This PDF file contains the editorial “Editorial: Top Reviewers of 2015” for JRS Vol. 10 Issue 01

Rapidly and accurately detecting individual tree crowns in satellite imagery is a critical need for monitoring and characterizing forest resources. We present a two-stage semiautomated approach for detecting individual tree crowns using high spatial resolution (0.6 m) satellite imagery. First, active contours are used to recognize tree canopy areas in a normalized difference vegetation index image. Given the image areas corresponding to tree canopies, we then identify individual tree crowns as local extrema points in the Laplacian of Gaussian scale-space pyramid. The approach simultaneously detects tree crown centers and estimates tree crown sizes, parameters critical to multiple ecosystem models. As a demonstration, we used a ground validated, 0.6 m resolution QuickBird image of a sparse forest site. The two-stage approach produced a tree count estimate with an accuracy of 78% for a naturally regenerating forest with irregularly spaced trees, a success rate equivalent to or better than existing approaches. In addition, our approach detects tree canopy areas and individual tree crowns in an unsupervised manner and helps identify overlapping crowns. The method also demonstrates significant potential for further improvement.

Microinertial sensors currently cannot provide long-term stability attitude information for a spinning projectile because of drift error. An attitude measuring method of a spinning projectile based on the background magnetic field (BMF) compensation is presented. The effect of the BMF is discussed, with particular attention to its implications for magnetometer-based attitude measuring. First, a compensation model of the BMF is established. Then, the error in measuring attitude caused by the BMF is analyzed using a theoretical model and numerical simulation. An attitude measuring algorithm for the spinning projectile based on the intersection ratio of magnetic sensors is proposed. Finally, semiphysical experiments are carried out to prove the effectiveness of this attitude measurement method. The experiment results indicate that the error of attitude angles is within ±1  deg and the attitude angle error of the combined magnetic sensors is not cumulated. Compared with the traditional geomagnetic attitude measurement method, the accuracy of our method is improved, which can be used for the trajectory correction of simple guided ammunition.

The georeferencing accuracy of a ground-based mobile mapping system designated for agricultural applications is tested. The system integrates a hyperspectral sensor, digital camera, global navigation satellite system receivers, and an inertial navigation system. Acquired imagery was synchronized with GPS time using custom hardware and software solutions developed in-house. The imaging platform was mounted on a forklift and used to conduct three imaging missions along a paved road segment and agricultural beds. Sixteen ground control points were established in each site and used to calibrate the system and test the positional accuracy. The control point coordinates were determined using GNSS and total station observations independent from the imaging data. The navigation data were postprocessed to extract sensor positions and attitude along the imaging trajectories. The pushbroom hyperspectral images were georeferenced using ReSe Parge software, while the digital camera images were analyzed using Agisoft PhotoScan software. Control point coordinates extracted from the georeferenced imagery were compared to corresponding ground-surveyed coordinates. The maximum root mean square errors obtained for the hyperspectral images in all experiments were 2.4 and 3.1 cm in the easting and northing directions, respectively. These results were achieved using only two control points at both ends of the scan line to estimate the boresight offsets. The RMSE values of the orthorectified image constructed using the digital camera images and two control points at each end of the agricultural site were 1.6 and 2.6 cm in the easting and northing directions.

Image registration is concerned with the precise overlap of two images. One challenging problem in this area is the registration of low-resolution synthetic aperture radar (SAR) images. In general, extracting feature points from such images is difficult due to the coarse observation and the severe speckle. The use of area similarity for image registration is another important branch to solve the problem. A similarity measure based on a conditional density function (cdf) is proposed. The cdf is specially tailored for SAR images, where the speckle is generally assumed as multiplicative gamma noise with unit mean. Additionally, a two-step procedure is devised for the registration of intro-model SAR images to improve the computational efficiency. First, the two images are roughly aligned considering only the translational difference. Then small blocks from the two images are accurately aligned and the center point of each block is treated as a control point, which is finally used to obtain the precise affine transformation between the two images. Five SAR image datasets are tested in the experiment part, and the results demonstrate the efficiency and accuracy of the proposed method.

Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise. Two approaches based on modeling the nonsubsampled Shearlet transform (NSST) coefficients are presented. Two-sided generalized Gamma distribution and normal inverse Gaussian probability density function have been used to model the statistics of NSST coefficients. Bayesian maximum a posteriori estimator is applied to the corrupted NSST coefficients in order to estimate the noise-free NSST coefficients. Finally, experimental results, according to objective and subjective criteria, carried out on both artificially speckled images and the true SAR images, demonstrate that the proposed methods outperform other state of art references via two points of view, speckle noise reduction and image quality preservation.

Hyperspectral unmixing aims at extracting pure material spectra, accompanied by their corresponding proportions, from a mixed pixel. Owing to modeling more accurate distribution of real material, nonlinear mixing models (non-LMM) are usually considered to hold better performance than LMMs in complicated scenarios. In the past years, numerous nonlinear models have been successfully applied to hyperspectral unmixing. However, most non-LMMs only think of sum-to-one constraint or positivity constraint while the widespread sparsity among real materials mixing is the very factor that cannot be ignored. That is, for non-LMMs, a pixel is usually composed of a few spectral signatures of different materials from all the pure pixel set. Thus, in this paper, a smooth sparsity constraint is incorporated into the state-of-the-art Fan nonlinear model to exploit the sparsity feature in nonlinear model and use it to enhance the unmixing performance. This sparsity-constrained Fan model is solved with the non-negative matrix factorization. The algorithm was implemented on synthetic and real hyperspectral data and presented its advantage over those competing algorithms in the experiments.

Hyperspectral instruments have been incorporated in satellite missions, providing large amounts of data of high spectral resolution of the Earth surface. This data can be used in remote sensing applications that often require a real-time or near-real-time response. To avoid delays between hyperspectral image acquisition and its interpretation, the last usually done on a ground station, onboard systems have emerged to process data, reducing the volume of information to transfer from the satellite to the ground station. For this purpose, compact reconfigurable hardware modules, such as field-programmable gate arrays (FPGAs), are widely used. This paper proposes an FPGA-based architecture for hyperspectral unmixing. This method based on the vertex component analysis (VCA) and it works without a dimensionality reduction preprocessing step. The architecture has been designed for a low-cost Xilinx Zynq board with a Zynq-7020 system-on-chip FPGA-based on the Artix-7 FPGA programmable logic and tested using real hyperspectral data. Experimental results indicate that the proposed implementation can achieve real-time processing, while maintaining the methods accuracy, which indicate the potential of the proposed platform to implement high-performance, low-cost embedded systems, opening perspectives for onboard hyperspectral image processing.

Mapping vegetation abundance by using remote sensing data is an efficient means for detecting changes of an eco-environment. With Landsat-8 operational land imager (OLI) imagery acquired on July 31, 2013, both linear spectral mixture analysis (LSMA) and multinomial logit model (MNLM) methods were applied to estimate and assess the vegetation abundance in the Wild Duck Lake Wetland in Beijing, China. To improve mapping vegetation abundance and increase the number of endmembers in spectral mixture analysis, normalized difference vegetation index was extracted from OLI imagery along with the seven reflective bands of OLI data for estimating the vegetation abundance. Five endmembers were selected, which include terrestrial plants, aquatic plants, bare soil, high albedo, and low albedo. The vegetation abundance mapping results from Landsat OLI data were finally evaluated by utilizing a WorldView-2 multispectral imagery. Similar spatial patterns of vegetation abundance produced by both fully constrained LSMA algorithm and MNLM methods were observed: higher vegetation abundance levels were distributed in agricultural and riparian areas while lower levels in urban/built-up areas. The experimental results also indicate that the MNLM model outperformed the LSMA algorithm with smaller root mean square error (0.0152 versus 0.0252) and higher coefficient of determination (0.7856 versus 0.7214) as the MNLM model could handle the nonlinear reflection phenomenon better than the LSMA with mixed pixels.

A modified matched subspace detector (MSD) has been recently proposed for detecting a barely discernible object in an additive Gaussian background clutter using a single pixel in a sequence of digital images. In contrast to this detector designed for the subpixel object, we developed a generalized likelihood ratio approach to the detection of a multipixel object of unknown shape, size, and position in an additive signal-dependent Gaussian background and noise. The proposed detector modifies the MSD by adding the additional term proportional to the square of the difference between the background variances under two statistical hypotheses. The performances of these detectors are evaluated for the example scenario of two multipixel floating objects on the agitated sea surface. The crucial characteristic of the proposed detector is that prior knowledge of the target size, shape, and position is not required. Computer simulation and experimental results have shown that the proposed detector outperforms the MSD, especially in the case of weak and poorly contrasted objects of unknown shape, size, and position.

Inverse synthetic aperture radar (ISAR) can achieve high-resolution two-dimensional images of maneuvering targets. However, due to the indeterminate relative motion between radar and target, ISAR imaging does not provide the three-dimensional (3-D) position information of a target and suffers from great difficulty in target recognition. To tackle this issue, a 3-D interferometric ISAR (InISAR) imaging algorithm based on the joint cross modified Wigner-Ville distribution (MWVD) is presented to form 3-D images of maneuvering targets. First, we form two orthogonal interferometric baselines with three receiving antennas to establish an InISAR imaging system. Second, after the uniform range alignment and phase adjustment, the joint cross MWVD is used for all range cell of each antenna pair to generate the separation of the scatterer as well as preserve the phase that contains position information of the scatterer. At last, the 3-D images of the target can be directly reconstructed from the distribution. Simulation results demonstrate the validity of the proposal.

We propose an imaging algorithm for downward-looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) in the circumstance of cross-track sparse and nonuniform array configuration. Considering the off-grid effect and the resolution improvement, the algorithm combines pseudo-polar formatting algorithm, reweighed atomic norm minimization (RANM), and a parametric relaxation-based cyclic approach (RELAX) to improve the imaging performance with a reduced number of array antennas. RANM is employed in the cross-track imaging after pseudo-polar formatting the DLSLA 3-D SAR echo signal, then the reconstructed results are refined by RELAX. By taking advantage of the reweighted scheme, RANM can improve the resolution of the atomic norm minimization, and outperforms discretized compressive sensing schemes that suffer from off-grid effect. The simulated and real data experiments of DLSLA 3-D SAR verify the performance of the proposed algorithm.

Disparity compensation (DC) and transform coding are incorporated into a hybrid coding to reduce the code-rate of multiview images. However, occlusion and inaccurate disparity estimations (DE) impair the performance of DC, especially in spaceborne images. This paper proposes an adaptive disparity-compensation scheme for the compression of spaceborne multiview images, including stereo image pairs and three-line-scanner images. DC with adaptive loop filter is used to remove redundancy between reference images and target images and a wavelet-based coding method is used to encode reference images and residue images. In occlusion regions, the DC efficiency may be poor because no interview correlation exists. A rate-distortion optimization method is thus designed to select the best prediction mode for local regions. Experimental results show that the proposed scheme can provide significant coding gain compared with some other similar coding schemes, and the time complexity is also competitive.

An efficient lossless compression scheme for hyperspectral images using conventional recursive least-squares (CRLS) predictor with adaptive prediction bands is proposed. The proposed scheme first calculates the preliminary estimates to form the input vector of the CRLS predictor. Then the number of bands used in prediction is adaptively selected by an exhaustive search for the number that minimizes the prediction residual. Finally, after prediction, the prediction residuals are sent to an adaptive arithmetic coder. Experiments on the newer airborne visible/infrared imaging spectrometer (AVIRIS) images in the consultative committee for space data systems (CCSDS) test set show that the proposed scheme yields an average compression performance of 3.29 (bits/pixel), 5.57 (bits/pixel), and 2.44 (bits/pixel) on the 16-bit calibrated images, the 16-bit uncalibrated images, and the 12-bit uncalibrated images, respectively. Experimental results demonstrate that the proposed scheme obtains compression results very close to clustered differential pulse code modulation-with-adaptive-prediction-length, which achieves best lossless compression performance for AVIRIS images in the CCSDS test set, and outperforms other current state-of-the-art schemes with relatively low computation complexity.

This study presents an approach for the automatic detection of vehicles using very high-resolution images and road vector data. Initially, road vector data and aerial images are integrated to extract road regions. Then, the extracted road/street region is clustered using an automatic histogram-based fuzzy C-means algorithm, and edge pixels are detected using the Canny edge detector. In order to automatically detect vehicles, we developed a local perceptual grouping approach based on fusion of edge detection and clustering outputs. To provide the locality, an ellipse is generated using characteristics of the candidate clusters individually. Then, ratio of edge pixels to nonedge pixels in the corresponding ellipse is computed to distinguish the vehicles. Finally, a point-merging rule is conducted to merge the points that satisfy a predefined threshold and are supposed to denote the same vehicles. The experimental validation of the proposed method was carried out on six very high-resolution aerial images that illustrate two highways, two shadowed roads, a crowded narrow street, and a street in a dense urban area with crowded parked vehicles. The evaluation of the results shows that our proposed method performed 86% and 83% in overall correctness and completeness, respectively.

Based on spectral independence of different materials, independent component analysis (ICA), a blind source separation technique, can be applied to separate mixed hyperspectral signals. For the purpose of detecting objects on the sea and improving the precision of target recognition, an original ICA method is applied by analyzing the influence exerted by spectral features of different materials and mixture materials on spectral unmixing results. Due to the complexity of targets on the sea, several measured spectra of different materials have been mixed with water spectra to simulate mixed spectra for mixture spectra decomposition. Synthetic mixed spectra are generated by linear combinations of different materials and water spectra to obtain separated results. We then compared the separated results with the measured spectra of each endmember by coefficient of determination. We conclude that these factors that will change the original spectral characteristics of Gaussian distribution have significant influence on the separated results and selecting a proper initial matrix, and processing spectral data with lower noise can help improve the ICA method for more accurate separated results from hyperspectral data.

A direction of arrival (DOA) estimation algorithm for coherent signals in the presence of unknown mutual coupling is proposed. A group of auxiliary sensors in a uniform linear array are applied to eliminate the effects on the orthogonality of subspaces brought by mutual coupling. Then, a Toeplitz matrix, whose rank is independent of the coherency between impinging signals, is reconstructed to eliminate the rank loss of the spatial covariance matrix. Therefore, the signal and noise subspaces can be estimated properly. This method can estimate the DOAs of coherent signals under unknown mutual coupling accurately without any iteration and calibration sources. It has a low computational burden and high accuracy. Simulation results demonstrate the effectiveness of the algorithm.

Soil salinization is a common desertification process, especially in arid lands. Hyperspectral remote sensing of salinized soil is favored for its advantages of being efficient and inexpensive. However, soil moisture often jointly has a great influence on the soil reflectance spectra under field conditions. It is a challenge to establish a model to eliminate the effect of soil moisture and quantitatively estimate the salinity contents of slightly and moderately salt-affected soil. A controlled laboratory experiment was conducted by way of continuously monitoring changes of soil moisture and salt content, which was mainly focused on the slightly and moderately salt-affected soil. We investigated the external parameter orthogonalization (EPO) method to remove the effect of soil moisture (4 to 36% in weight base) by preprocessing soil spectral reflectance and establishing the partial least squares regression after EPO preprocessing model (EPO-PLS) to predict soil salt content. Through comparing PLS with EPO-PLS model, R² and ratio of prediction to deviation rose from 0.604 and 1.063, respectively, to 0.874 and 2.865 for validation data. Root mean square error and bias were, respectively, reduced from 1.163 and 0.141  g/100  g to 0.718 and 0.044  g/100  g. The performance of the model after EPO algorithm preprocessing was improved significantly.

Currently, various satellite processing centers produce extensive data, with different solutions of the same field being available. For instance, the Gravity Recovery and Climate Experiment (GRACE) has been monitoring terrestrial water storage (TWS) since April 2002, while the Center for Space Research (CSR), the Jet Propulsion Laboratory (JPL), the GeoForschungsZentrum (GFZ), and the Groupe de Recherche de Géodésie Spatiale (GRGS) provide individual monthly solutions in the form of Stokes coefficients. The inverted TWS maps (or the regionally averaged values) from these coefficients are being used in many applications; however, as no ground truth data exist, the uncertainties are unknown. Consequently, the purpose of this work is to assess the quality of each processing center by estimating their uncertainties using a generalized formulation of the three-cornered hat (TCH) method. Overall, the TCH results for the study period of August 2002 to June 2014 indicate that at a global scale, the CSR, GFZ, GRGS, and JPL presented uncertainties of 9.4, 13.7, 14.8, and 13.2 mm, respectively. At a basin scale, the overall good performance of the CSR was observed at 91 river basins. The TCH-based results were confirmed by a comparison with an ensemble solution from the four GRACE processing centers.

Raw data generation for synthetic aperture radar (SAR) is very powerful for designing systems and testing imaging algorithms. In this paper, a raw data generation method based on Fourier analysis for one-stationary bistatic SAR is presented. In this mode, two-dimensional (2-D) spatial variation is the major problem faced by the fast Fourier transform–based raw data generation. To deal with this problem, a 2-D linearization followed by a 2-D frequency transformation is employed in this method. This frequency transformation can reflect the 2-D spatial variation. Residual phase compensation is also discussed. Numerical simulation verifies the method.

In recent years, the popularity of tree-based ensemble methods for land cover classification has increased significantly. Using WorldView-2 image data, we evaluate the potential of the oblique random forest algorithm (oRF) to classify a highly heterogeneous protected area. In contrast to the random forest (RF) algorithm, the oRF algorithm builds multivariate trees by learning the optimal split using a supervised model. The oRF binary algorithm is adapted to a multiclass land cover and land use application using both the “one-against-one” and “one-against-all” combination approaches. Results show that the oRF algorithms are capable of achieving high classification accuracies (<80%). However, there was no statistical difference in classification accuracies obtained by the oRF algorithms and the more popular RF algorithm. For all the algorithms, user accuracies (UAs) and producer accuracies (PAs) <80% were recorded for most of the classes. Both the RF and oRF algorithms poorly classified the indigenous forest class as indicated by the low UAs and PAs. Finally, the results from this study advocate and support the utility of the oRF algorithm for land cover and land use mapping of protected areas using WorldView-2 image data.

In high-resolution bistatic synthetic aperture radar (SAR) systems, parameter estimation is essential to moving target imaging quality. However, precise parameters are difficult to obtain without priori information due to the relative along-track and across-track velocities between the moving target and platforms that change with time. A parameter estimation and imaging approach for moving targets is proposed. First, slant range and relative velocities expression are deduced based on the geometry of bistatic SAR model with one stationary configuration. Then, range curvature term are compensated skillfully by fitting the range-compressed curve in two-dimensional time domain, meanwhile, the initial estimated range walk slope can be achieved. Finally, precise Doppler centroid is estimated through searching for the maximum contrast with folding search algorithm, which is giving consideration to both searching precision and computational complexity. Thus, the proposed algorithm provides an effective way for parameter estimation and imaging of moving target without prior information and interpolation operation. Experimental results show the effectiveness of the proposed method.

In inverse synthetic aperture radar (ISAR) imaging of targets with complex motion, the azimuth echoes have to be modeled as multicomponent cubic phase signals (CPSs) after motion compensation. For the CPS model, the chirp rate and the quadratic chirp rate deteriorate the ISAR image quality due to the Doppler frequency shift; thus, an effective parameter estimation algorithm is required. This paper focuses on a parameter estimation algorithm for multicomponent CPSs based on the local polynomial ambiguity function (LPAF), which is simple and can be easily implemented via the complex multiplication and fast Fourier transform. Compared with the existing parameter estimation algorithm for CPS, the proposed algorithm can achieve a better compromise between performance and computational complexity. Then, the high-quality ISAR image can be obtained by the proposed LPAF-based ISAR imaging algorithm. The results of the simulated data demonstrate the effectiveness of the proposed algorithm.

Remote sensing images usually need scale-up for visualization or representation, using only one original image. According to the performance of detective sensors, a new and more applicable method is proposed here. To enhance the high-frequency components, the modulation transform function compensation (MTFC) part focuses on how to adjust the spatial response before and after launch, under signal-to-noise ratio control. This largely reduces the ring phenomenon from incorrect point spread function guesses. Then a contour stencil prior manages to limit edge artifacts in the upscaled image after MTFC. An iterative backprojection operation with fast convergence is also utilized to bring about intensity and contour consistency. We finally present our analysis based on real images with parallel design for full speed. Compared with existing algorithms, the operator illustrates its potential to keep geometric features and extend the visual and quantitative quality for further analysis.

Inverse synthetic aperture radar (ISAR) imaging of maneuvering target is a main topic in the field of radar signal processing, and the received signal in a range bin can usually be characterized as multicomponent cubic chirps with constant amplitudes after motion compensation. In fact, the phenomenon of migration through resolution cell (MTRC) often occurs for the target’s complex motion, and this will induce the time-varying character for the amplitudes of cubic chirps. An algorithm for the parameters estimation of multicomponent cubic chirps with time-varying amplitudes based on the extension form of match Fourier transform is proposed, and by using it in ISAR imaging of maneuvering target, the quality of images can be improved significantly compared with the constant amplitudes model. Results of simulated and real data validate the effectiveness of the algorithm in this paper.

Classification of high-resolution remote sensing images is a challenging problem. Bag-of-words (BOW) based classification algorithms have obtained good performances in recent years. However, how the procedures of the BOW framework affect the classification result is still an open question. We present three visualization algorithms to reconstruct images from BOW. After visualization, we can see what the computer actually “sees” in an image feature. We also analyze the procedures of the BOW framework, namely, descriptor extraction and histogram generation, in detail. It is found that the descriptors should not be blamed for wrong classification. The histogram generation strategy should be improved to be robust with image transformation. Then some suggestions are posed for future improvement of BOW-based remote sensing image classification algorithms.

Remote sensing studies aiming at assessing woody biomass have demonstrated a strong relationship between canopy height and plot-level aboveground biomass, but most of these studies focused on closed canopy forests. To date, a few studies have examined the strength and reliability of this relationship using large footprint lidar in savannas. Furthermore, there have been few studies of appropriate methods for the comparison of models that relate aboveground biomass to canopy height metrics without consideration of variation in species composition (generic models) to models developed for individual species composition or vegetation types. We developed generic models using the classical least-squares regression modeling approach to relate selected canopy height metrics to aboveground woody biomass in a savanna landscape. Hierarchical Bayesian analysis (HBA) was then used to explore the implications of using generic or composition-specific models. Our study used the estimates of aboveground biomass from field data, canopy height estimates from airborne discrete return lidar, and a proxy for canopy cover (the Normalized Difference Vegetation Index) from Landsat 5 Thematic Mapper data, collected from the oak savannas of Tejon Ranch Conservancy in Kern County, California. Models were developed and analyzed using estimates of canopy height and aboveground biomass calculated at the level of 50-m diameter plots, comparable with footprint diameter of existing large footprint spaceborne lidar data. The two generic models that incorporated canopy cover proxies performed better than one model that did not use canopy cover information. From the HBA, we found out that for all models both the intercept and slope had interspecific variability. The valley oak dominated plots consistently had higher slopes and intercepts, whereas the plots dominated by blue oaks had the lowest. However, the intercept and slope values of the composition-specific models did not differ much from the generic (overall) values and their 95% credible intervals (CIs) overlapped the generic mean values. We conclude that the narrow range of the distribution and the overlap of the CIs of the composition-specific and generic parameters suggest that scaling rules do exist for savannas. The distribution of the posterior densities of the differences between composition-level and generic-level parameter values showed high support for the use of generic parameters, suggesting that all three models are applicable across the range of compositions in this study. Therefore, in this case, the choice of method depends more on secondary considerations such as data availability and scale of analyses.

The current study evaluates the application of a moderate resolution imaging spectroradiometer (MODIS) triangle-based method to estimate evapotranspiration (ET) in subalpine environments. Topographic corrections and improved soil moisture representation are applied to a previously developed net radiation (R_n) model and triangle algorithm to develop an 8-day average ET product based solely on satellite products. We evaluate modeled R_n and MODIS ET (MOD-ET) against ground-based values at four sites in the Sierra Nevada of northern California and also present a comparison between two monthly distributed ET datasets [operational simplified surface energy balance (SSEBop) and MODIS MOD16]. Modeled daily R_n results indicate a systematic underestimation (between −83 and −110  W/m² bias). Consequently, R_n is bias-corrected before calculating MOD-ET. MOD-ET validation shows correlations between 0.15 and 0.45 with errors between 73 and 126  W/m². MOD-ET and SSEBop ET report correlations of 0.36 and 0.20, respectively, on average, compared to ground-based monthly ET. MOD16 underestimates monthly totals, with bias values on the range of −14 to −144  W/m². Semiarid conditions and scale differences between the MODIS pixel and station contribute to errors with respect to observation. Overall, MOD-ET provides reasonable ET estimates and may better capture temporal dynamics in environments undergoing chronic disturbance.

Existing segmentation methods require manual interventions to optimally extract objects from cluttered background, so that they can hardly work well in automated surveillance systems. In order to automatically extract aircrafts from very high-resolution images, we proposed a segmentation method that combines bottom-up and top-down cues. Three essential principles from local contrast, global contrast, and center bias are involved to compute bottom-up cue. In addition, top-down cue is computed by incorporating aircraft shape priors, and it is achieved by training a classifier from a rich set of visual features. Iterative operations and adaptive fitting are designed to get refined results. Experimental results demonstrated that the proposed method can provide significant improvements on the segmentation accuracy.

The dynamics of land use/land cover change in the Lower Nhecolândia wetland are marked by deforestation for pasture expansion, resulting in a real threat to the ecological stability. The aim of our work was to analyze the spatial distribution of land cover changes in the Lower Nhecolândia from 1985 to 2013 and to predict changes in trends for 2040. The mapping of land cover changes was developed using Landsat satellite images of 1985, 1999, 2007, and 2013, based on geographic object-based image analysis approach. This study uses integrated Markov chains and cellular automata modeling and multicriteria evaluation techniques to produce transition probability maps and describe the trajectory analysis methodology to construct a continuity of spatial and temporal changes for the wetland. The results of the multitemporal change detection classification show that, from 1985 to 2013, the forest woodland decreased by 6.89% and the grassland class increased by 18.29%. On the other hand, all water bodies showed a reducing trend, while the bare soil class increased compared to 1985, but did not present a regular trend of increase or decrease. From the present day, the trend for the future is a reduction of almost 6.4% by 2040. We found that deforestation actions will be concentrated in the areas with the highest concentration of saline lakes, constituting a serious threat to the natural functioning of this environmental system.

Lithological mapping is a fundamental step in various mineral prospecting studies because it forms the basis of the interpretation and validation of retrieved results. Therefore, this study exploited the multispectral Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat 8 Operational Land Imager (OLI) data in order to map lithological units in the Bas Drâa inlier, at the Moroccan Anti Atlas. This task was completed by using principal component analysis (PCA), band ratios (BR), and support vector machine (SVM) classification. Overall accuracy and the kappa coefficient of SVM based on ground truth in addition to the results of PCA and BR show an excellent correlation with the existing geological map of the study area. Consequently, the methodology proposed demonstrates a high potential of ASTER and Landsat 8 OLI data in lithological units discrimination.

Estimation of vertical ground displacements due to seasonal periods of freezing and thawing was carried out on the Selenga river mouth using differential radar interferometry. The results of seasonal radar interferometry measurements from 2008 to 2010 have been compared with the survey data for 2011, under the assumption that the seasonal deformation is comparable for different years and lies within the limits of geodetic leveling error. The difference between the interferometric and geodetic measurements is <1  cm in winter months, whereas in summer, as the upper soil moisture content varies, the difference reaches 2 to 2.7 cm. It has been concluded that the moisture content and its profile should be taken into consideration in summer or when choosing the radar interferometry in winter.

Atmosphere causes delay distortions in synthetic aperture radar images. Numerical weather prediction models, which compute the forecast stepwise, are beneficial in correcting this distortion. After initialization, the model needs time to reach a balanced state, such that first prediction steps contain errors. The imbalance causes false predicted precipitation, which then affects the water vapor distribution. Correspondingly, the predicted zenith path delay (ZPD), which depends on this distribution, is affected by the initial imbalances. The digital filtering initialization (DFI) technique reduces these imbalances and the ZPD prediction disturbances, respectively. The objective of this paper is the accuracy gain for ZPD predictions, which is achieved by this technique. For the accuracy gain investigation, predicted ZPD time series of the weather research and forecasting (WRF) model with and without DFI are compared against Global Navigation Satellite System (GNSS)-derived time series from 233 GNSS stations mainly located in Germany. Three conclusions are found. First, the experiment confirms that the DFI technique improves the precipitation forecast. Second, the corresponding accuracy gain, i.e., the bias of ZPD predictions, improves by about 13%. Third, the accuracy gain is only valid for the first 4 h of the prediction.

Automatic identification of landslides based on remote sensing images is important for investigating disasters and producing hazard maps. We propose a method to detect shallow landslides automatically using Wordview2 images. Features such as high soil brightness and low vegetation coverage can help identify shallow landslides on remote sensing images. Therefore, soil brightness and vegetation index were chosen as indexes for landslide remote sensing. The back scarp of a landslide can form dark shadow areas on the landslide mass, affecting the accuracy of landslide extraction. To eliminate this effect, the shadow index was chosen as an index. The first principal component (PC1) contained >90% of the image information; therefore, this was also selected as an index. The four selected indexes were used to synthesize a new image wherein information on shallow landslides was enhanced, while other background information was suppressed. Then, PC1 was extracted from the new synthetic image, and an automatic threshold segmentation algorithm was used for segmenting the image to obtain similar landslide areas. Based on landslide features such as slope, shape, and area, nonlandslide areas were eliminated. Finally, four experimental sites were used to verify the feasibility of the developed method.

We propose an improved collaborative representation model with multitask learning using spatial support (ICRTD-MTL) for target detection (TD) in hyperspectral imagery. The proposed model consists of the following aspects. First, multiple features are extracted from the hyperspectral image to represent pixels from different perspectives. Next, we apply these features into the unified CRTD-MTL to acquire a collaborative vector for each feature. To adjust the contribution of each feature, a weight coefficient is included in the optimization problem. Once the collaborative vector is obtained, the class of the test sample can be determined by the characteristics of the collaborative vector on reconstruction. Finally, the spatial correlation and spectral similarity of adjacent neighboring pixels are incorporated into each feature to improve the detection accuracy. The experimental results suggest that the proposed algorithm obtains an excellent performance.

This paper proposes a robust method for the matching of attributed scattering centers (ASCs) with application to synthetic aperture radar automatic target recognition (ATR). For the testing image to be classified, ASCs are extracted to match with the ones predicted by templates. First, Hungarian algorithm is employed to match those two ASC sets initially. Then, a precise matching is carried out through a threshold method. Point similarity and structure similarity are calculated, which are fused to evaluate the overall similarity of the two ASC sets based on the Dempster–Shafer theory of evidence. Finally, the target type is determined by such similarities between the testing image and various types of targets. Experiments on the moving and stationary target acquisition and recognition data verify the validity of the proposed method.

The use of unmanned aerial vehicles (UAVs) for remote sensing applications is becoming more frequent. However, this type of information can result in several software problems related to the huge amount of data available. Object-based image analysis (OBIA) has proven to be superior to pixel-based analysis for very high-resolution images. The main objective of this work was to explore the potentialities of the OBIA methods available in two different open source software applications, Spring and OTB/Monteverdi, in order to generate an urban land cover map. An orthomosaic derived from UAVs was considered, 10 different regions of interest were selected, and two different approaches were followed. The first one (Spring) uses the region growing segmentation algorithm followed by the Bhattacharya classifier. The second approach (OTB/Monteverdi) uses the mean shift segmentation algorithm followed by the support vector machine (SVM) classifier. Two strategies were followed: four classes were considered using Spring and thereafter seven classes were considered for OTB/Monteverdi. The SVM classifier produces slightly better results and presents a shorter processing time. However, the poor spectral resolution of the data (only RGB bands) is an important factor that limits the performance of the classifiers applied.

A Ku-band low incidence backscatter model (KuLMOD) for retrieving wind speeds from Tropical Rainfall Mapping Mission (TRMM) precipitation radar (PR) data is proposed. The data set consisted of TRMM PR observations and collocated National Data Buoy Center (NDBC) and Tropical Ocean Global Atmosphere program buoy-measured wind and wave data. The TRMM PR data properties were analyzed with regard to their dependence on spatial resolution, wind speed, relative wind direction, and significant wave height. The KuLMOD model was developed using incidence angles (0.5 to 6.5 deg) and wind speeds (1.5 to 16.5  m/s) as inputs. The model coefficients were derived by fitting the collocated data. The KuLMOD-derived normalized radar cross section, σ₀, was compared with those obtained from the TRMM PR observations and a quasi-specular theoretical model and showed good agreement. With the KuLMOD, the wind speeds were retrieved from the TRMM PR data using the least squares method and validated with the buoy measurements, yielding a root mean square error of 1.45  m/s. The retrieval accuracies for the different incidence angles, wind speeds, and spatial resolutions were obtained.

This study introduces derivations of the soil isoline equation for the case of partial canopy coverage. The derivation relied on extending the previously derived soil isoline equations, which assumed full canopy coverage. This extension was achieved by employing a two-band linear mixture model, in which the fraction of vegetation cover (FVC) was considered explicitly as a biophysical parameter. A parametric form of the soil isoline equation, which accounted for the influence of the FVC, was thereby derived. The differences between the soil isolines of the fully covered and partially covered cases were explored analytically. This study derived the approximated isoline equations for nine cases defined by the choice of the truncation order in the parametric form. A set of numerical experiments was conducted using coupled leaf and canopy radiative transfer models. The numerical results revealed that the accuracy of the soil isoline increased with the truncation order, and they confirmed the validity of the derived expressions.

Digital elevation models (DEMs) are essential in many professional areas as they produce georeferenced elevation data that are critical for a wide range of studies, computations, decision-making processes, and derived products. Quality control thus becomes necessary to quantify the accuracy of the information provided. We assessed the accuracy of elevation data estimated by DEMs derived from LiDAR data representing diverse land cover types. For this purpose, we used the FUSION software and explored variations in accuracy in relation to the following factors: input data, interpolation methods, terrain slope, heterogeneity of land cover, and LiDAR point density. We selected and measured 1157 checkpoints by using total station and GPS techniques and following a stratified random design in order to validate the LiDAR-derived DEMs. We used robust estimators, nonparametric tests, and analysis of variance to examine the elevation errors. The study findings showed the following: (1) using the full set of LiDAR returns did not improve elevation accuracy relative to using the last-return data set; (2) using the minimum switch for interpolation did not improve accuracy compared to the default behavior of the interpolator; (3) land cover and slope significantly affected accuracy; (4) DEMs tended to underestimate elevation; and (5) the mean density of the returns classified as ground was significantly affected by land cover and slope factors.

Based on the model of Lombardini et al. [J. Atmos. Ocean. Technol.6(6), 882–890 (1989)], which can predict the hydrodynamic damping of rough sea surfaces in the presence of oil films, the influence of sea slicks on the sea surface roughness spectrum and sea surface geometrical structure is examined briefly in the present study. On this basis, the influence of sea slicks on the angular distribution of the bistatic scattering coefficient of sea surfaces and the Doppler spectrum signature of backscattered radar sea-echo is investigated in detail based on a frequency-domain numerical method of the parallel fast multiple method. Simulation results show that Doppler spectrum signatures including Doppler shift and spectral bandwidth of radar sea-echo are significantly affected by sea slicks, which are qualitatively consistent with wave-tank or open sea measurements. Moreover, simulation results indicate that the Doppler spectrum signature is a promising technique for remote sensing of oil films floating on sea surfaces.

Maximum likelihood classifier (MLC) and support vector machines (SVMs) are commonly used supervised classification methods in remote sensing applications. MLC is a parametric method, whereas SVM is a nonparametric method. In an environmental application, a hybrid scheme is designed to identify forest encroachment (FE) pockets by classifying medium-resolution remote sensing images with SVM, incorporating knowledge-base and GPS readings in the geographical information system. The classification scheme has enabled us to identify small scattered noncontiguous FE pockets supported by ground truthing. On Baratang Island, the detected FE area from the classified thematic map for the year 2003 was ∼202  ha, and for the year 2013, the encroachment was ∼206  ha. While some of the older FE pockets were vacated, new FE pockets appeared in the area. Furthermore, comparisons of different classification results in terms of Z-statistics indicate that linear SVM is superior to MLC, whereas linear and nonlinear SVM are not significantly different. Accuracy assessment shows that SVM-based classification results have higher accuracy than MLC-based results. Statistical accuracy in terms of kappa values achieved for the linear SVM-classified thematic maps for the years 2003 and 2013 is 0.98 and 1.0, respectively.

This paper proposes a method that combined hyperspectral remote sensing with super-low-frequency (SLF) electromagnetic detection to extract oil and gas reservoir characteristics from surface to underground, for the purpose of determining oil and gas exploration target regions. The study area in Xinjiang Karamay oil–gas field, China, was investigated. First, a Hyperion dataset was used to extract altered minerals (montmorillonite, chlorite, and siderite), which were comparatively verified by field survey and spectral measurement. Second, the SLF electromagnetic datasets were then acquired where the altered minerals were distributed. An inverse distance weighting method was utilized to acquire two-dimensional profiles of the electrical feature distribution of different formations on the subsurface. Finally, existing geological data, field work, and the results derived from Hyperion images and SLF electromagnetic datasets were comprehensively analyzed to confirm the oil and gas exploration target region. The results of both hyperspectral remote sensing and SLF electromagnetic detection had a good consistency with the geological materials in this study. This paper demonstrates that the combination of hyperspectral remote sensing and SLF electromagnetic detection is suitable for the early exploration of oil and gas reservoirs, which is characterized by low exploration costs, large exploration areas, and a high working efficiency.

Despite recent rapid advancement in remote sensing technology, accurate mapping of the urban landscape in China still faces a great challenge due to unusually high spectral complexity in many big cities. Much of this complication comes from severe spectral confusion of impervious surfaces with polluted water bodies and bright bare soils. This paper proposes a two-step land cover decomposition method, which combines optical and thermal spectra from different seasons to cope with the issue of urban spectral complexity. First, a linear spectral mixture analysis was employed to generate fraction images for three preliminary endmembers (high albedo, low albedo, and vegetation). Seasonal change analysis on land surface temperature induced from thermal infrared spectra and coarse component fractions obtained from the first step was then used to reduce the confusion between impervious surfaces and nonimpervious materials. This method was tested with two-date Landsat multispectral data in Shanghai, one of China’s megacities. The results showed that the method was capable of consistently estimating impervious surfaces in highly complex urban environments with an accuracy of R² greater than 0.70 and both root mean square error and mean average error less than 0.20 for all test sites. This strategy seemed very promising for landscape mapping of complex urban areas.

We aim to improve the predictive mapping of stem volume with airborne laser scanning (ALS) data acquired in Laos by adapting the area-based approach (ABA) to a tropical context. Separating laser returns of bushes from main stories with a cut-off threshold is a step very important to the ABA. The adaptation focused here on applying global and plot-adaptive cut-off thresholds to improve the extraction of canopy metrics. In order to select the optimal global cut-off threshold for removing understory bushes and ground objects, a sensitivity analysis of the modeling efficacy to the global cut-off threshold was conducted in the range from 0 to 5 m at 0.1-m intervals. To account for structural variation between plots, a simple plot-adaptive method was proposed for adjusting the threshold of each specific plot. The results showed that the optimal global cut-off threshold, which implicitly assumed the forest structure being homogeneous for all plots was 3.6 m. A model based on the plot-adaptive cut-off thresholds achieved better accuracy (RMSE 28%) than did the optimal global threshold-based model (RMSE 30%). It is concluded that the ALS-based canopy metrics extracted using the plot-adaptive method describe the structural heterogeneity of tropical forests adequately, whereas the global thresholding method is contingent on the forest structure being simple.

Forest cover change is one of the most important land cover changes. Intensified economic development and population growth have led to extensive deforestation and afforestation, and greatly altered the forest landscape patterns. This paper analyzed how socioeconomic drivers influenced the forest extent and landscape patterns in a lagged manner within the Li River Basin, Guangxi Zhuang Autonomous Region, China, during the period of 1991 to 2013. First, the temporal variations of forest extent and landscape patterns as quantified by six forest cover metrics were analyzed. The cross-correlation analysis was employed to examine the lagged responses of forest cover metrics to each socioeconomic driver across regions. Last, the fixed effects regression models were built to quantitatively assess the spatially variant relationships. The results demonstrated that the influence of socioeconomic drivers lagged, and the lagged influence varied across regions.

Traditional remote sensing change-detection algorithms only generate change-detection map and few quantitative evaluation indicators as the results, but they are unable to provide comprehensive analysis and further understanding of the detected changes. Aiming to assess regional development, we develop a comprehensive analysis method for human-driven environmental change by multitemporal remote sensing images. In order to adapt to analyze the time-varying multiple changed objects, an observed object-specified dynamic Bayesian network (i.e., OOS-DBN) is first proposed to adjust DBN structure and variables. Using semantic analysis for the relationship between multiple changed objects and regional development, all levels of situations and evidences (i.e., detected attributes of changed objects) are extracted as hidden variables and observed variables and inputted to OOS-DBN. Furthermore, conditional probabilities are computed by levels and time slices in OOS-DBN, resulting in the comprehensive analysis results. The experiments on the coastal region in Huludao, China, from 2003 to 2014 demonstrate that comprehensive analysis of changes reflecting that reclamation, construction of infrastructure, and New Huludao port contributed to the regional development. During four time slices, this region experienced rapid and medium-speed development, whose corresponding probabilities are 0.90, 0.87, 0.41, and 0.54, respectively, which is consistent with our field surveys.

Building extraction is one of the main research topics of the photogrammetry community. This paper presents automatic algorithms for building boundary extractions from aerial LiDAR data. First, segmenting height information generated from LiDAR data, the outer boundaries of aboveground objects are expressed as closed chains of oriented edge pixels. Then, building boundaries are distinguished from nonbuilding ones by evaluating their shapes. The candidate building boundaries are reconstructed as rectangles or regular polygons by applying new algorithms, following the hypothesis verification paradigm. These algorithms include constrained searching in Hough space, enhanced Hough transformation, and the sequential linking technique. The experimental results show that the proposed algorithms successfully extract building boundaries at rates of 97%, 85%, and 92% for three LiDAR datasets with varying scene complexities.

Using UV Raman Lidar for aerosol extinction (α_ext), and combining microwave radiometer-derived liquid water path (LWP) with multifilter rotating shadowband radiometer-derived cloud optical depth (τ_cod) to retrieve cloud droplet effective radius (R_eff), we observe clear signatures of the Twomey aerosol indirect effect (IE) under certain specialized conditions. The aerosol–cloud index (ACI) or IE slope relating cloud droplet radius to aerosol loading is calculated and shown to be quantitatively consistent with theoretical constraints. To demonstrate consistency, we use both a neural network multiband (default) approach and a dual-channel (DC) approach for the LWP and observe that the DC approach is generally more robust with more successful retrievals leading to a reduction of error in our regression analysis. We also perform an uncertainty analysis of the IE regression slope taking into account the major sources of error in cloud property retrieval and demonstrate that only sufficiently high values of the IE slope should be observable. Finally, based on the results of multiple cases, we observe the importance of vertical wind uptake on the IE signature.

The day/night band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi National Polar-orbiting Partnership (Suomi-NPP) represents a major advancement in night time imaging capabilities. DNB covers almost seven orders of magnitude in its dynamic range from full sunlight to half-moon. To achieve this large dynamic range, it uses four charge-coupled device arrays in three gain stages. The low gain stage (LGS) gain is calibrated using the solar diffuser. In operations, the medium and high gain stage values are determined by multiplying the gain ratios between the medium gain stage, and LGS, and high gain stage (HGS) and LGS, respectively. This paper focuses on independently verifying the radiometric accuracy and stability of DNB HGS using DNB observations of ground vicarious calibration sites under lunar illumination at night. Dome C in Antarctica in the southern hemisphere and Greenland in the northern hemisphere are chosen as the vicarious calibration sites. Nadir observations of these high latitude regions by VIIRS are selected during perpetual night season, i.e., from April to August for Dome C and from November to January for Greenland over the years 2012 to 2013. Additional selection criteria, such as lunar phase being more than half-moon and no influence of straylight effects, are also applied in data selection. The lunar spectral irradiance model, as a function of Sun–Earth–Moon distances and lunar phase, is used to determine the top-of-atmosphere reflectance at the vicarious site. The vicariously derived long-term reflectance from DNB observations agrees with the reflectance derived from Hyperion observations. The vicarious trending of DNB radiometric performance using DOME-C and Greenland under moon light shows that the DNB HGS radiometric variability (relative accuracy to lunar irradiance model and Hyperion observation) is within 8%. Residual variability is also discussed.

A three-dimensional electromagnetic model (3-D EM-model)–based scattering center matching method is developed for synthetic aperture radar automatic target recognition (ATR). 3-D EM-model provides a concise and physically relevant description of the target’s electromagnetic scattering phenomenon through its scattering centers which makes it an ideal candidate for ATR. In our method, scatters of the 3-D EM-model are projected to the two-dimensional measurement plane to predict scatters’ location and scattering intensity properties. Then the identical information is extracted for scatters in measured data. A two-stage iterative operation is applied to match the model-predicted scatters and the measured data-extracted scatters by combining spatial and attributed information. Based on the two scatter sets’ matching information, a similarity measurement between model and measured data is obtained and recognition conclusion is made. Meanwhile, the target’s configuration is reasoned with 3-D EM-model serving as a reference. In the end, data simulated by electromagnetic computation verified this method’s validity.

Land subsidence can be a major problem where there are large-scale underground activities such as oil extraction. This paper addresses the spatial variability of land subsidence over Minagish and Umm Gudair oil fields of Kuwait. Synthetic aperture radar interferometry (InSAR) with multiple reference scenes using a persistent scatterer InSAR toolchain was employed in this study. Twenty-nine scenes of advanced synthetic aperture radar data (for the period January 2005 to August 2009) were used to make 20 pairs of interferograms (with high coherence and low noise) of stable point-like reflectors. The output of this study is the land subsidence maps of Minagish and Umm Gudair oil fields with a spatial resolution of 40 m. The results indicate that there is land subsidence of 29.9  mm/year in the southern part of the oil field (Umm Gudair). This is the first detailed assessment of land subsidence in the Minagish–Umm Gudair oil fields; therefore, no ground-truth data are available to compare the subsidence results. The results were consistent, indicating their validity.

Atmosphere causes distortions in the geometry and phases of synthetic aperture radar images denoted by the atmospheric phase screen (APS). Numerical weather models are beneficial in correcting these disturbances. After initialization, the models need time to derive a physical valid state. This is called the spin-up time, and it affects delay predictions. The positive impact of a 12-h spin-up time on delay mitigation has not yet been reported and is the objective of this paper. Hence, four independent experiments are considered, revealing the best accuracy in the case of 12-h predictions and showing the best consistency of spatial frequencies. First, global positioning system zenith path delay (ZPD) series are compared with model-predicted ZPD series, which reports a 28% reduction of the root mean squared error. Second, the absolute ranging technique as an application of the delay prediction reports a 21% standard deviation decrease of position estimates. Third, a comparison of spatial frequencies between APS predictions and interferograms shows a closer consistency using a 12-h rather than a 6-h spin-up time. Fourth, APS mitigation in interferograms as an application of APS prediction is twice as good with respect to the 12-h spin-up time as with the 6-h spin-up time.

Differential synthetic aperture radar interferometry (D-InSAR) is characterized mainly by high spatial resolution and high accuracy over a wide coverage range. Because of its unique advantages, the technology is widely used for monitoring ground surface deformations. However, in coal mining areas, the ground surface can suffer large-scale collapses in short periods of time, leading to inaccuracies in D-InSAR results and limiting its use for monitoring mining subsidence. We propose a data-processing method that overcomes these disadvantages by combining D-InSAR with the probability integral method used for predicting mining subsidence. Five RadarSat-2 images over Fengfeng coal mine, China, were used to demonstrate the proposed method and assess its effectiveness. Using this method, surface deformation could be monitored over an area of thousands of square kilometers, and more than 50 regions affected by subsidence were identified. For Jiulong mine, nonlinear subsidence cumulative results were obtained for a time period from January 2011 to April 2011, and the maximum subsidence value reached up to 299 mm. Finally, the efficiency and applicability of the proposed method were verified by comparing with data from leveling surveying.

Lake level and volume are sensitive to climate change, and their changes can affect the sustainable utilization of regional water resources. Satellite radar/laser altimetry has effectively been used for monitoring water-level changes in recent years. In this study, satellite altimetry data and optical images were used to assess the changes in water level, area, and volume of Hulun Lake in north-eastern China. We derived a time series of lake levels for nearly two decades (1992 to 2010) from the altimetry data of two satellite sensors (Topex/Poseidon and Envisat RA-2); additionally, lake surface extent was extracted from Landsat TM/ETM+ images during the same period. The results indicate that the water level, area, and volume of Hulun Lake decreased over the past two decades. The water level shows a significant decrease (−0.36  m/year) of a total of −5.21  m from 1992 to 2010, specifically including a slight decrease (−0.4  m) during 1992 to 1999 and a sudden drop (−4.81  m) during 2000 to 2010. There has also been a consistent and significant reduction in lake area (−355.35  km²) and volume (−12.92  km³). An integrated examination on changes in temperature, evaporation, precipitation, and runoff during 1992 to 2010 shows that the main changes in the Hulun Lake area are correlated with increasing temperature (0.47°C/year) and evaporation (13.61  mm/year), as well as decreasing precipitation (−6.58  mm/year) and runoff (−1.04×10⁸  m³/year). Thus, we infer that climate warming is likely the main cause of the changes in water level, area, and volume of Hulun Lake. In addition, anthropogenic factors accelerate the degradation of the Hulun Lake wetland to some extent.

Images captured from airborne imaging systems can be mosaicked for diverse remote sensing applications. The objective of this study was to identify appropriate mosaicking techniques and software to generate mosaicked images for use by aerial applicators and other users. Three software packages—Photoshop CC, Autostitch, and Pix4Dmapper—were selected for mosaicking airborne images acquired from a large cropping area. Ground control points were collected for georeferencing the mosaicked images and for evaluating the accuracy of eight mosaicking techniques. Analysis and accuracy assessment showed that Pix4Dmapper can be the first choice if georeferenced imagery with high accuracy is required. The spherical method in Photoshop CC can be an alternative for cost considerations, and Autostitch can be used to quickly mosaic images with reduced spatial resolution. The results also showed that the accuracy of image mosaicking techniques could be greatly affected by the size of the imaging area or the number of the images and that the accuracy would be higher for a small area than for a large area. The results from this study will provide useful information for the selection of image mosaicking software and techniques for aerial applicators and other users.

The performance of two direct-detection atmospheric lidar systems with very different methods of generating and detecting laser radiation is compared as the result of a field experiment held in March 2015, in Chico, California. During the noncontinuous, 11-day test period, in which the systems operated side by side, the micropulse lidar was operated at its maximum pulse repetition frequency (15 kHz) and integrated elastic backscatter over the interpulse period of the analog direct-detection lidar (0.1 s). Operation at the high pulse repetition frequency resulted in second-trip echoes that contaminated portions of the data. The performance of the micropulse lidar varied with background brightness—as expected with a photon-counting receiver—yet showed equal or larger backscatter intensity signal-to-noise ratio throughout the experiment. Examples of wind fields and time series of wind vectors from both systems during the Chico experiment are presented. In addition, scans over the ocean that were collected by the micropulse lidar during a subsequent deployment on the northern California coast are presented. We conclude by reviewing the advantages and disadvantages of each system and make some suggestions to improve the design and performance of future systems.

This PDF file contains the errata for “JRS Vol. 10 Issue 01 Paper JARS-2016-0106-ERR” for JRS Vol. 10 Issue 01

This PDF file contains the errata for “JRS Vol. 10 Issue 01 Paper JARS-2016-0107-ERR” for JRS Vol. 10 Issue 01