Analytical forms of eigenvalues are given based on the coherence matrix simplified under the hypothesis of reflection symmetry, and three parameters P_S, P_D, and P_M representing the individual contribution of single, double, and multiple scattering mechanisms to the total backscattering are defined. The single scattering is confirmed as a direct description of the Bragg scattering while the double and multiple scatterings are supposed to be classified as non-Bragg scattering. Experiments using quad-polarization single look complex C-band RADARSAT-2 SAR images indicate that the oil slick mainly dampens the Bragg scattering, due to the reduction of single scattering over oil-covered sea surfaces. The decrease of P_S results in the corresponding increase of P_D and P_M, and the values of P_S over clean sea surfaces are higher than those of oil slicks on the P_S image. Therefore, P_S can be selected to discriminate oil slicks from clean sea surfaces. By comparing the two imagery experiments, we also note that the relative contribution of Bragg scattering to the backscattering over oil-covered surfaces is highly dependent on the local wind field.

Comprehensive and less costly forest inventory approaches are required to monitor the spatiotemporal dynamics of key species in forest ecosystems. Subpixel analysis using the earth resources data analysis system imagine subpixel classification procedure was tested to extract Olea europaea subsp. cuspidata and Juniperus procera canopies from Landsat 7 enhanced thematic mapper plus imagery. Control points with various canopy area fractions of the target species were collected to develop signatures for each of the species. With these signatures, the imagine subpixel classification procedure was run for each species independently. The subpixel process enabled the detection of O. europaea subsp. cuspidata and J. procera trees in pure and mixed pixels. Total of 100 pixels each were field verified for both species. An overall accuracy of 85% was achieved for O. europaea subsp. cuspidata and 89% for J. procera. A high overall accuracy level of detecting species at a natural forest was achieved, which encourages using the algorithm for future species monitoring activities. We recommend that the algorithm has to be validated in similar environment to enrich the knowledge on its capability to ensure its wider usage.

Through its association with proteins and plant pigments, leaf nitrogen (N) plays an important regulatory role in photosynthesis, leaf respiration, and net primary production. However, the traditional methods of measurement leaf N are rooted in sample-based spectroscopy in laboratory. There is a big challenge of deriving leaf N from the nondestructive field-measured leaf spectra. In this study, the original PROSPECT model was extended by replacing the absorption coefficient of chlorophyll in the original PROSPECT model with an equivalent N absorption coefficient to develop a nitrogen-based PROSPECT model (N-PROSPECT). N-PROSPECT was evaluated by comparing the model-simulated reflectance values with the measured leaf reflectance values. The validated results show that the correlation coefficient (R) was 0.98 for the wavelengths of 400 to 2500 nm. Finally, N-PROSPECT was used to simulate leaf reflectance using different combinations of input parameters, and partial least squares regression (PLSR) was used to establish the relationship between the N-PROSPECT simulated reflectance and the corresponding leaf nitrogen density (LND). The inverse of the PLSR-based N-PROSPECT model was used to retrieve LND from the measured reflectance with a relatively high accuracy (R²=0.77, RMSE=22.15  μg cm−²). This result demonstrates that the N-PROSPECT model established in this study can accurately simulate nitrogen spectral contributions and retrieve LND.

We present a hyperspectral image classification method based on sparse representation and superpixel segmentation. The presented method includes two main stages, which are sparse representation of extended multiattribute profiles (EMAPs) and superpixel segmentation of EMAPs. Specifically, we use the sparse representation of EMAPs to obtain the initial label of the pixel in the hyperspectral data. In addition, unsupervised superpixel segmentation is applied to EMAPs to generate the spatial constraint of the data. By refining the spectral classification results with the spatial constraints, the accuracy of classification is improved by a substantial margin. Our experiments reveal that the proposed approach yields state-of-the-art classification results for different hyperspectral datasets.

The Azul mining complex, located in the Carajás Mineral Province, Amazon region, encompasses the most important manganese mine in Brazil. Vale S.A. company operates three simultaneous open pit excavations (mines 1, 2, and 3) in the area, which are conducted on rock alteration products of low geomechanical quality related to sandstones, siltstones, and a lateritic cover. In order to monitor ground deformation, 33 TerraSAR-X (TSX-1) StripMap images covering the period of March 2012–April 2013 were used in the investigation. An advanced differential interferometric synthetic aperture radar (A-DInSAR) approach based on persistent scatterer interferometry (PSI) using an interferometric point target analysis algorithm was applied, and the results showed that most of the area was considered stable during the time span of the synthetic aperture radar acquisitions. However, persistent scatterers (PS) with high deformation rates were mapped over a waste pile, probably related to settlements, and also along the north flank of mine 1, indicative of cut slope movements toward the center of the pit. A spatial relationship of geological structures with PS was observed for this sector of the mine, given by PS showing deformation rates concentrated along a structural corridor with faults, fractures, and folds related to the Carajás fault system. Though only ground-based radar measurements for wall benches of mine 1 were available for a short time period of the TSX-1 coverage, the PS movement patterns showed concordance with geotechnical field measurements. The investigation emphasized the important role that satellite-based A-DInSAR can play for deformation monitoring and risk assessment in this kind of mining area.

We describe the capabilities and performance of a terrestrial laser scanning instrument built for the purpose of recording and retrieving the three-dimensional structure of forest vegetation. The dual-wavelength Echidna® lidar characterizes the forest structure at an angular resolution as fine as 1 mrad while distinguishing between leaves and trunks by exploiting their differential reflectances at two wavelengths: 1 and 1.5 μm. The instrument records the full waveforms of return signals from 5 ns laser pulses at half-nanosecond time resolution; obtains ±117 deg zenith and 360 deg azimuth coverage out to a radius of more than 70 m; provides single-target range resolution of 4.8 and 2.3 cm for the 1 and 1.5 μm channels, respectively (1σ); and separates adjacent pulse returns in the same waveform at a distance of 52.0 and 63.8 cm apart for the 1 and 1.5 μm channels, respectively. The angular resolution is in part controlled by user-selectable divergence optics and is shown to be <2 mrad for the instrument’s standard resolution mode, while the signal-to-noise ratio is 10 at 70 m range for targets with leaf-like reflectance for both channels. The portability and target differentiation make the instrument an ideal ground-based lidar suited for vegetation sensing.

The capabilities of different Earth Observation multispectral satellites are employed for detecting and tracking of desert dust coming from North Africa toward the Northern Italy area and for evaluating the impact of Saharan dust deposition in inland waters, such as those of Lake Garda. Absorbing and scattering spectral optical properties of desert aerosol in the atmospheric windows in the ultraviolet, visible-near-infrared, and infrared spectral ranges are exploited in the dust retrieval performed by OMI/Aura, MODIS/Terra-Aqua, and SEVIRI/MSG satellite sensors. Therefore, the direct link between dust deposition and increase in phytoplankton abundance has been assessed retrieving MERIS-based chlorophyll-a (chl-a) concentration for the desert dust events. Estimates of the increased chl-a in the lake have been derived with values in concentration from 30% to 170%. AERONET sun-photometer measurements, gravimetric particulate matter samplings, in situ chl-a concentration and surface temperature are employed to select events and assess the presence of desert dust and recognize a corresponding increase of the phytoplankton abundance in the analyzed inland waters. The improved observational features that will be provided by the next European Sentinels missions, namely Sentinel-2, 3, 4, 5P, together with MTG-I (Imager) and MTG-S (Sounder) will allow better monitoring atmospheric constituents and studying the environmental impacts of desert dust transport.

We propose a model for soil moisture change detection using phase information of synthetic aperture radar data. It is expected to be applied for drought monitoring over grasslands in north China. This model is developed from the coherent scattering model, which was originally studied for random oriented volume over ground scattering. Compared to the conventional water content estimation methods employing amplitude information, the methods based on phase information have advantages over change detection. In particular, the phases caused by topography can be removed by the use of external digital elevation model data with high accuracy. Simulations are presented to show the phase sensitivity on soil moisture variations, as well as soil moisture changes that can be feasibly inverted under different conditions of system phase accuracy and incidence angle. Then the inversion scheme is given on the basis of the proposed model. Finally, a relevant experiment in the anechoic chamber was implemented, in which good agreement is achieved between the model computations and the measurements. The results are discussed considering the practical limitations of potential applications.

Atmospheric spectra reconstructed from interferometric measurements are susceptible to scene motion, which can be caused by changing environment or instrument jitter. This leads to a coupling between the time series of the modulated scene radiance and the interferogram collected as a function of time. Spectral artifacts are generated when this occurs and appear as both narrow, isolated spikes in the measurement wavelength band as well as broad, out-of-band features. Here, we derive an analytical relationship that depends on jitter frequency, amplitude and phase, and the scene gradient. The observed radiance is expressed as a spatially weighted mixture of radiance originating from various points within the scene. Spectral artifacts created by relative scene motion are shown to be proportional to the radiometric scene difference within the instrument field of view (FOV) and jitter amplitude, and vary significantly with the jitter frequency. The analytical solution can be useful in various applications, where image instability is expected to have an effect on measurements in question, ranging from spectroscopy of turbine plumes, through airborne measurements of smoke density and chemical composition in natural fire or volcanic events, to atmospheric sounding from space.

Rapid determination of soil water content is urgently needed for monitoring and modeling ecosystem processes and improving agricultural practices, especially in arid landscapes. Far-infrared band application in soil water measurement is still limited. Various samples were arranged to simulate complex field condition and emissivity was obtained from a Fourier transform infrared spectrometer. Four spectral forms (including raw spectra, logarithm of reciprocal spectra, first-order derivate, and second-order derivate) were employed to develop a partial least squares regression model. The results indicate that the model with first-order derivate spectral form was identified with the highest performance (R²=0.87 and root mean square error=1.88%) at the range of 8.309 to 10.771  μm. Judging from the contribution of the bands to each principal component, the band region from 8.27 to 9.112  μm holds a great promise for soil water content estimation. Several channels of ASTER and MODIS correspond to the involved band domain, which show the potential of predicting and mapping soil water content on large scales. However, there are still constraints due to the differences in spectral resolution between instrument and sensors and the influence of complex factors under field conditions, which are still challenges for forthcoming studies.

A light detection and ranging canopy height model (CHM) was used as training data for a segment-based classification of woody patches. The classifier is accurate (∼92%) and suitable for use at the national scale. Height thresholds and percentage cover of vegetation from the CHM were used to produce larger quantities of reliable training data compared to other, mostly point or plot-based, ground-truthing approaches. It was found that the regional-scale differentiation between woody and nonwoody vegetation might be achieved by a combination of L-band dual-polarized Phased Array type L-band synthetic aperture radar data (HV) with multispectral optical data that include a short-wave infrared band. The application of a support vector machine algorithm to these data proved successful. The versatility of these algorithms regarding the discrimination function and their ability to solve classification problems with multiple output classes were critical factors for success. The identified and classified woody patches constitute a valuable addition and enhancement of the national land cover database.

A viable method to implement oil spill detection and monitoring based on marine radar is proposed. The primary data of this study are obtained from the X-band marine radar of the teaching–training ship, YUKUN, of the Dalian Maritime University on July 21, 2010, when a pipeline burst and an oil spill accident occurred at the Xingang Port in Dalian. Aiming at the working characteristics of marine radar, the adaptive median filter algorithm is improved to eliminate the radar shared-frequency interference by adding the identification of noise points and resetting the neighborhood window. A power attenuation correction method is proposed to solve the uneven distribution in resolution and echo intensity by acquiring the average power distribution of radar images simultaneously. Oil spill will be easily detected from different sea backgrounds after morphological processing, gray segmentation, and image smoothing. Comparison with the images extracted from a thermal infrared sensor on the same monitoring point demonstrates the validity of the extraction method for oil spill based on X-band marine radar.

Paddy rice areas in Larkana district in Sindh province, Pakistan, were mapped over eight years. Landsat 7 ETM+ satellite imagery was classified for rice areas using training data collected through visual interpretation and using a bagged decision tree approach. Within the rice areas, we estimated yield for the 2013 season using regression models based on Landsat-derived normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) values against historic, reported yield values. The annual cropped rice area estimated from satellite imagery was between 19% and 24% lower than the area reported by the Crop Reporting Service, Sindh. A positive and strong relationship with coefficient of determination (R2) of 0.94 was observed between the reported rice crop yield and NDVI at the peak of the growing season for the years 2006 to 2013. A fair relation (R2=0.875) between rice crop yield and RVI was observed for the same years. A strong relationship between observed and predicted rice production with model efficiency=0.925, mean bias error=−85,016t, and RMSE=80,726t was obtained. Thus, Landsat ETM+ has a high potential for estimating rice yield and production at the district level in Pakistan and elsewhere.

A previously proposed vegetation isoline equation suffers from errors if the soil background of a canopy layer is bright. These errors arise from the truncation of the second- and higher-order terms that represent photon interactions between the canopy and the soil. An isoline equation that includes a second-order interaction term is introduced. The equation was initially derived by explicitly including a second-order interaction term in both the red and near-infrared (NIR) reflectance spectra (symmetric approximation). We also examined an alternative model in which the interaction term was included only in the NIR band (asymmetric approximation). In this model, the derived isolines tend to shift upward (overcorrection effects). Numerical experiments revealed that the errors in the isoline obtained by the asymmetric approximation were reduced in magnitude to nearly one-fifth of the errors in the previously proposed method. Its accuracy was higher than that of the symmetric approximation, despite the fact that the asymmetric approximation included fewer terms than the symmetric approximation. The improved model accuracy resulted from the overcorrection effects, which compensated for the truncation error. With the simplicity and improved accuracy, the current isoline equations provide a good alternative to the previous approach.

Submarine sand waves are visible in optical sun glitter remote sensing images and multiangle observations can provide valuable information. We present a method for bathymetric mapping of submarine sand waves using multiangle sun glitter information from Advanced Spaceborne Thermal Emission and Reflection Radiometer stereo imagery. Based on a multiangle image geometry model and a sun glitter radiance transfer model, sea surface roughness is derived using multiangle sun glitter images. These results are then used for water depth inversions based on the Alpers–Hennings model, supported by a few true depth data points (sounding data). Case study results show that the inversion and true depths match well, with high-correlation coefficients and root-mean-square errors from 1.45 to 2.46 m, and relative errors from 5.48% to 8.12%. The proposed method has some advantages over previous methods in that it requires fewer true depth data points, it does not require environmental parameters or knowledge of sand-wave morphology, and it is relatively simple to operate. On this basis, we conclude that this method is effective in mapping submarine sand waves and we anticipate that it will also be applicable to other similar topography types.

We use a bio-optical model of the optical properties of natural seawater to investigate the effects of subsurface chlorophyll layers on passive and active remote sensors. A thin layer of enhanced chlorophyll concentration reduces the remote sensing reflectance in the blue, while having little effect in the green. As a result, the chlorophyll concentration inferred from ocean color instruments will fall between the background concentration and the concentration in the layer, depending on the concentrations and the depth of the layer. For lidar, an iterative inversion algorithm is described that can reproduce the chlorophyll profile within the limits of the model. The model is extended to estimate column-integrated primary productivity, demonstrating that layers can contribute significantly to overall productivity. This contribution also depends on the chlorophyll concentrations and the depth of the layer. Using passive remote sensing alone to estimate primary productivity can lead to significant underestimation in the presence of subsurface plankton layers. Active remote sensing is not affected by this bias.

Hyperspectral images can be used to identify savannah tree species at the landscape scale, which is a key step in measuring biomass and carbon, and tracking changes in species distributions, including invasive species, in these ecosystems. Before automated species mapping can be performed, image processing and atmospheric correction is often performed, which can potentially affect the performance of classification algorithms. We determine how three processing and correction techniques (atmospheric correction, Gaussian filters, and shade/green vegetation filters) affect the prediction accuracy of classification of tree species at pixel level from airborne visible/infrared imaging spectrometer imagery of longleaf pine savanna in Central Florida, United States. Species classification using fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH) atmospheric correction outperformed ATCOR in the majority of cases. Green vegetation (normalized difference vegetation index) and shade (near-infrared) filters did not increase classification accuracy when applied to large and continuous patches of specific species. Finally, applying a Gaussian filter reduces interband noise and increases species classification accuracy. Using the optimal preprocessing steps, our classification accuracy of six species classes is about 75%.

Evaluation of static three-dimensional (3-D) laser scanning point cloud accuracy has become a topical research issue. Point cloud accuracy is typically estimated by comparing terrestrial laser scanning data related to a finite number of check point coordinates against those obtained by an independent source of higher accuracy. These methods can only estimate the point accuracy but not the point cloud accuracy, which is influenced by the positional error and sampling interval. It is proposed that the point cloud error ellipsoid is favorable for inspecting the point cloud accuracy, which is determined by the individual point error ellipsoid volume. The kernel of this method is the computation of the point cloud error ellipsoid volume and the determination of the functional relationship between the error ellipsoid and accuracy. The proposed point cloud accuracy evaluation method is particularly suited for small sampling intervals when there exists an intersection of two error ellipsoids, and is suited not only for planar but also for nonplanar target surfaces. The performance of the proposed method (PM) is verified using both planar and nonplanar board point clouds. The results demonstrate that the proposed evaluation method significantly outperforms the existing methods when the target surface is nonplanar or there exists an intersection of two error ellipsoids. The PM therefore has the potential for improving the reliability of point cloud digital elevation models and static 3-D laser scanning-based deformation monitoring.

In the event of a natural disaster, such as a flood or earthquake, using fast and efficient methods for estimating the extent of the damage is critical. Automatic change mapping and estimating are important in order to monitor environmental changes, e.g., deforestation. Traditional change detection (CD) approaches are time consuming, user dependent, and strongly influenced by noise and/or complex spectral classes in a region. Change maps obtained by these methods usually suffer from isolated changed pixels and have low accuracy. To deal with this, an automatic CD framework—which is based on the integration of change vector analysis (CVA) technique, kernel-based C-means clustering (KCMC), and kernel-based minimum distance (KBMD) classifier—is proposed. In parallel with the proposed algorithm, a support vector machine (SVM) CD method is presented and analyzed. In the first step, a differential image is generated via two approaches in high dimensional Hilbert space. Next, by using CVA and automatically determining a threshold, the pseudo-training samples of the change and no-change classes are extracted. These training samples are used for determining the initial value of KCMC parameters and training the SVM-based CD method. Then optimizing a cost function with the nature of geometrical and spectral similarity in the kernel space is employed in order to estimate the KCMC parameters and to select the precise training samples. These training samples are used to train the KBMD classifier. Last, the class label of each unknown pixel is determined using the KBMD classifier and SVM-based CD method. In order to evaluate the efficiency of the proposed algorithm for various remote sensing images and applications, two different datasets acquired by Quickbird and Landsat TM/ETM+ are used. The results show a good flexibility and effectiveness of this automatic CD method for environmental change monitoring. In addition, the comparative analysis of results from the proposed method (O. A.: 95.75, kappa: 0.91) and the classical CD techniques, namely, the principal component analysis-based CD method [overall accuracy (O. A.): 69.41, kappa: 0.55], the independent component analysis-based CD method (O. A.: 85.48, kappa: 0.68), spectral angle mapper (O. A.: 75.38, kappa: 0.60), image subtraction CD (O. A.: 86.57, kappa: 0.69), and image rationing CD (O. A.: 76.45, kappa: 0.61) methods shows that the accuracy of the obtained change map can be considerably improved. Moreover, the results demonstrate that the proposed kernel-based CD methods do not depend on the instrument or the nature of change classes.

The real-time measurement of rainfall is a primary information source for many purposes, such as weather forecasting, flood risk assessment, and landslide prediction and prevention. In this perspective, remote sensing techniques to monitor rainfall fields by means of radar measurements are very useful. In this work, a technique is proposed for the estimation of cumulative rainfall fields averaged over a large area, applied on the Tuscany region using the Italian weather radar network. In order to assess the accuracy of radar-based rainfall estimates, they are compared with coincident spatial rain gauge measurements. Observations are compared with average rainfall over areas as large as a few tens of kilometers. An ordinary block kriging method is applied for rain gauge data spatialization. The comparison between the two types of estimates is used for recalibrating the radar measurements. As a main result, this paper proposes a recalibrated relationship for retrieving precipitation from radar data. The accuracy of the estimate increases when considering larger areas: an area of 900  km² has a standard deviation of less than few millimeters. This is of interest in particular for extending recalibrated radar relationships over areas where rain gauges are not available. Many applications could benefit from it, from nowcasting for civil protection activities, to hydrogeological risk mitigation or agriculture.

This paper highlights color component features that improve high-resolution satellite (HRS) images retrieval. Color component correlation across image lines and columns is used to define a revised color space. It is designed to simultaneously take both color and neighborhood information. From this space, color descriptors, namely rotation invariant uniform local binary pattern, histogram of gradient, and a modified version of local variance are derived through dual-tree complex wavelet transform (DT-CWT). A new color descriptor called smoothed local variance (SLV) using an edge-preserving smoothing filter is introduced. It is intended to offer an efficient way to represent texture/structure information using an invariant to rotation descriptor. This descriptor takes advantage of DT-CWT representation to enhance the retrieval performance of HRS images. We report an evaluation of the SLV descriptor associated with the new color space using different similarity distances in our content-based image retrieval scheme. We also perform comparison with some standard features. Experimental results show that SLV descriptor allied to DT-CWT representation outperforms the other approaches.

We propose an unsupervised method for slight change extraction and detection in multitemporal hyperspectral image sequence. To exploit the spectral signatures in hyperspectral images, autoregressive integrated moving average and fitting models are employed to create a prediction of single-band and multiband time series. Minimum mean absolute error index is then applied to obtain the preliminary change information image (PCII), which contains slight change information. After that, feature vectors are created for each pixel in the PCII using block processing and locally linear embedding. The final change detection (CD) mask is obtained by clustering the extracted feature vectors into changed and unchanged classes using k-means clustering algorithm with k=2. Experimental results demonstrate that the proposed method extracts the slight change information efficiently in the hyperspectral image sequence and outperforms the state-of-the-art CD methods quantitatively and qualitatively.

At present, the Soil Moisture and Ocean Salinity (SMOS) mission is severely affected by radio frequency interferences (RFIs), and the detection of low-level RFI-contamination brightness temperatures (BTs) is still a challenge in SMOS. A low-level RFI detection algorithm is proposed, which is based on the soil surface temperature products provided by the European Centre for Medium-Range Weather Forecasting. The algorithm is analyzed in terms of RFI-flagged snapshot, RFI-flagged probability, and localization accuracy. The performance of the algorithm is demonstrated by SMOS data. The results show this algorithm can detect and flag more low-level RFI-contamination BTs and show a better performance.

This study evaluated the effect of two readily available multispectral sensors: the newly launched 30 m spatial resolution Landsat 8 and the long-serving 1000 m moderate resolution imaging spectroradiometer (MODIS) datasets in the spatial representation of total evaporation in the heterogeneous uMngeni catchment, South Africa, using the surface energy balance system model. The results showed that sensor spatial resolution plays a critical role in the accurate estimation of energy fluxes and total evaporation across a heterogeneous catchment. Landsat 8 estimates showed better spatial representation of the biophysical parameters and total evaporation for different land cover types, due to the relatively higher spatial resolution compared to the coarse spatial resolution MODIS sensor. Moreover, MODIS failed to capture the spatial variations of total evaporation estimates across the catchment. Analysis of variance (ANOVA) results showed that MODIS-based total evaporation estimates did not show any significant differences across different land cover types (one-way ANOVA; F_1.924=1.412, p=0.186). However, Landsat 8 images yielded significantly different estimates between different land cover types (one-way ANOVA; F_1.993=5.185, p<0.001). The validation results showed that Landsat 8 estimates were more comparable to eddy covariance (EC) measurements than the MODIS-based total evaporation estimates. EC measurement on May 23, 2013, was 3.8  mm/day, whereas the Landsat 8 estimate on the same day was 3.6  mm/day, with MODIS showing significantly lower estimates of 2.3  mm/day. The findings of this study underscore the importance of spatial resolution in estimating spatial variations of total evaporation at the catchment scale, thus, they provide critical information on the relevance of the readily available remote sensing products in water resources management in data-scarce environments.

Estimating refractivity profiles from radar sea clutter is a complex nonlinear optimization problem. To deal with the ill-posed difficulties, an inversion algorithm, particle swarm optimization with a Lévy flight (LPSO), was proposed to be applied in the refractivity from clutter (RFC) technique to retrieve atmospheric duct in this paper. PSO has many advantages in solving continuous optimization problems, while in its late period it has slow convergence speed and low precision. Therefore, we integrate the Lévy flights into the standard PSO algorithm to improve the precision and enhance the capability of jumping out of the local optima. To verify the feasibility and validation of the LPSO for estimating atmospheric duct parameters based on the RFC method, the synthetic and Wallops98 experimental data are implemented. Numerical experiments demonstrate that the optimal solutions obtained from the hybrid algorithm are more precise and efficient. Additionally, to test the algorithm inversion performance, the antinoise ability of LPSO is analyzed. The results indicate that the LPSO algorithm has a certain antinoise ability. Finally, according to the experiment results, it can be concluded that the LPSO algorithm can provide a more precise and efficient method for near-real-time inversion of atmospheric refractivity from radar clutter.

Urban heat island (UHI) effect is a global phenomenon caused by urbanization. Because of the number and complexity of factors contributing to the urban thermal environment, traditional statistical methods are insufficient for acquiring data and analyzing the impact of human activities on the thermal environment, especially for identifying which factors are dominant. The UHI elements were extracted using thermal infrared remote sensing data to retrieve the land surface temperatures of Lanzhou city, and then adopting an object-oriented fractal net evolution approach to create an image segmentation of the land surface temperature (LST). The effects of urban expansion on the urban thermal environment were quantitatively analyzed. A comprehensive evaluation system of the urban thermal environment was constructed, the spatial pattern of the urban thermal environment in Lanzhou was assessed, and principal influencing factors were identified using spatial principal component analysis (SPCA) and multisource spatial data. We found that in the last 20 years, the UHI effect in Lanzhou city has been strengthened, as the UHI ratio index has increased from 0.385 in 1993 to 0.579 in 2001 and to 0.653 in 2011. The UHI expansion had a spatiotemporal consistency with the urban expansion. The four major factors that affect the spatial pattern of the urban thermal environment in Lanzhou can be ranked in the following order: landscape configuration, anthropogenic heat release, urban construction, and gradient from man-made to natural land cover. These four together accounted for 91.27% of the variance. A linear model was thus successfully constructed, implying that SPCA is helpful in identifying major contributors to UHI. Regression analysis indicated that the instantaneous LST and the simulated thermal environment have a good linear relationship, the correlation coefficient between the two reached 0.8011, highly significant at a confidence level of 0.001.

European Galileo global navigation system’s four in-orbit validation (IOV) satellites (E11, E12, E19, and E20) are able to calculate position accurately. The analysis of the IOV satellites’ measurements can provide insight into the performance of the Galileo system. To evaluate the performance of IOV satellites using measurements in the Shanghai, China, area signal-to-noise ratio (SNR) and multipath are used. We also suggest a method to calculate the four frequencies’ multipath error. When compared with global positioning system (GPS) satellites’ SNR, IOV satellites’ signal strength is stronger. In the aspect of multipath error, the IOV satellite is also less than GPS. The accuracy of single point positioning under open sky, under trees, and between tall buildings of a combined GPS/Galileo system is analyzed in the Shanghai area. The positioning result shows that the positioning accuracy of the combined GPS/Galileo system is better than the GPS system alone.

The tsunami that occurred on March 11, 2011, in Japan caused widespread devastation of infrastructure and rice cultivation areas. This study assessed the rice growing areas damaged due to this event and the restoration progress using moderate resolution imaging spectroradiometer data. The data were processed for 2010 to 2013, comprising four main steps: (1) data preprocessing to construct the smooth time-series enhanced vegetation index 2 data, (2) rice crop mapping using the dynamic time warping algorithm, (3) accuracy assessment, and (4) change analysis of rice cultivation areas. The mapping results validated using the ground reference data indicated overall accuracies and kappa coefficients generally >84.3% and 0.69, respectively. The results, also verified with the government’s rice area statistics, reaffirmed a close correlation between these two datasets (R²<0.77). When examining changes of rice cultivation between 2010 and 2013, the rice area damaged by the tsunami during 2010 to 2011 was ∼17,550  ha. The rice area restored after one year (2012) was ∼6663  ha, but this regressed slightly in 2013 (5656 ha). Such information could be used by officials to better understand the tsunami-damaged rice area and the restoration progress for crop management in the study region.

Vegetation optical depth (VOD) and effective vegetation optical depth (EVOD) are key factors for estimating soil moisture and vegetation parameters. Microwave vegetation indices (MVIs, including A and B parameters) have been recently developed for short-vegetation covered surfaces. The MVIs parameter B (MVIs_B) is mainly related to vegetation conditions, which makes it provide a potential way of EVOD retrieval. A theoretical expression deriving EVOD was deduced using MVIs_B from WindSat data. Global patterns of EVOD were analyzed subsequently. It has been shown that EVOD retrieved from MVIs performed a consistent global pattern and seasonal variation with normalized difference vegetation index. Time-series data from the Central Tibetan Plateau Soil Moisture/Temperature Monitoring Network, which is grassland dominated, was selected for temporal analysis. It was found that the temporal EVOD from WindSat MVIs can capture the growth trend of vegetation. Comparisons between EVOD estimations from MVIs and a radiative transfer model were also performed over this network. It was found that EVOD from the two methods exhibited comparable values and similar trends. MVIs_B-derived EVOD can be obtained without any other auxiliary data and has great potential in land-surface parameter retrieval over short-vegetation covered areas.

Shoreline evolution is a simple and common method to illustrate beach erosion or accretion in coastal engineering. Extracted waterlines on different satellite images are sometimes used for shoreline evolution. However, time-varying waterlines for tidal variation and wave runup are different from the shoreline at the mean water level. Waterline evolution may bring about misunderstanding of beach erosion or accretion. In a former study, the one-line shift method was proposed to determine the waterlines on a satellite image and to shift the waterlines to shorelines while only considering the tidal variation. The upward shift extension of the waterline due to wave runup and wave setup is considered. Some acceptable equations of wave runup were examined to correct the waterlines on three satellite images in one month when the foreshore beach slopes were specified. The suggested equation for wave runup includes both wave conditions and an average beachface slope at two points located shoreward and seaward away from mean water level by an equal distance of 62.5 m. When on-site shore bathymetrical measurement is sometimes unavailable, a method of minimizing the difference between the initially guessed and estimated foreshore beach slopes by the one-line shift method is proposed.

Airborne visible/infrared imaging spectrometer (AVIRIS) and spatially coincident hyperspectral thermal emission spectrometer (HyTES) data were used to map geology and alteration for a site in northern Death Valley, California and Nevada. AVIRIS with 224 bands from 0.4 to 2.5  μm were converted to reflectance. HyTES data with 256 bands covering 8 to 12  μm were converted to emissivity. Two approaches were investigated for integration of the datasets for full spectrum analysis. A combined (integrated) bands method utilized 332 spectral bands spanning both datasets. Spectral endmembers were extracted, and the predominant material at each pixel was mapped for the full spectral range using partial unmixing. This approach separated a variety of materials, but it was difficult to directly relate mapping results to surface properties. The second method used visible to near-infrared, shortwave infrared, and longwave infrared data independently to determine and map key endmembers in each spectral range. AVIRIS directly mapped a variety of specific minerals, while HyTES separated and mapped several igneous rock phases. Individual mapping results were then combined using geologically directed logical operators. The full-range results illustrate that integrated analysis provides advantages over use of just one spectral range, leading to improved understanding of the distribution of geologic units and alteration.

A pattern recognition model for aerosol classification with atmospheric backscatter lidars is proposed and studied in detail. The theoretical framework and the implementation process of the proposed model are presented. Computer simulations have been carried out to verify the practicability and robustness of this model. The k-fold cross-validation method is employed in the process of classifier designing to choose the proper decision rule, which is mainly based on statistical pattern recognition theory. At the same time, the validity of the model is evaluated. The generalized self-validation is also carried out in the computer simulations to verify the stability of the model. The analysis of the performances in reduced status, especially the instance of application to Cloud-Aerosol Lidar with Orthogonal Polarization, demonstrates the generalization ability and performance of this model.

We present the study and analysis of the variations of the bistatic electromagnetic (EM) signature of the sea surface contaminated by pollutants. Therefore, we start with the numerical analyses of the pollutant effect on the geometrical and physical characteristics of sea surface. Then, we evaluate the EM scattering coefficients of the clean and polluted sea surfaces observed in bistatic configuration by using the numerical forward-backward method. The obtained numerical results of the EM scattering coefficients are studied and given as a function of various parameters: sea state, wind velocity, type of pollutant (sea surface polluted by oil emulsion and sea surface covered by oil layer), incidence and scattering angles, frequencies bands (C, X, and Ku), and radar polarization.

In hyperspectral remote sensing, the surface compositional material can be identified by means of spectral matching algorithms. In many cases, the importance of each spectral band to measure spectral similarity is different, whereas the traditional spectral matching algorithms implicitly assume all wavelength-dependent absorption features are equal. This may yield an unsatisfactory performance for spectral matching. To remedy this deficiency, we propose methods called feature-enhanced spectral similarity measures. They are hybrids of the spectral matching algorithms combined with a feature-enhanced space projection, termed feature-enhanced spectral angle measure, feature-enhanced Euclidean distance measure, feature-enhanced spectral correlation measure, and feature-enhanced spectral information divergence. The proposed methods creatively project the original spectra into spectral feature-enhanced space, in which important features for measuring the spectral similarity will be increased to a high degree, whereas features of low importance will be suppressed. In order to demonstrate the effectiveness of the proposed approaches, performances are compared on real hyperspectral image data from Airborne Visible Infrared Imaging Spectrometer. The proposed methods are found to possess significant improvements over the original four spectral matching algorithms.

The polarimetric active radar calibrator (PARC) is extensively used as an external test target for system distortion compensation and polarimetric calibration for the high-resolution polarimetric radar. However, the signal undergoes distortion in the PARC, affecting the effectiveness of the compensation and the calibration. The system distortion compensation resulting from the distortion of the amplitude and phase in the PARC was analyzed based on the “method of paired echoes.” Then the correction method was proposed, which separated the ideal signals from the distorted signals. Experiments were carried on real radar data, and the experimental results were in good agreement with the theoretical analysis. After the correction, the PARC can be better used as an external test target for the system distortion compensation.

Accurate estimation of forest aboveground biomass (AGB) is crucial for monitoring ecosystem responses to environmental change. Optical remote sensing is the most widely used method for obtaining AGB information. However, there is a need for improving the accuracy of AGB estimation obtained in this way. A synergistic estimation model through the integration of spectral and textural features from Chinese high spatial resolution satellite data GaoFen-1 (GF-1) for AGB estimation in the arid region of Ejin Banner, Inner Mongolia Autonomous Region, China, was put forward. The proposed model combined the spectrum-alone model and texture-alone model, which were developed to describe the relationship between image parameters (spectral vegetation indices or texture parameters) obtained from GF-1 data and field measurements, and determined the contributions of spectral and textural sensitive indices to biomass estimation under different biomass conditions. The synergistic model was verified by comparison with the ground measurements and the results of the spectrum-alone and texture-alone models. The results indicate that the proposed synergistic estimation model is more effective than the spectrum-alone or texture-alone model, and shows considerable potential in forest AGB estimation by combining spectral and textural information.

A new scheme of a pulse-Doppler radar for the effective detection of metal objects in a short range of millimeter waves is presented. An omnidirectional transmitting antenna (TXA) is used to provide full angular coverage while a directional receiving antenna (RXA) collects the coded signals for the determination of range and angle of the arriving signal. A Doppler shift is created by making RXA horizontally rotate along the axis of TXA, which creates a relative velocity between the RXA and the target under detection. It is shown that the system may detect targets as small as 0.2  m² with a high resolution.

In this study, an analytical methodology based on four typical statistical analysis methods was proposed to understand soil moisture (SM) dynamics and their response to climate change in Central Asia and Xinjiang over 30 years using the essential climate variable-soil moisture dataset and the Climate Research Unit (CRU) dataset. The results are as follows: (1) In general, the SM of the study area decreased significantly over the last 30 years. The significant warming trend dominated the soil desiccation. (2) The soil desiccation trend is more severe in Central Asia than in Xinjiang, while the SM in Xinjiang has increased gradually since 2004. The trends of soil desiccation in Central Asia and Xinjiang consistently and negatively feedback to the significant warming trend. (3) The SM for all five countries of Central Asia distinctly decreased. The significant increase in temperature dominated the soil desiccation in the other four countries of Central Asia except for Kyrgyzstan, while precipitation had no significant impact on SM. (4) The regions with drying and warming trends and with drying and cooling trends (∼89% of the total area) were largely distributed in major agricultural areas; these trends are unfavorable to the sustainable development of agriculture.

Cotton root rot is a destructive disease affecting cotton production. Accurate identification of infected areas within fields is useful for cost-effective control of the disease. The uncertainties caused by various infection stages and newly infected plants make it difficult to achieve accurate classification results from airborne imagery. The objectives of this study were to apply fuzzy set theory and nonlinear stretching enhancement to airborne multispectral imagery for unsupervised classification of cotton root rot infections. Four cotton fields near Edroy and San Angelo, Texas, were selected for this study. Airborne multispectral imagery was taken and the color-infrared (CIR) composite images were used for classification. The intensity component was enhanced by using a fuzzy-set based method, and the saturation component was enhanced by a nonlinear stretching image enhancement algorithm. The enhanced CIR composite images were then classified into infected and noninfected areas. Iterative self organization data analysis and adaptive Otsu’s method were used to compare the performance of the proposed image enhancement method. The results showed that image enhancement has improved the classification accuracy of these two unsupervised classification methods for all four fields. The results from this study will be useful for detection of cotton root rot and for site-specific treatment of the disease.

After the vast volcanic eruption that took place around 1613 BC in Santorini island, Greece, broadly known as the “minoan,” activity continued inside the caldera that was created during historical times, with minor eruptions and lava extrusions. As a result, two new volcanic islands named Palea Kammeni and Nea Kammeni rose from the depths of the sea. These islands are made up of dacite lavas of different known ages. A discrimination of the lavas is attempted using a Landsat-8 satellite image, an air-photo, and light detection and ranging (LIDAR) data. Results are compared to the geological map of the islands. Enhanced satellite images offer a new perspective in mapping lavas of the same mineralogical composition. LIDAR intensity reflectance values generally increase with the age of lavas.

This paper proposes a scaling transfer model based on fractal theory to retrieve the leaf area index (LAI) at different spatial resolutions and to evaluate the scaling bias on the LAI retrieved from coarse resolution images. The LAI scaling transfer model was developed by establishing the double logarithmic linear relationship between the scale n (spatial resolution) and average LAIs of the image at different scales. Thereafter, the influences of four factors, namely, coefficients of LAI retrieval model, image size, spatial resolution, and image standard deviation, which may have impact on the scaling transfer model were analyzed. The results indicated that the scaling transfer model performed well in estimating LAI with a determination coefficient (R²) value of 96.99% and in evaluating the scaling bias with a root-mean-square error of 0.0188. The scaling transfer model was considerably influenced by the image standard deviation. As the model parameter, the fractal dimension of image was highly correlated with the standard deviation of the normalized difference vegetation index image. Results indicated that the proposed method based on fractal theory is feasible for LAI spatial scaling transformation.

Landslides and debris flow events triggered by substantial precipitation in typhoon events often cause enormous damage. It is crucial to rapidly acquire, process, and distribute the most updated information of the disaster areas, particularly optical images with high-spatial resolution. An unmanned aerial vehicle (UAV) provides an innovative approach to remote sensing that is much cheaper, safer, and more flexible for deployment in a small area, ranging from a few tens of square kilometers. This paper demonstrates the application of a low-cost UAV to rapid landslide assessment in difficult access and climatic conditions, as well as presents two examples of rapid response to the natural disaster events at Mudan and Manjhou triggered by Typhoon Nanmadol on August 29, 2011. Various approaches were successfully integrated and implemented in an automatic mission planning and image processing system to plan a flight mission and generate three levels of georeferenced products. Comparing with 14 check points identified on the 25-cm resolution aerial orthophoto, the accuracy of the orthophoto product can achieve a root mean square error of less than 2.81 pixels. All orthophotos are further processed to one seamless, color-balanced, and georeferenced mosaic that can be published on the free-access Google Earth within 24 h.

For more accurate classification of earthquake-induced damaged regions, a high-resolution satellite image is required to extract textural and spatial features of the damage. In addition to using textural features, spectral features may improve the identification of the damaged regions. Earthquake-induced damage that occurred in the city of Bam in Iran was identified by a nonparametric and nonlinear classifier called support vector selection and adaptation (SVSA) using both the textural and the spectral features. SVSA can achieve the performance of nonlinear support vector machines (NSVM) without the need for a kernel function. Our aim is to show the effectiveness of the SVSA algorithm compared with the linear support vector machines, NSVM, and K-nearest neighbor (KNN) methods in terms of classification accuracy when using the textural features. A nonparametric weighted feature extraction was also implemented before the classification in order to increase the classification accuracy further by assigning a different weight to the textural feature. The results indicate that SVSA is significantly better than the linear SVM (LSVM) and KNN classifiers, and it is quite competitive with NSVM in terms of damage detection accuracy.

New and effective methods for burned area detection from remote sensing data are required by diverse applications. Burned area detection from a large heterogeneous landscape is a challenging task because of the low-interclass dissimilarity of burned and unburned pixels. Burned and unburned pixels may reflect similar spectral signatures but retain distinct temporal and spatial dependence in its vicinity. This change in temporal and spatial domains can be used to supplement spectral information. The existing methods use only spectral information of the images for detecting burned area and ignore the local temporal and spatial associations. We propose a new method to improve burned area detection accuracy by combining spectral information with local temporal and spatial associations. A method for simultaneous incorporation of these associations by means of cross- and autocorrelation is put forward and tested here under the logistic regression framework. The proposed method was assessed over two different study areas and showed significant increment of 5% to 9% in Kappa accuracy. We demonstrated that the use of cross- and autocorrelation leads to substantial increase in burned area detection accuracy.

The Yellow River Delta, the largest and most rapidly growing delta in China, has undergone severe land subsidence. However, land subsidence has not been regularly and fully measured in this large region and no consensus has been reached as to the primary cause. Here, the small baseline subset interferometric synthetic aperture radar (InSAR) method is applied to retrieve time-series deformation and the full land subsidence pattern in the Yellow River Delta. Spirit leveling data and a standard deviation map are used to verify the InSAR results and measurement accuracy. The major subsidence regions, which have a maximum subsidence rate ranging from 20 to more than 70  mm/year, cover a total of ∼800  km² and are mainly concentrated in the groundwater source areas. An intimate connection of surface deformation with groundwater suggests that land subsidence is primarily caused by excessive exploitation of groundwater. In addition, comparison of the surface deformation with the precipitation record indicates that the seasonal land subsidence correlates with the rainfall rate, with a lag time of around 1 to 2 months between the precipitation peak and the minimum land subsidence displacement. The results provide new insights into land subsidence mechanisms in the Yellow River Delta. A key policy priority should, therefore, be to plan for controlling anthropogenic activities and better management of groundwater.

Application of remote sensing and geographic information systems to the characterization of wildlife habitats is an area of growing significance for conservation. We examine the use of space of a group of bearded capuchin monkeys, a species unique in their use of stone tools to extract encapsulated foods. We define important landscape variables associated with the monkeys’ behavior, especially the use of stone tools. Maximum entropy modeling is used to define the landscape characteristics associated with the monkeys’ use of space. The variables evaluated in model building include normalized difference vegetation index, distance to roads, distance to areas of human influence, distance to vertical scarps, elevation, land cover/land use class, and percentages of green vegetation, bare soil, and shadow from spectral mixture analysis. Distance to areas of human influence and distance to vertical scarps were the variables most closely associated with capuchin habitat suitability (permutation importance 31.7% and 21%, respectively). Stone tool use occurred in areas of lower elevation and higher percent green vegetation relative to other behavior. These results may inform efforts for conserving the unique stone tool use of this species, especially relevant due to the recent expansion and intensification of industrial agriculture in the region.

This paper presents an example of using the ground-based synthetic aperture radar (GBSAR) technique for the emergency monitoring of a deep foundation excavation. The process includes a quality evaluation for the interferometric data acquired by the IBIS-L radar from Ingegneria Dei Sistemi S.p.A. Atmospheric effects were eliminated through calibration with ground control points (two triangular reflectors) to improve the measuring accuracy of the emergency monitoring. Accuracy of the data obtained was then compared at six check points between the GBSAR technique and a Leica total station TC2003 with prisms. The results indicate that the GBSAR technique is a suitable method of emergency displacement monitoring within a deep foundation excavation and has several significant advantages. These include a higher level of accuracy, near real-time monitoring intervals, and site-wide displacement maps obtained for each observation sampling interval.

To overcome the inefficiency of incremental learning for hyperspectral remote sensing images, we propose a binary detection theory-sequential minimal optimization (BDT-SMO) nonclass-incremental learning algorithm based on hull vectors and Karush-Kuhn-Tucker conditions (called HK-BDT-SMO). This method can improve the accuracy and efficiency of BDT-SMO nonclass-incremental learning for fused hyperspectral images. But HK-BDT-SMO cannot effectively solve class-incremental learning problems (an increase in the number of classes in the newly added sample sets). Therefore, an improved version of HK-BDT-SMO based on hypersphere support vector machine (called HSP-BDT-SMO) is proposed. HSP-BDT-SMO can substantially improve the accuracy, scalability, and stability of HK-BDT-SMO class-incremental learning. Ultimately, HK-BDT-SMO and HSP-BDT-SMO are applied to the classification of land uses with fused hyperspectral images, and the classification results are compared with other incremental learning algorithms to verify their performance. In nonclass-incremental learning, the accuracy of HSP-BDT-SMO and HK-BDT-SMO is approximately the same and is higher than the others, and the former has the best learning speed; while in class-incremental learning, HSP-BDT-SMO has a better accuracy and more continuous stability than the others and the second highest learning speed next to HK-BDT-SMO. Therefore, HK-BDT-SMO and HSP-BDT-SMO are excellent algorithms which are respectively suitable to nonclass and class-incremental learning for fused hyperspectral images.

The Amazon Basin experienced an abrupt transition from extreme drought to flood during 2010–2012, causing significant loss and damage to the property of thousands of families. We used datasets derived from the latest products of the Gravity Recovery and Climate Experiment (GRACE), Tropical Rainfall Measuring Mission (TRMM), and the Global Land Data Assimilation System (GLDAS) to assess the extent, intensity, and dynamics of the 2010–2012 abrupt transition from extreme drought to flood in the Amazon. The monthly developing processes during the abrupt transition from extreme drought to flood between 2010 and 2012 were reproduced and examined by comparisons between GRACE terrestrial water storage anomaly and the precipitation derived from TRMM satellite estimates and GLDAS datasets. Accumulated precipitation during the peak of 2010 drought and 2012 flood looks very much similar to terrestrial water storage deficit and surplus, both at the temporal and spatial scales. Furthermore, strong correlations between the 2010 and 2012 extreme drought/flood events over the Amazon and El Niño-Southern Oscillation were also detected. This study can be helpful for archiving historical information on disasters that can contribute to the elaboration of regional scale drought/flood disaster prevention and mitigation strategies in the Amazon.

The Geostationary Ocean Color imager (GOCI) is the first geostationary ocean color satellite sensor that collects hourly images eight times per day during daylight. This high frequency image acquisition makes it possible to study more detailed dynamics of red tide blooms, sediment plumes, and colored dissolved organic matter plumes, and can aid in the prediction of biophysical phenomena. We apply the red band difference and the fluorescence line height algorithms to GOCI imagery to separate waters with high algal and nonalgal particles and validate the results with the MODIS imagery. We also track optical features using hourly GOCI imagery and assess their movement through comparisons with predicted ocean currents derived from the navy coastal ocean model and tidal data.

We propose two approaches of multiresolution image fusion using multistage guided filter and difference of Gaussians (DoGs). In a multiresolution image fusion problem, the given multispectral (MS) and panchromatic (Pan) images have high spectral and high spatial resolutions, respectively. One can obtain the fused image using these two images by injecting the missing high frequency details from the Pan image into the MS image. The quality of the final fused image will then depend on the method used for high frequency details extraction and also on the technique for injecting these details into the MS image. Specifically, we have chosen the guided filter and DoGs for detail extraction since these are more versatile in applications involving feature extraction, denoising, and so on. The detail extraction process in the fusion approach using a guided filter exploits the relationship between the Pan and MS images by utilizing one of them as a guidance image while extracting details from the other. The final fused image is obtained by adding the extracted high frequency details to the corresponding MS image. This way, the spatial distortion of the MS image is reduced by consistently combining the details obtained using both MS and Pan images. In the fusion method using DoGs, the high frequency details are extracted in the first and second levels by subtracting the blurred images of the original Pan. The extracted details at both DoGs are added to the MS image to obtain the final fused image. Advantages and disadvantages of each method are discussed and the comparison of the results is shown between the two. The results are also compared with the traditional and the state-of-the-art methods using the images captured using different satellites such as Quickbird, Ikonos-2, and Worldview-2. The quantitative assessment is evaluated using the conventional measures as well as using a relatively new index, i.e., quality with no reference which does not require a reference image. The results and measures clearly show that there is promising improvement in the quality of the fused image using the proposed approaches.

A simplified adaptive volume scattering model (SAVSM) for RADARSAT-2 polarimetric synthetic aperture radar (PolSAR), based on the n’th power sine and cosine functions, is developed to characterize the changes in crop phenology within the growing season. A three-component model-based decomposition (TCMD) with SAVSM is also implemented with the non-negative eigenvalue decomposition. Multitemporal RADARSAT-2 data acquired in southwestern Ontario, Canada, are used for validating the proposed TCMD-SAVSM. The TCMD-SAVSM is first compared with the adaptive model-based decomposition proposed by Arii et al. in which ∇f_v is set to 0.01, demonstrating that TCMD-SAVSM not only consumes much less computing time but also the decomposed surface and double-bounce components are also more representative of the land cover reality. Using the percentage of power of the remainder matrix <0.001 as the evaluation criterion, the SAVSM achieved the best performance in corn, soybean and wheat fields compared with other popular volume scattering models (VSMs), with the highest mean and lowest standard deviation. It suggests that the SAVSM is a suitable VSM for describing seasonal changes of crop fields. Last, the decomposed surface, double-bounce and volume scattering components of wheat, soybean and corn at various growth stages are analyzed at the individual crop level, and the results demonstrate that their variations are in concert with the crop’s phenological development.

Assessment of human health impact from the exposure to PM₁₀ air pollution is crucial for evaluating environmental damage. We established an empirical model to estimate ground PM₁₀ mass concentration from satellite-derived aerosol optical depth and adopted the dose-response model to evaluate the annual average human health risks and losses related to PM₁₀ exposure over China from 2010 to 2014. Unlike the traditional human health assessment methods, which relied on the in situPM₁₀ concentration measurements and statistical population data issued by administrative district, the approach proposed in this study obtained the spatial distribution of human health risks in China by analyzing the distribution of PM₁₀ concentration estimated from satellite observations and population distribution based on the relationship to the spatial distribution of land-use type. It was found that the long-term satellite observations have advantages over the ground-based observations in estimating human health impact from PM₁₀ exposure.

Scale-invariance feature transform (SIFT)–based algorithms often suffer from false matches of keypoints while being utilized to register multispectral remote sensing images. It is even worse for the registration of large water areas due to the lack of keypoints and high similarity of textures in such regions. To tackle these problems, a robust and fast SIFT match algorithm for multispectral image registration based on keypoint classification is proposed. This algorithm establishes match candidates through classifying the keypoints based on their scale spaces, where the keypoint matching is restricted on the same scale group. To obtain an accurate scale group, the ground sampling distance of images to be registered is normalized, while the initial scale ratio is estimated, relying upon the seed matches. Furthermore, for refining the match candidates and improving the matching robustness on water areas of the image, the keypoints are classified into keypoints of the land region (L-KPs) and keypoints of the water area (W-KPs). The L-KPs of the match candidates with sparse region on the two-dimensional representation of the location offset are excluded by the iterative bivariate histogram, and then the average location offset of the correct land matches is employed to exclude the outliers of the candidates associated with the W-KPs. Meanwhile, to reduce the computational cost, the images to be registered are divided into corresponding overlapped blocks, providing parallel computing and local rectification. Experiments were conducted on large-size remote sensing images at different resolutions and the results demonstrate the effectiveness of the proposed approach.

Quantitative analysis of trends in vegetation cover, especially in Kogi state, Nigeria, where agriculture plays a major role in the region’s economy, is very important for detecting long-term changes in the phenological behavior of vegetation over time. This study employs the use of normalized difference vegetation index (NDVI) [global inventory modeling and mapping studies 3g (GIMMS)] data from 1983 to 2011 with detailed methodological and statistical approach for analyzing trends within the NDVI time series for four selected locations in Kogi state. Based on the results of a comprehensive study of seasonalities in the time series, the original signals are decomposed. Different linear regression models are applied and compared. In order to detect structural changes over time a detailed breakpoint analysis is performed. The quality of linear modeling is evaluated by means of statistical analyses of the residuals. Standard deviations of the regressions are between 0.015 and 0.021 with R² of 0.22–0.64. Segmented linear regression modeling is performed for improvement and a decreasing standard deviation of 33%–40% (0.01–0.013) and R² up to 0.82 are obtained. The approach used in this study demonstrates the added value of long-term time series analyses of vegetation cover for the assessment of agricultural and rural development in the Guinea savannah region of Kogi state, Nigeria.

The land surface temperature (LST) retrieval in an urban environment by thermal remote sensing is a widespread scientific topic and several studies have been made to point out the correspondence between spectral indices and LST pattern. This work evaluates the potential of the spectral indices introduced in the literature, more than 30, by assessing their correlation with the summer LST in the heterogeneous urban area of Rome, Italy, considering two different triplets of images acquired during 2009 and 2011 by the Landsat Thematic Mapper (TM). The spectral indices have been divided into vegetation and built up-soil indices employing the reflective TM bands, and then ranked on the basis of their linear and monotonic relationship with the LST. Vegetation indices have a strong negative correlation with LST: vegetation area (VA), nonlinear index, modified soil adjusted vegetation index exhibit a greater Pearson and Spearman correlation coefficient with LST. The more useful spectral indices for built up and soil analysis, exhibiting a greater positive correlation with LST, are the impervious surface area (ISA), the bare soil index, the index-based built-up index, and the normalized difference built-up index. Interesting indications of the impact on the spectral index performance of specific land-cover classes embedded in an urban environment, such as the bare soil and the water classes were pointed out; for example, the reduction of the ISA and VA capability to display the full dynamic range of the LST pattern.

We investigate the potential of hyperspectral imaging spectrometry for the analysis of fresh sediment cores. A sediment-core-scanning system equipped with a camera working in the visual to near-infrared range (400 to 1000 nm) is described and a general methodology for processing and calibrating spectral data from sediments is proposed. We present an application from organic sediments of Lake Jaczno, a freshwater lake with biochemical varves in northern Poland. The sedimentary pigment bacteriopheophytin a (BPhe a) is diagnostic for anoxia in lakes and, therefore, an important ecological indicator. Calibration of the spectral data (BPhe a absorption ∼800 to 900 nm) to absolute BPhe a concentrations, as measured by high-performance-liquid-chromatography, reveals that sedimentary BPhe a concentrations can be estimated from spectral data with a model uncertainty of ∼10%. Based on this calibration model, we use the hyperspectral data from the sediment core to produce high-resolution intensity maps and time series of relative BPhe a concentrations (∼10 to 20 data points per year, pixel resolution 70×70  μm²). We conclude that hyperspectral imaging is a very cost- and time-efficient method for the analysis of lake sediments and provides insight into the spatiotemporal structures of biogeochemical species at a degree of detail that is not possible with wet chemical analyses.

Remote sensing for unmanned aerial vehicles (UAVs) is becoming the preferred method for Earth observations. Data and command communication between an airborne remote sensing load and the ground terminal is crucial for the realization of real-time and smart observation for remote sensing. However, the current remote sensing observation equipment for UAVs cannot meet these requirements. In order to solve this problem, a real-time and smart remote control and data transmission system for UAVs is designed. The design and implementation of several key functions are presented, including multitask and multithread data transmission, transmission resumption, and task scheduling by priority.

Optimizing the productivity of native rangelands has received considerable attention in range management. Rangeland fertilizer application has emerged as a popular intervention for improving rangeland quality. To achieve optimal range quality from such intervention, there is a need for quick and accurate methods of assessing the effects of different fertilizer combinations. The utility of in situ hyperspectral data and multivariate techniques in distinguishing 12 complex ammonium nitrate, ammonium sulfate, lime, and phosphorus fertilizer combinations on a grassland is assessed. Partial least squares regression discriminant analysis (PLS-DA) and discriminant analysis (DA) classification results derived using hyperspectral grass reflectance that were (1) fertilized using 11 combinations of ammonium sulfate, ammonium nitrate, phosphorus, and lime and (2) unfertilized experimental plots were compared. Results illustrate the strength of in situ hyperspectral data and multivariate techniques in detecting and discriminating grasses with different fertilizer treatments. Specifically, four bands within the red edge (731 and 737 nm) and the shortwave infrared (1310 and 1777 nm) regions of the electromagnetic spectrum demonstrated a high potential for discriminating the effects of fertilizer treatments on grasslands. DA outperformed PLS-DA in discriminating complex combinations of ammonium nitrate, ammonium sulfate combined with lime and phosphorus, as well as unfertilized grasses. Overall, spectroscopy and DA offer great potential for discriminating complex fertilizer combinations.

An edge-pixels-based support vector data description (EPSVDD) method has been developed for improving one-class classification accuracy. The proposed method was validated in two experiments: a simulated experiment and an actual experiment. In the simulated experiment, a ring segmentation search method was performed to segment the wheat spectral feature for deriving training samples of different spectral responses. As the training data moved from the center to the edge of wheat distribution, the hypersphere expanded and the overall accuracy (OA) simultaneously increased, highlighting the potential advantage of edge pixels in SVDD classification. In the actual experiment, edge training samples were manually acquired from geographical parcel boundary and minimum noise fraction (MNF) scatterplots for both wheat and bare-land classes. For the wheat class, EPSVDD yielded an improved classification with an OA of 92.71% and a producer’s accuracy of 95.81%, which were higher than those of conventional SVDD method using typical training samples. Similarly, for the bare-land class, the OA of the EPSVDD was 92.53%, which was also significantly higher than traditional SVDD method. Then, SVDD classifications were carried out and repeated 10 times using different training set sizes. Mean OAs were almost higher than 0.9 with variance less than 0.03 using edge training samples, while highest OAs for wheat and bare land classes were 0.74 and 0.81, respectively, using random sampling method. The EPSVDD can effectively select the informative training sample for SVDD classifier to improve the accuracy of one-class classification.

Gaofen-1 panchromatic/multispectral (GF-1 PMS) data have both high spatial and temporal resolutions, and this research aims at evaluating the potential of GF-1 PMS data for crop classification. Three PMS images (at days 110, 192, and 274) were acquired in Manas County of Xinjiang. The images were first segmented and all objects were then visually interpreted based on ground reference data. Some indices and textual features were then extracted at the object level. Subsequently, the Jeffries–Matusita (JM) distance was employed to estimate the class separability among all pair-wise comparisons of each time period. Afterward, a random forest algorithm was used to calculate importance scores of all features and classify crop types for every possible image combination. Additionally, to evaluate the influence of feature number on classification accuracy, features were added one by one based on the importance of scores. The result showed that GF-1 PMS images with high-spatial resolution had the potential to identify the boundary of the crop fields. Relatively high JM distance (above 1.5) and classification accuracy (above 90%) indicated that day 192 image contributed the most to the crop identification in the study area. For multi-image combinations, days 110 to 192 combination can achieve high overall accuracy (around 93%) and more images cannot substantially improve the classification performance. As for features, normalized difference vegetation index and near infrared (NIR) band had the highest importance scores and textual features contributed to distinguishing tree from crop land. Finally, classification accuracy increased together with the augmentation of feature number when only a few features were used. After accuracies reached saturation points, however, more features only slightly improved the classification performance.

Woody cover was found to be an easily measurable and significant variable for estimating woody above-ground biomass (AGB) based on in situ measurements in an arid zone of central Australia. In addition, the potential of woody cover to estimate woody AGB based on Landsat imagery was further tested. Linear spectral mixture analysis (LSMA) using multitemporal endmembers was employed to estimate the woody cover for Landsat imagery and the LSMA method was tested for different Landsat images under various drought conditions. The results show that the accuracy of the woody cover estimation increased as the accumulated rainfall prior to the image acquisition date decreased. Woody AGB across the study area was finally retrieved using the combination of plot-level woody AGB model and woody cover derived from dry-period Landsat images closest to the acquisition time of the field data, with an root mean square error of 0.798  t/ha. It is concluded that woody cover is able to replace tree basal area to estimate woody AGB at plot-level scale, and it is much more suitable for estimating woody AGB than the normalized difference vegetation index-AGB model for this arid zone covered with low-cover shrubs; however, woody AGB estimated using the proposed method is underestimated for some areas.

High-dimensional features and limited labeled training samples often lead to dimensionality disaster for hyperspectral image classification. Semisupervised learning has shown great significance in hyperspectral image processing. A semisupervised classification algorithm based on spatial-spectral clustering (SC-S²C) was proposed. In the proposed framework, spatial information extracted by Gabor filter was first stacked with spectral information. After that, an active learning (AL) algorithm was used to select the most informative unlabeled samples. Then a probability model based support vector machine combined with the SC-S²C technique was used to predict the labels of the selected unlabeled data. The proposed algorithm was experimentally validated on real hyperspectral datasets, indicating that the proposed framework can utilize the unlabeled data effectively and achieve high accuracy compared with state-of-the-art algorithms when small labeled data are available.

We propose a remote sensing technique to measure sound in the upper ocean. The objective is a system that can be flown on an aircraft. Conventional acoustic sensors are ineffective in this application, because almost none (∼0.1%) of the sound in the ocean is transmitted through the water/air interface. The technique is based on the acoustic modulation of naturally occurring bubbles near the sea surface. It is clear from the ideal gas law that the volume of a bubble will decrease if the pressure is increased, as long as the number of gas molecules and temperature remain constant. The pressure variations associated with the acoustic field will therefore induce proportional volume fluctuations of the insonified bubbles. The lidar return from a collection of bubbles is proportional to the total void fraction, independent of the bubble size distribution. This implies that the lidar return from a collection of insonified bubbles will be modulated at the acoustic frequencies, independent of the bubble size distribution. Moreover, that modulation is linearly related to the sound pressure. A laboratory experiment confirmed the basic principles, and estimates of signal-to-noise ratio suggest that the technique will work in the open ocean.

Existing automatic target recognition of synthetic aperture radar (SAR ATR) schemes mainly focus on target chips, but there is very little research for a large-scale and high-resolution SAR image that is more practical for SAR image interpretation. How to recognize targets efficiently and accurately from a large-scale and high-resolution SAR image is still a challenge. We present a scheme based on the combination of a salient detection approach, an active contour model (ACM), an affine-invariant shape descriptor, and the corresponding shape context. During the detection stage, the spectral residual approach is utilized to efficiently preselect salient regions. The proposed convex ACM, based on a ratio distance and distribution metric which makes it more robust to multiplicative speckled noise, is then adopted to get accurate candidate target chips. For the discrimination stage, a cumulative sum of multiscale lacunarity feature is proposed to select vehicle chips from clutter chips. Finally, affine-invariant shape features, obtained from the contours by our proposed ACM, are combined with a corresponding shape context to make the classification more accurate. Experimental results demonstrate that our SAR ATR system, integrating all the proposed methods, is feasible in ATR from a high-resolution and large-scale SAR image.

Two forestry-change detection methods are described, compared, and contrasted for estimating deforestation and growth in threatened forests in southern Peru from 2000 to 2010. The methods used in this study rely on freely available data, including atmospherically corrected Landsat 5 Thematic Mapper and Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous fields (VCF). The two methods include a conventional supervised signature extraction method and a unique self-calibrating method called MODIS VCF guided forest/nonforest (FNF) masking. The process chain for each of these methods includes a threshold classification of MODIS VCF, training data or signature extraction, signature evaluation, k-nearest neighbor classification, analyst-guided reclassification, and postclassification image differencing to generate forest change maps. Comparisons of all methods were based on an accuracy assessment using 500 validation pixels. Results of this accuracy assessment indicate that FNF masking had a 5% higher overall accuracy and was superior to conventional supervised classification when estimating forest change. Both methods succeeded in classifying persistently forested and nonforested areas, and both had limitations when classifying forest change.

Woody vegetation encroachment into grasslands or bush thickening is transforming the southern African grassland systems into savanna-like landscapes. Estimation of woody plant density and canopy biomass is important to rangeland scientists for assessing the impact on grass production and for calculating grazing and browsing capacity. We investigated the utility of remote sensing data for modeling grazing and browsing capacity at landscape level. Tree density or tree equivalents (TEs) and total leaf mass (LMASS) data, derived using the biomass estimation for canopy volume program, were assessed. The random forest (RF) regression algorithm was evaluated to predict TE and LMASS using vegetation indices calculated on red and near-infrared bands of Satellite Pour l’Observation de la Terre-5. The RF model predicted LMASS with R²=0.64 and a root mean square error (RMSE) of 1256  kg/ha compared to a mean of 2291  kg/ha. TE was predicted with R²=0.56 and a RMSE of 1614  TE/ha compared to a mean of 3746  TE/ha. Next, maps of LMASS/ha and TE/ha were derived using separate RF models. The resultant maps were then used in conventional grazing and browsing capacity models. The study provides a sound platform for integrating available and future remote sensing data into rangeland carrying capacity modeling and monitoring.