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This work presents an interesting case-study regarding a big dam monitoring thanks to the deployment of the electrical resistivity tomography (ERT). In particular, we are concerned with the largest rock-fill dam in Europe, i.e., the dam on Sinni river in Basilicata region (Italy), which is one of the main civil strategic infrastructures in Southern Italy. The presence of fractures visible on the hanging waterproof dam has motivated the investigation about the possible water infiltration in the dam. ERT surveys were carried out to detect the inner areas characterized by lower electrical resistivity values as indicators of the undesired presence of water inside the structure. The imaging capability of the ERT investigation was the key to detect and localize the areas of the dam affected by the presence of water. On the basis of ERT results, the managing body of the dam activated other diagnostic tools in view of a proper planning of future restoration works. [ABSTRACT FROM PUBLISHER]

Due to the rapid technological development of various different satellite sensors, a huge volume of high-resolution image data sets can now be acquired. How to efficiently represent and recognize the scenes from such high-resolution image data has become a critical task. In this paper, we propose an unsupervised feature learning framework for scene classification. By using the saliency detection algorithm, we extract a representative set of patches from the salient regions in the image data set. These unlabeled data patches are exploited by an unsupervised feature learning method to learn a set of feature extractors which are robust and efficient and do not need elaborately designed descriptors such as the scale-invariant-feature-transform-based algorithm. We show that the statistics generated from the learned feature extractors can characterize a complex scene very well and can produce excellent classification accuracy. In order to reduce overfitting in the feature learning step, we further employ a recently developed regularization method called “dropout,” which has proved to be very effective in image classification. In the experiments, the proposed method was applied to two challenging high-resolution data sets: the UC Merced data set containing 21 different aerial scene categories with a submeter resolution and the Sydney data set containing seven land-use categories with a 60-cm spatial resolution. The proposed method obtained results that were equal to or even better than the previous best results with the UC Merced data set, and it also obtained the highest accuracy with the Sydney data set, demonstrating that the proposed unsupervised-feature-learning-based scene classification method provides more accurate classification results than the other latent-Dirichlet-allocation-based methods and the sparse coding method. [ABSTRACT FROM PUBLISHER]

High-quality pansharpened multispectral (MS) images are rarely obtained from fast, efficient, and robust algorithms. In most cases, effective pansharpening methods have huge computational complexity, as in the case of variational methods, or algorithms based on sparse representations. Moreover, injection models are often application dependent, not sufficiently general to be applied to different scenarios, and the resulting algorithm implementations cannot process large-size images. The proposed pansharpening method is accurate and fast and can be successfully applied to huge images. It also solves the problem of contextadaptive schemes that tune the spatial injection parameters on local statistics: Instabilities and blocky artifacts can be generated by pansharpening methods whose parameters are computed on local windows. The proposed method is an extension of the classical component-substitution algorithms: An optimal detail image (in the mmse sense) extracted from the panchromatic band is calculated for each MS band by evaluating band-dependent generalized intensities. It overcomes window-based local estimation of parameters by applying a nonlocal parameter optimization through K-means clustering. Very high quality scores, both at degraded and full scale, and excellent visual quality of the fused images demonstrate the validity of the method. [ABSTRACT FROM PUBLISHER]

This paper reports on the absolute 3-D localization of radar corner reflectors and persistent scatterers (PSs) by stereo synthetic aperture radar (SAR) carried out with TerraSAR-X and TanDEM-X. The novel combination of rigorously linearized range-Doppler equations with thorough sensor modeling, consideration of atmospheric delays, geodynamic signals, satellite dynamics, and geometrical calibration allows a direct target localization at the centimeter level. Therefore, there is no need for the application of geocoding since our approach delivers 3-D positions directly in the International Terrestrial Reference Frame (ITRF) 2008. Four radar corner reflectors located at the Wettzell Geodetic Observatory, Metsähovi Geodetic Observatory, and German Antarctic Receiving Station O'Higgins were localized in 3-D with a precision better than 4 cm. By introducing an updated geometrical calibration of TerraSAR-X and TanDEM-X, the absolute accuracy of the same level becomes possible, which was demonstrated by externally validating the reflector positions at Metsähovi and Wettzell against results of terrestrial geodetic surveying. Furthermore, PSs located in Berlin were retrieved with a precision of 10 cm, making the method a suitable tool for radar “positioning” in urban environments. Potential fields of application for our approach are the joint analysis with phase-based methods, geometrical calibration of radar satellites, and direct integration of SAR observations into Global Navigation Satellite System networks. [ABSTRACT FROM PUBLISHER]

In this paper, we design a fast hierarchical clustering algorithm for high-resolution hyperspectral images (HSI). At the core of the algorithm, a new rank-two nonnegative matrix factorization (NMF) algorithm is used to split the clusters, which is motivated by convex geometry concepts. The method starts with a single cluster containing all pixels and, at each step, performs the following: 1) selects a cluster in such a way that the error at the next step is minimized and 2) splits the selected cluster into two disjoint clusters using rank-two NMF in such a way that the clusters are well balanced and stable. The proposed method can also be used as an endmember extraction algorithm in the presence of pure pixels. The effectiveness of this approach is illustrated on several synthetic and real-world HSIs and is shown to outperform standard clustering techniques such as k-means, spherical k-means, and standard NMF. [ABSTRACT FROM PUBLISHER]

The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), the successor mission to ICESat, is planned to launch in 2017. The ICESat-2 spacecraft will carry the Advanced Topographic Laser Altimeter System (ATLAS). ATLAS will be the most precise space-based photon-counting laser altimeter to date, and its measurement strategy requires the development of sophisticated onboard receiver algorithms to ensure success in downlinking the science data in the telemetry and the subsequent development of science data products. A set of databases, the digital elevation model and digital relief map (DRM), has been developed for use in ATLAS onboard signal processing. A number of elevation data sets were combined to create the global elevation and relief databases, and a method for calculating along-track relief from raster elevation data sets was devised. A technique for deriving the accuracy of the DRM relative to the magnitude of relief was developed to inform the selection of DRM margin values. [ABSTRACT FROM PUBLISHER]

The Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime lights imagery has proven to be a powerful remote sensing tool to monitor urbanization and assess socioeconomic activities at large scales. However, the existence of incompatible digital number (DN) values and geometric errors severely limit application of nighttime light image data on multiyear quantitative research. In this paper, we extend and improve previous studies on intercalibrating nighttime lights image data to obtain more compatible and reliable nighttime lights time-series (NLT) image data for China and the U.S. through four steps, namely, intercalibration, geometric correction, steady-increase adjustment, and population data correction. We then use gross domestic product (GDP) data to test the processed NLT image data indirectly and find that sum light (summed DN value of pixels in a nighttime light image) maintains apparent increase trends with relatively large GDP growth rates but does not increase or decrease with relatively small GDP growth rates. As nighttime light is a sensitive indicator for economic activity, the temporally consistent trends between sum light and GDP growth rate imply that brightness of nighttime lights on the ground is correctly represented by the processed NLT image data. Finally, through analyzing the corrected NLT image data from 1992 to 2008, we find that China experienced apparent nighttime lights development in 1992-1997 and 2001-2008, respectively, and the U.S. showed nighttime lights decay in large areas after 2001. [ABSTRACT FROM PUBLISHER]

The radiometric correction of airborne imagery aims at providing unbiased spectral information about the Earth's surface. Correction steps include system calibration, geometric correction, and the compensation for atmospheric effects. Such preprocessed data are affected by the bidirectional reflectance distribution function (BRDF), which requires an additional compensation step. We present a novel method for a surface-cover-dependent BRDF effects correction (BREFCOR). It uses a continuous index based on bottom-of-atmosphere reflectances to tune the Ross-Thick Li-Sparse BRDF model. This calibrated model is then used to correct for observation-angle-dependent anisotropy. The method shows its benefits specifically for wide-field-of-view airborne systems where BRDF effects strongly affect image quality. Evaluation results are shown for sample data from a multispectral photogrammetric Leica ADS camera system and for HYSPEX imaging spectroscopy data. The scalability of the procedure for various kinds of sensor configurations allows for its operational use as part of standard processing systems. [ABSTRACT FROM PUBLISHER]

With the increased spatial resolution of current sensor constellations, more details are captured about our changing planet, enabling the recognition of a greater range of land use/land cover classes. While pixeland object-based classification approaches are widely used for extracting information from imagery, recent studies have shown the importance of spatial contexts for discriminating more specific and challenging classes. This paper proposes a new compact representation for the fast query/classification of compound structures from very high resolution optical remote sensing imagery. This bag-of-features representation relies on the multiscale segmentation of the input image and the quantization of image structures pooled into visual word distributions for the characterization of compound structures. A compressed form of the visual word distributions is described, allowing adaptive and fast queries/classification of image patterns. The proposed representation and the query methodology are evaluated for the classification of the UC Merced 21-class data set, for the detection of informal settlements and for the discrimination of challenging agricultural classes. The results show that the proposed representation competes with state-of-the-art techniques. In addition, the complexity analysis demonstrates that the representation requires about 5% of the image storage space while allowing us to perform queries at a speed down to 1 s/ 1000 km2/CPU for 2-m multispectral data. [ABSTRACT FROM PUBLISHER]

We present a method for assessing land cover change on a continental scale and with high spatial resolution. This is a postclassification method, but instead of tracking transitions of land cover classes on a cell-by-cell basis, the method measures the change at a tile level by quantifying a difference between local patterns of land cover at two different time steps. Pattern-based change assessment is well suited for the large-scale survey as it addresses landscape dynamics rather than just simple land class transitions. A tile is defined as a local area consisting of a large enough number of land cover cells to sample a distribution of landscape but small enough to detect change with high spatial resolution; 4.5 km × 4.5 km square tiles are used. The level of change is measured as the dissimilarity between motifs of tile patterns at two time steps and is calculated using information-theoretic metric called the JSS. The method is able to discriminate between different types of change, including change in geometric pattern, change in class composition, and numerous class transitions without significant changes in either pattern or composition. The methodology is applied to the National Land Cover Dataset to obtain a 2001-2006 change map of the conterminous U.S. The resultant map shows (in a high resolution of 3 km/cell) a spatial distribution of the degree to which the landscape has changed in this time period. Both large regions (southeastern and Gulf regions, Pacific Northwest region, and the state of Maine) of heightened landscape dynamics and small regions of sudden change due to fires, urban growth, etc., are clearly identifiable from the map. A fully featured online application for fast and convenient exploration of the change map together with original land cover maps in their full resolutions is available at http://sil.uc.edu/dataeye/. [ABSTRACT FROM PUBLISHER]

Constrained energy minimization (CEM) has been widely used for subpixel detection. It takes advantage of inverting the global sample correlation matrix R to suppress background so as to enhance detection of targets of interest. This paper presents a progressive band processing of CEM (PBP-CEM) which can perform CEM for target detection progressively band by band according to band sequential format. In doing so, a new concept, called causal band correlation matrix (CBCM), is introduced to replace the global sample correlation matrix R. It is a global correlation matrix formed by only those bands that were already visited up to the band currently being processed while excluding bands yet to be visited in the future. The proposed PBP-CEM allows CEM to be processed whenever bands are available, without waiting for completing band collection. With such an advantage, CEM has potential in data transmission and communication, specifically in satellite data processing. [ABSTRACT FROM PUBLISHER]

Historical variations in the extent of mountain glaciers give insight into natural and forced changes of these bellwethers of the climate. Because of the limited number of ground observations relative to the number of glaciers, it is useful to develop techniques that permit for the monitoring of glacier systems using satellite imagery. Here, we propose a new approach for identifying the glacier terminus over time from Landsat images. The proposed method permits for detecting inflection points in multispectral satellite imagery taken along a glacier's flow path in order to identify candidate terminus locations. A gated tracking algorithm is then applied to identify the best candidate for the glacier terminus location through time. Finally, the long-term trend of the terminus position is estimated with uncertainty bounds. This is achieved by applying nonparametric regression to the temporal sequence of estimated terminus locations. The method is shown to give results consistent with ground-based observations for the Franz Josef and Gorner glaciers and is further applied to estimate the retreat of Viedma, a glacier with no available ground measurements. [ABSTRACT FROM PUBLISHER]

This paper presents a framework for using remote sensing imagery and image processing techniques to analyze the interactions among tropical depressions (TDs) and typhoons. The purpose is to properly understand and predict this phenomenon, in order to reduce their influence on the regions where they pass by. Improvements on the understanding of the interactions among TDs and typhoons are obtained and presented. They carry the potential to improve forecast of severe weather system. In this paper, we analyzed the physical responses of TDs on two typhoons Tembin and Bolaven (2012), including TD's appearance, development, interaction, and mergence. According to the Central Weather Bureau of Taiwan, Tembin and Bolaven were formed about 1550 km apart. In general, the Fujiwhara effect happens when two storms are within a distance of 1400 km. Does the TD have impacts on the typhoons, and could we also call this impact as Fujiwhara effect? The 3-D profiles of the weather systems are reconstructed using the multifunctional transport satellite (MTSAT) infrared cloud images by the image reconstruction technique (IRT). The 3-D profiles of typhoons were implemented for investigating the in-depth distribution of the cloud top from a surface cloud image. The main parameters, which have been analyzed in this paper, are distance, height difference, rotation, and sizes of the two TDs and the two typhoons. The results showed that two TDs occurred between typhoons Tembin and Bolaven, which interacted with each other through upward convection and attraction between two typhoons and the TDs, respectively. The mergence of the two TDs caused the typhoons to start rotating and generate strengthened power. The findings of the TD effects on two typhoons represent a new aspect of the interaction between TD and typhoon. [ABSTRACT FROM PUBLISHER]

We present a semisupervised method for single-pixel classification of hyperspectral images. The proposed method is designed to address the special problematic characteristics of hyperspectral images, namely, high dimensionality of hyperspectral pixels, lack of labeled samples, and spatial variability of spectral signatures. To alleviate these problems, the proposed method features the following components. First, being a semisupervised approach, it exploits the wealth of unlabeled samples in the image by evaluating the confidence probability of the predicted labels, for each unlabeled sample. Second, we propose to jointly optimize the classifier parameters and the dictionary atoms by a task-driven formulation, to ensure that the learned features (sparse codes) are optimal for the trained classifier. Finally, it incorporates spatial information through adding a Laplacian smoothness regularization to the output of the classifier, rather than the sparse codes, making the spatial constraint more flexible. The proposed method is compared with a few comparable methods for classification of several popular data sets, and it produces significantly better classification results. [ABSTRACT FROM PUBLISHER]

Dimension reduction (DR) is a necessary and helpful preprocessing for hyperspectral image (HSI) classification. In this paper, we propose a spatial and spectral regularized local discriminant embedding (SSRLDE) method for DR of hyperspectral data. In SSRLDE, hyperspectral pixels are first smoothed by the multiscale spatial weighted mean filtering. Then, the local similarity information is described by integrating a spectral-domain regularized local preserving scatter matrix and a spatial-domain local pixel neighborhood preserving scatter matrix. Finally, the optimal discriminative projection is learned by minimizing a local spatial-spectral scatter and maximizing a modified total data scatter. Experimental results on benchmark hyperspectral data sets show that the proposed SSRLDE significantly outperforms the state-of-the-art DR methods for HSI classification. [ABSTRACT FROM PUBLISHER]

Satellite image classification is an important technique used in remote sensing for the computerized analysis and pattern recognition of satellite data, which facilitates the automated interpretation of a large amount of information. Today, there exist many types of classification algorithms, such as parallelepiped and minimum distance classifiers, but it is still necessary to improve their performance in terms of accuracy rate. On the other hand, over the last few decades, cellular automata have been used in remote sensing to implement processes related to simulations. Although there is little previous research of cellular automata related to satellite image classification, they offer many advantages that can improve the results of classical classification algorithms. This paper discusses the development of a new classification algorithm based on cellular automata which not only improves the classification accuracy rate in satellite images by using contextual techniques but also offers a hierarchical classification of pixels divided into levels of membership degree to each class and includes a spatial edge detection method of classes in the satellite image. [ABSTRACT FROM PUBLISHER]

This paper presents a reliable technology for GPS/Global'naya Navigatsionnaya Sputnikovaya Sistema (GLONASS) surveying under difficult observational conditions, i.e., in forest environments. The technology is based on the rapid static and Real-Time Kinematic (RTK) methods. Test surveys for the developed technology were carried out on a point in a dense forest, with a high degree of difficulty for satellite access. The coordinates of the point were determined by three GPS/GLONASS receivers positioned in a line on a special base, which were separated by a fixed distance of 0.5 m. The test surveys were conducted using Topcon HiPer Pro receivers with 10-Hz RTK positioning and gathering data at a sampling rate of 10 Hz. Practical tests for the presented technology showed that rapid static surveys based on three rover GPS/GLONASS receivers were a quite efficient technology in forest environments (unlike the RTK) and allowed for reliable centimeter accuracy of the determined positions to be obtained in 5-min observational sessions. [ABSTRACT FROM PUBLISHER]

The objective of this paper is to provide a better understanding of the capabilities of the BIOMASS tomography concerning the retrieval of forest biomass and height in tropical areas. The analysis presented in this paper is carried out on airborne data acquired by Office National d'Etudes et de Recherches Aérospatiales (ONERA) over the site of Paracou, French Guiana, during the European Space Agency campaign TropiSAR. This high-resolution data set (125-MHz bandwidth) was reprocessed in order to generate a new data stack consistent with BIOMASS as for the bandwidth (6 MHz) and the azimuth resolution (about 12 m). To do this, two different processing approaches have been considered. One approach consisted of degrading the resolution of the airborne data through the linear filtering of raw data, followed by standard SAR processing. The other approach consisted of recovering the 3-D distribution of the scatterers at a high resolution, which was then reprojected onto the BIOMASS geometry. The latter procedure allows us to obtain a data stack that is the most realistic emulation of BIOMASS imaging capabilities. In both approaches, neither ionospheric disturbances nor temporal decorrelation has been considered. The connection to the forest biomass has been examined in both cases by investigating the correlation between the backscatter at different forest heights and the above-ground biomass (AGB) values from in situ data. As expected, the reduction of the system bandwidth to 6 MHz resulted in significant vertical resolution losses compared with the original airborne data. Nevertheless, it was possible to retrieve the forest height to within an accuracy of better than 4 m, whereas the backscattered power at the volume height (30 m above the ground) exhibited a correlation higher than 0.8 with the in situ data and no bias phenomena over the AGB values ranging from 250 to 450 t/ha. [ABSTRACT FROM PUBLISHER]

A unique method for investigating continental surfaces uses backscatter data measured by a radar altimeter at the nadir point of a satellite, in contrast to other microwave sensors designed to work at oblique angles, such as scatterometers and synthetic aperture radar. To improve the altimetry resolution over land, we generated 0.5° × 0.5° merged altimetry backscatter maps covering 66° N to 66° S over the global land surface every six days for the period from January 2002 to June 2009 by combining three altimeter data sets (Jason1, Envisat, Jason2) in the Ku-band. The four backscatter products of Envisat RA2 from different retracking algorithms were evaluated prior to merging with the Jason1 and Jason2 data. The global pattern and the seasonal variation of the merged altimetry backscatter were examined, which show the merged results have better spatial sampling for the regional to global geophysical process due to the combination of three altimeters. To understand how the altimetry backscatter is related to land surface parameters, the advanced integral equation model for bare soil and water cloud model for vegetation are used to simulate the Ku-band backscatter response to soil and vegetation parameters at an incidence of 0°. Furthermore, we compared the time series of merged altimetry backscatter with the leaf area index (LAI) determined by an optical sensor and the backscatter coefficients obtained from a scatterometer (QuikSCAT) over seven selected vegetated areas over six years. The results confirm the sensitivity of ocean altimetry to vegetation. Further studies relating altimetry backscatter to geophysical parameter are needed. [ABSTRACT FROM PUBLISHER]

We propose a new automatic method called the rotational pixel swapping (RPSW) method to detect circular features in binary images of remote sensing images. The method is based on a multiplication operation between the original image and the rotated images. We show that the RPSW selectively enhances rotational symmetric patterns and weakens nonrotational symmetric patterns, including noise components, without any noise reduction processes. The method can detect not only simple circles but also more complex circular features such as incomplete ring structures or several concentric rings. Furthermore, we demonstrate that the RPSW provides the stable detection of circular features such as terrestrial impact structures, which are irregular imperfect circular shapes, in binary images based on Earth-observation satellite images. The RPSW would provide a potential method of future surveys or statistical studies using huge data sets of multiband or hyperspectral images obtained by Earth-observation satellites. [ABSTRACT FROM PUBLISHER]

Noise pollution caused by vehicular traffic is a common problem in urban environments that has been shown to affect people's health and children's cognition. In the last decade, several studies have been conducted to assess this noise, by measuring the equivalent noise pressure level (called Leq) to acquire an accurate sound map using wireless networks with acoustic sensors. However, even with similar values of Leq, people can feel the noise differently according to its frequency characteristics. Thus, indexes, which can express people's feelings by subjective measures, are required. In this paper, we analyze the suitability of using the psychoacoustic metrics given by the Zwicker's model, instead of just only considering Leq. The goal is to evaluate the hardware limitations of a low-cost wireless acoustic sensor network that is used to measure the annoyance, using two types of commercial and off-the-shelf sensor nodes, Tmote-Invent nodes and Raspberry Pi platforms. Moreover, to calculate the parameters using these platforms, different simplifications to the Zwicker's model based on the specific features of road traffic noise are proposed. To validate the different alternatives, the aforementioned nodes are tested in a traffic congested area of Valencia City in a vertical and horizontal network deployment. Based on the results, it is observed that the Raspberry Pi platforms are a feasible low-cost alternative to increase the spatial-temporal resolution, whereas Tmote-Invent nodes do not confirm their suitability due to their limited memory and calibration issues. [ABSTRACT FROM PUBLISHER]

In this study, a novel methodology based upon the information-theoretic measures of entropy and mutual information was implemented to downscale soil moisture (SM) observations from 10 km to 1 km. It included a transformation function that related auxiliary remotely sensed (RS) products at high resolution to in situ SM observations to obtain first estimates of SM at 1 km and merging this estimate with SM at coarse resolutions through Principle of Relevant Information (PRI). The PRI-based estimates were evaluated using synthetic observations in NC Florida for heterogeneous agricultural land covers (LC), with two growing seasons of sweet corn and one of cotton, annually. The cumulative density function showed an overall error in SM of <; 0.03 cubic meter/cubic meter in the region, with a confidence interval of 95% during the simulation period. The PRI estimates at 1 km were also compared with those from the method based upon Universal Triangle (UT). The spatially averaged root mean square error (RMSE) aggregated over the vegetative LC were 0.01 cubic meter/cubic meter and 0.15 cubic meter/cubic meter using the PRI and UT methods, respectively. The RMSE for downscaled estimates using the UT method increased to 0.28 cubic meter/cubic meter when Laplacian errors are used, while the corresponding RMSE for the PRI remains the same for both Laplacian or Gaussian errors. The Kullback-Liebler divergence (KLD) for estimates using PRI is about 50% lower than those using the method based upon UT indicating that the probability density function (PDF) of the PRI estimate is closer to PDF of the true SM, than the UT method. [ABSTRACT FROM PUBLISHER]

The Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (S-NPP VIIRS) instrument contains a visible imaging band designed to produce imagery during both daytime and nighttime, which is called the day-night band (DNB). The DNB is a three-gain-stage backside-illuminated charge-coupled device (CCD) with four detector arrays that aggregate the individual CCD pixels into 32 different aggregation modes across scan, yielding imagery with a roughly constant horizontal sampling interval. The highest gain stage is over 100 000 times more sensitive than the lowest gain stage; the combination of the three gain stages allows for imagery with radiances ranging from 10-10 to 10-2 W · cm-2 · sr-1. The initial DNB on-orbit calibration relies on monthly sensor special operations. This offline calibration approach results in discrete calibration and the loss of some science data. In this paper, we will present a new calibration method based solely on VIIRS onboard calibrators (OBCs). The calibrator data collected on the nighttime side of an orbit are used to determine the dark offset and the data collected over the daytime side of the orbit, and the day-night terminators are used to compute the cross-stage gain ratios. The results showed that the dark offset and the gain ratio derived from the initial method could be biased up to ten digital numbers (DN) and 12%, respectively, due to nighttime airglow and Earth scene stray light. The calibration is also continuous as calibrator data are recorded for each scan. Because no special operation and offline analysis are required, this method was approved for VIIRS operational implementation to improve the DNB radiometric calibration and sensor on-orbit operations. [ABSTRACT FROM PUBLISHER]

Light detection and ranging (LiDAR) technology has been extensively used for estimating forest attributes. Although high-spatial-density LiDAR data can be used to accurately derive attributes at single tree level, low-density LiDAR data are usually acquired for reducing the cost. However, a low density strongly affects the estimation accuracy due to the underestimation of the tree top and the possible loss of crowns that are not hit by any LiDAR point. In this paper, we propose a 3-D model-based approach to the estimation of the tree top height based on the fusion between low-density LiDAR data and high-resolution optical images. In the proposed approach, the integration of the two remotely sensed data sources is first exploited to accurately detect and delineate the single tree crowns. Then, the LiDAR vertical measures are associated to those crowns hit by at least one LiDAR point and used together with the radius of the crown and the tree apex location derived from the optical image for reconstructing the tree top height by a properly defined parametric model. For the remaining crowns detected only in the optical image, we reconstruct the tree top height by proposing a k-nearest neighbor trees technique that estimates the height of the missed trees as the average of the k reconstructed height values of the trees having most similar crown properties. The proposed technique has been tested on a coniferous forest located in the Italian Alps. The experimental results confirmed the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

Spatial and temporal variability in landscape freeze- thaw (FT) status at higher latitudes and elevations significantly impacts land surface water mobility and surface energy partitioning, with major consequences for regional climate, hydrological, ecological, and biogeochemical processes. With the development of new-generation spaceborne remote sensing instruments, future L-band missions, including the NASA Soil Moisture Active and Passive mission, will provide new operational retrievals of landscape FT state dynamics at moderate (~3 km) spatial resolution. We applied theoretical simulations of L-band radar backscatter using first-order radiative transfer models with two and three-layer modeling schemes to develop a modified seasonal threshold algorithm (STA) and FT classification study over Alaska using 100-m-resolution satellite Phased Array L-band Synthetic Aperture Radar (PALSAR) observations. The backscatter threshold distinguishes between frozen and nonfrozen states, and it is used to classify the predominant frozen or thawed status of a grid cell. An Alaska FT map for April 2007 was generated from PALSAR (ScanSAR) observations and showed a regionally consistent but finer FT spatial pattern than an alternative surface air temperature-based classification derived from global reanalysis data. Validation of the STA-based FT classification against regional soil climate stations indicated approximately 80% and 75% spatial classification accuracy values in relation to respective station air temperature and soil temperature measurement-based FT estimates. An investigation of relative spatial scale effects on FT classification accuracy indicates that the relationship between grid cell size and classified frozen or thawed area follows a general logarithmic function. [ABSTRACT FROM PUBLISHER]

To retrieve the soil salinity by microwave remote sensing, we must clarify the relation of dielectric properties of saline soils with soil salinity. The objective of this paper was to determine how dielectric properties are affected by soil salinity in the remote sensing range and develop an improved model applying for the saline soil. Laboratory measurements of dielectric constant are made with a microwave vector network analyzer using soil mixture samples of various soil moistures and salinities prepared artificially from natural soils. The results confirmed that the real part is strongly affected by soil moisture, whereas the imaginary part depends on both the soil moisture and salinity, particularly at lower frequencies (1-6 GHz). Thus, as a key factor, soil salinity is introduced into the expression of the imaginary part of dielectric constant in the Dobson semiempirical dielectric mixing model, combining the dielectric model for saline water and the impact of electrical conductivity of soil solution. The improved model yields results, which are in good agreement with the laboratory measurements, the slopes of fitting curves between measurements and simulation nearly being equal to 1, and coefficient R2 are higher than 0.89. In addition, the improve model is independent of soil-texture parameters. However, it should be noted that the improved model can be applied only to the C-band remote sensing. [ABSTRACT FROM PUBLISHER]

Light detection and Ranging (LiDAR) is becoming an increasingly used tool to support decision-making processes within forest operations. Area-based methods that derive information on the condition of a forest based on the distribution of points within the canopy have been proven to produce reliable and consistent results. Individual tree-based methods, however, are not yet used operationally in the industry. This is due to problems in detecting and delineating individual trees under varying forest conditions resulting in an underestimation of the stem count and biases toward larger trees. The aim of this paper is to use high-resolution LiDAR data captured from a small multirotor unmanned aerial vehicle platform to determine the influence of the detection algorithm and point density on the accuracy of tree detection and delineation. The study was conducted in a four-year-old Eucalyptus globulus stand representing an important stage of growth for forest management decision-making process. Five different tree detection routines were implemented, which delineate trees directly from the point cloud, voxel space, and the canopy height model (CHM). The results suggest that both algorithm and point density are important considerations in the accuracy of the detection and delineation of individual trees. The best performing method that utilized both the CHM and the original point cloud was able to correctly detect 98% of the trees in the study area. Increases in point density (from 5 to 50 points/m2) lead to significant improvements (of up to 8%) in the rate of omission for algorithms that made use of the high density of the data. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel covariation approach for seismic wavelet estimation under the assumption that a real seismic signal follows non-Gaussian α-stable distributions. Since the non-Gaussian α-stable signals do not have finite second or higher order moments, the traditional methods of Gaussian distribution may not get a suitable solution. Based on the principle of fractional lower order statistics, the covariation approach deconvolution objective function matrix was given, and the details of wavelet estimation with the covariation approach were presented. Furthermore, computer simulation experiments on theoretical synthetic data and real seismic data were conducted. In the experiments, the effect of moments was considered. Among the estimated wavelets with different moments, the best wavelet should be the one with moment less than characteristic but close to characteristic. To verify the correctness and effectiveness of the proposed method, the extracted real wavelet was applied in real seismic acoustic impedance inversion. The result from the inversion of the 2-D real data set is consistent with the well log interpretation very well. [ABSTRACT FROM PUBLISHER]

Hyperspectral sensors record radiances in a large number of wavelengths of the electromagnetic spectrum and can be used to distinguish different tree species based on their characteristic reflectance signatures. Reflectance spectra were measured from airborne hyperspectral AISA Eagle/Hawk imagery in order to identify different Mediterranean tree species at a coastal test site in Portugal. A spectral range from 400 to 2450 nm was recorded at 2-m spatial resolution. The hyperspectral data are divided into five spectral data ranges. The chosen ranges for segmentation are based on statistical properties as well as on their wavelengths, as radiances of a particular wavelength may overlap with neighboring wavelengths. Principal component analysis (PCA) is applied individually to each spectral range. The first three principal components (PCs) of each range are chosen and are fused into a new data segment of reduced dimensionality. The resulting 15 PCs contain 99.42% of the information content of the original hyperspectral image. These PCs were used for a maximum likelihood classification (MLC). Spectral signatures were also analyzed for the hyperspectral data, and were validated with ground data collected in the field by a handheld spectro-radiometer. Different RGB combinations of PC bands of segmented PC image provide distinct feature identification. A comparison with other classification approaches (spectral angle mapper and MLC of the original hyperspectral imagery) shows that the MLC of the segmented PCA achieves the highest accuracy, due to its ability to reduce the Hughes phenomenon. [ABSTRACT FROM PUBLISHER]

The first-generation time-series synthetic aperture radar interferometry (TSInSAR) technique persistent-scatterer (PS) InSAR has been proven effective in ground deformation measurement over areas with high reflectivity by taking advantage of coregistered temporally coherent pointwise scatterers. In order to increase the spatial density of measurement points and quality of displacement time series over moderate reflectivity scenes, a second-generation TSInSAR called SqueeSAR was developed to extract displacement information from both PSs and distributed scatterers, by taking into account their temporal coherence and their spatial statistical behavior. In this paper, we propose a new second-generation TSInSAR, which is referred to as joint-scatterer (JS) InSAR, to measure the line-of-sight surface displacement using the neighboring pixel stacks. A novel goodness-of-fit testing approach is proposed to analyze the similarity between two JS vectors based on time-series likelihood ratios. By taking advantage of the proposed test, a new spatially adaptive filter is developed to estimate the covariance matrix. Based on the estimated covariance matrix, the projection of the joint signal subspace onto the corresponding joint noise subspace is applied to retrieve phase history. With coherence information of neighboring pixel stacks, JSInSAR is able to provide reliable geophysical parameters in the presence of large coregistration errors. The effectiveness of the proposed technique is verified with a time series of high-resolution SAR data from the TerraSAR-X satellite. [ABSTRACT FROM PUBLISHER]

The goal of this paper is to develop an efficient method for forest change detection using multitemporal stereo panchromatic imagery. Due to the lack of spectral information, it is difficult to extract reliable features for forest change monitoring. Moreover, the forest changes often occur together with other unrelated phenomena, e.g., seasonal changes of land covers such as grass and crops. Therefore, we propose an approach that exploits kernel Minimum Noise Fraction (kMNF) to transform simple change features into high-dimensional feature space. Digital surface models (DSMs) generated from stereo imagery are used to provide information on height difference, which is additionally used to separate forest changes from other land-cover changes. With very few training samples, a change mask is generated with iterated canonical discriminant analysis (ICDA). Two examples are presented to illustrate the approach and demonstrate its efficiency. It is shown that with the same amount of training samples, the proposed method can obtain more accurate change masks compared with algorithms based on k-means, one-class support vector machine, and random forests. [ABSTRACT FROM PUBLISHER]

We assessed the reproducibility of forest inventory metrics derived from an unmanned aerial vehicle (UAV) laser scanning (UAVLS) system. A total of 82 merged point clouds were captured over six 500-m2 plots within a Eucalyptus globulus plantation forest in Tasmania, Australia. Terrain and understory height, together with plot- and tree-level metrics, were extracted from the UAVLS point clouds using automated methods and compared across the multiple point clouds. The results show that measurements of terrain and understory height and plot-level metrics can be reproduced with adequate repeatability for change detection purposes. At the tree level, the high-density data collected by the UAV provided estimates of tree location (mean deviation (MD) of less than 0.48 m) and tree height (MD of 0.35 m) with high precision. This precision is comparable to that of ground-based field measurement techniques. The estimates of crown area and crown volume were found to be dependent on the segmentation routine and, as such, were measured with lower repeatability. The precision of the metrics found within this paper demonstrates the applicability of UAVs as a platform for performing sample-based forest inventories. [ABSTRACT FROM PUBLISHER]

With the availability of real-time high-rate GPS observations and precise satellite orbit and clock products, the interest in the real-time precise point positioning (PPP) technique has greatly increased to construct displacement waveforms and to invert for source parameters of earthquakes in real time. Furthermore, PPP ambiguity resolution approaches, developed in the recent years, overcome the accuracy limitation of the standard PPP float solution and achieve comparable accuracy with relative positioning. In this paper, we introduce the real-time PPP service system and the key techniques for real-time PPP ambiguity resolution. We assess the performance of the ambiguity-fixed PPP in real-time scenarios and confirm that positioning accuracy in terms of root mean square of 1.0-1.5 cm can be achieved in horizontal components. For the 2011 Tohoku-Oki (Japan) and the 2010 El Mayor-Cucapah (Mexico) earthquakes, the displacement waveforms estimated from ambiguity-fixed PPP and those provided by the accelerometer instrumentation are consistent in the dynamic component within few centimeters. The PPP fixed solution not only can improve the accuracy of coseismic displacements but also provides a reliable recovery of earthquake magnitude and of the fault slip distribution in real time. [ABSTRACT FROM PUBLISHER]

The mean power of the reflected Global Navigation Satellite System (GNSS) signals acquired by a GNSS-Reflectometry (GNSS-R) receiver can be modeled through the integral bistatic radar equation by weighting the contributions of all scatterers on the surface by the system impulse response. The geophysical properties of the scattering surface affect the magnitude of the reflected navigation signals through the bistatic scattering coefficient which, in case the observed surface is land, is a function of the soil dielectric properties, surface roughness, and vegetation cover. In this paper, the GNSS-R signal simulator developed in the framework of the Land MOnitoring with Navigation signal (LEiMON) Project, supported by European Space Agency, is presented. The simulator is able to predict the power reflected by land, taking as input the system and observation parameters, as well as the land surface parameters. The latter are used to simulate both the coherent and the incoherent scattering, taking advantage of widely used theoretical models of bistatic scattering from bare soils and vegetated surfaces. First, the geometrical formulation is discussed, and then, the problem of polarization mismatch due to real antennas at circular polarization is faced following the polarization synthesis approach. Finally, a comparison with some experimental data collected during the LEiMON campaign is presented. The simulations display the same trend of the experimental data, thus showing that the simulator can be used as an efficient tool for the interpretation of GNSS-R measurements. [ABSTRACT FROM PUBLISHER]

A modified version of the MODerate resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) algorithm is presented for use in the angular normalization of surface reflectance data gathered by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard the geostationary Meteosat Second Generation (MSG) satellites. We present early and provisional daily nadir BRDF-adjusted reflectance (NBAR) data in the visible and near-infrared MSG channels. These utilize the high temporal resolution of MSG to produce BRDF retrievals with a greatly reduced acquisition period than the comparable MODIS products while, at the same time, removing many of the angular perturbations present within the original MSG data. The NBAR data are validated against reflectance data from the MODIS instrument and in situ data gathered at a field location in Africa throughout 2008. It is found that the MSG retrievals are stable and are of high-quality across much of the SEVIRI disk while maintaining a higher temporal resolution than the MODIS BRDF products. However, a number of circumstances are discovered whereby the BRDF model is unable to function correctly with the SEVIRI observations-primarily because of an insufficient spread of angular data due to the fixed sensor location or localized cloud contamination. [ABSTRACT FROM PUBLISHER]

TanDEM-X and TerraSAR-X platforms form together the first spaceborne single-pass polarimetric interferometer in space. This allows, for the first time, the acquisition of spaceborne polarimetric synthetic aperture radar interferometry (Pol-InSAR) data without the disturbing effect of temporal decorrelation. This paper aims to assess the potential of such data for forest applications. For this, single- and dual-pol data acquired over a boreal, a temperate, and a tropical site were investigated to characterize X-band penetration and polarization diversity of the interferometric coherence measurements. Pol-InSAR forest height inversion schemes have been proposed and implemented for the singleand dual-pol cases and cross validated against LIDAR reference measurements for all sites. The single-pol inversion relies on an external ground digital terrain model (DTM) and performed well for all sites with correlation coefficients r2 between 0.80 and 0.98. The dual-pol inversion does not require an external DTM but depends on the visibility of the whole forest layer. Accordingly, its performance varied with forest structure and season: The best performance was achieved for the summer acquisition of the boreal test site (r2 = 0.86) and for the winter acquisition of the temperate test site (r2 = 0.77). For the tropical test site, only a weak correlation (r2 = ~0.50) could be established. [ABSTRACT FROM PUBLISHER]

Seismic noise attenuation to improve signal-to-noise ratio plays an important role in seismic data processing. In recent years, time-frequency peak filtering (TFPF) has been introduced and applied to seismic random noise attenuation successfully. However, in the conventional TFPF, the window length (WL) is fixed and used for all frequency components. As a consequence, serious loss of the effective components is unavoidable due to the inappropriate WL. The recently proposed radial-trace TFPF adapts radial-trace transform to reduce the dominant frequencies of the effective signals. Nevertheless, the radial traces with a fixed inclination angle have some limitations for bent reflection events. To resolve these shortcomings, this paper presents a novel variable-eccentricity hyperbolic-trace TFPF. In this novel method, the noisy record is first resampled along a family of spatial-temporal hyperbolic filtering traces of different bending degrees. In this way, the spatial correlation between the adjacent channels is taken into account, the linearity of the input signals is enhanced, and the estimation bias of the instantaneous frequency is reduced. Moreover, there is little difference between the reduced dominant frequencies. A fixed WL is suitable for all reduced dominant frequencies without distortion of the effective components. Finally, we evaluate the performance of our method on some synthetic records and field data. The experimental results illustrate that our proposed method attenuates random noise effectively and recovers the effective reflection events smoothly and more continuously compared with the other methods. [ABSTRACT FROM PUBLISHER]

We report the design, fabrication, and application of temperature and electrical conductivity sensor arrays for studying the harsh environments of hot springs at Yellowstone National Park. Centimeter-scale measurements are necessary to measure sharp gradients in geochemical parameters across the photosynthetic/nonphotosynthetic transition zone known as the photosynthetic fringe. Platinum, parylene-C, and fused quartz were the critical materials used in fabrication of both sensor arrays. More than 700 temperature and 90 conductivity measurements were taken in the mixing zone, where two geochemically distinct hot spring channels converged. We present a new technique, whereby microscale temperature measurements can be used to predict ion concentrations that represent the bulk ion chemistry via electrical conductivity (or other conservative chemicals) in this mixing zone. High-spatial-resolution data confirm that the strong temperature gradients across these regions largely determine where photosynthetic organisms appear.appear. [ABSTRACT FROM PUBLISHER]

A new geometric optical (GO)-radiative transfer (RT) model with a multiple scattering scheme suitable for sloping forest canopies is developed in this study. It is based on a Geometrical-Optical model for Sloping Terrains and an RT method. This new model overcomes the difficulty to prescribe bidirectional reflectance factors (BRFs) of shaded components (shaded foliage and background) in GO modeling through simulating radiation multiple scattering within a sloping forest. A case study shows that multiply scattered radiation depends on topographic factors and leaf area index. The contributions of the shaded components to stand-level BRF are less than 3% in the red band and can reach up to 40% in the near-infrared (NIR) band. The “multiangle” Moderate Resolution Imaging Spectroradiometer (MODIS) data over sloping pixels are selected to validate the modeled forest BRF. Considering the multiple scattering schemes and topographic factors, the modeled BRF is closer to the MODIS surface reflectance (BRF product) (red band: R2 = 0.8614, rRMSE = 0.1339; NIR band: R2 = 0.7573, rRMSE = 0.0850) than the modeled BRF (red band: R2 = 0.7771, rRMSE=0.1839; NIR band: R2 =0.5176, rRMSE = 0.1155) without topographic consideration. It is also shown that the MODIS surface reflectance of sloping forests at multiple angles can be simulated well using the newly developed model. [ABSTRACT FROM PUBLISHER]

Geosynchronous (GEO) SAR has been proposed as a means for obtaining observations of the Earth with finer temporal sampling than possible with a single satellite from a lower orbit. However, standard algorithms developed for low-Earth-orbit SAR imaging are inadequate for GEO, where the typical assumptions of quasi-linear trajectory and “stop-and-go” transmit/receive propagation break down because of the long integration time and the very long range between the satellite and the Earth. This paper proposes a curved trajectory model to overcome these limitations and considers the impact of the “stop-and-go” assumption. According to the proposed range model, an accurate 2-D analytical spectrum is deduced under the curved trajectory model based on a series reversion method, leading to an improved frequency domain imaging algorithm involving a high-order-phase coupling function and a range migration correction function. An adaptive azimuth compression function overcomes the space variance for large-scene focusing. Simulation results validate that the improved imaging algorithm performs well over the expected range of applicability for GEO SAR. [ABSTRACT FROM PUBLISHER]

This paper presents a new automatic change detection technique for synthetic aperture radar (SAR) time series, i.e., Method for generalIzed Means Ordered Series Analysis (MIMOSA). The method compares only two different temporal means between the amplitude images, whatever the length of the time series. The method involves three different steps: 1) estimation of the amplitude distribution parameters over the images; 2) computation of the theoretical joint probability density function between the two temporal means; and 3) automatic thresholding according to a given false alarm rate, which is the only change detection parameter. The procedure is executed with a very low computational cost and does not require any spatial speckle filtering. Indeed, the full image resolution is used. Due to the temporal means, the data volume to process is reduced, which is very helpful. Moreover, the two means can be simply updated using the new incoming images only. Thus, the full time series is not processed again. Change detection results between image pairs are presented with the airborne sensor CARABAS-II, using a public data release, and with TerraSAR-X data. In the case of time series, change detection results are illustrated using a TerraSAR-X time series. In every case, the MIMOSA method produces very good results. [ABSTRACT FROM PUBLISHER]

Various sparsity-based methods have been widely used in hyperspectral image (HSI) classification. To determine the class label of a test sample, traditional sparsity-based frameworks mainly use the sparse vectors to compute the residual error for classification. In this paper, a novel sparsity-based framework is proposed, which adopts the max pooling operation for HSI classification. Compared with the traditional sparsity-based frameworks using residual error, sparse vectors in our proposed framework are utilized to generate the feature vectors using max pooling operation. Experimental results demonstrate that our proposed framework can achieve the state-of-the-art classification performance. [ABSTRACT FROM PUBLISHER]

Mapping land use and land cover change (LULCC) over large areas at regular time intervals is a key requisite to improve our understanding of dynamic land systems. In this study, we developed and tested an automated approach for mapping LULCCs at annual time intervals using data from the Moderate Resolution Imaging Spectroradiometer (MODIS). Our approach characterizes changes between land cover types based on annual time series of per-pixel land cover probabilities. We used the temporal segmentation algorithm MODTrendr to identify trends and changes in the probability time series that were associated with land cover/use conversions. Accuracy assessment revealed good performance of our approach (overall accuracy of 92.0%). The method detected conversions from forest to grassland with a user's accuracy of 94.0 ± 2.0% and a producer's accuracy of 95.6 ± 1.6%. Conversions between cropland and grassland were detected with a user's and a producer's accuracy of 65.8 ± 4.8% and 72.2 ± 9.2%, respectively. We here present for the first time an approach that combines probabilities derived from machine learning (random forest classification) with time-series-based analysis (MODTrendr) for land cover/use change analysis at MODIS scale. [ABSTRACT FROM PUBLISHER]

Despite using multiple input datasets for effective crop classification, it is important to select an appropriate method that efficiently integrates these multiple datasets to produce accurate classification results. In this paper, we present an endorsement theory-based crop classification approach that considers the qualitative information, in terms of prediction probabilities, from different input datasets and integrates them efficiently to produce final classification results. Three different input datasets are used in this study: 1) spectral; 2) texture; and 3) indices from multitemporal (spring, early summer) WorldView-2 multispectral imagery. A multilayer perceptron classifier is trained with the multitemporal datasets separately using a backpropagation learning algorithm, and prediction probabilities are produced for each pixel as evidence against each crop class. An integration rule based on endorsement theory is applied to these multiple evidence by considering their individual contribution, and the most probable class of a pixel is identified. Integration of the three multidate datasets by the proposed method is found to produce higher overall classification accuracy (91.2%) when compared to conventional winner-takes-all approach (89%). In order to determine which individual dataset is more useful for crop discrimination, the dataset's performance is compared using evidence and contributions produced in the proposed integration method for four selected crops, for both single- and multidate. The results of this analyses showed that seasonal textures information outperformed both spectral and indices. To verify this finding, results of individual dataset classification are examined. The highest overall classification accuracy of 88.8% is achieved by the use of multidate texture, where multidate spectral and indices resulted in 86.3% and 84.4%, respectively. [ABSTRACT FROM PUBLISHER]

The amount of images used in multitemporal classification studies has greatly increased along with enhanced temporal sensor capacities. Handling large intra-annual time series leads to the question of how the selection of image acquisition dates could be optimized. In this study, an empirical approach for evaluating the relative classification power of single acquisition dates is introduced for the differentiation of seminatural grassland vegetation. The main question is how many acquisitions from which phenological origins are preferable to achieve a certain classification accuracy target. The tested time series contains 24 single RapidEye scenes from 2009 to 2011. The vegetation index composites of these images were iteratively classified into different combinations of acquisition dates using the support vector machine (SVM) algorithm. The subsequent results were tested for significant accuracy improvements over single acquisition dates. These acquisition dates are subsumed under phenological seasons to evaluate adequate temporal acquisition windows. The results show that a three-scene composite reaches more than 0.8 overall accuracy (OAA). The best tradeoff amount between number of acquisition dates and classification accuracy is achieved using a seven-scene NDVI composite. The most important season for the differentiation of seminatural grassland is early summer (ESu). Full spring (FuS), late summer (LSu), and midsummer (MSu) can also be identified as influential temporal windows for data acquisition. [ABSTRACT FROM PUBLISHER]

Continuous monitoring leaf area index (LAI) of field crops in a growing season has a great challenge. The development of remote sensing technology provides a good tool for timely mapping LAI regionally. In this study, hyperspectral reflectance data (405-835 nm) obtained from an airborne hyperspectral imager (Pushbroom Hyperspectral Imager) were used to model LAI of winter wheat canopy in the 2002 crop growing season. LAI was modeled based on its semi-empirical relationships with six vegetation indices (VIs), including ratio vegetation index (RVI), modified simple ratio index (MSR), normalized difference vegetation index (NDVI), a newly proposed index NDVI-like (which resembles NDVI), modified triangular vegetation index (MTVI2), and modified soil adjusted vegetation index (MSAVI). To assess the performance of these VIs, root mean square errors (RMSEs) and determination coefficient (R2) between estimated LAI and measured LAI were reported. Our result showed that NDVI-like was the most accurate predictor of LAI. The inclusion of a green band in MTVI2 trended to give a rise to a much quicker saturation with increase of LAI (e.g., over 3.5). MSAVI and MTVI2 showed comparable but lower potential than NDVI-like in estimating LAI. RVI and MSR demonstrated their lowest prediction accuracy, implying that they are more likely to be affected by environmental conditions such as atmosphere and cloud, thus cannot properly reflect the properties of winter wheat canopy. Our results support the use of VIs for a quick assessment of seasonal variations in winter wheat LAI. Among the indices we tested in this study, the newly developed NDVI-like model created the most accurate and reliable results. [ABSTRACT FROM PUBLISHER]

In the evening of 20 March 2010, after about two centuries of quiescence, an effusive eruption took place at Eyjafjöll (Iceland) volcano, from a small vent localized on the northeast flank (Fimmvörduháls Pass) of the volcano edifice. On 31 March, a new eruptive fissure opened on the same region emitting lava. About 2 weeks later, on 14 April, a strong explosive eruption took place under the Eyjafjallajökull glacier, injecting copious amounts of ash in the atmosphere and causing an unprecedented air traffic disruption in Northern and Central Europe. In this paper, the changes in thermal signals occurring at Eyjafjöll volcano during 1 March-20 April 2010 are investigated, testing the RSTVOLC algorithm for the first time in a subpolar environment. Outcomes of this retrospective study, performed by means of infrared Moderate Resolution Imaging Spectroradiometer (MODIS) data, show that both effusive and explosive eruptions of the Eyjafjöll volcano could be identified in a timely manner and well monitored from space. Moreover, in spite of a lack of pre-eruptive hot spots detection, this paper reveals a general increasing trend of the middle infrared signal at crater area, beginning 2 weeks before the explosion, stimulating and suggesting further investigations devoted to better characterize the thermal behavior of the monitored volcano. [ABSTRACT FROM PUBLISHER]

This paper proposes two approaches to change detection in bitemporal remote sensing images based on concurrent self-organizing maps (CSOM) neural classifier. The first one performs change detection in a supervised way, whereas the second performs change detection in an unsupervised way. The supervised approach is based on two steps: 1) concatenation (CON); and 2) CSOM classification. CSOM classifier uses two SOM modules: 1) one associated to the class of change; and 2) the other to the class of no-change for the generation of the training set. The unsupervised change detection approach is based on four steps: 1) image comparison (IC), consisting of either computation of difference image (DI) for passive sensors or computation of log-ratio image (LRI) for active sensors; 2) unsupervised selection of the pseudotraining sample set (USPS); 3) concatenation (CON); and 4) CSOM classification. The proposed approaches are evaluated using two datasets. First dataset is a LANDSAT-5 TM bitemporal image over Mexico area taken before and after two wildfires, and the second one is a TerraSAR-X image acquired in the Fukushima region, Japan, before and after tsunami. Experimental results confirm the effectiveness of the proposed approaches. [ABSTRACT FROM PUBLISHER]

Many ecological and agricultural studies require accurate information about the phenological state of the vegetation such as the start, peak/plateau, and end of the growing season. This study investigates the impact of the day of observation in image composites on time-series generation and analysis. It employs daily Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance and 16-day vegetation index (VI) composites and combines data streams from Terra and Aqua. Results of a time series that take into account the day of observation and time series that assume the starting, ending, or middle day of the compositing period were compared to a reference of daily observations. A temporal shift of approximately 7.5 days with a high variation in error is introduced if the start of the compositing period is assumed. The middle day mitigates the mean error close to zero but cannot fully compensate for temporal delays. Only a time series that takes into account the actual day of observation can be used for the correct estimation of temporal characteristics. In addition, the study addresses that the day of observation for time-series generation from image composites is imperative when combining data of different data streams with phased production cycles such as MODIS VI composites from Terra and Aqua. [ABSTRACT FROM PUBLISHER]

In this paper, one-dimensional (1-D) geophysical time series are regarded as series of significant time-scale events. We combine a wavelet-based analysis with a Gaussian mixture model to extract characteristic time-scales of 486 144 detected events in the Sea Surface Temperature Anomaly (SSTA) observed from satellite at global scale from 1985 to 2009. We retrieve four low-frequency characteristic time-scales of Niño Southern Oscillation (ENSO) in the 1.5- to 7-year range and show their spatial distribution. High-frequency (HF) SSTA event spatial distribution shows a dependency to the ENSO regimes, pointing out that the ENSO signal also involves specific signatures at these time-scales. These fine-scale signatures can hardly be retrieved from global EOF approaches, which tend to exhibit uppermost the low-frequency influence of ENSO onto the SSTA. In particular, we observe at global scale a major increase by 11% of the number of SSTA HF events during Niño periods, with a local maximum of 80% in Europe. The methodology is also used to highlight an ENSO-induced frequency shift during the major 1997-2000 ENSO event in the intertropical Pacific. We observe a clear shift from the high frequencies toward the 3.36-year scale with a maximum shift occurring 2 months before the ENSO maximum of energy at 3.36-year scale. [ABSTRACT FROM PUBLISHER]