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Geographic information science (GIScience) and remote sensing have long provided essential data and methodological support for natural resource challenges and environmental problems research. With increasing advances in information technology, natural resource and environmental science research faces the dual challenges of data and computational intensiveness. Therefore, the role of remote sensing and GIScience in the fields of natural resources and environmental science in this new information era is a key concern of researchers. This study clarifies the definition and frameworks of these two disciplines and discusses their role in natural resource and environmental research. GIScience is the discipline that studies the abstract and formal expressions of the basic concepts and laws of geography, and its research framework mainly consists of geo-modeling, geo-analysis, and geo-computation. Remote sensing is a comprehensive technology that deals with the mechanisms of human effects on the natural ecological environment system by observing the earth surface system. Its main areas include sensors and platforms, information processing and interpretation, and natural resource and environmental applications. GIScience and remote sensing provide data and methodological support for resource and environmental science research. They play essential roles in promoting the development of resource and environmental science and other related technologies. This paper provides forecasts of ten future directions for GIScience and eight future directions for remote sensing, which aim to solve issues related to natural resources and the environment.

SAR Interferometry (InSAR) has been proven to be effective for measuring landslides deformation. However, the InSAR application of landslide mapping is limited by the blind observation areas caused by SAR geometric distortion or vegetation decorrelation. We proposed an approach of landslide detection and mapping with a priori assessment of InSAR applicability, which can not only guide the selection of SAR data, but also prejudge the observable areas. Meanwhile, it can assist to analyze the reliability of landslide detection and help to reveal the reason of detection failure of occurred landslides. We employed this approach to evaluate the InSAR applicability over the whole Guizhou Province at first. Then, we took Shuicheng County as an example to conduct time-series InSAR landslide detection combined with a priori applicability. Six large active landslides were identified from the results of three SAR data stacks (ALOS-2 PALSAR-2 and ascending/descending Sentinel-1 from April 2017 to July 2019). The reliabilities of landslide detection results were analyzed and the possible reasons for the detection failure of Jicangzhen landslide were revealed. We inferred that the Jichangzhen landslide suddenly happened induced by short-term heavy rainfalls and presented no precursors, which cannot be captured by SAR satellites with intermittent observations.

The study of urban area is one of the hottest research topics in the field of remote sensing. With the accumulation of high-resolution (HR) remote sensing data and emerging of new satellite sensors, HR observation of urban areas has become increasingly possible, which provides us with more elaborate urban information. However, the strong heterogeneity in the spectral and spatial domain of HR imagery brings great challenges to urban remote sensing. In recent years, numerous approaches were proposed to deal with HR image interpretation over complex urban scenes, including a series of features from low level to high level, as well as state-of-the-art methods depicting not only the urban extent, but also the intra-urban variations. In this paper, we aim to summarize the major advances in HR urban remote sensing from the aspects of feature representation and information extraction. Moreover, the future trends are discussed from the perspectives of methodology, urban structure and pattern characterization, big data challenge, and global mapping.

Using global navigation satellites to construct bi-static synthetic aperture radar for imaging has been a major research hotspot in passive radar. However, the low range resolution of Global Naviga...

Accurate delineation of global built-up area (BUA) is fundamental to a better understanding of human development and the impacts on global environmental change. Existing global datasets of human settlement were mostly generated at medium and coarse spatial resolutions, including BUA and other impervious surfaces. With multiple high-resolution satellite constellations now available (e.g., ZiYuan-3 (ZY3), SPOT-5/6/7, Cartosat-1/2, and WorldView-2/3), identifying the global BUA explicitly from the complex landscapes becomes possible. In this study, a novel method was proposed for automated extraction of BUA at the global scale, by fusing a series of building features. Specifically, two planar features, the Morphological Building Index (MBI) and Harris corner detector, were employed to characterize the structure and corner attributes of buildings. Moreover, two multi-angular built-up indices (MABIs), i.e., Ratio Multi-angular Built-up Index (RMABI) and Normalized Difference Multi-angular Built-up Index (NDMABI), were proposed to represent the vertical properties of buildings based on multi-view images, which can further complement the planar features. 45 global cities were selected to validate the performance of the proposed method with images acquired by the ZY3 satellite constellation. The results show that the fusion of MBI and Harris corner can achieve satisfactory accuracy, i.e., 91.12%, 88.85%, 82.82% and 0.85, for the average overall accuracy (OA), user's accuracy (UA), producer's accuracy (PA), and F1-score, respectively, for all the test cities. After fusing the MABIs with the planar features, the average OA, UA, PA and F values of the final results were 92.00%, 86.20%, 89.14% and 0.87 for the RMABI, and 91.83%, 85.51%, 89.62% and 0.87 for the NDMABI, respectively. In particular, addition of the MABIs can further reduce the omission errors where medium/high rise buildings with low local contrast exist. We compared our results with two existing state-of-the-art global BUA products, Global Human Settlement Layer (GHSL) and Global Urban Footprint (GUF), which further corroborated the effectiveness of our method.

Accurate mapping the forest stand mean height (FSMH) and aboveground biomass (AGB) with a high spatial resolution are important for monitoring carbon stocks on Earth and the variability and trends of terrestrial carbon fluxes. The recently launched Sentinel-1 (SAR) and Sentinel-2 (multispectral) missions offers a new opportunity to map FSMH and AGB. Here we present a methodological framework to map the FSMH and AGB at a resolution of 10 m in Yichun, Northeast China, by integrating field plots, Sentinel imagery, topographic data, and national geographical conditions monitoring data. First, a spatial continuous FSMH product was retrieved using an empirical model, which adopts the backscattering of SAR Sentinel-1B and the fraction of vegetation cover (FVC) variable from multispectral Sentinel-2A imagery. Subsequently, three AGB estimation models were developed for different forest types to link the field measurements to the FSMH, biophysical variables, spectral vegetation index, and topographic variables using the random forest algorithm. The mapping results show that the FSMH estimated using SAR backscatter values from VH polarization is more robust and accurate than that based on VV polarization. Furthermore, the three AGB estimation models based on three different forest types perform better than the model built by grouping all forest types together. The determination coefficient (R2) and root-mean-squared error (RMSE) range from 0.69 to 0.74 and 23.38 Mg/ha to 24.21 Mg/ha, respectively. Overall, our study demonstrates that the proposed methodological framework can be used to map the FSMH and AGB products at a high spatial resolution utilizing freely accessible Sentinel-1 SAR and Sentinel-2 multispectral imagery.

The dynamic characteristics of seasonal snow cover are critical for hydrology management, climate system, and ecosystem function. Although optical satellite remote sensing has proved to be an effective tool for monitoring global and regional variations of snow cover, it is still problematic to accurately capture the snow dynamics characteristics at a finer spatiotemporal resolution, because the observations from optical satellite sensors are seriously affected by clouds and solar illumination. Besides, traditional methods of mapping snow cover from passive microwave data only provide binary information with a 25-km spatial resolution. In this study, we first present an approach to predict fractional snow cover over North America under all-weather conditions, derived from the enhanced resolution passive microwave brightness temperature data (6.25 km). This estimation algorithm used Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products between 2010 and 2017 to create the reference fractional snow cover data as the "true" observations. Further, the influence of many factors, including land cover, topography, and location, were incorporated into the retrieval models. The results show that the proposed retrieval models based on random forest regression technique perform much better using independent test data for all land cover classes, with higher accuracy and no out-of-range estimated values, when compared to the other three approaches (linear regression, artificial neural networks (ANN), and multivariate adaptive regression splines (MARS)). The results of the output evaluated by using independent data indicate that the root-mean-square error (RMSE) of the estimated fractional snow cover ranges from 16.7 % to 19.8 %. In addition, the estimated fractional snow cover is verified in the snow mapping aspect by using snow cover observation data from meteorological stations (more than 0.31 million records). The result shows that the binary snow cover obtained by the proposed retrieval algorithm is in a good agreement with the ground measurements (kappa: 0.67). The accuracy of our algorithm estimation in the snow cover identification shows significant improvement when benchmarked against the Grody’s snow cover mapping algorithm: overall accuracy is increased by 18 % (from 0.71 to 0.84), and omission error is reduced by 71 % (from 0.48 to 0.14). Daily time-series and full space-covered sub-pixel snow cover area data are urgently needed for climate and reanalysis studies. According to our experiment results, we can conclude that it is feasible for estimating fractional snow cover from passive microwave brightness temperature data, and this strategy also has a great advantage in detecting snow cover area.

Satellites-based microwave sensors are sensitive to soil moisture at the surface of the Earth, but their performance is limited by their continuously varying footprints due to the repeat cycle of s...

Earth observation (EO) data, such as high-resolution satellite imagery or LiDAR, has become one primary source for forests Aboveground Biomass (AGB) mapping and estimation. However, managing and an...

InSAR technology provides a powerful tool to detect potentially unstable slopes across wide areas and to monitor surface displacements of a single landslide. However, conventional time series InSAR methods such as persistent scatterer interferometry (PSI) and small-baseline subset (SBAS) can rarely identify sufficient measurement points (MPs) in mountainous areas due to decorrelations caused by steep terrain and vegetation coverage. In this study, we developed a new InSAR approach, coherent scatterer InSAR (CSI), to map landslide surface displacements in the radar line-of-sight (LOS) direction by combining persistent scatterers (PS) and distributed scatterers (DS). The key ideas of CSI include the employment of the generalized likelihood ratio (GLR) test for the identification of statistically homogeneous pixels (SHPs) and the use of the phase linking algorithm to estimate optimal phase for each DS pixel. The joint exploitation of PS and DS targets dramatically increases the spatial density of MPs, which makes the phase unwrapping more reliable. To demonstrate the effectiveness of the CSI approach, we applied it to retrieve the historical displacements of the Jiaju landslide in Danba County of southwest China using 19 L-band ALOS PALSAR images (2006–2011) and nine C-band ENVISAT ASAR images (2007–2008). Multiple comparisons clearly illustrated the big advantages of CSI over PSI and SBAS in mapping landslide displacements with more details owing to much higher (> 10 times) MP density. Furthermore, the superiority of L-band SAR data over C-band for landslide investigation in rural environments was confirmed. Quantitative validation of the CSI results for PALSAR data against in-situ GPS measurements suggested an accuracy of about 10.5 millimeters per year (mm/year) in terms of root mean square error (RMSE). Afterwards, the spatial-temporal characteristics of the Jiaju landslide surface displacements were summarized, with a new upper boundary for the active northern part delineated. Particularly, the northern part of the landslide moved faster than the southern part, exhibiting a maximum LOS displacement rate of around 120 mm/year. Subsequently, the fluvial erosion by the Dajinchuan River was identified as the predominant impact factor for the instability of the Jiaju landslide. Finally, the major problems and challenges for the application of CSI method were discussed, and the conclusions were given.

Deformation monitoring has been brought to the fore and extensively studied in recent years. Global Navigation Satellite System Reflectometry (GNSS-R) techniques have so far been developed in deformation estimation applications, which however, are subject to the influence of mobile satellites. Rather than compensating for the path delay variations caused by mobile satellites, adopting Beidou geostationary Earth orbit (GEO) satellites as transmitters directly eliminates the satellite-motion-induced phase error and thus provides access to stable phase information. This paper presents a novel deformation monitoring concept based on GNSS-R utilizing Beidou GEO satellites. The geometrical properties of the GEO-based bistatic GNSS radar system are explored to build a theoretical connection between deformation quantity and the echo carrier phases. A deformation retrieval algorithm is proposed based on the supporting software receiver, thus allowing echo carrier phases to be extracted and utilized in deformation retrieval. Two field validation experiments are conducted by constructing passive bistatic radars with reflecting plates and ground receiver. Utilizing the proposed algorithm, the experimental results suggested that the GEO-based GNSS reflectometry can achieve deformation estimations with an accuracy of around 1 cm when the extracted phases does not exceed one complete cycle, while better than 3 cm when considering the correct integer number of phase cycles. Consequently, based on the passive bistatic radar system, the potential of achieving continuous, low-cost deformation monitoring makes this novel technique noteworthy.

The use of multipath signals to estimate soil moisture is an important application of Global Navigation Satellite System (GNSS) reflectometry. In most studies the data used to estimate the soil moi...

Ground control points (GCPs) are necessary in order to achieve precise georeferencing of high-resolution satellite (HRS) imagery. However, measuring GCPs is costly, laborious, and time consuming. In some remote areas, we cannot even obtain well-defined GCPs. In this study, a strip constraint model is established. Based on the bias-compensated rational function model and the strip constraint model, a feasible extrapolated georeferencing approach for HRS imagery is presented. The presented approach remains effective even when the intermediate images in the strip are unavailable. Experimental results of the two ZiYuan-3 (ZY-3) nadir datasets show that the direct georeferencing accuracy of the ZY-3 nadir images reaches only 9 to 12 pixels. With four GCPs in the first image, the georeferencing accuracy of the other images in the strip is improved to better than 2 pixels through extrapolated georeferencing.

The ZiYuan-3 (ZY-3) satellite, equipped with three-line array scanners, is China’s first civilian stereo mapping satellite. In this paper, the first assessment of the stereoscopic capacities of the ZY-3 imagery for building height estimation in heterogeneous urban areas is performed. Digital surface models (DSMs) are generated and optimized by different stereo pairs of a ZY-3 triplet for the city of Wuhan, China. The normalized DSMs (nDSMs) are computed as the height of the off-terrain objects by using morphological top-hat by reconstruction of the DSMs. We adopt random forest classification and object-based building segmentation to detect the buildings from the orthographic image, and estimate the building height by assigning the maximum nDSM value within the building regions. The actual heights of 400 buildings are used as reference data. Comparisons between the building heights extracted from the different stereo pairs are presented, indicating a higher accuracy by the nadir-forward stereo pair...

BeiDou Navigation Satellite System reflectometry (BDS-R) is an emerging area of BD (BeiDou) applications that uses multipath reflected signals in microwave remote sensing. Soil moisture estimation is one of the potential important applications of BDS-R. This study aims to investigate the feasibility of soil moisture estimation based on BD B1 band interference signals, which are composed of direct and reflected BD signals and can be readily captured using low-cost commercial BDS receivers. In this paper, a theoretical background for soil estimation from interference signals was introduced, and an analysis of field experimental data was conducted. First, a cosine model for the interference signal power was built, and a non-uniform power spectrum analysis was performed on the received interference signals to determine its main frequency. Then, a least squares curve fitting was applied on the interference signal power to extract its phase. The relationship between the soil moisture and the phase was then determined. Two months of experimental data were collected at BaoXie, Wuhan and analyzed for further inversion. Experimental results show that the phase of the interference signals increases with the increase of soil moisture. The correlation coefficient between the phase and the in-situ soil moisture value is approximately 0.8. Finally, the relationship between the phase and soil moisture is employed to estimate soil moisture. Results show that using BDS-R to measure soil moisture is feasible, which demonstrates a great potential of new application of the BD system.

Owing to the large biases of laboratory-calibrated imaging parameters, the initial direct georeferencing accuracy of Ziyuan-3 ( zy -3) three-line camera ( tlc ) images can only reach the kilometer level. In this paper, a line-based geometric calibration model is established by using the line-coplanarity constraint, and a feasible geometric calibration approach for the zy -3 tlc s using ground control lines ( gcl s) is presented. Experimental results show that the calibration accuracy achieved by using gcl s reached about 1.0 pixel, which was almost the same as that achieved by using ground control points ( gcp s). After substituting gcl s for gcp s in the geometric calibration, the georeferencing accuracy of the zy -3 tlc images without ground controls was significantly improved from the kilometer level to better than 11 m in planimetry and 8 m in height. A better georeferencing accuracy of about 3.0 m in planimetry and 2.5 m in height was achieved after the block adjustment with four gcp s.

Existing methods that incorporate spatial information into a traditional Bag-of-Visual-Words (BoVW) model consider the spatial arrangement of an image but ignore pixel homogeneity in land-use remote sensing images. In this letter, we present an improved correlaton model to jointly integrate appearance, spatial correlation, and pixel homogeneity using multiscale segmentation. The effectiveness of the proposed method was tested on a ground truth image data set of 21 land-use classes manually extracted from high-resolution remote sensing images. The experimental results demonstrate that our improved correlaton model can promote classification and outperforms existing methods such as the traditional BoVW model, spatial pyramid matching model, and the traditional correlaton model.

Void filling and anomaly replacement in conjunction with auxiliary sources of data have been widely used to improve the quality of existing problematic high-resolution digital elevation models. However, the traditional interpolation methods used for this purpose have always failed to eliminate the discrepancies between different data-sets. In this paper, an improved ANUDEM method is presented for DEM interpolation, which incorporates the idea of topographic correction using high correlation of topological structure between contour lines (CLs) from multi-scale digital elevation models (DEM). Firstly, the terrain topological structure is extracted from the CLs of a low-resolution DEM. The topographic surface correction is then undertaken based on the extracted structure, which recovers the topographic information of the sharp depressions and eminences to fit the high-resolution representation. Finally, the breaklines of the terrain surface are distilled and integrated into the denser DEM generation. The exp...

Surface soil moisture is an important environment variable that is dominant in a variety of research and application areas. Acquiring spatiotemporal continuous soil moisture observations is therefore of great importance. Weather conditions can contaminate optical remote sensing observations on soil moisture, and the absence of remote sensors causes gaps in regional soil moisture observation time series. Therefore, reconstruction is highly motivated to overcome such contamination and to fill in such gaps. In this paper, we propose a novel image reconstruction algorithm that improved upon the Satellite and In situ sensor Collaborated Reconstruction (SICR) algorithm provided by our previous publication. Taking artificial neural networks as a model, complex and highly variable relationships between in situ observations and remote sensing soil moisture is better projected. With historical data for the network training, feedforward neural networks (FNNs) project in situ soil moisture to remote sensing soil moisture at better performances than conventional models. Consequently, regional soil moisture observations can be reconstructed under full cloud contamination or under a total absence of remote sensors. Experiments confirmed better reconstruction accuracy and precision with this improvement than with SICR. The new algorithm enhances the temporal resolution of high spatial resolution remote sensing regional soil moisture observations with good quality and can benefit multiple soil moisture-based applications and research.

The launching of CBERS-01 (China Brazil Earth Resource Satellite) in 1999, China’s first land observation satellite, signifies an unprecedented milestone in Chinese satellite remote sensing history. Since then, a large number of applications have been developed that drew upon solely CBERS-01 and other Chinese land observation satellites. The application development evolves from one satellite to multiple satellites, from one series of satellites to multiple series, from scientific research to industrial applications. Six aspects of the Chinese land observation satellite program are discussed in this paper: development status, data sharing and distribution, satellite calibration, industrial data applications, future prospects, and conclusion.

For multi-sensor integrated systems, such as a mobile mapping system (MMS), data fusion at sensor-level, i.e., the 2D-3D registration between optical camera and LiDAR, is a prerequisite for higher level fusion and further applications. This paper proposes a line-based registration method for panoramic images and LiDAR point cloud collected by a MMS. We first introduce the system configuration and specification, including the coordinate systems of the MMS, the 3D LiDAR scanners, and the two panoramic camera models. We then establish the line-based transformation model for panoramic camera. Finally, the proposed registration method is evaluated for two types of camera models by visual inspection and quantitative comparison. The results demonstrate that the line-based registration method can significantly improve the alignment of the panoramic image and LiDAR datasets under either the ideal spherical or the rigorous panoramic camera model, though the latter is more reliable.

Synthetic Aperture Radar Interferometry (InSAR) provides an effective tool to study slow-moving landslides. However, InSAR observations are often contaminated by tropospheric artefacts due to spatial and temporal variations of atmospheric refractivity. Particularly, the topography-dependent stratified delays may introduce seasonal oscillation biases into InSAR-measured deformation time series under steep terrains, which cannot be removed by conventional spatial and temporal filtering. In this study we proposed two complementary approaches to correct the stratified tropospheric delays for time series InSAR analysis when studying single landslides. One is the Iterative Linear Model (ILM) as an improved version of the traditional Linear Model (LM). The other is to fuse tropospheric delays predicted by several global weather models (FDWM) with different temporal intervals and spatial resolutions. Both methods are integrated into the standard Small BAseline Subset (SBAS) time series analysis procedure. We evaluated the proposed methods in three landslide-prone areas in southwest China using Sentinel-1 datasets. The experimental results demonstrated that the ILM method removed the seasonal stratified delays mixed in deformation time series, unaffected by the deforming points. The FDWM method achieved an optimal combination of tropospheric delay predictions by four weather models, i.e. ERA-Interim, ERA5, HRES ECMWF, and MERRA-2. Validations using in-situ GPS measurements suggested that the original Root Mean Squared (RMS) values of interferometric phases declined by more than 35% after both ILM and FDWM corrections. The ILM had better performances than the FDWM to correct stratified delay for single landslides, whereas the FDWM can be an effective alternative when the ILM is inapplicable in case of limited coherent points.

This letter focuses on scattering mechanism classification of polarimetric synthetic aperture radar (PolSAR) images. Scattering mechanism classes are defined as the different combinations of dominant and secondary scattering mechanisms. By analysing the general characteristics of surface scattering, double-bounce scattering and volume scattering, we propose a maximum likelihood classifier to classify PolSAR pixels into nine classes with three PolSAR metrics. The probability density functions of various classes are obtained via extensive simulations. This method is not only a good classification method free of polarimetric decomposition but can also serve as a pre-classification step for advanced classification scheme as well. Furthermore, it is able to simplify incoherent polarimetric decomposition, so that we can employ incoherent scattering models for all components.

In the field of biomass estimation, terrain radiometric calibration of airborne polarimetric SAR data for forested areas is an urgent problem. Illuminated area correction of σ -naught could not completely remove terrain features. Inspired by Small and Shimada, this paper tested gamma-naught on one mountainous forested area using airborne Uninhabited Aerial Vehicle Synthetic Aperture Radar data and found it could remove most terrain features. However, a systematic increasing trend from far range to near range is found in airborne SAR cases. This paper made an attempt to use the relationship between distance to SAR sensor and γ-naught to calibrate γ -naught. Two quantitative evaluation methods are proposed. Experimental results demonstrate that variation of γ -naught can be constrained to a limited extent from near range to far range. Since this method is based on ground range images, it avoids complicated orthorectification.

Multiple InSAR techniques are increasingly being developed for earth observation. However, among them, persistent scatterers-based InSAR (PSI) techniques fail to obtain enough measurement points (MPs) in rural mountainous area due to the lack of persistent scatterers (PSs), such as ma-made structures, rocks, and outcrops, etc. In this paper, Distributed Scatterers-based InSAR (DS-InSAR) algorithm, exploiting both persistent scatterers and distributed scatterers (DSs) widely spreading in rural areas, is proposed to make up the limitation of persistent scatterers-based technique when monitoring mountainous landslides. There are two key steps to preprocess DSs in DS-InSAR algorithm: selecting DS candidates and estimating optimal interferometric phases. The selected DSs and PSs are combined for further processing using traditional PSI procedure. A qualitative and quantitative simulation analysis was operated to validate the DS-InSAR algorithm. Then, both PSI and DS-InSAR were implemented to monitor Xishan landslide in western mountainous region of Sichuan province, based on high-resolution TerraSAR-X images. The obtained results demonstrate that DS-InSAR could detect much more MPs and provide more reliable deformation information.

Differential morphological profiles (DMPs) are widely used for the spatial/structural feature extraction and classification of remote sensing images. They can be regarded as the shape spectrum, depicting the response of the image structures related to different scales and sizes of the structural elements (SEs). DMPs are defined as the difference of morphological profiles (MPs) between consecutive scales. However, traditional DMPs can ignore discriminative information for features that are across the scales in the profiles. To solve this problem, we propose scale-span differential profiles, i.e., generalized DMPs (GDMPs), to obtain the entire differential profiles. GDMPs can describe the complete shape spectrum and measure the difference between arbitrary scales, which is more appropriate for representing the multiscale characteristics and complex landscapes of remote sensing image scenes. Subsequently, the random forest (RF) classifier is applied to interpret GDMPs considering its robustness for high-dimensional data and ability of evaluating the importance of variables. Meanwhile, the RF “ out-of-bag ” error can be used to quantify the importance of each channel of GDMPs and select the most discriminative information in the entire profiles. Experiments conducted on three well-known hyperspectral data sets as well as an additional WorldView-2 data are used to validate the effectiveness of GDMPs compared to the traditional DMPs. The results are promising as GDMPs can significantly outperform the traditional one, as it is capable of adequately exploring the multiscale morphological information.

This paper reviewed the developments of the last ten years in the field of international high-resolution earth observation, and introduced the developmental status and plans for China’s high-resolution earth observation program. In addition, this paper expounded the transformation mechanism and procedure from earth observation data to geospatial information and geographical knowledge, and examined the key scientific and technological issues, including earth observation networks, high-precision image positioning, image understanding, automatic spatial information extraction, and focus services. These analyses provide a new impetus for pushing the application of China’s high-resolution earth observation system from a “quantity” to “quality” change, from China to the world, from providing products to providing online service.

Flooding in general is insignificant event worldwide and also in Thailand. The Central plain, the Northern plain and the northeast of Thailand are frequently flooded areas, caused by yearly monsoons. The Thai government has extra expenditure to provide disaster relief and for the restoration of flood affected structures, persons, livestock, etc. Current flood detection in real time or near real time has become a challenge in the flood emergency response. In this paper, an automatic instant time flood detection approach consisting of a data retrieval service, flood sensor observation service (SOS), flood detection web processing service (WPS) under a sensor web environment, is presented to generate dynamically real-time flood maps. A scenario of a RADARSAT and MODIS sensor web data service for flood detection cover of the Thailand Central plain is used to test the feasibility of the proposed framework. MODIS data are used to overview the wide area, while RADARSAT data are used to classify the flood area. The proposed framework using the transactional web coverage service (WCS-T) for instant flood detection processes dynamic real-time remote sensing observations and generates instant flood maps. The results show that the proposed approach is feasible for automatic instant flood detection.

Recent advances in sensor web geospatial data capture, have led to the generation of large numbers of real-time or near-real-time observations and measurements. As the magnitude of Web Coverage Service (WCS) and Sensor Observation Service (SOS) becomes increasingly large, a major problem is how to make fast and fully robust use of such data services in a Web-ready sensors environment. A new method is proposed, Real-time Coverage Service (RCS), for serving observational data based on the integration of WCS and SOS. The RCS method hides the complexity of a series of information models and service interfaces in the Earth Observation (EO) and Sensor Web world, allowing near-real-time on-demand access to geospatial observations. The core components-dynamical schema transformer and automatic information extractor-are designed and implemented based on service middleware technology. The Observations & Measurements (O&M) schema of SOS and coverage schema of WCS are matched by schema transformer dynamically. The coverage information is extracted from a SOS “GetObservation” operation by an information extractor and served by a WCS “GetCoverage” operation on-demand. Experiments on the feasibility of Earth Observing-1 (EO-1) Hyperion data retrieval for the RCS method and OGC Sensor Web SOS method were conducted and their efficiency compared. The results show that the proposed RCS has architecture that is more robust and performs more efficiently than the SOS method.

Aiming at the storage and management problems of massive remote sensing data, this paper gives a comprehensive analysis of the characteristics and advantages of thirteen data storage centers or systems at home and abroad. They mainly include the NASA EOS, World Wind, Google Earth, Google Maps, Bing Maps, Microsoft TerraServer, ESA, Earth Simulator, GeoEye, Map World, China Centre for Resources Satellite Data and Application, National Satellite Meteorological Centre, and National Satellite Ocean Application Service. By summing up the practical data storage and management technologies in terms of remote sensing data storage organization and storage architecture, it will be helpful to seek more suitable techniques and methods for massive remote sensing data storage and management.

Recent advances in Sensor Web geospatial data capture, such as high-resolution in satellite imagery and Web-ready data processing and modeling technologies, have led to the generation of large numbers of datasets from real-time or near real-time observations and measurements. Finding which sensor or data complies with criteria such as specific times, locations, and scales has become a bottleneck for Sensor Web-based applications, especially remote-sensing observations. In this paper, an architecture for use of the integration Sensor Observation Service (SOS) with the Open Geospatial Consortium (OGC) Catalogue Service-Web profile (CSW) is put forward. The architecture consists of a distributed geospatial sensor observation service, a geospatial catalogue service based on the ebXML Registry Information Model (ebRIM), SOS search and registry middleware, and a geospatial sensor portal. The SOS search and registry middleware finds the potential SOS, generating data granule information and inserting the records into CSW. The contents and sequence of the services, the available observations, and the metadata of the observations registry are described. A prototype system is designed and implemented using the service middleware technology and a standard interface and protocol. The feasibility and the response time of registry and retrieval of observations are evaluated using a realistic Earth Observing-1 (EO-1) SOS scenario. Extracting information from SOS requires the same execution time as record generation for CSW. The average data retrieval response time in SOS+CSW mode is 17.6% of that of the SOS-alone mode. The proposed architecture has the more advantages of SOS search and observation data retrieval than the existing sensor Web enabled systems.

Various methods have been developed to detect and assess building’s damages due to earthquakes using remotely sensed data. After the launch of the high resolution sensors such as IKONOS and QuickBird, it becomes realistic to identify damages on the scale of individual building. However the low accuracy of the results has often led to the use of visual interpretation techniques. Moreover, it is very difficult to estimate the degree of building damage (e.g. slight damage, moderate damage, or severe damage) in detail using the existing methods. Therefore, a novel approach integrating LiDAR data and high resolution optical imagery is proposed for evaluating building damage degree quantitatively. The approach consists of two steps: 3D building model reconstruction and rooftop patch-oriented 3D change detection. Firstly, a method is proposed for automatically reconstructing 3D building models with precise geometric position and fine details, using pre-earthquake LiDAR data and high resolution imagery. Secondly, focusing on each rooftop patch of the 3D building models, the pre- and post-earthquake LiDAR points belonging to the patch are collected and compared to detect whether it was destroyed or not, and then the degree of building damage can be identified based on the ratio of the destroyed rooftop patches to all rooftop patches. The novelty of the proposed approach is to detect damages on the scale of building’s rooftop patch and realize quantitative estimation of building damage degree.

Aiming at object fragmentation and poor detection results caused by discontinuous segmentation scale in object-level change detection, a new object-level change detection method based on the full-scale object tree is presented in this paper. The core idea of this new algorithm is to establish the full-scale object tree based on convexity model theory and integrate full-scale image segmentation techniques and change detection into the whole process. Some Wenchuan Earthquake images are taken as an example to discuss the new method for earthquake damage detection and evaluation in urban area, landslide detection, and extraction of barrier lake boundary. The application shows that the new method is robust and it provides an advanced tool for the quantitative detection and evaluation of earthquake damage.

In this paper, an unsupervised change-detection approach was proposed to detect new urban areas from multi-temporal SAR images. The novelty of the proposed approach is the joint use of coherence and intensity characteristics of SAR imagery. The approach involves two main steps: (a) the extraction of difference feature containing information on changed areas, and (b) the unsupervised two-dimensional (2D) thresholding. First, two difference features based on the concepts of long-term coherence and backscattering temporal variability are extracted from a series of multitemporal SAR images. Then, the resulting features that represent the INSAR signal temporal variability of changed areas are merged, and a 2D thresholding technique based on the maximum 2D Renyi’s entropy criterion is developed to obtain the change-detection results. The effectiveness of the proposed approach is confirmed with experimental results obtained from a set of six ERS-1/2 SLC SAR images acquired in Shanghai, China.

We used publicly available digital spatial datasets to study the area extents and their horizontal variations of two water bodies within the Danjiangkou Reservoir, China. Between 2003 and 2005, the water levels varied from 140 to 149 m above mean sea level as measured by the Geoscience Laser Altimeter System (GLAS). Detailed procedures to derive the horizontal extents and variations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) coupled with GLAS data and to verify the extents and variations spatially were provided. For the water bodies on the north and west, the surface water extents derived from four MODIS images varied between 174 and 218 km2 and from 96 to 135 km2, respectively. The extents by inundating the DEM using the GLAS data were 178-212 km2 for the water body on the north and 104-118 km2 for the water body on the west. The spatial verifications of surface water extents derived from the MODIS images versus DEM coupled with GLAS data agreed 83-93%. Within the ring areas between water/land boundaries at elevations of 140 and 147 m, and 140 and 149 m, the spatial agreement was 52-75%.

Remote sensing information services have characteristics of multi-dimension, heterogeneity, and high dynamic performance, so it is very difficult to realize online search by use of UDDI registry of business search engines. It has become a bottle-neck restricting the farther development of remote sensing information services. Considering the characteristics of remote sensing information services, the paper introduces remote sensing information ontologies into the online search of the remote sensing information services and proposes a semantics clustering based and multi-QoS (Quality of Service) constraints method to improve the performance of remote sensing information services discovery. Experimental results show that the method proposed in the paper can attain well search performance.

This paper provides a new solution to manage and display the massive LiDAR point cloud data based on virtual global platform, either airborne or terrestrial LiDAR data, which helps understanding the overall conditions of the collected data from different sources. Given overall consideration of management and virtualization, the study designs the out-of-core multi-resolution data structures. The structures for airborne and terrestrial LiDAR data are discussed separately, due to the different ways of data acquisition and different scales. The data structures are used to implement a LiDAR data management and display module in the virtual earth system - GeoGlobe, which is a spatial information sharing platform developed by Wuhan University.

With the continuously increment of the available amount of geospatial data, a huge and scalable data warehouse is required to store those data, and a web-based service is also highly needed to retrieve geospatial information. The emergence of Cloud Computing technology brings a new computing information technology infrastructure to general users, which enable the users to requisition compute capacity, storage, database and other service. The Web Coverage Service supports retrieval of geospatial data as digital geospatial information representing space and time varying phenomena. This paper explores the feasibility of utilizing general-purpose cloud computing platform to fulfill WCS specification through a case study of implementing a WCS for raster image on the Amazon Web Service. Challenges in enabling WCS in the Cloud environment are discussed, which is followed by proposed solutions. The resulting system demonstrates the feasibility and advantage of realizing WCS in Amazon Cloud Computing platform.

Earth Observation interacts with space, remote sensing, communication, and information technologies, and plays an increasingly significant role in Earth related scientific studies, resource management, homeland security, topographic mapping, and development of a healthy, sustainable environment and community. Geospatial Technology for Earth Observation provides an in-depth and broad collection of recent progress in Earth observation. Contributed by leading experts in this field, the book covers satellite, airborne and ground remote sensing systems and system integration, sensor orientation, remote sensing physics, image classification and analysis, information extraction, geospatial service, and various application topics, including cadastral mapping, land use change evaluation, water environment monitoring, flood mapping, and decision making support. Geospatial Technology for Earth Observation serves as a valuable training source for researchers, developers, and practitioners in geospatial science and technology industry. It is also suitable as a reference book for upper level college students and graduate students in geospatial technology, geosciences, resource management, and informatics.

An algorithm for satellite tracking and orbit prediction is presented in detail. Firstly, the satellite's position and velocity are calculated by the Simplified General Perturbations Version 4 and Simplified Deep-space Perturbation Version 4 (SGP4/SDP4) orbit propagation algorithms. And we put forward "Two-Point-Pair" which means two points that the CCD of satellite's IFOV rays intersecting with the earth. We make use of the "Two-Point-Pair" to calculate the accuracy bounding box of satellite at the instantaneous time through the satellite's position and velocity above. Besides, we build a system called GeoGlobe to simulate the algorithm. The system can be used to simulate all kinds of satellites that the Two-Line Element (TLE) Sets files can provide.

In this research, we focus on the spatial pattern of the urban expansion. The spatial pattern of the urban area can be quantitatively delineated by many spatial variables. Numerous spatial variables have been examined to evaluate their applicability to the urban change. These metrics include road network accessibility, built-up density and some landscape metrics. Remote sensing technology was used for monitoring dynamic urban change. Multi-temporal Landsat TM images (1988, 1991, 1994, 1997, 2000, and 2002) were used for the change detection using post-classification comparison method. The road network and its change were extracted from multitemporal images using the GDPA algorithm. Contagion, one of the landscape metrics, was selected, because it it can describe the heterogeneity of the suburban area, where the landuse change is most likely to happen. Analysis has also been conducted to identify the relationship between urban change and these spatial variables.

A simulation system is developed for satellite tracking and orbit prediction. The program displays the location of Earth satellites and predicts the location of any satellite at any time. And the simulation of satellite tracking and orbit prediction take the swaths into consideration. The orbit prediction model and Two-Line Element (TLE) Sets are present first, and the Simplified General Perturbations Version 4 and Simplified Deep-space Perturbation Version 4 (SGP4/SDP4) orbit propagation algorithms are introduced then. In the end, we introduce how we build our system in detail. In the end we present the comparison of the prediction result between the system of ours and STK's. The result shows that SGP4/SDP4 are efficient and valid in our system.