional analysis. The proposed algorithm merges the parameters from different decomposed algorithms and the optimal parameters describing the backscattering characters of the targets are explored. The difference of parameters’ locations in three-dimensional spaces is the important basis of target differentiation. Based on the selected parameters, PolSAR images are classified using the object-oriented analysis and decision tree method. The experimental results indicate that the overall accuracy and Kappa coefficient of the classification using the integrated multidimensional parameters were higher than those using Freeman and H/A/α decomposed parameters. The advantage of this algorithm is to select optimal parameter combinations in multidimensional space by integrating many parameters from different decomposed algorithms.

We describe an algorithm for creating a virtual, statistically estimated 13.3-μm band for the Visible Infrared Imaging Radiometer Suite (VIIRS), an instrument aboard the National Oceanic and Atmospheric Administration’s (NOAA’s) operational satellite, Suomi NPP. VIIRS does not have a 13.3-μm band, although this band has important applications such as estimating cloud-top pressure. We demonstrate that a reliable estimate of the missing data can be created with a multisensor approach, using other VIIRS bands at 4, 9, 11, and 12 μm, as well as input from the Cross-track Infrared Sounder, on board the same satellite, which produces data at a much finer spectral resolution but lower spatial resolution. In addition, we evaluate the algorithm by applying it to data from the Moderate Resolution Image Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS), both on the Aqua satellite. MODIS and AIRS provide a benchmark for measuring the accuracy of the algorithm since, unlike VIIRS, MODIS makes measurements in the 13.3-μm band.

Rice crop monitoring using synthetic aperture radar (SAR) polarimetry is one of the thrust areas of research in radar remote sensing due to unavailability of optical data in critical growth stages of crop because of dense cloud cover during the growing season (monsoon) in India and Southeast Asia. The prime objective of this study was to assess the potential of polarimetric C-band SAR data to estimate the leaf area index (LAI) of two varieties (non-basmati and basmati) of paddy rice. Seven fine Quad-pol single look complex (SLC) RADARSAT-2 data were acquired over part of the Indo-Gangetic plain, India, in 2011 and 2012, and ground data were collected during the same period of the satellite pass. The backscatter of rice in different polarizations over different growth periods was analyzed and LAI estimated using a semi-empirical model. The performance of the model was evaluated by statistical parameters, namely. root mean square error and coefficient of determination (R 2 ). The model performed well for the LAI estimation of non-basmati rice using HV backscatter while achieving acceptable accuracy for others. Results of this study provided the promising approach for LAI prediction using SAR data in India.

Remote sensing measurements of ammonia emitted by a near-monotypic seabird colony established on an islet in the Salton Sea (Imperial Valley, California) are described. The compact (3 ha) nature of the island affords a constrained environment that provides an ideal case study for validating models of ammonia emission from seabird colonies. Incorporated as part of a coordinated approach to future field campaigns, the techniques demonstrated would provide a means for validation and refinement of current seabird ammonia emission models on a case study basis. This would contribute to an improved understanding of the nitrogen cycle, especially in remote ocean locales.

Remote sensing of plant stress holds promise for detecting environmental pollution by oil. However, in oil-rich delta regions, waterlogging is a frequent source of plant stress that has similar physiological effects to oil pollution. This study investigated the capabilities of remote sensing for discriminating between these two sources of plant stress. Bean plants were subjected to oil pollution, waterlogging, and combined oil and waterlogging treatments. Canopy physiological, hyperspectral, and thermal measurements were taken every two to three days after treatment to follow the stress responses. For plants treated with oil, spectral and thermal responses were evident six days before symptoms could be observed visually. In waterlogged plants, only spectral responses were observed, but these were present up to eight days before visual symptoms. A narrowband reflectance ratio was efficient in detecting stress caused by oil and waterlogging. Canopy temperature and a thermal index were good indicators of oil and combined oil and waterlogging stress, but insensitive to waterlogging alone. Hence, this study provides evidence that combined hyperspectral and thermal remote sensing of vegetation has potential for monitoring oil pollution in environments that are also subjected to waterlogging.

We used 35 years of brightness temperature data (1978 to 2013) from the scanning multichannel microwave radiometer (SMMR) and special sensor microwave/imager (SSM/I) to analyze the freezing, ablation, and duration time of ice on Nam Co Lake and validated the results using data from the advanced microwave scanning radiometer for Earth observation system and moderate resolution image spectroradiometer. The results indicate that the SMMR and SSM/I data can be applied to monitor lake ice phenology variability for a long time and the results are reliable. Since 1978, the duration of Nam Co lake ice has decreased by 19 to 20 days, with the freezing onset date delayed by 9 days and the ablation date advanced by 9 to 10 days. Between 1978 and 2010, there was a negative correlation between temperature and the duration of lake ice in Nam Co; after 2000, the temperature increased significantly in the Nam Co Basin. This caused a clear downward trend of lake ice duration. Therefore, the freezing onset date, ablation end date, and duration of lake ice are effective indicators of regional climate change.

The distribution of the daily average air temperature with high spatial resolution is vital for hydro-ecological applications. The air temperature usually recorded at fixed-point stations provides little distribution information and easily suffers from the scarce amount and uneven distribution of the stations in the data sparse regions. In this study, a method based on multisource spatial data was developed to estimate the spatial distribution of daily average temperature, especially for data sparse regions. In this method, the instantaneous temperature was retrieved first using the moderate resolution imaging spectroradiometer data, which was then transformed to a daily value using transformation equations. Second, the global land data assimilation system air temperature data were spatially downscaled and used to improve the data accuracy from step 1 at low temperatures. This method was applied in the Ili River basin in Central Asia, and the results were evaluated against data from two stations’ observations and in situ data from a field test site. The results showed the correlation coefficient varies from 0.90 to 0.94 and the root mean square deviation is ∼3°C , indicating the generated temperature matched the observations well. This suggests the method is an alternative for data sparse regions.

The Wahba problem is the task of constrained optimization seeking the matrix from SO(3), which maximally converges (based on the least squares criterion) two sequences of unit vectors. The solution of this task is vital for satellite attitude determination using star trackers. An iterative method for solving the Wahba problem is proposed. Each iteration of the proposed method is reduced to sequential rotation of the vectors and solving the system of linear algebraic equations. Usage of the method implies that the corresponding vectors of both sequences are located sufficiently close to each other. Two variants of the method are proposed, having linear and quadratic convergence. The Wahba problem solution is interpreted in terms of finding the angular velocity of a system of material points, which have certain angular momentum. Taking into consideration the characteristics of state-of-the-art star trackers, one to two iterations are sufficient for finding the optimal solution using the small-angle rotation method. The primary advantage of the proposed method as compared with classical methods based on calculation of eigenvectors and singular decomposition is the simplicity of its implementation.

Classification of different tree species in semiarid areas can be challenging as a result of the change in leaf structure and orientation due to soil moisture constraints. Tree species mapping is, however, a key parameter for forest management in semiarid environments. In this study, we examined the suitability of 5-band RapidEye satellite data for the classification of five tree species in mopane woodland of Botswana using machine leaning algorithms with limited training samples.We performed classification using random forest (RF) and support vector machines (SVM) based on EnMap box. The overall accuracies for classifying the five tree species was 88.75 and 85% for both SVM and RF, respectively. We also demonstrated that the new red-edge band in the RapidEye sensor has the potential for classifying tree species in semiarid environments when integrated with other standard bands. Similarly, we observed that where there are limited training samples, SVM is preferred over RF. Finally, we demonstrated that the two accuracy measures of quantity and allocation disagreement are simpler and more helpful for the vast majority of remote sensing classification process than the kappa coefficient. Overall, high species classification can be achieved using strategically located RapidEye bands integrated with advanced processing algorithms.

Change detection in Tai’an city of eastern China using a pair of qual-polarimetric Advanced Land Observing Satellite phased array type L-band synthetic aperture radar (ALOS PALSAR) data was studied. The procedures consisted of polarimetric features extraction, optimal polarimetric feature group selection, supervised classification, and result accuracy assessment. Feature extraction from PALSAR data was performed first, and then the polarimetric features were categorized into several groups. Polarimetric optimum index factor (POIF) and distance factor (DF) were selected to measure and evaluate the suitability of each feature group for urban change detection. The best group of features was identified including linear polarization correlation coefficient (ρ HH–VV ), right–left (R–L) circular polarization correlation coefficient (ρ RR–LL ), total power (TP), and cross-polarization isolation (XPI). Afterward, four difference images of the identified features extracted from the two PALSAR data were derived, respectively. Then, the random forest (RF) classifier was employed to perform a supervised classification of the four difference images. Three classes were quantified, including no-change, change from undeveloped area to developed area, and vice versa. The overall accuracy of change detection was about 84% and Cohen’s Kappa coefficient was 0.71. Consequently, satisfactory outcomes were obtained in the application of the polarimetric ALOS PALSAR data of moderate resolution in detecting urban land use and land cover type changes.

With the development of population and economy, the problems of deficit in water resources and degradation in water environment are increasingly serious in the Taihu watershed of China. The information on spatial and temporal availabilities of water will be helpful for the optimum utilization of water resources. In this study, we apply precipitation (P) from the Tropical Rainfall Measuring Mission (TRMM) products, evapotranspiration (ET) derived from MODIS data, and ground-observed runoff. Then annual water budgets in the Taihu watershed from 2005 to 2007 and the variation of water budget components in spatial and temporal (monthly and annually) scales were evaluated. The results indicated that ET was the most notable component of water consumption in the watershed. The annual mean ratio of the ET to the precipitation was 0.73 to 0.89 in the watershed and 1.1 to 1.3 in Lake Taihu area from 2005 to 2007. The analysis of water balance in the watershed indicated that the amount of water input and output were approximately equal for the watershed and the lake areas with imbalance percentages of 1.4% to 4.4% and 0.1% to 4.0%, respectively.

Two popular synthetic aperture radar (SAR) reconstruction algorithms, namely the back-projection (BP) and the frequency wavenumber (ω−k ) algorithms, were tested and compared against each other, especially for their use in ground-based (GB) SAR applications directed to foreign object debris removal. For this purpose, an experimental setup in a semi-anechoic chamber room was accomplished to obtain near-field SAR images of objects on the ground. Then, the 90 to 95 GHz scattering data were acquired by using a stepped frequency continuous-wave radar operation. The performances of the setup and the imaging algorithms were then assessed by exploiting various metrics including point spread function, signal-to-clutter ratio, integrated side-lobe ratio, and computational complexity. Results demonstrate that although both algorithms produce almost accurate images of targets, the BP algorithm is shown to be superior to the ω−k algorithm due to its some inherent advantages specifically suited for short-range GB-SAR applications.

Optical remote sensing is the most widely used method for obtaining forest biomass information. This research investigated the potential of using topographical and high-resolution optical data from Quickbird for measurement of black locust plantation aboveground biomass (AGB) grown in the hill-gully region of the Loess Plateau. Three different processing techniques, including spectral vegetation indices (SVIs), texture, and topography were evaluated, both individually and combined. Simple linear regression and stepwise multiple-linear regression models were developed to describe the relationship between image parameters obtained using these approaches and field measurements. SVI and topography-based approaches did not yield reliable AGB estimates, accounting for at best 23 and 19% of the observed variation in AGB. Texture-based methods were better, explaining up to 70% of the observed variation. A combination of SVIs, texture, and topography yielded an even better R 2 value of 0.74 with the lowest root mean square error (17.21  t/ha ) and bias (−1.85  t/ha ). The results suggest that texture information from high-resolution optical data was more effective than SVIs and topography to estimate AGB. The performance of AGB estimation can be improved by adding SVIs and topography results to texture data; the best results can be obtained using a combination of these three data types.

Spatial variation of phenology is a central feature of global change research. Satellite remote sensing is used for continental to global monitoring due to the limitations of long-term field observations of plant phenology. A threshold method was used to estimate the start of the season, length of the season, maximum normalized difference vegetation index (NDVI), and integral NDVI for selected tree species using remote sensing based NDVI data acquired by the VEGETATION instrument on board Satellite Pour l’Observation de la Terre (SPOT VGT NDVI). Afterward, the spatial patterns in the satellite-derived phenological metrics for four dominant tree species (i.e., beech, birch, pine, and spruce) across Europe were characterized. The results indicate that: (1) The SOS occurs 1.6–2.9 days later and the average LOS is 2.7–3 days shorter per 1 deg of latitude increase from south to north. (3) The SOS occurs 0.7–1.8 days later and the LOS was 0.6–2 days shorter per 100-m increase in altitude for the four species. (4) The SOS and LOS across Europe are well correlated with the mean annual air temperature (1°C correlates with a 4.5-day advance in the SOS and a 7-day extension in the LOS). Our research is the first one to characterize the spatial and temporal variations of phenology for different tree species across Europe using remote sensing.

The presence of clouds is the biggest obstacle in the investigation of land cover, and many techniques have been developed to detect clouds. However, few indicators have been proposed for the detection of cloud-free conditions. To address this, we propose two indicators for use in compositing 8-day cloud-free images: the B 1/7 ratio is the ratio of the band 1 reflectance to the band 7 reflectance of Terra surface reflectance images (MOD09GA), and saturation refers to the color saturation of these images. Here, we describe the principles underlying these two indicators and analyze their characteristics for vegetation, water, urban, and nonvegetation pixels under cloud-free, cloud shadow, and cloudy conditions using MOD09GA from October 16 to 23, 2007, in North China (sample A) and using data published by the U.S. Geological Survey (USGS). We found that the B 1/7 ratio and saturation are suitable for extracting cloud-free pixels over land and water, respectively; therefore, we combined these two indicators to develop a single-unified model. In particular, our results demonstrate that the pixels exhibiting the lowest B 1/7 ratios should be adopted as cloud-free pixels over land when the value of B 1/7 for land surfaces is between 0 and 1, and the surface reflectance of Moderate Resolution Imaging Spectroradiometer band 1 is less than 0.3. Otherwise, the pixels exhibiting the greatest saturation values should be adopted. We used our model to composite cloud-free images for two additional regions in China: the Tarim basin (sample B) from October 2 to 9, 2012, and the coastal areas of southeastern China (sample C) from April 15 to 23, 2013. We compared the cloud-free images of these regions with the 8-day surface reflectance product (MOD09A1) with respect to clouds, cloud shadow, and cirrus clouds, and we found that our proposed cloud-free image compositing approach can accurately eliminate both clouds and cirrus clouds. Specifically, the percentage of residual cloud pixels in sample C was found to be less than that in MOD09A1. Moreover, in the cloud-free images obtained using our newly developed method, cloud-free pixels are typically associated with greater sensor zenith angles and smaller scatter angles than those in MOD09A1. However, our method retains some limitations. In particular, 9.68, 33.22, and 33.00% of cloud-shadow pixels remain in the cloud-free images for samples A, B, and C, respectively.

Low-level jet (LLJ)-generated gravity waves were observed over Oklahoma City by a scanning Doppler wind lidar during the transition periods of atmospheric boundary layer (ABL) from stable to convective conditions in the mornings after sunrise. The temperature profiles had a multilayer structure with a shallow neutral layer immediately above the ground and a stable cap layer above the neutral layer. The wind profiles exhibited a typical shape of a LLJ with nearly linear growth of wind speed with respect to height, and maximum wind speed occurred at the top of the stable cap layer. The average wavelength and its relation with mean wind and temperature profiles are characterized with data from Doppler wind lidar, radiosonde, and wind profiler. A linear stability analysis was performed to check the stratification conditions for wave occurrence. The wind signals from sonic anemometers near the ground were separated into waves and turbulence parts using a wavelet decomposition method, and the momentum fluxes due to the wave motions and turbulence were computed. The downward gravity wave momentum flux was 1.5 to 3.0 times of turbulent momentum flux. The analysis indicated that gravity wave momentum transport from the stable cap layer downward is one of the mechanisms of stable-to-convective transition in the LLJ-dominated ABL.

Flight duration, which consists of strip time and turning time, is an important element of flight planning of airborne surveys as it affects the cost of data acquisition. The turning time dominates over the strip time and thus dictates the flight duration. However, the current practices of flight planning assume it as a constant for easier calculations in flight planning. Further, there is no literature available explicitly on turning mechanisms for airborne surveys. Instead of assuming a constant or following the complicated models of turning, this paper advocates simple heuristic-based models and turning mechanisms. The models and turning mechanisms are developed by considering the feasibility of flight operations, experiences of pilots, and the available approximate models of turning in the literature. The paper illustrates consecutive and nonconsecutive turning mechanisms and derives comprehensive mathematical formulations to represent these. An algorithmic scheme for selection of an optimal turning mechanism is presented. The paper argues that the discussed turning mechanisms and algorithms can be combined with the strip time for arriving at the optimal flight plan using the genetic and evolutionary algorithms. The developed approach can be easily incorporated into the present knowledge base of flight planning.

Anomaly detection (AD) is a particular case of target detection applications that aim to find the objects in the image scene which are anomalous in comparison to their surrounding background. In many applications, like real-time detection of environmental pollution, not only the accuracy of the algorithm, but also its runtime is important. Because AD algorithms run on high-dimensional data cubes, their runtimes are normally not optimal. This paper presents two high-speed AD algorithms: the first method is based on the maximum and minimum values of the spectral signature of hyperspectral data pixels; the other employs simple statistical and mathematical tools to calculate the abnormality of image pixels. The proposed methods have been compared with the Kernel-RX detector which is a well known, high performance AD. The AD methods have been applied to four hyperspectral datasets acquired by Airborne Visible/Infrared Imaging Spectrometer, Hyperspectral Mapper, and SpecTIR sensors. The evaluation of the algorithms has been done using receiver operation characteristic (ROC) curve, visual investigation, and runtime of the algorithms. Experimental results show that the detection performance of the proposed methods is almost equal to Kernel-RX detector. In addition, the proposed AD methods have a much better runtime than Kernel-RX detector and are fast enough to be implemented in real-time environmental applications.

The comb jellyfish Mnemiopsis leidyi invaded vastly the whole Caspian Sea in summer 2001. Sea-viewing wide field-of-view sensor and moderate resolution imaging spectroradiometer (MODIS) satellite data from 1998 to 2006 and bio-optical field measurements along six transects in the southern Caspian Sea from 2001 to 2006 were used to detect the relationships between M. leidyi abundances with satellite driven sea surface temperature (SST) and chlorophyll-a . MODIS chlorophyll-a and SST monthly composite average value showed a positive linear correlation with M. leidyi abundance in the southern Caspian Sea. Spatiotemporal distribution of MODIS chlorophyll-a high-level patches (∼5  mg⋅m −3 ) were also confirmed with the highest recorded M. leidyi and the lowest zooplankton abundances. However, there are several other factors that affect the concentration of chlorophyll-a , and it is not clear how much of the chlorophyll-a variation is related to M. leidyi abundances.

For à trous wavelet-based remote sensing image fusion, it is significant to determine the number of decomposition levels (NDL) to derive the best tradeoff between the spatial and spectral qualities of the fused image. The tradeoff is modeled as a cost function from a novel three-dimensional variational point of view. By solving this cost function, a new fusion scheme is proposed that can be used to customize the tradeoff by weighting the wavelet planes with a factor. The optimal NDL is decided by the intersection of the characteristic curves of the spatial and spectral qualities. Three pairs of remote sensing images are used to test the analysis. The experimental results show that the proposed method provides the practitioner with a reference for selecting the fused image with different spatial and spectral qualities for different applications.

Stream bank condition is an important physical form indicator for streams related to the environmental condition of riparian corridors. This research developed and applied an approach for mapping bank condition from airborne light detection and ranging (LiDAR) and high-spatial resolution optical image data in a temperate forest/woodland/urban environment. Field observations of bank condition were related to LiDAR and optical image-derived variables, including bank slope, plant projective cover, bank-full width, valley confinement, bank height, bank top crenulation, and ground vegetation cover. Image-based variables, showing correlation with the field measurements of stream bank condition, were used as input to a cumulative logistic regression model to estimate and map bank condition. The highest correlation was achieved between field-assessed bank condition and image-derived average bank slope (R 2 =0.60 , n=41 ), ground vegetation cover (R 2 =0.43 , n=41 ), bank width/height ratio (R 2 =0.41 , n=41 ), and valley confinement (producer’s accuracy=100% , n=9 ). Cross-validation showed an average misclassification error of 0.95 from an ordinal scale from 0 to 4 using the developed model. This approach was developed to support the remotely sensed mapping of stream bank condition for 26,000 km of streams in Victoria, Australia, from 2010 to 2012.

The current study investigates a MODIS-based remote-sensing-based algorithm for the assessment of daily latent heat flux (LE), or evapotranspiration (ET), with a focus on semi-arid regions. The approach uses the triangle method, relying on remotely sensed inputs and a previously developed net radiation model. A major difference from previous studies is that we utilize only MODIS products for estimation of ET at the daily timestep for clear and cloudy days. The algorithm is evaluated at four flux tower locations in the San Pedro River basin in Arizona. The mean daily LE varies significantly between the sites, ranging from 144 to 179  W/m 2 in the riparian areas to 36 to 76  W/m 2 at the rangeland sites. Comparison of the flux towers shows good correlation and low root mean square error at the riparian sites (0.81 to 0.82 and 37 to 51  W/m 2 , respectively) with slightly larger errors at the upland sites, where ET is strongly correlated to precipitation events. The model assumption of a linear variation in evaporative fraction across the triangular domain (LST/EVI space) results in more uncertainty under water-stressed conditions such as those found at the upland sites. Overall, the proposed MODIS-based algorithm provides reasonable estimates of riparian and upland plant water use and unique spatial and temporal information.

This paper presents the practical spaceborne synthetic aperture radar (SAR) data focusing method based on the realistic end-to-end spaceborne SAR simulation. Our simulation reflects main factors of the satellite SAR that induce errors to degrade the focused image severely, which are related to the sensor hardware, the antenna beam pointing, the effective velocity, and the Doppler frequency. To minimize errors due to them in the spaceborne SAR image formation, we suggest and utilize the preprocessing as the internal calibration, the analysis of orbital data of an SAR satellite, the calculation of an effective velocity and the Doppler frequency utilizing the two-way slant range equation, and the usage of the phase gradient algorithm combined with the extended chirp scaling algorithm based on the azimuth signal deramping. The processing results for realistic simulated raw data of a spaceborne SAR are presented to validate the proposed methods.

Numerous modeling approaches have been proposed to estimate evapotranspiration (ET) and its partitioning between evaporation from soil (E) and transpiration from vegetation (T) over the last several decades. Although these ET models claimed to give reasonable E and T partitioning, few studies have compared their modeling results with direct E and T observations. In this study, a hybrid dual source scheme and trapezoid framework based evapotranspiration model (HTEM) fed with MODIS data was applied in a Chihuahuan Desert shrubland during the growing season of 2003 and validated with direct ET measurement using the Bowen-ratio technique and T measurement using scaled-up sap-flow measurements. Results show that the HTEM is capable of decomposing the remotely sensed land surface temperature into temperature components (soil and canopy temperatures) and providing accurate E and T estimates. At satellite overpass time, the root-mean-square error (RMSE) of estimated latent heat flux (LE) is 47.7  W/m 2 . The agreement between estimated and simulated LE was largely improved when observed net radiation and ground heat flux were used (35.1  W/m 2 ). At daily scale, the RMSE of estimated daily ET, E, and T are 0.52, 0.36, and 0.41  mm/day , respectively.

The giant magnetic impedance (GMI) effect in ferromagnetic materials has been investigated for sensing applications. The GMI properties were evaluated via numerical solution of the complex magnetic permeability of the material. MATLAB® simulation was carried out to study the frequency dependence of magnetic permeability via obtaining solutions of the Landau–Lifshitz–Gilbert (LLG) and the Maxwell’s equations. The results indicate that the complex magnetic permeability peaks at a frequency of 6 GHz, corresponding to the ferromagnetic resonant (FMR) frequency, where the energy loss is maximum. A variation of the Gilbert damping parameter (α ) associated with the LLG equation inversely affects this peak value. The area under the curve of complex magnetic permeability, calculated through counting the number of pixels within the image, provides an estimate of the average energy loss density within the material and appears to be consistent with the variation of the peak intensity.

A complete solution for effectively, automatically, and accurately reconstructing the three-dimensional (3-D) panoramic terrain from aerial imagery is presented. With enough premeasured and identified georeferences, we first estimate every camera’s accurate intrinsic and extrinsic parameters by implementing bundle adjustment, which is introduced in detail. Afterward, the adjacent relationship of imagery is acquired from the cameras’ position parameters. In addition, the formulas for corresponding area prediction and image rectification are derived according to the camera parameters. Subsequently, feature-based matching is conducted between adjacent image pairs to provide much more constraints for bundle adjustment. Area-based matching is applied to pairs of horizontal epipolar imagery for dense correspondence to produce dense spatial point cloud. Eventually, the mosaicked digital ortho map and digital elevation model of the whole imaging area are produced automatically by a series of steps including spatial intersection, Tin generation, differential correction, and color blending. Experimental results show that the root mean square (RMS) residual errors of check points in planimetry and altitude are, respectively, 0.039 and 0.170 m, demonstrating the high accuracy of camera orientation. The visualized panoramic 3-D realistic scene validates the feasibility and effectiveness of the proposed solution.

The rapid transmission of vector map data over the Internet is becoming a bottleneck of spatial data delivery and visualization in web-based environment because of increasing data amount and limited network bandwidth. In order to improve both the transmission and rendering performances of vector spatial data over the Internet, we propose a proxy map server enabling parallel vector data fetching as well as caching to improve the performance of web-based map servers in a dynamic environment. Proxy map server is placed seamlessly anywhere between the client and the final services, intercepting users’ requests. It employs an efficient parallelization technique based on spatial proximity and data density in case distributed replica exists for the same spatial data. The effectiveness of the proposed technique is proved at the end of the article by the application of creating map images enriched with earthquake seismic data records.

Based on ENVISAT ASAR images taken from 2004 to 2010, we obtain the deformation time series of the Changbaishan Tianchi volcano by using persistent scatterer interferometric synthetic aperture radar (PSInSAR) technique. Then, the magma chamber parameters are derived by modeling the PSInSAR deformation field based on a Mogi point source. We draw the following conclusions. First, the magma chamber is located immediately beneath the caldera, with a depth of 9 km below sea level. Second, the Changbaishan Tianchi volcano inflated between August 2006 and August 2008, which may be a result of an episodic magma intrusion event leading to pressurization and a volume increase of the magma chamber. The deformation pattern changed oppositely during August 2008 to June 2010, with maximum subsidence more than 10 mm, which may be caused by magma cooling and crystallization in the crustal chamber. Moreover, the deformation fields consist of several components, including large-scale deformation caused by magmatic activity, the local deformation caused by the active faults, and others. In conclusion, our results indicate that the anomalous activity of the Changbaishan Tianchi volcano is obvious in recent years, providing a basis for further research on monitoring this active volcano to reduce hazards and risks of future eruptions.

Observation was made to understand the spectral behavior of aerosol from the NASA moderate resolution imaging spectroradiometer (MODIS-Aqua) sensor data. The 869 nm central wavelength band MODIS-Aqua-derived images were retrieved and interpreted. The aerosol concentration was found to be higher during December (Northeast monsoon) over the Bay of Bengal (BoB). Its characterized distinct plume with higher aerosol optical depth (AOD) ranged between 0.05 and 0.25. The coastal aerosol concentration was higher than the offshore region, with decreasing trend (AOD∼0.05 ). To understand aerosol’s link with ocean biological productivity in terms of phytoplankton pigment, the ocean surface chlorophyll was retrieved from 8-years monthly datasets (2002 to 2010). MODIS-Aqua-derived monthly scale aerosol and chlorophyll data were analyzed in the northern Indian Ocean (NIO) region during July 2002 to June 2010. Their interrelationship was found to be significant (r 2 =0.663 and 0.584) over pockets in the BoB and Arabian Sea. Land to seaward movement of wind and aerosol plays a significant role to increasing ocean productivity. This study is very important and interesting to understand the precise composition of aerosol dust like iron (Fe) and increase in sulfate ions, nitrates, etc., and their link to phytoplankton biomass and the related productivity in the Indian water and attempted for the first time in the NIO water.

Hyperspectral remote sensing can provide information about species richness over large areas and may be useful for species discrimination in tropical environments. Here, we analyze the main sources of variability in leaf spectral signatures of tropical trees and examine the potential of spectroscopic reflectance measurements (450 to 2450 nm) for tree species discrimination. We assess within- and among-species spectral variability and perform a feature selection procedure to identify wavebands in which the species most differ from each other. We assess the discriminative power of these wavebands by calculating a separability index and then classifying the species. Finally, leaf chemical and structural parameters of each species are retrieved by inversion of the leaf optical model PROSPECT-5. Among-species spectral variability is almost five times greater than within-species spectral variability. The feature selection procedure reveals that wavebands, where species most differ, are located at the visible, red edge, and shortwave infrared regions. Classification of the species using these wavebands reaches 96% overall accuracy. Leaf chemical and structural properties retrieve by model inversion show that differences in leaf pigment concentrations and leaf internal structure are the most important parameters controlling the spectral variability of species. These parameters also contribute to the variation in red edge position among species.

A rigorous methodology for mapping thermal and RGB images on three-dimensional (3-D) models of building façades is presented. The developed method differs from most existing approaches because it relies on the use of thermal images coupled with 3-D models derived from terrestrial laser scanning surveying. The primary issue for an accurate texturing is the coregistration of the geometric model of the façade and the thermal images in the same reference system. This task is done by using a procedure standing out from other approaches adopted in current practice, which are mainly based on the independent registration of each image on the basis of homography or space resection techniques. A rigorous photogrammetric orientation of both thermal and RGB images is computed together in a combined bundle adjustment. This solution allows one to have a better control of the quality of the results, especially to reduce errors and artifacts in areas where more images are mosaicked onto the 3-D model. Several products can be obtained: 3-D triangulated textured models or raster products like orthophotos, having the temperature as radiometric value. The proposed approach is tested on different buildings of Politecnico di Milano University. Applications demonstrated the performance of the procedure and its technical applicability in routine thermal surveys.

A new image reconstruction algorithm is presented that will remove the effect of atmospheric turbulence on motion compensated frame average images. The primary focus of this research was to develop a blind deconvolution technique that could be employed in a tactical military environment where both time and computational power are limited. Additionally, this technique can be employed to measure atmospheric seeing conditions. In a blind deconvolution fashion, the algorithm simultaneously computes a high resolution image and an average model for the atmospheric blur parameterized by Fried’s seeing parameter. The difference in this approach is that it does not assume a prior distribution for the seeing parameter, rather it assesses the convergence of the image’s variance as the stopping criteria and identification of the proper seeing parameter from a range of candidate values. Experimental results show that the convergence of variance technique allows for estimation of the seeing parameter accurate to within 0.5 cm and often even better depending on the signal to noise ratio.

In order to understand the response of gross primary production (GPP) and above-ground biomass (AGB) to warming, a field warming experiment using open-top chambers was conducted in an alpine meadow at three elevations (i.e., 4313, 4513, and 4693 m) on the Northern Tibetan Plateau in May 2010. We calculated GPP from the moderate-resolution imaging spectroradiometer algorithm and AGB using the surface measured data in 2012. Average GPP and AGB at elevation 4313 m was significantly decreased by experimental warming, whereas the declines at elevations 4513 and 4693 m were not statistically significant across all sampling dates. The negative effects of experimental warming on GPP and AGB may be related to experimental warming-induced soil drying. The different responses of GPP and AGB to experimental warming among the three alpine meadow sites could be dependent on climate conditions. Our findings suggested that experimental warming did not enhance GPP and AGB in the alpine meadow, and its effects differed among alpine meadows on the Tibetan Plateau.

A semiautomated classification methodology was implemented using historic, high-resolution aerial photography in a dominant agricultural landscape. An object-based segmentation approach was applied to study land cover change from 1930 through 1990 in Johnson County, Iowa, in the Midwestern United States. A critical analysis of the approach is discussed, emphasizing the ability of the methodology to generate landscape metrics that can accurately characterize the quality of the landscape, particularly the high-resolution landscape features that are so important in a highly modified landscape. Landscape analysis includes a discussion of both the changes in the areal composition of land cover types and also the structural changes that are captured using both patch- and landscape-level metrics. Results were compared with countywide statistics from the United States Department of Agriculture as well as similar landscape studies, and provide evidence of agricultural intensification. Results also indicate some counterintuitive processes occurring from what is expected of a landscape undergoing this type of transformation, suggesting that altering the scale of study may provide different insight into land cover change dynamics.

The Haiyuan fault zone is a major discontinuity in the northeastern margin of the Tibetan plateau. A magnitude 8.5 earthquake occurred there in 1920. Both geological investigations and GPS measurements show that this fault zone is still highly active, with a slip rate of 3 to 10  mm/year , exhibiting a large range of variance in both space and time. We attempt to use the permanent scatterers interferometric (PSI) synthetic aperture radar technique to better detect the near-field motion of this fault zone. We process and analyze 38 scenes of ENVISAT/ASAR images from two neighboring descending orbits using the PSI method. The results show a remarkable velocity gradient of about 5  mm/year across the central segment of the fault zone and a rate of about 5 to 6  mm/year on its eastern segment. The motion senses are consistent with a left-lateral strike slip. The motion histories of most PS points show a stable linear variation trend in time series. In addition to these motion features that agree with those from geological, GPS and other observations, the dense PS analysis also reveals spatially continuous variations of crustal motion around the fault zone.

Climate change may represent the greatest future risk to the sagebrush ecosystem. Improved ways to quantify and monitor gradual change resulting from climate influences in this ecosystem are vital to its future management. For this research, the change over time of five continuous field cover components including bare ground, herbaceous, litter, sagebrush, and shrub were measured on the ground and by satellite across six seasons and four years. Ground-measured litter and herbaceous cover exhibited the highest variation annually and herbaceous cover the highest variation seasonally. Correlation of ground measurements to corresponding remote-sensing predictions indicated that annual predictions tracked ground measurements more closely than seasonal ones, and QuickBird predictions tracked ground measurements more closely than Landsat predictions. When annual linear slope values from ground plots and sensor predictions were correlated by component, the direction of ground-measured change was tracked better with QuickBird components than with Landsat components. Component predictions were correlated to annual and seasonal DAYMET precipitation. QuickBird components on average had the best response to precipitation patterns, followed by Landsat components. Overall, these results demonstrate the ability of sagebrush ecosystem components as predicted by regression trees to incrementally measure changing components of a sagebrush ecosystem.

A neural network (NN) algorithm to invert biomass of rice plants using quad-polarization radar datasets of ground-based scatterometer and spaceborne RADARSAT-2 has been studied. The NN is trained with pairs of multipolarization radar backscattering and biomass data. The backscattering data are simulated from a Monte Carlo backscatter model that uses the outputs from a growth model of the rice plant. The growth model is developed from the plant data collected in growing cycles of several years. In addition to producing parameters needed by the backscatter model, the growth model outputs the biomass value of the plant. Multipolarization data collected by a ground-based scatterometer at eight stages during the 2012 growing cycle are input to the NN to invert biomass. Satisfactory results are obtained due to a small root mean squared error (RMSE) of 0.477  kg/m 2 and a high correlation coefficient of 0.989 when the inverted and measured biomass values are compared. Finally, RADARSAT-2 synthetic aperture radar images acquired on four different dates during the 2012 growth period are analyzed to delineate rice paddies within the study area and to invert biomass using the NN. Inversion results from the delineated rice paddies are encouraging because the RMSE is 0.582  kg/m 2 and correlation coefficient is 0.983.

Point cloud data collected by small-footprint lidar scanning systems have proven effective in modeling the forest canopy for extraction of tree parameters. Although line-of-sight visibility (LOSV) in complex forests may be important for military planning and search-and-rescue operations, the ability to estimate LOSV from lidar scanners is not well developed. A new estimator of below-canopy LOSV (BC-LOSV) by addressing the problem of estimation of lidar under-sampling of the forest understory is created. Airborne and terrestrial lidar scanning data were acquired for two forested sites in order to test a probabilistic model for BC-LOSV estimation solely from airborne lidar data. Individual crowns were segmented, and allometric projections of the probability model into the lower canopy and stem regions allowed the estimation of the likelihood of the presence of vision-blocking elements for any given LOSV vector. Using terrestrial lidar scans as ground truth, we found an approximate average absolute difference of 20% between BC-LOSV estimates from the airborne and terrestrial point clouds, with minimal bias for either over- or underestimates. The model shows the usefulness of a data-driven approach to BC-LOSV estimation that depends only on small-footprint airborne lidar point cloud and physical knowledge of tree phenology.

A new technique for denoising and compression of multispectral satellite images to remove the effect of noise on the compression process is presented. One type of multispectral images has been considered: Landsat Enhanced Thematic Mapper Plus. The discrete wavelet transform (DWT), the dual-tree DWT, and a simple Huffman coder are used in the compression process. Simulation results show that the proposed technique is more effective than other traditional compression-only techniques.

Based on the Système Pour l’Observation de la Terre-5 imagery, two main techniques of classifying land-use categories in a tropical landscape are compared using two supervised algorithms: maximum likelihood classifier (MLC) and K -nearest neighbor object-based classifier. Nine combinations of scale level (SL10, SL30, and SL50) and the nearest neighbor (NN3, NN5, and NN7) are investigated in the object-based classification. Accuracy assessment is performed using two main disagreement components, i.e., quantity disagreement and allocation disagreement. The MLC results in a higher total disagreement in total landscape as compared with object-based image classification. The SL30-NN5 object-based classifier reduces allocation error by 250% as compared with the MLC. Therefore, this classifier shows a higher performance in land-use classification of the Langat basin.

Characteristic spectral parameters such as the wavelength and depth of absorption bands are widely used to quantitatively estimate the composition of samples from hyperspectral reflectance data in soil science, mineralogy as well as vegetation study. However, little research has been conducted on the spectral characteristic of rare earth elements (REE) and their relationship with chemical composition of aqueous solutions. Reflectance spectra of ore leachate solutions and contaminated stream water from a few REE mines in the Jiangxi Province, China, are studied for the first time in this work. The results demonstrate that the six diagnostic absorption features of the rare earths are recognized in visible and near-infrared wavelengths at 574, 790, 736, 520, 861, and 443 nm. The intensity of each of these six absorption bands is linearly correlated with the abundance of total REE, with the r 2 value >0.95 and the detection limit at ≥75,000  μg/L . It is suggested that reflectance spectroscopy provides an ideal routine analytical tool for characterizing leachate samples. The outcome of this study also has implications for monitoring the environmental effect of REE mining, in particular in stream water systems by hyperspectral remote sensing.

Large uncertainties in calculating radiative forcings from aerosols due to their location, loading, and types pose a great challenge to global climate modeling. Trying to improve retrievals in a statistical manner normally requires detailed knowledge of uncertainty statistics and bias due to possible error sources such as different measurement viewing geometries, instrument calibration, and dynamically changing atmospheric and earth surface conditions. However, a priori estimates of these error sources are not, in general, available. The use of a neural network (NN) approach to compensate for biases and systematic errors of aerosol optical thickness (AOT) from the Moderate Resolution Imaging Spectrometer (MODIS) operational retrieval algorithm is explored. By utilizing the NN as an estimator, we can compensate against unknown sources of errors, nonlinearity in the data sets, and the presence of non-normal distributions. In this study, the highly accurate ground-based Aerosol Robotic Network (AERONET) measurements are used as the ground truth (GT). Our results show that the adjusted AOT with NN has decreased root mean square errors, improved correlations with GT data by 4% to 6%, and increased the number of NN-adjusted data falling within the published expected error envelope by ∼10% .

Islands are an important part of the marine ecosystem. Increasing impervious surfaces in the Zhoushan Islands due to new development and increased population have an ecological impact on the runoff and water quality. Based on time-series classification and the complement of vegetation fraction in urban regions, Landsat thematic mapper and other high-resolution satellite images were applied to monitor the dynamics of impervious surface area (ISA) in the Zhoushan Islands from 1986 to 2011. Landsat-derived ISA results were validated by the high-resolution Worldview-2 and aerial photographs. The validation shows that mean relative errors of these ISA maps are <15 % . The results reveal that the ISA in the Zhoushan Islands increased from 19.2  km 2 in 1986 to 86.5  km 2 in 2011, and the period from 2006 to 2011 had the fastest expansion rate of 5.59  km 2 per year. The major land conversions to high densities of ISA were from the tidal zone and arable lands. The expansions of ISA were unevenly distributed and most of them were located along the periphery of these islands. Time-series maps revealed that ISA expansions happened continuously over the last 25 years. Our analysis indicated that the policy and the topography were the dominant factors controlling the spatial patterns of ISA and its expansions in the Zhoushan Islands. With continuous urbanization processes, the rapid ISA expansions may not be stopped in the near feature.

Lyzenga proposed a shallow-water reflectance model that describes the exponential relationship between the remote-sensing reflectance (R ) and water depth [Appl. Opt.17, 379383 (1978)]. The model has been widely used in remote sensing of water depth to estimate the depth from R , and in remote sensing of bottom type to remove the effect of depth from R . Although it was derived from radiative transfer theory ignoring internal reflection at the water surface, no study has quantitatively validated it following the theory. In this study, we examine its accuracy under various conditions using Monte Carlo radiative transfer simulations. Although internal reflection contributed significantly to R in some cases, the model, if fitted to (calibrated with) data covering the entire target depth range, described the relationship between R and depth reasonably accurately (R 2 >0.9935 ). This was because the internally reflected component of R , as well as the other component, decreases exponentially with depth. However, because the sum of two exponentially decreasing functions is not strictly exponential, the model does not accurately estimate the depth using R when the calibration data did not cover the entire depth range of interest: the model significantly underestimated the depth when used for extrapolation.

The Tsunami classification model with real-time sensors placed at different locations and at different depths is proposed. To exclude the artifact effects in the sensor values, a wavelet-based denoising scheme is integrated in the model. In addition, a downsampling approach has been proposed to achieve maximum flat delay response, and the present results are compared with the Pc McClellan method. Various parameters such as conductivity, salinity, pressure, temperature, and dissolved oxygen are measured and sensed using multisensor grid architecture. Our results show that by sensing the above parameters and subject them online, it is possible to clearly distinguish the pre- and post-tsunami behaviors.

The analysis of the spatial–temporal variability and trends of aerosols over China based on ∼11 years (February 2000 to December 2010) of Terra-Multiangle Imaging SpectroRadiometer (MISR) Level 3 aerosol products is the focus. The study shows that the MISR aerosol optical depth (AOD) is in good agreement with corresponding AOD from AERONET stations and suggests that MISR aerosol products have higher applicability compared with MODIS AOD products over China. The spatial–temporal distribution and trends for annual and seasonal AOD values over the middle and eastern regions of China are the main focus. The results of our analysis show the significant characteristics and seasonal variation of the aerosol distribution over the study areas. The seasonal distribution in AOD over the study areas is not consistent with the MODIS data. An insignificant trend (5.3%) is observed over the whole study area during the study period. The larger positive tendencies are found in the Yangtze River Delta and the Pearl River Delta (PRD); the values increased by 24.4% and 18% and are considered to be significant with a confidence level of >95% . However, in contrast to the recently reported negligible AOD trends of 0.002 per decade using MODIS data over Sichuan, significant increasing trends of 0.046 per 11 years for MISR over the same areas have been measured. The analysis of seasonal variation revealed the seasonal trend in the AOD and the main types of aerosols. During spring, the largest increasing trend (30.2%) is observed in the PRD with a stable composition of fine and coarse aerosols. A pronounced decreasing trend is found over Shanxi and decreased by −18.7% in spring during the period, which has not been previously reported. MODIS-Terra currently shows artificial negative AOD trends over land. Therefore, trends derived from MODIS-Terra may not reflect the realistic decadal aerosol changes and variability. The study shows that seasonal distribution and trend analyses are inconsistent when using MISR AOD products and MODIS AOD products over study areas, which indicates that the analysis of aerosol variation using MISR sensors is necessary.

Coastline change, erosion-accretion evolution, and their relationship in the Pearl River Estuary (PRE) of China over the past 25 years are analyzed using six remote-sensing images from 1986 to 2011 and two nautical charts. Due to land reclamation in the period from 1986 to 2011, the total length of the PRE coastline increased by 149.2 km, which is equivalent to a growth of 0.57% per year, and the coastal land increased by 251.76  km 2 , which is equivalent to a growth of 0.23% per year; in addition, water depth change showed a trend in that foreshores became shallower, while deep channels became deeper. Areas where the coastline extended seaward had deposition to some extent, except for deepwater ports. Human activities played an important role in coastline change and erosion-accretion evolution in the PRE, which intervened with the natural variation of coastline and erosion-accretion. In addition, pollutants from the reclamation land became the major factors of coastal water pollution, which may significantly influence the environment of the PRE in a negative way.

This study examined vegetation distribution and tree line change in the Sangong River watershed in the Tianshan Mountains, Xinjiang, China, using aerial photographs from 1962 and 1981 and a QuickBird image from 2006. An object-oriented classification method was used to map vegetation distribution, and the postclassification comparison approach was used to detect vegetation change. The results were linked to digital elevation model data for exploring the relationships between vegetation distribution/expansion and topographic factors. It shows that Schrenkiana is mainly distributed in the region with elevations less than 2,600 m, but its tree line can reach 2,880 m. Sabina is mainly distributed in the region with elevations within 2,600 to 2,900 m, and its highest elevation can be 3,270 m. Approximately 73% of Schrenkiana areas are distributed in aspects of 0 to 45 deg and 270 to 360 deg and 52% of Sabina areas are in aspects of 45 to 90 deg and 225 to 270 deg. The Schrenkiana expansion is mainly distributed in slopes of 20 to 40 deg (accounting for 82%) and aspects of 0 to 90 deg and 270 to 315 deg (accounting for 77%). The elevation of Schrenkiana tree lines increased by 2 to 3 m from 1962 to 2006 and of Sabina increased by 7 to 12 m from 1981 to 2006. Both Schrenkiana and Sabina expansion is mainly located in vegetation frontiers.

The retrieval of aerosols using remote sensing requires an accurate estimation of both atmospheric and land surface parameters. Radiative transfer codes along with simultaneous field measurements are used to solve the forward and the inverse problems. This study uses the simple model for atmospheric radiative transfer to simulate the aerosol optical depth (AOD) over Dehradun, India from the top of atmosphere signals recorded by first band of AWiFS sensor onboard RESOURCESAT. The analysis has been carried out for five satellite datasets acquired at different time periods with single scattering approximation (SSA) for aerosols and molecules. The results have been compared with those obtained from MODIS aerosol product. A maximum difference of 0.156 has been found between the calculated AOD from the model and those obtained from MODIS product. The differences in the results may be attributed to exclusion of multiple scattering contributions, different time, and geometry of image acquisitions along with the difference in relative spectral response of the two sensors. The algorithm in SSA is found to be best suited to retrieve AODs for clear days with low particle loading.

The surveillance and inspection tasks in large exterior critical infrastructures have arisen as critical processes. More complex challenges are now present, and the traditional approaches are sometimes obsolete for facing these new menaces. An alternative system—a mobile sensor, spherical shaped—that provides a flexible, versatile, and reliable way to perform measurements is proposed. Even more, thanks to its original traction method (based on center of gravity destabilization), the system has a result as an all-terrain vehicle that guarantees a safe and friendly interaction with the environment. It has been widely tested, verifying as well the accurate acquisition performance, resulting in this system as a suitable sensing and monitoring alternative.

The Sunderban Biosphere Reserve of West Bengal, India, is an ideal locale where hyperspectral image data may be successfully utilized for accurate mapping of dominant mangrove species that exist there. This study attempts to apply and analyze automated end member detection algorithms such as N-Finder (N-FINDR) where N is the number of end members and automated target generation process (ATGP) on Hyperion data of the study area to enable species-level discrimination of mangroves. The identified end members have been further unmixed using constrained and unconstrained linear unmixing and the fractional abundance images of individual end members generated. It has been found that classification results generated by unconstrained linear unmixing with N-FINDR algorithm shows higher accuracy than the unconstrained classification results of ATGP. The classified output of unconstrained linear unmixing also shows higher accuracy than constrained linear unmixing with ATGP and N-FINDR. The subpixel classification output identifies dominant species on the study area to be Avicennia Marina, Avicennia Officinallis, Excoecaria Agallocha, Ceriops Decandra, Phoenix Paludosa, Bruguiera Cylindrica, and Aegialitis. The results also identify mixed patches of Ceriops-Excoecaria Agallocha and Aegialitis-Avicennia Marina var aquitesima in many places. The accuracy assessment of subpixel classification showing mangrove species distribution has been done by generation of confusion matrix and calculation of kappa coefficient of field data collected during ground survey. It has been observed that unconstrained linear unmixing with N-FINDR has been more successful in detecting more target species as compared to the other algorithms applied.

The purpose of the study is to evaluate the suitability of moderate-resolution imaging spectroradiometer (MODIS) data to study the land use land cover over India. The study is based on secondary data sets pertaining to forest, cropland, pasture, and barrenland obtained from Directorate of Economics and Statistics (DES) and MODIS (Terra) global land use land cover data yearly composite from 2002 to 2005. A family of statistical and mathematical techniques is adopted here in order to compare the MODIS data with DES statistics. The comparison at the country level shows estimated forest cover has least uncertainty compared to pasture and barrenland. Comparison at the state level, on the other hand, shows high degree of association between the data sets in cropland (R 2 =0.9 ), followed by forest cover and pastureland. Barrenland shows weakest association between DES and MODIS. The computed average accuracy in cropland shows a level of 84% and has been chosen as the best fitted land cover category among all land cover classes selected for the study. Hierarchical clustering of the MODIS cropland at the state level based on the estimated accuracy shows that, except for Andhra Pradesh, Tamilnadu, Haryana, West Bengal, Chhattisgarh, and Orissa, which are far off from the true estimate, the rest of the states are in closer correspondence of the cropland statistics reported by Ministry of Agriculture.

In hyperspectral imagery, endmember extraction is the process of finding a pure spectrum set within the materials present in a hyperspectral scene. However, various endmember extraction algorithms (EEAs) can yield different endmember spectrum sets. This research presents a comparison of four EEAs: pixel purity index, automatic target generation process (ATGP), N-finder (N-FINDR), and simplex identification via split augmented Lagrangian. To perform the comparison, a ground reference geological map is first coregistered with the hyperspectral scene. Then, 12 geological ground truth spectra are chosen. The four EEAs are then used to extract endmember spectra from the scene. Next, the extracted endmember spectra are applied to generate abundance maps using fully constrained least squares. The largest spectrum magnitude in the abundance map is considered the endmember. Finally, the spectral angle mapper and root-mean-square error between the extracted endmember spectrum and the chosen geological spectral spectra are computed, using the angle and minimum error to evaluate similarities. The results of this EEA comparison show that only the ATGP algorithm could consistently identify endmembers with the least computation time. Additionally, the N-FINDR algorithm is able to extract the most endmembers with the closest endmember similarity measures, although this required the highest computation time.

Pan-sharpening of optical remote sensing multispectral imagery aims to include spatial information from a high-resolution image (high frequencies) into a low-resolution image (low frequencies) while preserving spectral properties of a low-resolution image. From a signal processing view, a general fusion filtering framework (GFF) can be formulated, which is very well suitable for a fusion of multiresolution and multisensor data such as optical-optical and optical-radar imagery. To reduce computation time, a simple and fast variant of GFF-high-pass filtering method (HPFM)—is proposed, which performs filtering in signal domain and thus avoids time-consuming FFT computations. A new joint quality measure based on the combination of two spectral and spatial measures was proposed for quality assessment by a proper normalization of the ranges of variables. Quality and speed of six pan-sharpening methods—component substitution (CS), Gram-Schmidt (GS) sharpening, Ehlers fusion, Amélioration de la Résolution Spatiale par Injection de Structures, GFF, and HPFM—were evaluated for WorldView-2 satellite remote sensing data. Experiments showed that the HPFM method outperforms all the fusion methods used in this study, even its parentage method GFF. Moreover, it is more than four times faster than GFF method and competitive with CS and GS methods in speed.

Socotra Island (Yemen), a global biodiversity hotspot, is characterized by high geomorphological and biological diversity. In this study, we present a high-resolution vegetation map of the island based on combining vegetation analysis and classification with remote sensing. Two different image classification approaches were tested to assess the most accurate one in mapping the vegetation mosaic of Socotra. Spectral signatures of the vegetation classes were obtained through a Gaussian mixture distribution model, and a sequential maximum a posteriori (SMAP) classification was applied to account for the heterogeneity and the complex spatial pattern of the arid vegetation. This approach was compared to the traditional maximum likelihood (ML) classification. Satellite data were represented by a RapidEye image with 5 m pixel resolution and five spectral bands. Classified vegetation relevés were used to obtain the training and evaluation sets for the main plant communities. Postclassification sorting was performed to adjust the classification through various rule-based operations. Twenty-eight classes were mapped, and SMAP, with an accuracy of 87%, proved to be more effective than ML (accuracy: 66%). The resulting map will represent an important instrument for the elaboration of conservation strategies and the sustainable use of natural resources in the island.

The retrieval of surface reflectance information from the same single pixel of the Hyperion image atmospherically corrected by using image-based [internal average relative reflectance (IARR), log residuals, and flat field] and radiative transfer model (RTM)-based [the fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH) and the Atmospheric and Topographic Correction 2 (ATCOR-2)] approaches and the spectral feature characteristics of this information were quantitatively and comparatively examined based on measured ground spectral reflectance data. The spectral features quantitative analysis results of the reflectance data showed that spectral reflectances that are suitable and best fitting to the ground spectral reflectances which were obtained from the pixels of FLAASH, ATCOR-2, and flat field–corrected images, respectively. The retrieval of surface reflectance from the FLAASH-corrected image pixels, in general, produced high scores in spectral parameter analyses. Of the image-based approaches, only in flat field–derived reflectance data, results were obtained which are high and nearest to those of RTM and ground spectral reflectance data. Generally, low scores obtained in the spectral parameter analyses of the surface reflectance values retrieved from single pixels of IARR and log residuals-corrected images showed the results that fit worst to the measured ground spectral reflectance.