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The constraints of spatiotemporal heterogeneity and spatial resolution constitute two crucial challenges in the establishment of remote sensing inversion models. Spatiotemporal heterogeneity gives rise to an inadequate generalization capacity of remote sensing models, demanding extensive manual parameter adjustment for each model construction. This not only escalates the task's work intensity but also leads to unstable performance. The limited spatial resolution of remote sensing images leads to suboptimal inversion accuracy for sampling points influenced by mixed pixel effects. To tackle these problems, we take the case of non-photosensitive water quality parameter inversion in the narrow rivers of Longnan area. By integrating advanced Hyper-Parameter Optimization (HPO) techniques, such as Optuna from machine learning, an inversion model was developed, incorporating the bands of Sentinel-2 and Sentinel-3 as model features. Among these features, bands with lower spatial resolution are employed to furnish surrounding information, thereby enhancing the inversion accuracy. The research outcomes demonstrate that: 1) The model constructed based on the HPO method, Optuna, attained favourable inversion results, with R2 values of 0.68, 0.77, 0.35, and 0.60 for Total Nitrogen (TN), Total Phosphorus (TP), Ammonia Nitrogen (NH3-N), and Chemical Oxygen Demand (COD), respectively. 2) The fusion of Sentinel-2 and Sentinel-3 data enhanced the inversion accuracy compared to using them separately, highlighting the considerable significance of multi-source data fusion methods in improving inversion accuracy. This research fills a void in the remote sensing inversion domain and lays the groundwork for future endeavours. [ABSTRACT FROM AUTHOR]

Monitoring changes in carbon stocks through forest biomass assessment is crucial for carbon cycle studies. However, challenges in obtaining timely and reliable ground measurements hinder creation of the spatially continuous maps of forest aboveground biomass density (AGBD). This study proposes an approach for generating spatially continuous maps of forest aboveground biomass density (AGBD) by combining Global Ecosystem Dynamics Investigation (GEDI) LiDAR-based data with open-access earth observation (EO) data. The key contribution of the study lies in the systematic evaluation of various model configurations to select the optimal model for AGBD generation. The evaluation considered various model configurations, including predictor sets, spatial resolution, beam selection, and sensitivity thresholds. We used a Random Forest model, trained through five-fold cross-validation on 80% of the total data, to estimate AGBD in the Indian forest region. Model performance was assessed using the 20% independent test dataset. Results, using Sentinel-1 and 2 predictors, yielded R2 values of 0.55 to 0.60 and RMSE of 48.5 to 56.3 Mg/ha. Incorporating forest and agroclimatic zone attributes improved performance (R2: 0.59 to 0.69, RMSE: 42.2 to 53.3 Mg/ha). The selection of the top 15 predictors, which favoured features from Sentinel-2, DEM, forest attributes, and agroclimatic zones, and GEDI data with sensitivity >0.98, yielded the optimal model with an R2 of 0.64 and RMSE of 46.59 Mg/ha. The results underscore the significance of incorporating attributes like forest and agro-climatic zones and the need for an optimal model selection considering predictor types and GEDI shot characteristics. The top-performing model is validated in Simdega, Jharkhand (R2: 0.74, RMSE: 39.3 Mg/ha), demonstrating the methodological potential of this approach. Overall, this study emphasizes the methodological prospects of integrating multi-source open-access EO data to produce spatially continuous aboveground biomass (AGB) maps through data fusion. [ABSTRACT FROM AUTHOR]

Remote sensing-assisted monitoring of forest health entails methods that can provide up-to-date and accurate information on decline and mortality of individual trees, while maintaining time and cost efficiency. However, the trade-off of applying consumer-grade UAV-RGB data as the most affordable and accessible data source at the catchment level is constrained by its poor spectral information content. We developed a method based on the fusion of UAV-RGB data with space-borne Sentinel-2 Multispectral Instrument (MSI) at the level of tree crowns, with the specific target of supporting studies on semi-arid tree decline. We applied linear spectral unmixing (Spectral Unmixing-Based data Fusion method, LSUBF) by considering a limited number of endmembers and calculating the abundances (fractional covers) from the UAV data, and evaluated the results by high-resolution MSI space-borne data including SPOT-6 (1.5 m spatial resolution) and PlanetScope (3 m spatial resolution). This method suggested an increase in the coefficient of determination of the applied generalized additive model for decline severity estimation at tree crown level from 0.61 to 0.69, while it was improved from 0.70 to 0.91 when fitting a non-parametric random forest model. The results of sensitivity analysis demonstrated that the additional spectral information obtained from the proposed method results in higher accuracy in estimating decline severity. We suggest this method as a cost-effective alternative to monitor periodical tree decline, in particular across semi-arid ecosystems. [ABSTRACT FROM AUTHOR]

Olives are a crucial economic crop in Mediterranean countries. Detailed spatial information on the distribution and condition of crops at regional and national scales is essential to ensure the continuity of crop quality and yield efficiency. However, most earlier studies on olive tree mapping focused mainly on small parcels using single-sensor, very high resolution (VHR) data, which is time-consuming, expensive and cannot feasibly be scaled up to a larger area. Therefore, we evaluated the performance of Sentinel-1 and Sentinel-2 data fusion for the regional mapping of olive trees for the first time, using the Izmir Province of Türkiye, an ancient olive-growing region, as a case study. Three different monthly composite images reflecting the different phenological stages of olive trees were selected to separate olive trees from other land cover types. Seven land-cover classes, including olives, were mapped separately using a random forest classifier for each year between 2017 and 2021. The results were assessed using the k-fold cross-validation method, and the final olive tree map of Izmir was produced by combining the olive tree distribution over two consecutive years. District-level areas covered by olive trees were calculated and validated using official statistics from the Turkish Statistical Institute (TUIK). The K-fold cross-validation accuracy varied from 94% to 95% between 2017 and 2021, and the final olive map achieved 98% overall accuracy with 93% producer accuracy for the olive class. The district-level olive area was strongly related to the TUIK statistics (R2 = 0.60, NRMSE = 0.64). This study used Sentinel data and Google Earth Engine (GEE) to produce a regional-scale olive distribution map that can be scaled up to the entire country and replicated elsewhere. This map can, therefore, be used as a foundation for other scientific studies on olive trees, particularly for the development of effective management practices. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNN) are useful models for spatiotemporal fusion, especially under strong temporal changes. Convolutional layers with receptive fields of various sizes produce hierarchical features that can improve prediction performance. In this study, we aimed to develop a CNN model for spatiotemporal fusion, which can extract hierarchical features and fully use them. The proposed network is composed of residual dense blocks with local dense connections that effectively utilize the hierarchical features. We conducted the experiments in the newly proposed Kansas dataset, which includes Sentinel-2 and Sentinel-3 image pairs with large resolution differences and strong temporal changes. Based on both quantitative and qualitative evaluation, experiments revealed that the STFRDN model outperformed the Flexible Spatiotemporal DAta Fusion (FSDAF) 2.0, Reliable and Adaptive Spatiotemporal Data Fusion (RASDF), Fit-FC, and DMNet methods in all of the test groups. The second major finding was that the STFRDN model was able to produce sufficiently successful predictions against cases with such large resolution differences. To get more accurate prediction results when there is a significant resolution difference, it is important to extract the right features that can model the discrepancy. Loss of spatial detail can be avoided by making full use of hierarchical features without any manipulation after extraction. The results of this study provide insights into the significance of extracting and fully utilizing the hierarchical features. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach for automatic 3D surface reconstruction from uncalibrated and multi-view Google Earth images by using a multi-view stereo method and 3D projective to metric transformation. Without the Rational Polynomial Coefficients, it is impossible to obtain the metric reconstruction of the 3D surface from multi-view satellite images. We solve the uncalibrated multi-view satellite image problem by employing a multi-view stereo vision technique followed by a projective to metric transformation. The virtual pose parameters of the satellite images are obtained by using COLMAP, and the virtual 3D projective reconstruction is done by using EnSoft3D. For projective to metric transformation, we propose to employ 3D homography transformation. Eight 3D correspondence pairs on the viewing frustums between the virtual reference camera and the ideal nadir camera are used to derive a 3D homography matrix. Using the 3D homography matrix, we finally obtain the metric reconstruction of 3D surface up to an unknown height scale in a reference coordinate system of the Google Earth desktop software. Experiments are done in several world locations on Google Earth including building and vegetation areas. Reconstruction error analysis with the Data Fusion Contest 19 dataset is also presented. The average of MAE and RMSE of five tile regions in the dataset are 1.596 m and 2.083 m, respectively. [ABSTRACT FROM AUTHOR]

With the continuous improvement of radar resolution, distributed characteristics are presented with regard to maritime moving targets, which occupy multi-resolution cells in Synthetic Aperture Radar (SAR) image domain. However, the current constant false alarm rate (CFAR) detection algorithms rarely consider the impact of distributed characteristics on target detection performance, and thus the corresponding performance evaluation methods could not be applicable to this issue. In this paper, a multi-station fusion detection method for maritime moving target (MMT) is presented based on three-dimensional (3D) sliding window. Firstly, the multi-station echoes in Cartesian-Doppler frequency rate (DFR) domain are obtained under the simple-transmitting and multiple-receiving operation configuration, and the 3D sliding window is designed to achieve the optimal matching for a specified target with any moving direction in terms of its prior information, i.e. Radar Cross Section (RCS), radar resolution and target size. Then, the target cells within the sliding window are directly processed by means of the M/N criterion, and thus to avoid target detection performance loss caused by the artificial construction of extended detection statistics. Finally, a novel quantitative evaluation method in regards to a high-resolution radar is designed by mining the relationship between the target detection performance and the occupied cell number, which could greatly improve detection probability of distributed targets on the premise of a constant false alarm rate. The proposed algorithm does not need to design complex extended detection statistics, which provides a feasible way for robust detection and performance evaluation for a high-resolution radar. Simulation results verify the effectiveness of this research. [ABSTRACT FROM AUTHOR]

Rapid and accurate determination of soil salt content (SSC) and its spatial distribution are of great significance for the prevention and improvement of soil salinization. Satellite and unmanned aerial vehicle (UAV) remote sensing data have complementary advantages. The fusion of satellite and UAV multisource remote sensing data to improve the accuracy of SSC based on inversion methods has become a hot topic, and the appropriate fusion level of multisource remote sensing data needs to be explored and determined. The objective of this study was to determine the appropriate fusion level of Sentinel-2A Multispectral Instrument (Sentinel-MSI) and UAV image data for SSC inversion by comparing the fusion effect of three levels (spectral data, spectral index, and spectral model). A numerical regression method was employed to analyse the relationship between Sentinel-MSI and UAV image data (MSI-UAV), and MSI-UAV data were fused at different levels. Then, the appropriate fusion level and best inversion model were optimized to realize regional SSC inversion. The results indicate that spectral data fusion was better than spectral index fusion for enhancing the SSC spectral response, with the correlation between spectral indices and SSC increasing by 0.139–0.167 after fusion. After spectral data fusion, the model improved the SSC inversion accuracy most obviously, with a calibration R2 of 0.623, validation R2 of 0.571, and ratio of performance to deviation (RPD) of 1.821. Therefore, spectral data fusion was found to be superior in enhancing the spectral response of soil salinity and in improving the accuracy of the estimation model. This research optimized spectral data fusion as the appropriate fusion level of MSI-UAV for SSC inversion and formed a set of high-precision MSI-UAV multisource remote sensing fusion inversion approaches for SSC. [ABSTRACT FROM AUTHOR]

Sea ice detection has played an important role in climate protection and strategic deployment. Due to the autonomous learning characteristics of deep learning, it has been gradually applied to the classification of remote sensing sea ice images. At present, deep learning models are mostly used in sea ice classification of single source remote sensing data. Due to the limitations of single source data and the information loss of deep learning model in the process of feature extraction layer by layer, it is inevitable to encounter a bottleneck in sea ice detection requiring fine classification. To solve the above problems, this paper proposes a sea ice image classification method based on ResNet16-feature pyramid networks-spatial pyramid pooling-gated fusion network (ResFPG) and heterogeneous data fusion. In the feature extraction part, the method uses the improved ResNet16 to extract the multi-level feature information of sea ice in synthetic aperture radar data and optical data, reducing information loss in the feature extraction process, then mines and fuses the low-level spatial information and high-level semantic information through the improved feature pyramid networks (FPN), and then collects and fuses the output features of different scales through the spatial pyramid pooling (SPP) network. In the feature fusion part, a gated feature-level fusion strategy is designed to further improve the overall classification accuracy by adaptively adjusting the feature contribution of two heterogeneous sources of data through the gated fusion network (GFN). In order to verify the effectiveness of this method, we use two sets of heterogeneous sea ice remote sensing data located in Hudson Bay area for experiments. The experimental results show that compared with other image classification methods, the proposed method fully excavates and integrates the multi-scale and multi-level features in heterogeneous data, effectively distinguishes the feature contribution, and achieves better classification results (97.14% and 95.85%). [ABSTRACT FROM AUTHOR]

Soil moisture is a vital surface physical quantity in studying the earth's ecology. It plays a crucial role in the hydrological cycle, crop yield estimation, and ecological monitoring. Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technology inversion to obtain high accuracy soil moisture is a hot topic of current research. However, due to the limited available sites, it's difficult to obtain an extensive and continuous range of soil moisture based on this technique. It is necessary to build algorithms for encryption based on known sites' data, combined with the corresponding geographic environmental elements. This paper extracted the surface environmental factors affecting soil moisture using high-precision optical remote sensing images. The contribution of each surface environmental element to the soil moisture inversion was analysed using back propagation (BP) neural network optimized by the genetic algorithm (GA). Based on this, ten surface environmental elements (latitude and longitude information, precipitation, temperature, land cover type, normalized difference vegetation index (NDVI), elevation, slope, slope direction, and shading) were identified as critical factors, and a multi-data fusion soil moisture inversion model was constructed. The results showed that the constructed model could better describe the relationship between soil moisture and these elements, and the Pearson correlation coefficient R reached 0.8724, and the RMSE was 0.0346 cm3 cm−3. GNSS-IR technology provides an effective technical means for inversing soil moisture over a large area with high spatial and temporal resolution. [ABSTRACT FROM AUTHOR]

In this paper, a novel automated coastline extraction method from SAR (Synthetic Aperture Radar) data is presented. The method is designed to exploit radar backscatter coefficients ( σ 0 ) from multipolarization SAR acquisitions (the 4 classic co- and cross-polarized polarizations), whereas single-pol data are employed in the majority of methods in this field, implementing data fusion through the use of an autoencoder neural network and producing the coastline by harnessing a Pulse-Coupled Neural Network (PCNN). Main results are presented throughout the paper, demonstrating superiority and comparability with established methods and with a recent automated algorithm that can be considered among the state-of-the art techniques in this field; furthermore, effectiveness of data fusion and segmentation obtained through the mentioned neural networks has been compared to that of several combinations of the same networks with different frameworks: a different data fusion framework, obtained through the use of linear Principal Component Analysis (PCA), and a different binarization framework, based on the use of Expectation-Maximization (EM) image segmentation. Main achievements of presented technique consist in enabling a possible faster processing as well as the opportunity of operating with an improved fused information content on coastline, together with very high accuracy results. [ABSTRACT FROM AUTHOR]

The spatiotemporal image fusion technique is regarded as an effective and efficient remedy to blend two or more geometrically registered remote sensing images of the same scene acquired from different sensors into a single image with both high temporal and high spatial resolutions. A number of existing fusion methods developed for generating Landsat-like images from the Moderate Resolution Imaging Spectroradiometer (MODIS) have successfully solved limitations on vegetation dynamics monitoring. However, these methods either treat the weighting parameters as constants or adopt insufficient linear regression coefficients (considering only spatial, temporal or spectral weights), which may contribute to blocky artefacts and dissimilar pixels in the predicted image. In this study, a boundary finding-based vegetation index spatiotemporal fusion model (BESFM) is newly proposed to generate high spatial and high temporal resolutions Enhanced Vegetation Index (EVI) images in homogeneous and heterogeneous regions with rapid vegetation changes. The cloud-free Landsat-8 Operational Land Imager (OLI) and the MODIS (MOD09GA) data acquired from two study sites, located in Queensland and New South Wales and Australia, were selected to evaluate the performance of the proposed method. Compared with the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and flexible spatiotemporal data fusion (FSDAF), the BESFM outperforms in both visible image quality (reducing the spatial distortion and blocky artefacts in prediction caused by abrupt land cover changes) and quantitative indices (with RSME minimum to 0.011 and R2 maximum to 0.956). Since the BESFM method not only uses adjustable linear regression assumptions for heterogeneous and homogeneous regions differently but also implements boundary detection-based spatial weight with temporal and spectral weights together to enhance the predicted spatial details, it has the potential to increase the availability of fine spatial resolution data. [ABSTRACT FROM AUTHOR]

Gas leaks can occur not only in industrial environments such as refineries but also in biogas plants. The main compound of the emitted biogas is methane, which is a greenhouse gas with a higher global warming potential than carbon dioxide (CO 2 ). Considering that the climate change and the reduction of greenhouse gases are becoming more and more important, it is crucial to be able to not only localize but also to quantify methane leakages. Based on the quantification of methane leakages, not only the effect of the effluent gases to the atmosphere but also the economic loss can be estimated. Another aspect is safety: biogas consists of different gases, mainly methane which can create an explosive mixture with air, but also hydrogen sulphide, which is toxic. This motivates remotely measuring the gas emissions. In this paper a remote measurement system and model is presented to estimate the methane volume flow due to a leak based on infrared optical sensors and sensor data fusion. The sensors are calibrated and the system is tested in the laboratory as well as in biogas plants. The laboratory results show that the measurement model allows an estimation of the methane volume flow if the assumptions of the measurement model are met. The highest relative deviations occur for small reference methane volume flows while the absolute deviations are similar. In contrast to the laboratory measurements the results of the measurements in biogas plants show partially high deviations from the reference. These deviations can have multiple reasons which cannot be determined for each single case because many of them occur at the same time. Future work includes improvements in order to reduce the deviations of the methane volume flow estimation. This could be done not only by improving the processing steps of the velocity estimation but also by using more accurate sensors. [ABSTRACT FROM AUTHOR]

Hyperspectral images have high spectral resolution but low spatial resolution, which results in a large number of mixed pixels. As an economical and effective means to improve image quality, the fusion of hyperspectral and multispectral data from different sensors can achieve the reconstruction of super-resolution images. As a representative of fusion method, coupled non-negative matrix factorization is an ill-posed problem, in which the number of endmembers was set to be no less than the groundtruth without requiring an accurate value. However, this often results in spectral shadows and spatial information redundancy, especially when the observed images are contaminated by noise. To address these problems above, this article incorporates sparse and low-rank regularization to reformulate a bi-convex fusion problem for the removal of shadows and noise, in which the logarithmic sum function is employed to suppress the small singular components of endmember and abundance matrices. Then, an efficient solver is designed to obtain the closed-form solutions via matrix-vector operators, in which the alternating direction method of multipliers is utilized to split the variables using equality constraints. The experimental results of real datasets demonstrate that the proposed fusion method can effectively enhance the quality of reconstructed super-resolution images especially in high-noise environments, which also verifies the validation of incorporated regularization. [ABSTRACT FROM AUTHOR]

Remote Sensing image super-resolution aims to improve the spectral and/or spatial resolution of the satellite imageries. In order to improve the performance of the CNN-based super-resolution methods, increasing the depth of the network is commonly used. However, this increases computational complexity and training difficulties only with small improvement of the performance. Meanwhile, the CNN kernels treat all the channels equally and cannot take the advantage of the abundant high-frequency information contained in the low-resolution images. To address these problems, Channel attention is one of the mechanisms and has been proven to be useful in many tasks. In this research, we proposed a channel attention-based framework for Remote Sensing Image Super-resolution (CARS) by constructing a novel residual channel attention block (RCAB) to further extract the features. In addition, a densely residual channel attention block (RCAB+) and densely residual spatial attention block (RSAB) were proposed to improve the performance. We adopted a post-upsampling architecture to reduce the computational complexity and time cost. Moreover, transfer learning strategy (CARS+T) was introduced to further improve the SR performance and proved to generate finer edge details. Experimentally, our proposed CARS, CARS_SA and CARS+T achieved competitive quantitative and qualitative results both on Data Fusion Contest Dataset and Pleiades Dataset that we created. [ABSTRACT FROM AUTHOR]

Remote sensing is based on the extraction of data, acquired by satellites or aircrafts, through multispectral images, that allow their remote analysis and classification. Analysing those images with data fusion techniques is a promising approach for identification and classification of forest types. Fusion techniques can aggregate various sources of heterogeneous information to generate value-added maps, facilitating forest-type classification. This work applies a data fusion algorithm, denoted FIF (Fuzzy Information Fusion), which combines computational intelligence techniques with multicriteria concepts and techniques, to automatically distinguish Eucalyptus trees from satellite images. The algorithm customization was performed with a Portuguese area planted with Eucalyptus. After customizing and validating the approach with several representative scenarios to assess its suitability for automatic classification of Eucalyptus, we tested on a large tile obtaining a sensitivity of 69.61%, with a specificity of 99.43%, and an overall accuracy of 98.19%. This work demonstrates the potential of our approach to automatically classify specific forest types from satellite images, since this is a novel approach dedicated to the identification of eucalyptus trees. [ABSTRACT FROM AUTHOR]

The latest MODerate resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) Aerosol Optical Depth (AOD) products provide AOD datasets at 3 km and 10 km spatial resolutions retrieved by the Dark Target (DT) and Deep Blue (DB) algorithms, i.e., DT3K and DB10K, respectively. Despite their respective values in aerosol-related studies, the two AOD products compromise between their spatial resolution and spatial coverage, which limits their utilities in studies that require fine-scale resolution AOD over large geographical areas (e.g., continental or global scale). This study proposes a SParse representation-based Gap Filling model (SPGF) to blend the higher spatial resolution of DT3K AOD and the broader spatial coverage of DB10K AOD, i.e., fill the gaps in DT3K AOD data. Specifically, this model downscales DB10K AOD based on a resolved sparse representation matrix of a learned overcomplete AOD dictionary pair (10 km and 3 km), which introduces the spatial information from DT3K AOD and the spatial coverage from DB10K AOD. To validate the fused 3 km AOD products, we conduct accuracy assessments based on two types of criteria that are evaluated qualitatively and quantitatively to demonstrate the contribution of our data fusion method. The results show that the proposed method accurately blends the spatial resolution of DT3K AOD and the spatial coverage of DB10K AOD. The improved 3 km AOD products extend the spatial coverage over the original DT3K AOD and contain more information content and spatial details than the original DB10K AOD. Consequently, the proposed data fusion model can compensate for the deficiency of existing MODIS C6.1 AOD products in a low-cost manner, which is of great significance for increasing the usage and benefit for more detailed studies related to AOD dynamics. [ABSTRACT FROM AUTHOR]

Few studies provide effective guidance for detecting subpixel urban impervious surface in desert environments. Such an environmental setting is substantially different from the study areas of most existing studies, especially with complicated desert landforms. To improve the accuracy of subpixel mapping of urban impervious surface in desert environments, an integrative approach is proposed to estimate fractional urban impervious surface at the 30-m resolution. This is done by the synergistic use of open-source datasets taken from day and night that characterize different aspects of human activities at a different time of a day. We carefully analyse the performance of three methods with model input combinations from different sources, i.e. the day and night image synergy, daytime images only, and georectified night-time International Space Station (ISS) photographs only. Three major findings are concluded from the analyses. First, the collective usage of remote sensing images derived from day and night yields reasonable results in all desert cities and outperforms any single-source data for subpixel urban impervious surface mapping. Second, among all the input features from multiple data sources, night light variables have a higher contribution than other variables of daytime images for subpixel urban impervious surface mapping in desert cities, regardless of lighting types. Third, since desert cities remain understudied in previous studies, the proposed synergy method fills this gap in the literature. This data fusion of multi-source remotely sensed data is promising, which can be employed for better subpixel urban mapping in support of various environmental and socioeconomic applications in the future. [ABSTRACT FROM AUTHOR]

Land cover and its change are one of the important factors of global environmental change. The new GaoFen-2 (GF-2) satellite provides abundant spectral features and texture information, and airborne light detection and ranging (lidar) provides accurate three-dimensional coordinates at a finer scale. Fusing these data has the potential to improve land-cover classification. In the article, we selected the Random Forest (RF) as a classifier. The spectral bands of GF-2, normalized difference vegetation index (NDVI), normalized digital surface model derived from lidar data, and their grey-level co-occurrence matrix (GLCM) textures including mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation were generated to create seven scenarios with different combination of RF input variables. We estimated the classification performance on GF-2 satellite data, compared and assessed the individual and combined contributions of GF-2 and lidar data with regard to classification accuracy using fusion data, and identified the optimum combination of input variables that best balances classification performance with the computational challenges for land-cover classification. The results showed that GF-2 multispectral data alone or the fusion of GF-2 data and other sources can provide relatively good classification mapping accuracy in complex urban environments. The classification accuracy of fused data from the GF-2 satellite and lidar data exceeded those of GF-2 or lidar data alone. Moreover, GLCM textures significantly improved the classification performance whether using GF-2 satellite data or lidar height data. A fusion of GF-2 multispectral data, NDVI, lidar data, and their texture features (102 RF variables) achieved the best classification accuracy in seven scenarios. Using the optimum set of variables (55 variables) for RF yielded the most satisfactory classification result, achieving a total accuracy and kappa coefficient of 94.51% and 0.93, respectively. The producer's accuracy and user's accuracy exceeded 90% for almost all classes. This study aims to provide a reference for the efficient improvement of land-cover classification and offer support for extending the applications of classification algorithms and data sources. [ABSTRACT FROM AUTHOR]

Unmanned aerial systems (UASs) have been already proven to be useful in fields and disciplines such as agriculture, forestry, or environmental mapping, and they have also found application during natural and nuclear disasters. In many cases, the environment is inaccessible or dangerous for a human being, meaning that the widely used technique of aerial imagery georeferencing via ground control points cannot be employed. The present article introduces a custom-built multi-sensor system for direct georeferencing, a concept that enables georeferencing to be performed without an access to the mapping area and ensures centimetre-level object accuracy. The proposed system comprises leading navigation system technologies in the weight category of micro and light UASs. A highly accurate Global Navigation Satellite System receiver integrating the real-time kinematic technology supports an inertial navigation system, where data from various sensors are fused. Special attention is paid to the time synchronization of all sensors, and a method for the field calibration of the system is designed. The multi-sensor system is completely independent of the used UASs. The authors also discuss the verification of the proposed system's performance on a real mission. To make the results credible, a high number of test points are used, with both direct and indirect georeferencing techniques subjected to comparison, together with different calibration methods. The achieved spatial object accuracy (about 4 cm root mean square error (RMSE)) is sufficient for most applications. [ABSTRACT FROM AUTHOR]

Multi-sensor data from different satellites were analysed to study the interannual variability of the cold domes northeast of Taiwan. From the empirical orthogonal function analysis of along-track satellite altimeter data and sea surface temperature, both revealed a cold dome phenomenon located at approximately 122°36´E and 25°24´N, and their principal components demonstrated a variation of 4-6 years. The cold dome variation correlates best with the Oceanic Niño index and poorly with Pacific Decadal Oscillation index. The forcing could be wind stress curl and Kuroshio volume transport, which produce an upwelling around the Men-Hua Canyon. Positive wind stress curl and the on-shelf water transport of the Kuroshio Current through the North Men-Hua Canyon increase the chance of the cold dome formation which may be responsible for the interannual variability of cold domes. [ABSTRACT FROM AUTHOR]

A new method for remote-sensing image fusion based on variational methods and the image objective evaluation model is proposed. Different from the previous methods, the proposed method does not make big improvement on the variational model but focuses on how to make the calculation of existing method more accurate. The problem is that in the solving process of some variational models, it cannot be determined by the information of input images to gain the accurate calculation results. To solve this problem, a new model based on the average gradient of the objective evaluation index is proposed. The measured value of the proposed model is used in the iterations of the fusion algorithm as a feedback to adaptively adjust the algorithm to improve the quality of the fused results. Experiments show that the proposed adaptive method significantly improves the spatial information and well preserves the spectral information in the view of the subjective and objective evaluations. [ABSTRACT FROM AUTHOR]

This article introduces a method for road network extraction from satellite images. The proposed approach covers a new fusion method (using data from multiple sources) and a new Markov random field (MRF) defined on connected components along with a multilevel application (two-level MRF). Our method allows the detection of roads with different characteristics and decreases by around 30% the size of the used graph model. Results for synthetic aperture radar (SAR) images and optical images obtained using the TerraSAR-X and Quickbird sensors, respectively, are presented demonstrating the improvement brought by the proposed approach. In a second part, an analysis of different types of data fusion combining optical/radar images, radar/radar images, and multitemporal SAR (TerraSAR-X and COSMO-SkyMed) images is described. The qualitative and quantitative results show that the fusion approach improves considerably the results of the road network extraction. [ABSTRACT FROM AUTHOR]

This paper proposes a new decision fusion method accounting for conditional dependence (correlation) between land-cover classifications from multi-sensor data. The dependence structure between different classification results is calculated and used as weighting parameters for the subsequent fusion scheme. An algorithm for fusion of correlated probabilities (FCP) is adopted to fuse the prior probability, conditional probability, and obtained weighting parameters to generate a posterior probability for each class. A maximum posterior probability rule is then used to combine the posterior probabilities generated for each class to produce the final fusion result. The proposed FCP-based decision fusion method is assessed in land-cover classification over two study areas. The experimental results demonstrate that the proposed decision fusion method outperformed the existing decision fusion methods that do not take into account the correlation or dependence. The proposed decision fusion method can also be applied to other applications with different sensor data. [ABSTRACT FROM PUBLISHER]

Although the combined use of Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data in land-cover classification has been widely adopted, the majority of such use of Landsat and MODIS data is done at the pixel level or feature input level in land-cover classification. We propose in this research a new method to make integrated use of different satellite data by adaptively weighted decision-level fusion. Training and validation samples were collected independently. Training samples were obtained from 329 regions and validation samples from 439 randomly distributed single-point positions. A Support Vector Machine (SVM) classifier was applied to the Landsat 8 data for classification and probability estimation. A Random Forests (RF) classifier was applied to the MODIS time-series data for probability estimation. Weight values were computed based on decision credibility, and reliability values were computed based on data quality. Three decision fusion procedures were performed. In the first procedure, decisions obtained from a Landsat 8 pixel and its corresponding MODIS pixel were fused for improvements (FUSION1). In the second, decisions obtained from the spatial neighbours of the Landsat 8 pixel were added to FUSION1 (FUSION2). In the third, decision fusion only among the Landsat 8 pixel and its spatial neighbours was performed (FUSION3) for comparison. Overall accuracies for the results with Landsat data only, FUSION1, FUSION2, and FUSION3 are 74.0%, 79.3%, 80.6%, and 75.6%, respectively. As a comparison, we also experimented on the use of Landsat and MODIS data by concatenating their features directly. Two classifiers, SVM and RF, were trained and validated on the concatenated features. The overall accuracies were 72.9% and 75.4%, respectively. Results show that the proposed method can utilize information selectively, so that considerable improvements can be obtained and fewer errors introduced. Moreover, it can be easily extended to handle more than two types of data source. [ABSTRACT FROM PUBLISHER]

The Rio Negro basin is characterized by the extensive occurrence of podzol-type soils that store large amounts of organic matter in depth, resulting in the storage of carbon able available to the atmosphere with climate change. The quantification of this carbon requires determination of podzol types and their spatial distribution. Remote-sensing techniques would be helpful in indirect spatializing and segmentation of soil groups in Amazonian podzols. Here we associated remote-sensing images (Shuttle Radar Topographic Mission (SRTM), Operational Land Imager sensor/Landsat 8, and Thermal Infrared Sensor/Landsat 8) and field sample data in order to achieve carbon stock mapping. We found that a multi-sensor approach was critical for a proper segmentation of vegetation groups and spatial distribution of areas with different hydrologic soil regimes. [ABSTRACT FROM AUTHOR]

Multi-sensor aerosol data sets are analysed to examine the aerosol characteristics over the Delhi national capital region. Both the Multiple-angle Imaging Spectroradiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS) capture the seasonal cycle of aerosol optical depth (AOD) as observed by ground-based measurements. However, AOD from MISR shows a low bias relative to AOD from MODIS, which increases linearly at high AOD conditions. A large difference (by >25 W m–2per unit AOD) in the top-of-atmosphere direct radiative forcing efficiency derived from MODIS and MISR-retrieved AOD is observed during the winter and pre-monsoon seasons relative to the other seasons. The ubiquitous presence of dust (as indicated by non-spherical particle fraction to AOD and linear depolarization ratio values) is observed throughout the year. The aerosol layer is mostly confined to within 2 km of surface in the winter and post-monsoon seasons, while it expands beyond 6 km in the pre-monsoon and monsoon seasons. Columnar AOD is found to be highly sensitive to aerosol vertical distribution. The applicability of multi-sensor data sets and climatic implications are discussed. [ABSTRACT FROM PUBLISHER]

Regional- to global-scale lake maps can now be produced using existing technology and freely available data and serve as powerful tools for a variety of lake- and water-related studies. The accuracy of these studies depends in part on the accuracy of the lake map that they use. Mapping lakes using remote sensing requires a careful study of error and uncertainty. Errors in lake maps are caused by sensor-specific, lake-specific, and processing-specific factors. These can be further broken down to spatial, spectral/radiometric, and temporal factors. In this study, we analyse and compare these factors using modern and historical Landsat images along with intensive ground surveys of lakes in northern Alaska. Percentage error in lake area (relative to lake size) decreases for larger and more circular lakes, making a minimum size threshold an effective error mitigation practice. Image resampling involved in image transformation significantly increased error in lake area and is easily avoided by performing co-registration in the vector domain. Spectral properties varied for individual lakes due to depth, suspended sediment, vegetation, and otherin situfactors, necessitating a normalized water index and independently derived threshold values for each lake. For lake change detection studies, spatially degrading a finer resolution image to the resolution of the coarser image (a common practice) does not significantly affect the difference in observed lake area. Due to the large numbers of lakes, particularly in the climatologically sensitive Arctic region, small errors in individual lake areas can compound to significantly impact results on regional to global scales. This study is intended to inform future static and multitemporal lake remote-sensing studies by evaluating errors and uncertainties in lake area, as measured by remote sensing. [ABSTRACT FROM AUTHOR]

A spatial mismatch of hazard data and exposure data (e.g. population) exists in risk analysis. This article provides an integrated approach for a rapid and accurate estimation of population distribution on a per-pixel basis, through the combined use of medium and coarse spatial resolution remote-sensing data, namely the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) night-time imagery, enhanced vegetation index (EVI), and digital elevation model (DEM) data. The DMSP/OLS night-time light data have been widely used for the estimation of population distribution because of their free availability, global coverage, and high temporal resolution. However, given its low-radiometric resolution as well as the overglow effects, population distribution cannot be estimated accurately. In the present study, the DMSP/OLS data were combined with EVI and DEM data to develop an elevation-adjusted human settlement index (EAHSI) image. The model for population density estimation, developed based on the significant linear correlation between population and EAHSI, was implemented in Zhejiang Province in southeast China, and a spatialized population density map was generated at a resolution of 250 m × 250 m. Compared with the results from raw human settlement index (59.69%) and single night-time lights (35.89%), the mean relative error of estimated population by EAHSI has been greatly reduced (17.74%), mainly due to the incorporation of elevation information. The accurate estimation of population density can be used as an input for exposure assessment in risk analysis on a regional scale and on a per-pixel basis. [ABSTRACT FROM PUBLISHER]

Integration of multisensor data provides the opportunity to explore benefits emanating from different data sources. A fusion between fraction images derived from spectral mixture analysis of Landsat-7 ETM+ and phased array L-band synthetic aperture radar (PALSAR) is introduced. The aim of this fusion is to improve the estimation accuracy of above-ground biomass (AGB) in lowland mixed dipterocarp forest. Spectral mixture analysis was applied to decompose a mixture of spectral components of Landsat-7 ETM+ into vegetation, soil, and shade fractions. These fraction images were integrated with PALSAR data using the discrete wavelet transform (DWT) and Brovey transform. As a comparison, spectral reflectance of Landsat-7 ETM+ was fused directly with PALSAR data. Backscatter of horizontal–horizontal and horizontal–vertical polarizations was also used to estimate AGB. Forest inventory was carried out in 77 randomly distributed plots, the data being used for either model development or validation. A local allometric equation was applied to calculate AGB per plot. Regression models were developed by integrating field measurements of 50 sample plots with remotely sensed data, e.g. fraction images, reflectance of Landsat-7 ETM+, and PALSAR data. The models developed were validated using 27 independent sample plots. The results showed that not all fused images significantly improved the accuracy of AGB estimation. The model based on Brovey transform using the reflectance of Landsat-7ETM+ and PALSAR produced anR2of only 0.03–0.10. By contrast, fusion between PALSAR data and fraction images using Brovey transform improved the accuracy ofR2to 0.33–0.46. Further improvement in the accuracy of estimating AGB was observed when DWT was applied to integrate PALSAR with the reflectance of Landsat-7ETM+ (R2= 0.69–0.72) and PALSAR with fraction images (R2= 0.70–0.75). [ABSTRACT FROM PUBLISHER]

For remote sensing image registration, we find that affine transformation is suitable to describe the mapping between images. Based on the scale-invariant feature transform (SIFT), affine-SIFT (ASIFT) is capable of detecting and matching scale- and affine-invariant features. Unlike the blob feature detected in SIFT and ASIFT, a scale-invariant edge-based matching operator is employed in our new method. To find the local features, we first extract edges with a multi-scale edge detector, then the distinctive features (we call these ‘feature from edge’ or FFE) with computed scale are detected, and finally a new matching scheme is introduced for image registration. The algorithm incorporates principal component analysis (PCA) to ease the computational burden, and its affine invariance is embedded by discrete sampling as ASIFT. We present our analysis based on multi-sensor, multi-temporal, and different viewpoint images. The operator shows the potential to become a robust alternative for point-feature-based registration of remote-sensing images as subpixel registration consistency is achieved. We also show that using the proposed edge-based scale- and affine-invariant algorithm (EBSA) results in a significant speedup and fewer false matching pairs compared to the original ASIFT operator. [ABSTRACT FROM PUBLISHER]

A host of remote-sensing and mapping applications require both high spatial and high spectral resolutions. Availability of high spatial and spectral details at different resolutions from a suite of satellite sensors has necessitated the development of effective image fusion techniques that can effectively combine the information available from different sensors and take advantage of their varied capabilities. A common problem observed in the case of multi-sensor multi-temporal data fusion is spectral distortion of the fused images. Performance of a technique also varies with variation in scene characteristics. In this article, two sets of multi-temporal CARTOSAT-1 and Indian Remote Sensing satellite (IRS-P6) Linear Imaging and Self Scanning sensor (LISS-IV) image sub-scenes, with different urban landscape characteristics, are fused with an aim to evaluate the performance of five image fusion algorithms – high-pass filtering (HPF), Gram–Schmidt (GS), Ehlers, PANSHARP and colour-normalized Brovey (CN-Brovey). The resultant fused data sets are compared qualitatively and quantitatively with respect to spectral fidelity. Spatial enhancement is assessed visually. The difference in the performance of techniques with variation in scene characteristics is also examined. For both scenes, GS, HPF and PANSHARP fusion techniques produced comparable results with high spectral quality and spatial enhancement. For these three methods, the variation in performance over different scenes was not very significant. The Ehlers method resulted in spatially degraded images with a more or less constant negative offset in data values in all bands of one scene and in the first two bands in the other. The CN-Brovey method produced excellent spatial enhancement but highly distorted radiometry for both sub-scenes. [ABSTRACT FROM PUBLISHER]

In this article we investigate the scale dependence of spatial heterogeneity in multiresolution and multisensor data using the wavelet transform. The landscape analysed with the wavelets retains the same dominant pattern irrespective of the original pixel size of the image. In agricultural areas, typically characterized by a mosaic of cultivated fields, the wavelet transform quantified consistently a median dominant scale of 512 m in the Orthophoto, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat Enhanced Thematic Mapper Plus (ETM + ). The dominant scale represented the dominant field size of cultivated areas. The shape of the average wavelet energy curves was also similar among the images. In semi-natural areas the wavelet transform quantified consistently a median dominant scale of 128 m in the Orthophoto and ASTER. The median dominant scale of ETM + was slightly smaller and located at 64 m. We characterized the spatial heterogeneity of agricultural and semi-natural areas in Andalucia (Spain) using multisensor data not time coincident ranging from 1 m (Orthophoto), to 15 m (ASTER) to 28.5 m (ETM + ). The contrast in vegetation cover was measured using Normalized Difference Vegetation Index (NDVI) in ASTER and ETM + and red band in Orthophoto. We performed a multiresolution analysis using a Haar two-dimension discrete wavelet transform to quantify and compare the intensity (maximum degree of contrast in vegetation cover), the dominant scale (the scale at which the maximum intensity occurs) and the wavelet energy curve (intensity plotted as a function of the scale) of different images at intervals of power of 2 within the scale range from 2 to 4096 m. [ABSTRACT FROM AUTHOR]

This paper introduces a new and general method for change detection based on the normalized difference change detection (NDCD) technique. A case study shows the use of the NDCD technique for flood mapping. Flood maps for the city of New Orleans (Louisiana, USA) resulting from the passage of Hurricane Katrina in 2005 were produced from the data processing of Satellite Pour l'Observation de la Terre (SPOT)-4 High-Resolution Visible Infra-Red (HRVIR) and Landsat-5 Thematic Mapper (TM) images and the accuracies of the maps were verified using as ground truth the flood extension map of the city of New Orleans produced at the Dartmouth Flood Observatory (Dartmouth College, USA). The potentialities and performances of the NDCD technique in flood mapping were also compared to other standard change detection methods such as: (i) the near-infrared (NIR) normalized difference, (ii) unsupervised post-classification comparison, (iii) change vector analysis (CVA) and (iv) spectral-temporal minimum noise fraction (STMNF) transformation. When using the SPOT-4 HRVIR data, the NDCD technique led to better results than the other change detection methods considered here, while for the Landsat-5 TM data processing the closeness of the post-flood image to the Katrina landfall influenced the overall performances negatively. However, with respect to flood mapping in the urban area alone, which may be of major interest in most cases, the NDCD technique also performed better than all the other change detection methods considered here when using the Landsat-5 TM data. [ABSTRACT FROM AUTHOR]

In this paper we evaluate the potential of ENVISAT-Medium Resolution Imaging Spectrometer (MERIS) fused images for land-cover mapping and vegetation status assessment in heterogeneous landscapes. A series of MERIS fused images (15 spectral bands; 25 m pixel size) is created using the linear mixing model and a Landsat Thematic Mapper (TM) image acquired over the Netherlands. First, the fused images are classified to produce a map of the eight main land-cover types of the Netherlands. Subsequently, the maps are validated using the Dutch land-cover/land-use database as a reference. Then, the fused image with the highest overall classification accuracy is selected as the best fused image. Finally, the best fused image is used to compute three vegetation indices: the normalized difference vegetation index (NDVI) and two indices specifically designed to monitor vegetation status using MERIS data: the MERIS terrestrial chlorophyll index (MTCI) and the MERIS global vegetation index (MGVI). Results indicate that the selected data fusion approach is able to downscale MERIS data to a Landsat-like spatial resolution. The spectral information in the fused images originates fully from MERIS and is not influenced by the TM data. Classification results for the TM and for the best fused image are similar and, when comparing spectrally similar images (i.e. TM with no short-wave infrared bands), the results of the fused image outperform those of TM. With respect to the vegetation indices, a good correlation was found between the NDVI computed from TM and from the best fused image (in spite of the spectral differences between these two sensors). In addition, results show the potential of using MERIS vegetation indices computed from fused images to monitor individual fields. This is not possible using the original MERIS full resolution image. Therefore, we conclude that MERIS-TM fused images are very useful to map heterogeneous landscapes. [ABSTRACT FROM AUTHOR]

This research examines the utility of QuickBird imagery and Light Detection and Ranging (LiDAR) data for estimating population at the census-block level using two approaches: area-based and volume-based. Residential-building footprints are first delineated from the remote-sensing data using image segmentation and machine-learning decision-tree classification. Regression analysis is used to model the relationship between population and the area or volume of the delineated residential buildings. Both approaches result in successful performance for estimating population with high accuracy (coefficient of determination = 0.8-0.95; root-mean-square error = 10-30 people; relative root-mean-square error = 0.1-0.3). The area-based approach is slightly better than the volume-based approach because the residential areas of the study sites are generally homogeneous (i.e. single houses), and the volume-based approach is more sensitive to classification errors. The LiDAR-derived shape information such as height greatly improves population estimation compared to population estimation using only spectral data. [ABSTRACT FROM AUTHOR]

Image fusion is the process of combining images captured by different sensors under different conditions. These images usually have different geometric and radiometric properties. The enormous increase in the volume of remotely sensed data has created the need for robust data processing techniques that can fuse data observed by different acquisition systems. This need is motivated by the fact that collected data by these sensors are complementary in nature. Therefore, simultaneous utilization of the collected data would guarantee a full understanding of the object/phenomenon under consideration. In this regard, a data-fusion procedure can be defined as being concerned with the problem of how to combine data and/or information from multiple sensors in order to achieve improved accuracies and better inference about the environment than could be attained through the use of a single sensor. Fusion of multi-source imagery captured under different conditions is a challenging problem. The difficulty is attributed to the varying radiometric and geometric resolutions of the acquired imagery. Image registration is considered as one of the most critical requirement for accurate data fusion. The most appropriate primitives, transformation function and similarity measure have been incorporated in a matching strategy to solve the registration problem. Experimental results using real data proved the feasibility and the robustness of the suggested method. [ABSTRACT FROM AUTHOR]

In this work, an adaptive neural-fuzzy-based multi-sensor data fusion (ANF-MDF) architecture for target tracking systems is presented. In this architecture, neural networks are employed to detect and estimate target manoeuvres, and adaptive-network-based fuzzy inference systems (ANFIS) are used to adjust the measurement noise covariance matrices. They are combined as an adaptive mechanism to cooperate with Kalman filters to process measurements from multiple sensors, whose outputs are fused by a specific neural network to obtain optimal results. The results of simulations demonstrate this architecture can adjust system parameters and respond quickly to avoid mistracking effectively. [ABSTRACT FROM AUTHOR]

Image fusion is one of the most commonly used image enhancement techniques for improving the spatial quality of the source image with minimal spectral distortion in remote sensing. Until now, data fusion algorithms were developed and applied to improve the spatial resolution of the multispectral images and also their performances were evaluated depending on the source images such as Landsat Enhanced Thematic Mapper Plus (ETM+), Landsat Multispectral (MS)/Panchromatic (PAN), Satellite pour l'Observation de la Terre (SPOT) XS/PAN and IKONOS MS/PAN datasets. This paper assesses whether hyperspectral images, having very narrow bands compared to multispectral images, can be fused with high spatial resolution panchromatic images using common and current new algorithms including Intensity-Hue Saturation (IHS), Principal Component Substitution (PCS), Gram Schmidt Transformation (GST), Smoothing Filter-based Intensity Modulation (SFIM), Discrete Wavelet Transform (DWT), wavelet-based IHS (DWT-IMS) and PCS (DWT-PCS) and Fast Fourier Transform (FFT)-enhanced IHS. We also examine the performance of the fused hyperspectral images with respect to the fused multispectral images. For this purpose, two different source datasets (EO1 Hyperion/ALI PAN and EO1 ALI MS/PAN) were used. Some qualitative and quantitative analyses were implemented to assess the spatial and spectral quality of the fused images. The results show that it was possible to carry out the fusion of a narrow-band hyperspectral image and a high spatial resolution panchromatic image. The fusion of EO1 Hyperion/ALI PAN and EO1 ALI MS/PAN datasets using the SFIM, DWT-PCS, DWT-IHS and FFT-IHS algorithms produces better results than other techniques. Also, the results show that the fusion methods behaved for both datasets in the same performances except the DWT algorithm. The DWT method has a lower performance for the hyperspectral image compared to the multispectral image. Therefore the DWT algorithm should be further studied to improve the spectral qualities of a fused hyperspectral image based on wavelet transformation. [ABSTRACT FROM AUTHOR]

The joint use of multiresolution sensors from different satellites offers many opportunities to describe vegetation and its dynamics. This paper introduces the concept of a virtual constellation (defined as an ensemble of all Earth Observation satellites in orbit that satisfy common requirements) for agricultural applications and contributes to providing the necessary inter-sensor calibration methodology for spectral reflectances and NDVI. For this purpose, we performed an observational study, comparing reflectances and the Normalized Difference Vegetation Index (NDVI), from near-synchronous image pairs of Landsat 7 Enhanced Thematic Mapper Plus (ETM+), as the reference sensor and Landsat 5 Thematic Mapper (TM), IRS 1C/D LISS-III (LISS), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), QuickBird, and NOAA Advanced Very High-resolution Radiometer (AVHRR). Linear relationships were found for the intercalibration of reflectances and NDVI from one sensor to another, for all sensors, provided that some spatial aggregation was performed. The main source of data dispersion in our linear cross-sensor translation equations is the geolocation uncertainty inherent in the process of geometric correction. Consequently, spatial aggregation always needs to be performed if (different or the same) sensors are to be used to derive time-series of biogeophysical parameters over heterogeneous areas. The homogenous zone approach developed here is recommended as an excellent tool for deriving robust new cross-sensor relationships, provided that the selected homogeneous crops cover the full NDVI range. The linear cross-sensor relationships derived from one image pair were shown to be valid for the whole season and for all areas with similar vegetation and climate. [ABSTRACT FROM AUTHOR]

A new approach aimed at a better understanding of the state of pollution of the Black Sea coastal zone is suggested. It consists of the combined use of all available quasi-concurrent satellite information (NOAA AVHRR, TOPEX/Poseidon, Jason-1, Terra/Aqua MODIS, Envisat ASAR, ERS-2 SAR and QuikSCAT) and was first applied during an operational seawater monitoring campaign in the coastal zone of the northeastern Black Sea conducted in 2006. The monitoring is based on daily receiving, processing and analysis of data different in nature (microwave radar images, optical and infrared data), resolution and surface coverage. These data allow us to retrieve information on seawater pollution, sea surface and air-sea boundary layer conditions, seawater temperature and suspended matter distributions, chlorophyll-a concentration, mesoscale water dynamics, near-surface wind, and surface wave fields. Such an approach helps in oil spill detection with synthetic aperture radar (SAR), especially in distinguishing oil slicks from look-alikes. The focus is on coastal seawater circulation mechanisms and their impact on the evolution of pollutants. [ABSTRACT FROM AUTHOR]

With many remote-sensing instruments onboard satellites exploring the Earth's atmosphere, most data are processed to gridded daily maps. However, differences in the original spatial, temporal, and spectral resolution—as well as format, structure, and temporal and spatial coverage—make the data merging, or fusion, difficult. NASA Goddard Earth Sciences Data and Information Services Center (GES-DISC) has archived several data products for various sensors in different formats, structures, and multi-temporal and spatial scales for ocean, land, and atmosphere. In this investigation using Earth science data sets from multiple sources, an attempt was made to develop an optimal technique to merge the atmospheric products and provide interactive, online analysis tools for the user community. The merged/fused measurements provide a more comprehensive view of the atmosphere and improve coverage and accuracy, compared with a single instrument dataset. This paper describes ways of merging/fusing several NASA Earth Observing Systems (EOS) remote-sensing datasets available at GES-DISC. The applicability of various methods was investigated for merging total column ozone to implement these methods into Giovanni, the online interactive analysis tool developed by GES-DISC. Ozone data fusion of MODerate resolution Imaging Spectrometer (MODIS) Terra and Aqua Level-3 daily data sets was conducted, and the results were found to provide better coverage. Weighted averaging of Terra and Aqua data sets, with the consequent interpolation through the remaining gaps using Optimal Interpolation (OI), also was conducted and found to produce better results. Ozone Monitoring Instrument (OMI) total column ozone is reliable and provides better results than Atmospheric Infrared Sounder (AIRS) and MODIS. However, the agreement among these instruments is reasonable. The correlation is high (0.88) between OMI and AIRS total column ozone, while the correlation between OMI and MODIS Terra/Aqua fused total column ozone is 0.79. [ABSTRACT FROM AUTHOR]

This paper investigates the applicability and limitations of combining multi-sensor data through data fusion, to increase the usefulness of the datasets. This study focuses on merging daily mean aerosol optical thickness (AOT), as measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, to increase spatial coverage and produce complete fields to facilitate comparison with models and station data. The fusion algorithm used the maximum likelihood (ML) technique to merge the pixel values where available, and then the optimal interpolation method to fill the remaining gaps. The algorithm was applied to a regional AOT subset. The results illustrate that the fusion algorithm can produce complete AOT fields with reasonably good data values and acceptable errors. The cumulative semivariogram (CSV) was found to be sensitive to the spatial distribution and fraction of gap areas and, thus, useful for assessing the sensitivity of the fused data to spatial gaps. [ABSTRACT FROM AUTHOR]

Remotely sensed multitemporal, multisensor data are often required in Earth observation applications. A common problem associated with the use of multisource image data is the grey value differences caused by non-surface factors such as different illumination, atmospheric, or sensor conditions. Such differences make it difficult to compare images using the same colour metric system. Image normalization is required to reduce the radiometric influences caused by non-surface factors and to ensure that the grey value differences between temporal images reflect actual changes on the surface of the Earth.A variety of image normalization methods, such as pseudoinvariant features (PIF), dark and bright set (DB), simple regression (SR), no-change set determined from scattergrams (NC), and histogram matching (HM), have been published in scientific journals. These methods have been tested with either Landsat TM data, MSS data or both, and test results differ from author to author. However, whether or not existing methods could be adopted for normalizing high resolution multispectral satellite images, such as IKONOS and QuickBird, is still open for discussion because of the dramatic change in spatial resolution and the difference of available multispectral bands. In this research, the existing methods are introduced and employed to normalize the radiometric difference between IKONOS and QuickBird multispectral images taken in different years. Some improvements are introduced to the existing methods to overcome problems caused by band difference and to achieve more stable and better results. The normalized results are compared in terms of visual inspection and statistical analysis. This paper examined whether or not existing methods can be directly adopted for image normalization with high resolution satellite images, and showed how these methods can be modified for use with such images. [ABSTRACT FROM AUTHOR]

Land-cover classification with remotely sensed data in moist tropical regions is a challenge due to the complex biophysical conditions. This paper explores techniques to improve land-cover classification accuracy through a comparative analysis of different combinations of spectral signatures and textures from Landsat Enhanced Thematic Mapper Plus (ETM+) and Radarsat data. A wavelet-merging technique was used to integrate Landsat ETM+ multispectral and panchromatic data or Radarsat data. Grey-level co-occurrence matrix (GLCM) textures based on Landsat ETM+ panchromatic or Radarsat data and different sizes of moving windows were examined. A maximum-likelihood classifier was used to implement image classification for different combinations. This research indicates the important role of textures in improving land-cover classification accuracies in Amazonian environments. The incorporation of data fusion and textures increases classification accuracy by approximately 5.8-6.9% compared to Landsat ETM+ data, but data fusion of Landsat ETM+ multispectral and panchromatic data or Radarsat data cannot effectively improve land-cover classification accuracies. [ABSTRACT FROM AUTHOR]

To safeguard the goals of flood protection and nature development, a river manager requires detailed and up-to-date information on vegetation structures in floodplains. In this study, remote-sensing data on the vegetation of a semi-natural floodplain along the river Waal in the Netherlands were gathered by means of a Compact Airborne Spectrographic Imager (CASI; spectral information) and LiDAR (structural information). These data were used to classify the floodplain vegetation into eight and five different vegetation classes, respectively. The main objective was to fuse the CASI and LiDAR-derived datasets on a pixel level and to compare the classification results of the fused dataset with those of the non-fused datasets. The performance of the classification results was evaluated against vegetation data recorded in the field. The LiDAR data alone provided insufficient information for accurate classification. The overall accuracy amounted to 41% in the five-class set. Using CASI data only, the overall accuracy was 74% (five-class set). The combination produced the best results, raising the overall accuracy to 81% (five-class set). It is concluded that fusion of CASI and LiDAR data can improve the classification of floodplain vegetation, especially for those vegetation classes which are important to predict hydraulic roughness, i.e. bush and forest. A novel measure, the balance index, is introduced to assess the accuracy of error matrices describing an ordered sequence of classes such as vegetation structure classes that range from bare soil to forest. [ABSTRACT FROM AUTHOR]

We report on the actions of the first daily revisit satellite, FORMOSAT-2, in the recent Indian Ocean tsunami disaster. Starting from the first images of Banda Aceh and Phuket taken on 28 December 2004, FORMOSAT-2 used its unique orbit and pointable sensor system to demonstrate the extent to which it is able to respond to emergencies. A total of 137 images throughout the Indian Ocean rim countries were taken within a month. The data were immediately analysed and turned into damage-assessment maps and other information resources for humanitarian aid. This paper focuses on the image-processing procedure followed for a fast response to the South Asia tsunami event. The imageodesy technique is used to coregister the level-2 product of FORMOSAT-2 image at high accuracy and speed. A novel approach for spectral reservation data fusion has also been proposed. With the advantages of accurate coregistration and reliable spectral property, the colour composites of FORMOSAT-2 imagery have been used as the principle source of information for our tsunami hazard assessment. The potential of FORMOSAT-2 for disaster monitoring is discussed. The technique developed in this research will be adapted to produce pan sharpened images as a standard value added product of FORMOSAT-2. [ABSTRACT FROM AUTHOR]

Various fusion methods have been developed for improving data spatial resolution. The methods most encountered in the literature are the intensity-hue-saturation (IHS) transform, the Brovey transform, the principal components algorithm (PCA) fusion method, the Gram-Schmidt fusion method, the local mean matching method, the local mean and variance matching method, the least square fusion method, the discrete wavelet fusion method including Daubechies, Symlet, Coiflet, biorthogonal spline, reverse biorthogonal spline, and Meyer wavelets, the wavelet-PCA fusion method, and the crossbred IHS and wavelet fusion method. Using various evaluation indicators such as two-dimensional correlation, relative difference of means, relative variation, deviation index, entropy difference, peak signal-to-noise ratio index and universal image quality index, as well as photo-interpretation methods and techniques, results of the above fusion methods were compared and comments on the fusion methods and potential of evaluation indicators were made. Among data fusion methods and indicators the local mean and variance matching methods proved the most efficient and the peak signal-to-noise ratio indicator proved the most appropriate for the evaluation of data fusion results. [ABSTRACT FROM AUTHOR]

The detection of changes affecting continental surfaces has important applications in hydrological, meteorological and climatic modelling. Using remote sensing data, numerous change indices have already been proposed. Previous work showed the interest of combining several of these to improve change detection performance, using the Dempster–Shafer evidence theory framework. This study analyses the performance of different change indices and their combination in different cases of application: forest logging either in pine forest or in mixed forest, and winter vegetation cover of fields in intensive farming areas, in comparison to the forest fire case presented in previous work. The interest of indices derived from Information Theory, some of which are original, is shown. [ABSTRACT FROM AUTHOR]

A high‐level data fusion system that uses Bayesian statistics involving weights‐of‐evidence modelling is described to combine disparate information from airborne digital data such as digital surface model (DSM), colour, thermal infrared (TIR) and hyperspectral images at different time periods. To determine the efficacy of the system, an analysis of change detection was performed. The data fusion system is capable of detecting changes in man‐made features automatically in a densely populated area where there is little prior information. Multiclass segmented images were obtained from the data captured by four airborne remote sensing sensors. The system performs data fusion modelling by using binary images of each theme class and a total of 40 binary patterns were obtained. Through Bayesian methods, involving weights‐of‐evidence modelling, all the binary images were analysed and finally four binary patterns (indicator images) were identified automatically as significant for the change‐detection application. A weighted index overlay model available in the system combines these four patterns. Data fusion by weights‐of‐evidence modelling is found to be straightforward and unequivocal for predicting newly transformed locations. The results of the Bayesian method are accurate as the weights are based on statistical analysis. Changes in features such as colour of roofs, parking areas, openland areas, newly built structures, and the presence or absence of vehicles are extracted automatically by using the high‐level data fusion approach. The final predictor image shows the probability of change‐detected areas in a densely populated city in Japan. [ABSTRACT FROM AUTHOR]