As a supplement to time-series interferometric synthetic aperture radar (TS-InSAR), time-series multiple-aperture InSAR (TS-MAI) can measure the spatiotemporal changes in SAR along-track surface deformation. TS-MAI is often applied with low-frequency SAR data (e.g., L-band data) due to its ability to retain high interferometric coherence. However, the low-frequency SAR signal is vulnerable to ionospheric delays, which can significantly degrade the measurement accuracy of TS-MAI. This letter presents an approach to correct the ionospheric errors in TS-MAI. A polynomial cubic model is employed to constrain the ground deformation, which is then incorporated into the observation model for effectively separating the deformation signal and the ionospheric delays. The proposed method is tested using the L-band ALOS-1 PALSAR-1 datasets covering the Tocopilla area in Chile between November 2007 and March 2011. The correction performance and accuracy of the proposed method are demonstrated by comparing the range split-spectrum interferometry (RSSI)-based method and the local GPS data, respectively. The root mean square error (RMSE) improvement rates between TS-MAI and GPS are 72.17% for the SRGD site and 84.51% for the VLZL site, and their correlation coefficients increase from 0.23 and 0.50 to 0.52 and 0.61 after the correction.

Ionospheric phase delay is a critical error source in Time Series Interferometric Synthetic Aperture Radar (TS-InSAR) for the purpose of monitoring ground surface deformation with SAR data obtained from low-frequency radar systems. Recently, the Range Split-Spectrum Interferometry (RSSI) method has been employed to estimate and rectify ionospheric errors in TS-InSAR. However, the performance of the RSSI method is largely restricted by the significant linear scale factors resulting from the current small SAR bandwidth. In this study, we propose a Reformulating RSSI (Re-RSSI)-based method for correcting the ionospheric error in TS-InSAR by optimizing the linear scale factors, with the aim of improving the accuracy of TS-InSAR measurements. We evaluate the Re-RSSI method using 121 ALOS-1 PALSAR images that cover two distinct regions: the low-latitude Lazufre volcano region and the high-latitude Anaktuvuk River tundra fire region. Our results demonstrate that the Re-RSSI method can effectively remove time series ionospheric errors at both test sites, where we detected ionospheric delays of approximately 2.5 cm/yr and 2.0 cm/yr, respectively. Using Global Navigation Satellite System (GNSS) measurements as ground truth, we achieved an 86.59% improvement rate in root mean square error (RMSE) with the Re-RSSI method, which is significantly higher than the 66.40% improvement rate achieved with the traditional RSSI method.

Virtual geographic environment (VGE) has experienced a more than 20-year development, which evolved from an idea to an independent geographic research field. The theoretical system of VGE has gone through a leap from theory to practice, from simple application to a large-scale engineering project. Now it has become an essential tool for studying geographic issues and is going towards geographic cognition. With the development of big data, VR technology, 5G technology, artificial intelligence technology, digital twin technology and metaverse, VGE as a platform for geographic scientific research is becoming more and more powerful. Still, the present VGE cannot reach the real-time and simulation requirements. To maintain the advancement of the theory of virtual geographic environment in the age of ever-changing technology, a new framework of twin virtual geographic environment (TVGE) has been proposed to support the sustainable development of the VGE theoretical system. In this context, we elaborate the TVGE framework, including definition, reasoning, characteristics, version management, and geographic cognition, which updates the VGE theory with the emerging technologies and lays the foundation for the VGE platform implementations.

The multiscale geographically weighted regression (MGWR) model is a useful extension of the geographically weighted regression (GWR) model. MGWR, however, is a kind of Nadaraya–Watson kernel smoother, which usually leads to inaccurate estimates for the regression function and suffers from the boundary effect. Moreover, the widely used calibration technique for the MGWR with a back-fitting estimator (MGWR-BF) is computationally demanding, preventing it from being applied to large-scale data. To overcome these problems, we proposed a local linear-fitting-based MGWR (MGWR-LL) by introducing a local spatially varying coefficient model in which coefficients of different variables could be characterised as linear functions of spatial coordinates with different degrees of smoothness. Then the model was calibrated with a two-step least-squared estimated algorithm. Both simulated and actual data were implemented to validate the performance of the proposed method. The results consistently showed that the MGWR-LL automatically corrected for the boundary effect and improved the accuracy in most cases, not only in the goodness-of-fit measure but also in reducing the bias of the coefficient estimates. Moreover, the MGWR-LL significantly outperformed the MGWR-BF in computational cost, especially for larger-scale data. These results demonstrated that the proposed method can be a useful tool for the MGWR calibration.

Hyperspectral image (HSI) denoising has been regarded as an effective and economical preprocessing step in data subsequent applications. Recent nonlocal low-rank approximation on each full band patch group has demonstrated their superiority for HSI denoising. These methods, however, directly design the low-rank regularization to the grouped patch image itself (i.e., original domain), which ignores the spatial information of the grouped patch image and cannot explores the potential structure. To address these issues, this paper proposes a nonlocal group sparsifying transform learning method (dubbed TLNLGS) for HSI denoising. Motivated by the global spectral correlation in the HSI, we firstly impose a certain low-dimensional subspace hypothesis over the HSI to prevent the heavy computation burden with the spectral band increases, and then explore a discriminatively intrinsic nonlocal group sparse prior of the reduced image by transform model. The learned group sparse prior can not only excavate the nonlocal self-similarity as recent nonlocal low-rank approximation methods but also preserve the local spatial smooth structure of the image. Moreover, compared with the fixed transform domain (e.g., gradient and discrete cosine transformation domains), the transform learning scheme can improve the sparse representation ability. An efficient block coordinate descent (BCD) algorithm is developed to solve the proposed model. Extensive experiments, including simulated and real HSI datasets, indicate the superiority of the proposed TLNLGS method over the state-of-the-art HSI denoising approaches.

Sonneratia have posed a threat to native mangrove species in Hong Kong. Early detection of individual Sonneratia when they are introduced and naturalized before invasion is essential for native mangrove species protection, especially for Sonneratia with a strong ability of propagation. This letter aims to provide an effective way to the accurate detection of individual Sonneratia. Specifically, using very high spatial resolution remotely sensed data, we adapt the RetinaNet, incorporating multiscale features for sapling detection and convolutional neural networks for detecting the Sonneratia distributed scatteredly among native species. The Sonneratia were detected with a higher mean average precision (mAP) 0.50 of 0.3891 with a precision of 0.5465 than that from the deformable part model. In addition, 3678 Sonneratia were detected at early stage. This letter can support the government for mangrove forest management and offer a scientific guidance for adequate response to the species invasion, like annual removal of Sonneratia, and then reduce the consumption of labor and time over a large scale. In addition, it can provide a quantitative survey for Sonneratia management.

High-resolution urban impervious surface (UIS) is essential for social and environmental analysis. However, shadows have become a major challenge to the accurate UIS mapping in high-resolution optical images, as the low reflectance usually leads to misclassification of shadows as roads or waters. To solve this problem, we proposed a shadow free multisource stack sparse autoencoder (ShdFree-MS-SSAE) for urban shadow detection and compensation. Multisource data, including optical, SAR and LiDAR were used for the occlusion information recover. First, MS-SSAE was proposed for urban land cover classification, including shadow and non-shadow area. Then, shadow area in optical data was enhanced with a linear compensation method. Finally, MS-SSAE was applied to classify the enhanced shadow area and the non-shadow area. The results demonstrated that ShdFree-MS-SSAE framework was effective for UIS mapping, with an average improvement of 10%.

Zonation of mangrove species is the predictable and discrete ordering of mangrove species caused by a unique, intertidal environment. Mangrove zonation pattern is formed by complex abiotic and biotic environmental factors and, at the same time significantly influences the associate flora and fauna communities even the whole coastal ecosystem and local carbon cycle. In this study, high resolution time-series satellite images were employed to investigate the characteristics and inner structures of mangrove zonation using landscape metrics during the past decade in the Deep Bay area, Hong Kong SAR and Shenzhen, China. Both native and exotic mangrove species were discriminated and analyzed. The results of this study shown that native mangrove stands in the Deep Bay showed relatively clearer zonation during the past decade, presenting a sequence of the species in this tide-dominated shore with higher aggregation and reunion degree. In comparison, the distribution pattern of exotic species is in higher degree of fragmentation and less connectivity. The patch-based landscape metrics were proven to be effective methods in measuring and describing the spatial distribution pattern of mangrove zonation based on high resolution satellite images.

Great strides have been made in Geographic Information Systems (GIS) research over the past half-century. However, this progress has created both opportunities and challenges. From a geographic perspective, certain challenges remain, including the modelling of geographic-featured environments with GIS data model, the enhancement of GIS’s analysis functions for comprehensive geographic analysis and achieving human-oriented geographic information presentation. Several basic theoretical and technical ideas that follow the workflow and processes of geographic information induction, geographic scenario modelling, geographic process analysis and geographic environment representation are proposed to fill the gaps between GIS and geography. We also call for designing methods for big geographic data-oriented analysis, making best use of videos and developing virtual geographic scenario-based GIS for further evolution.

Atmospheric artifacts and phase unwrapping errors have unfavorable effects on differential synthetic aperture radar interferometry (DInSAR) deformation monitoring. In this letter, we present an alternative small-baseline DInSAR inversion algorithm, velocity-constraint L1-norm minimization. The proposed algorithm improves the robustness of time series deformation estimation by combining a smoothing constraint and L1-norm minimization. The smoothing constraint can minimize temporal atmospheric artifacts, and the L1-norm minimization outperforms L2-norm minimization in the presence of phase unwrapping errors. The iteratively reweighted least square algorithm is employed to adjust the weights of DInSAR observations and smoothing constraints in L1-norm minimization. The proposed algorithm is validated using simulated data and TerraSAR-X data. The experimental results show that the proposed algorithm is suitable for the inversion of approximately linear deformation processes affected by both atmospheric artifacts and unwrapping errors.

Not only the urbanization development but also its ecological process lays emphasis on the Impervious Surface Area (ISA) extraction, whereas, the ISA extraction from high-resolution images is challenging for both the phenomenon of the mixed pixels and shadow effects. To solve the problem, a Multi-Source Dictionary Sparse Representation Classification (MSD-SRC) method using WorldView-2 and TerraSAR-X dataset is proposed. First, it uses multi-source data and fuzzy samples by Low Pass Filtering (LPF) to solve the problem of road and building misclassification; second, learning Multi-Source Dictionary for non-shadow and shadow classes, then using discriminative sparse coding method for classification, therefore to reduce shadow effects and improve the ISA extraction accuracy. Experimental results demonstrated the effectiveness of the proposed method.

Incorporating optical and SAR data to estimate impervious surface is useful but challenging due to their different geometric imaging mechanism. The recent development of deep convolutional networks (DCN) opens a promising opportunity. In this study, the typical DCN, AlexNet, was modified to estimate the impervious surface from optical and SAR data. GoogLeNet and the Support Vector Machine (SVM) were employed for comparison. Experimental results indicated the effectiveness of AlexNet with an accuracy of over 99%, outperforming both GoogLeNet and SVM. Furthermore, 60~80% of training samples outperformed the results from the whole training set under certain number of epochs, indicating that large number of training samples may not necessarily produce better results, depending on other factors (e.g. number of epochs). Generally, AlexNet was able to fuse the optical and SAR data and improved the accuracy of estimating impervious surface by about 2% compared with that using optical data alone.

In this paper, we extend the previously proposed Tomo-PSInSAR method to detect homogeneous objects in the urban environment. Tomo-PSInSAR integrates conventional persistent scatterer (PS) interferometry and multidimensional SAR tomography to monitor complex built environments [1]. It can jointly detect single and overlaid PSs by constructing a two-tier hierarchical network. Robust estimators (M-estimator and ridge estimator) are introduced to improve the robustness of estimation. To monitor the semi-artificial regions (e.g, pavements and small grassed lands) that are normally distributed scatterers (DSs) in SAR images [2], we analyze homogeneous pixels on the basis of Tomo-PSInSAR. Before estimating the geophysical parameters, we perform a two-sample Anderson-Darling test for the identification of statistically homogeneous pixels at the stage of interferometry. In the first-tier network, the most reliable PSs are identified and they will be used as reference points in the second-tier network. In the second-tier network, the geophysical parameters (e.g., height, deformation velocity) of overlaid PSs are estimated using tomographic imaging [3] and the geophysical parameters of DSs are estimated using the Capon-Beamforming algorithm [4]. The removal of atmospheric delay in the second-tier network is accomplished by subtracting the phase of adjacent PSs that are detected in the first-tier network. In this sense, the proposed integrated method as shown in Fig. 1 can jointly monitor single PSs, overlaid PSs, and DSs according to specific cases. TerraSAR-X/TanDEM-X images are used to validate this method. The results are shown in Fig. 2-4.

In this paper, we develop a SAR tomography-based persistent scatterer interferometry (Tomo-PSInSAR) method to detect single and double persistent scatterers (PSs) in urban built environments. By constructing a two-tier network, we can jointly detect single and double PSs with no need for preliminary removal of the atmospheric phase screen (APS) in the whole area. This technique is more applicable in high-rise built environments (e.g., Hong Kong) with cloudy and rainy weather where there is much uncertainty when removing the APS. In the first-tier network, we aim to detect the most reliable single PSs (SPSs) by constructing a Delaunay triangulation network. To improve the robustness of estimation, we combine beamforming with an M-estimator for parameter estimation at the arcs, and introduce a ridge-estimator for network adjustment. In the second-tier network, we detect the remaining SPSs and all of the double PSs (DPSs) by constructing local star networks that use the SPSs detected in the first-tier network as reference points. To simplify the detection of DPSs, we employ a local maximum ratio (LMR) method for extracting overlaid DPSs. Finally, TerraSAR-X images are used to validate the Tomo-PSInSAR method.

In this paper, we develop a synthetic aperture radar (SAR) tomography-based persistent scatterer interferometry (Tomo-PSInSAR) method to detect single and double persistent scatterers (PSs) in urban built environments. By constructing a two-tier network, we can jointly detect single and double PSs with no need for preliminary removal of the atmospheric phase screen (APS) in the whole area. This technique is more applicable in high-rise built environments (e.g., Hong Kong) with cloudy and rainy weather, where there is much uncertainty when removing the APS. In the first-tier network, we aim to detect the most reliable single PSs (SPSs) by constructing a Delaunay triangulation network. To improve the robustness of estimation, we combine beamforming with an M-estimator for parameter estimation at the arcs and introduce a ridge estimator for network adjustment. In the second-tier network, we detect the remaining SPSs and all of the double PSs (DPSs) by constructing local star networks that use the SPSs detected in the first-tier network as reference points. To simplify the detection of DPSs, we employ a local maximum ratio method for extracting overlaid DPSs. Finally, 56 Hong Kong TerraSAR-X images are used to validate the Tomo-PSInSAR method.

L1 minimization has proven to be useful for tomographic synthetic aperture radar (SAR) imaging because it has super-resolution capability and produces no sidelobes. However, it cannot always derive the sparsest solution and often yields outliers in recovery. Consequently, it is usually difficult to extract true persistent scatterers straightforwardly in practice. To enhance the sparsity, we introduce iterative reweighted L1 minimization for sparse inversion. The weight factor is computed in each iteration, according to the previous tomographic magnitude to establish a more democratic penalization rule. Our simulation results indicate that the reweighted algorithm can achieve perfect recovery when noise is lower. Specifically, when the signal-to-noise ratio is equal to 5 dB, two reweighted iterations can improve the probability of true sparsity from 29.2% to 99.8% for single scatterers and from 0.2% to 95.4% for double scatterers. Due to the enhanced sparsity, we can directly identify scatterers without the need for further model selection. The method is validated using 44 TerraSAR-X/ TanDEM-X images. Single and double scatterers are detected in urban areas. Verification using light detection and ranging (LiDAR) data indicates that we achieve submeter accuracy of the height estimates.

We investigated the joint use of the high-resolution WorldView-2 optical satellite images and the multitemporal TerraSAR-X synthetic aperture radar (SAR) satellite images to extract building height information in high-density urban areas. The main idea of the proposed fusion approach is to take full advantage of both data sets in building height retrieval. The proposed approach includes two main stages. First, initial building height estimates are extracted from WorldView-2 stereo images and multitemporal SAR images. These initial results are then combined using a novel object-based fusion approach, in which the heights of points for the same building footprint are retrieved and integrated. Experiments on the Mong Kok area of Hong Kong showed that the proposed approach using both data sets outperforms the use of either stereo images or SAR images alone. According to the results of the proposed approach, the average absolute height retrieval error is 6.53 m, which is much lower than using stereo and SAR images (9.08 and 12.24 m, respectively). The proposed fusion approach is suitable for building height retrieval in urban areas where single satellite data have limitations.

Land use/land cover (LULC) classification using optical and synthetic aperture radar (SAR) remote sensing images is becoming increasingly important to produce more accurate LULC products. As an important step, feature normalization techniques have been studied by the areas of pattern recognition. Nevertheless, because of the totally different imaging mechanisms of optical and SAR sensors, most of the existing normalization approaches are not suitable for optical and SAR data fusion. Moreover, whether normalization is a significant step remains unclear regarding optical and SAR fusion. Taking the Satellite Pour l'Observation de la Terre (SPOT-5) and the Advanced Land Observing Satellite (ALOS)/Phased Array type L-band SAR (PALSAR) (HH and HV polarizations) as the optical and SAR data, this letter aims to evaluate the impact of feature normalization. Experimental results indicated that feature normalization is not necessarily significant depending on fusion methods. For instance, distribution-dependent classifiers (e.g., a maximum likelihood classifier) are independent of feature normalization; thus, it has no impact on the results when using these classifiers. Moreover, advanced classifiers (e.g., a support vector machine) with built-in normalization are also not influenced by feature normalization. In contrast, a minimum distance classifier and an artificial neural network (ANN) depend on the input values of optical and SAR features and thus can be influenced by feature normalization. However, our experiments showed a fluctuation in classification accuracy using an ANN with normalized features. Therefore, more experiments are required to investigate the optimal normalization approaches for the optical and SAR images when using an ANN as the fusion method.

Geladandong Ice Field is one of the largest ice fields in central Qinghai-Tibetan Plateau, also the water source of Yangtze River, Selin Co Lake and Chibozhang Co Lake. In this study, we aim to monitor the glacier flow rates and its change in 1990s and 2000s by Differential SAR Interferometry and offset tracking. We obtained SAR images acquired by ERS-1/2 and Envisat/ASAR in 1990s and 2000s then processed them with offset-tracking method, also validated with D-InSAR method. The result indicates that most glaciers in Geladandong Ice Fields kept stable flow rates at 15-30m/a during 1990s and 2000s. Some glaciers are identified as surge type. We selected two glaciers and studied the time series of flow velocity profiles. During the surging period, flow rate could be 3 to 10 time as normal years. These two glaciers forwarded their terminus during their surging period by Landsat optical monitoring. We concluded that during 1990s and 2000s most glaciers kept a stable flow velocity and some glacier surged during the study period therefore terminus forwarding cannot equal to mass gaining.

Polarimetric features retrieved from CTLR (circularly transmit and linearly receive) Synthetic Aperture Radar (SAR) data was analysed in details. A new parameter, namely, Damping Status Sensitivity Index (DSSI) was proposed for quantitatively evaluating the PolSAR characteristics' capability of discriminating different damping patterns of oil slicks and clean seawater. The L-band Uninhabited Aerial Vehicle SAR (UAVSAR) data was utilized in the experiments. It was shown that polarimetric characteristics retrieved from CTLR compact polarimetric SAR data were nearly as same as those derived from fully polarimetric SAR data and can be applied for discriminating different damping status of oil spill and look-likes.

In this paper a polarimetric SAR data study is undertaken over the critical and peculiar case of the Deepwater Horizon (DWH) oil spill accident occurred in the Gulf of Mexico in 2010. It is a complex case that still calls for deeper physical/chemical characterization due to its special nature. Such an oil spill accident is in fact related to a deep/ultra-deep sea drilling, that although of paramount interest in gas and oil exploitation, need special care and safety actions to manage high oil pressure and potential vast environmental oil spill long-term and short-term impact.

Synthetic Aperture Radar (SAR) data has been reported to be able to provide complementary information towards optical remote sensing data for improving the urban impervious surface estimation. However, most existing researches were focused on using only single polarization SAR data. This study presents a preliminary experiment on the combined use of multispectral optical data and dual polarization SAR data for impervious surfaces estimation. Experimental results using SPOT-5 and ALOS PALSAR images showed a consistent result compared with our previous result using single polarization SAR data. Comparison results showed that not every polarimetric feature was able to provide positive effect to the impervious surfaces estimation. Compared with using only optical and SAR data, the separated HH and HV polarization data provided a positive effect to the result by improving the accuracy. The incorporation of both Entropy and Alpha features were also able to improve the accuracy. However, the HH/HV ratio and the separated use of Entropy did not provide positive results. A combination of all the features turned out to obtain the highest accuracy compared with using a subset of the features.

Transportation of dangerous goods (DGs) can significantly affect human life and the environment if accidents occur during the transportation process. Therefore, safe DG transportation is of vital importance, especially in high-density living environments. Effective routing of DG shipments is thus essential to the lowering of risk associated with DG transportation. DG routing is inherently a multicriteria, multiobjective problem in which various factors, such as cost, safety, public and environmental exposure, need to be simultaneously considered. We develop in this paper a multiobjective genetic algorithm (MOGA) for the determination of optimal routes for DG transportation under conflicting objectives. Implemented within the geographical information system environment, the MOGA approach is applied to the transportation of liquefied petroleum gas in the road network of Hong Kong. Experimental results in this case study substantiate the conceptual arguments and demonstrate the good performance of the proposed approach.

Spatial index is a key component of Geographic Information Systems (GISystems). To date, an increasing number of spatial indexes have been developed to enhance the efficiency of spatial analysis and spatial query. Approximate expressions are adopted in the foundation of spatial index construction, to assist data organisation, e.g., bounding box of vector elements can be employed to build R-tree index. However, R-tree index using such bounding box expresses elements approximately, which usually results in redundancy and excessiveness due to inherent roughness of this method. This study proposes a characteristic bitmap coding method, termed the QCODE method, which generates approximate expressions of vector elements based on self-adaptive gridding. Based on the sizes of vector elements, this method selects the grid at an appropriate level in a self-adaptive manner, discretises the vector elements into grids through a rasterisation operation, as well as compresses and encodes this information as the code of a characteristic bitmap, i.e., the QCODE. The ‘bit’ data type is used in the design of QCODE to restrict the approximate expression of vector elements into finite bytes, providing more precise filtering as compared to the case in which only bounding box is used. With its distinct characteristics, the QCODE is introduced for the improvement of R-tree index. The results of experiments show that, combined with R-tree index, this method can reduce the filtering amount of vector elements as required for spatial analysis, and accelerate the execution efficiency of the entire process of GIS spatial analysis.

In this study, the permafrost evolution in Tibetan Plateau (TP) was monitored through the detection of the overlaying active layer motion using space-observed Persistent Scatterers SAR Interferometry (PSI). Two bands SAR data, 38 scenes of C-band Envisat ASAR and 19 scenes of L-band ALOS PALSAR data were employed. The small baseline strategy was introduced to minimize the seasonal decorrelation effect. The derived surface motions (primarily in the range of -20 mm/yr to 20 mm/yr) over Beiluhe section, Qinghai, China indicated that human activities aggravate the surface motions, e.g. the construction and operation of Qinghai-Tibet Railway (QTR). SAR interferometry-derived results were firstly cross-compared, and then further validated by the precise leveling data.

Permanent Scatterers (PS) based on Synthetic Aperture Radar (SAR) data has been approved to be a feasible way to detect and monitor wide area ground subsidence at a low cost. Classical PS algorithms are focused on exploring the point-like radar targets while Quasi-PS (QPS) technique was proposed to manage the detection of both distributed and decorrelating targets. In this work, we explore the potential ability of PS and QPS analysis for subsidence monitoring with L- and X-band. A case study was conducted in Tianjin suburbs and the exploited SAR datasets were composed of 23 ALOS images acquired from 2009/4/24 to 2010/10/28 and 37 TerraSAR-X images from 2009/4/29 to 2010/11/11. The average subsidence velocity map and displacement history have been retrieved by PS and QPS analysis and the validation indicates good agreement between INSAR time series results and leveling data.

TerraSAR X-band (TSX) data can provide a high resolution of 1m, short revisit period of 11 days, and sensitive ground subsidence information. Permanent Scatterers (PS) technology was developed as a powerful tool for extracting the ground displacement information from TSX images. In this work, we exploit the potential of TSX for monitoring ground subsiding for the urban area in Hong Kong. A total of 57 TSX images and 11 TanDEM-X (TDX) images acquired between 25thOctober 2008 and 4thMay 2012 were used in the InSAR time series analysis for retrieving subsidence in the study area. A Corner Reflector (CR) validation test is conducted in the area to quantitatively analyze the performance of PS analysis, and the result indicates that the analysis can achieve millimetric accuracy for its monitoring the ground subsidence.

Accurate land covers classification is challenging in urban areas due to the diversity of urban land covers. This study presents a classification strategy with combined optical and Synthetic Aperture Radar (SAR) images using Random Forest (RF). Optimization of RF is conducted, indicating the optimal number of decision trees is 10 and the optimal number of features is 4 for splitting each tree node. The overall accuracy (OA) and Kappa coefficient are used to assess the classification. Result shows that classification with combined optical and SAR images (OA: 69.08%; Kappa: 0.6288) is higher than that with single optical image (OA: 81.43%; Kappa: 0.7770). Benefits of the combined use of optical and SAR images mainly come from reducing the confusions between water and shade, and between bare soil and dark impervious surfaces.

In the paper we proposed a comparison methodology between GPS delay and SAR Atmospheric Phase Screen (APS) in both differential and pseudo-absolute mode. ENVISAT ASAR and synchronous GPS campaign data in Como, Italy were collected and processed. APS from PSInSAR has been divided into even groups according to their height for analysis of stratification sensitivity. Then the stratification and assumed turbulent terms from SAR APS and GPS were compared. The stratified ratio from GPS and SAR APS in differential mode is in agreement of 7.7 mm/km with bias of 3.4 mm/km, with a correlation coefficient higher than 0.7 in the ascending case. The atmospheric total delay coincides with STD of differences smaller than 4 mm (~0.65 mmPWV) with correlation coefficients higher than 0.6. The results predicted the extent on which atmospheric measurements from GPS and InSAR are comparable.

Both the resolution SAR image and acquisition period are significantly improved with the dramatically development in astronautics and microelectronic techniques, providing new opportunities for SAR specialists to analyze data whose information has never been as abundant as nowadays. In this work, we introduce two algorithms to handle high resolution SAR images: for spatial SAR comparison, we employ a normalized difference algorithm and a Wiener filter; for time domain amplitude analysis, we conduct analysis of variance, or ANOVA. The validation of both algorithms is verified by our campus experiment and successfully applied in the administration work of Hong Kong government.

TerraSAR X-band provides high resolution of 1m, short revisit period of 11 days, and sensitive subsidence information, while it cannot afford relatively so high temporal and spatial coherence and wide area coverage as ALOS L-band data. In this work, we exploited the potential of combining Land X-band for subsidence monitoring. A case study was conducted in Tianjin induced by water withdrawal. A total of 9 ALOS images acquired from 2009/4/24 to 2010/10/28 and 37 TerraSAR-X images acquired from 2009/4/29 to 2010/11/11 were used in INSAR time series analysis for retrieving subsidence in the study area. DINSAR analysis based on Minimum Spanning Tree (MST) was applied into L-band to identify potential deformation area and X-band Permanent Scatterers (PS) analysis for this area was carried out for subsidence time series study.

Synthetic Aperture Radar Interferometry (InSAR) is an alternative technique to obtain measurements of surface displacement providing better spatial resolution and comparable accuracy at an extremely lower cost per area than conventional surveying methods. InSAR is becoming more and more popular in monitoring urban deformations, however, the technique requires advanced tools and high level competence to be successfully applied. In this paper we report important results obtained by analyzing new high resolution SAR data in urban areas. Using the SARPROZ InSAR tool and integrating the results with Google Earth, we discovered the (not public) precise tracks of new subways lines in Shanghai by detecting the millimetric surface subsidence caused by tunnel excavation. The data used in this work have been acquired by the Italian sensor Cosmo-SkyMed. About 1.2 million of individual and independent targets have been detected in 600 sqkm, revealing impressive details of the ground surface deformation. Through the high spatial resolution (about 2 m) and millimetric sensitivity in the Line Of Sight (LOS), Cosmo is able to highlight very localized subsidence with remarkable accuracy.

A new integrated SAR signal processor, geo-coding and interferometric processing commercial software (GeoWatch software) has been developed. It supports high speed and large data processing based on ERS-1&2, ENVISAT, ALOS PALSAR, JERS-1, RADARSAT-1&2, TerraSAT and CSK SAR data, with user friendly GUI and parallel processing on the latest 64 bit Windows and UNIX/Linux systems and both personal computers and powerful multicore CPU+GPU cluster platforms. It has been applied to the DSM estimation in a mountain area and subsidence estimation at an airport using its application-oriented step-by-step pipeline processing, and the results showed the following key features: 1. Deformation monitoring coverage expansion from coherent points to distributed scatters; 2. Deformation monitoring period reduction and accuracy improving by multi-track SAR interferometric processing; 3. Truly ortho-rectified interferogram, image, digital surface model (DSM) and deformation map production.

The rapid development of World Wide Web and the dominance of networked computers for information transfer and communication have enabled the rise of virtual worlds (e.g. Second Life™). This paper introduces our virtual campus-Virtual CUHK (VCUHK) project which builds a 3D virtual campus of the Chinese University of Hong Kong (CUHK) in networked virtual worlds. VCUHK is a 3D shared networked virtual world of the real campus of CUHK built with Open Simulator platform, an open source virtual world server project that has an adaptation of the basic server functionality of the Second Life™ servers. The real CUHK campus occupies approximately the area of 2 square km with hilly terrain and diverse and unique landscape, and has a great number of buildings with very sophisticated architecture. By using Second Life™ viewer (an open source 3D virtual world viewer) or other compatible viewers, users can access VCUHK from any Internet-connected personal computer running MS windows or Linux. Users can immerse in VCUHK being anything with the form of 3D modeling avatar and experience virtual education and other diverse campus activities.

The TerraSAR-X SAR system provides high spatial resolution and geometric accuracy imagery which supports well the mapping of the 3-D structure of large-scale civil infrastructure and its motions (4-D). In this work, we investigated the potential of TerraSAR-X observations for monitoring rapid land subsidence induced by reclamation activities. A case study was conducted in Hong Kong Disneyland Theme Park (DTP) at the Penny's Bay, one of the largest land reclamation projects worldwide. A total of 16 TerraSAR-X scenes were used in a small-baseline PSI method for retrieving residual reclamation settlement in the study area. The preliminary results indicate that a remarkably high density of high coherent point targets (>2,500 PS point/km2) was identified in the reclamation area and a large land subsidence rate on the order of 35 cm/yr could be detected in the center of reclaimed land. The high detail level of deformation filed as well as the high sensitivity regarding rapid residual settlement makes TerraSAR-X interferometry a remarkable potential Earth Observation technique to enable the detection of ground deformation related to the large-scale infrastructure development in reclaimed land.

To improve the efficiency of planning and designing silt dam systems, this article employs theories and technologies of collaboration and distributed virtual geographic environments (VGEs) to construct a collaborative virtual geographic environment (CVGE) system. The CVGE system provides geographically distributed users with a shared virtual space and a collaborative platform to implement collaborative planning. Many difficulties have been found in integrating data resources and model procedures for the planning of silt dam systems because of their diversity in heterogeneous environments. Unlike most of the current distributed system applications, the proposed CVGE system not only supports multi-platform and multi-program-language interoperability in the dynamically changing network environment, but also shares programs, data and software in the collaborative environment. Based on creating a shared 3D space by virtual reality technology, agent and grid technologies were tightly coupled to develop the CVGE system. A grid-based multi-agent system service framework was designed to implement this new paradigm for the CVGE system, which efficiently integrates and shares geographically distributed resources as well as having the ability to build modelling procedures on different platforms. At the same time, mobile agent computing services were implemented to reduce the network load, process parallel tasks, enhance communication efficiency and adapt dynamically to the changing network environment. Using Java, JMF (Java Media Framework API), Globus Toolkits (GT) core, Voyager, C++, and the OpenGL development package, a prototype system was developed to support silt dam systems planning in the case study area, the Jiu-Yuan-Gou watershed of the Loess Plateau, China. Compared with the traditional workflow, the CVGE system can reduce the workload by between one third and a half.

This paper presents an automatic meteorological data mining approach based on analyzing and mining heterogeneous remote sensed image datasets. The cloud structures are firstly identified and tracked in satellite remote sensed images, after which heterogeneous cloud features and properties are extracted and integrated to form a unified dataset. The C4.5 decision tree algorithm and dependency network analysis are then employed to discover useful knowledge for weather forecasting, by which a group of derivation rules and a conceptual model for metrological environment factors are generated. Experimental results have shown that the system reduces the heavy workload of manual weather forecasting and provides meaningful interpretations to the forecasted results.

The Xianshuihe fault of Sichuan province, southwest China, is a highly active strike-slip fault approximately 350 km long. Previous studies have described up to 10-17 mm/yr of left-lateral slip on the Xianshuihe fault during the last decades, as indicated from geological criteria and GPS observations. The satellite InSAR observation is an alternative technique effectively used to measure the crustal deformation on the active fault. However, the conventional strip mode SAR imagery (i.e. ENVISAT/ASAR IM image) covers usually a 100 km wide stripe and will therefore not be sufficient for such large-scale deformation study. In this paper, we presented ScanSAR interferometry applied to ENVISAT/ASAR WSM data to produce about 400×400 km2 deformation map over the Xianshuihe fault zone and the Garzê-Yushu fault zone. The preliminary stacked deformation results of six WS-WS interferograms indicated a northeast-southwest deformation trend nearly perpendicular to the two fault zones and demonstrated the potential to monitor such wide-stretched deformation using ENVISAT WSS data.

Results of ocean surface wind speed retrieval from C-band ENVISAT ASAR images using a physics wind model are shown. The physics model is based on the radar backscatter theory in which the radar cross section is calculated considering the contribution of both Bragg scattering or resonance and specular reflection from the sea surface. The wind speeds retrieved from VV- or HH-polarized ASAR images using the physics model were compared to both buoy measurements in Hong Kong coastal waters and that retrieved using the empirical C-band algorithms (CMOD4, CMOD5, CMOD_IRR2). The results show the feasibility of the physics model for ocean surface wind speed retrieval from both VV and HH-polarized ASAR images at moderate wind condition.

Most paddy rice in southern China grows in warm, humid and rainy areas where it is hard to acquire optical remote sensing data. In this study, a semi-empirical backscattering model was proposed to estimate leaf area index (LAI) of rice in the area using ENVISAT Advanced Synthetic Aperture Radar (ASAR) alternating polarization data. Ground measurements of LAI, water content and height of rice in the test site were collected and the model fitted at the same time as the acquisition of ASAR data. LAI estimated from the model was compared with ground measurements to evaluate the accuracy of the model. The results showed that the model provides a promising alternative to optical remote sensing data for predicting LAI of rice in southern China.

Currently, most of the operations performed for the construction of marine charts are still done manually. However, with the development of more and more powerful techniques, new processing methods must be developed in order to deal with the increasing amount of data and to achieve automatic construction. For that purpose, a new method for line smoothing is introduced in this paper and is applied to the generalisation of isobathymetric lines (lines connecting points at a same depth). The lines are modelled by B-spline curves which maintain their smooth feature. Smoothing is performed by reducing the curvature using a snake model. The generalisation constraint of navigation safety is satisfied by applying position constraints on the line. Spatial conflicts are also taken into consideration and are removed during the process. Parameters are automatically defined so that the method can be applied to large sets without user intervention. The method has been applied on real data sets and examples are provided and discussed for scale reduction of lines.

Recent advances in positioning and communication technologies have made it possible to collect trajectories of moving objects, and thus, corresponding applications that handle trajectories come into sight, e.g. applying vehicle trajectories to traffic studies, where various queries about trajectories are necessary. However, due to their special spatio‐temporal query conditions, trajectory queries are different from existing GIS queries, and there is still a lack of efficient data‐access methods to support queries about trajectories. In view of this, we develop a data‐access method, named a Topology‐based Mixed Index Structure (TMIS), to index network‐constrained trajectories for connectivity‐based queries. The TMIS indeed is not a novel index structure but a novel combination of simple and classical index structures linked by the topology of a network. The TMIS can support more query types through different combinations of simple and classical index structures and can be applied to a large network. Existing database management systems can be employed to implement the TMIS. In this paper, we first define network‐constrained trajectories and connectivity‐based queries, and then introduce the principles, architecture, algorithms, and query processing of the TMIS. Finally, we implement the TMIS and validate it through a series of analyses and experiments.

This paper presents a computer-aided cloud-analysis approach by effectively modeling the integration of heterogeneous satellite-observed data and remote sensing images. First, automatic cloud detection and tracking methods are proposed to identify the georeferenced cloud objects in satellite remote sensing images. Then, a data integration modeling mechanism is designed to collect meaningful properties of those detected clouds by integrating the heterogeneous satellite-observed data and imaging into a unified cloud database. Finally, based on the integrated global data schema, a two-phase data mining method employing the decision tree algorithm is implemented to analyze and forecast the meteorological activities of all the cloud objects. Experimental results have shown that the proposed data integration model can effectively extract and synthesize all the useful information from heterogeneous data sources to generate a unified view of knowledge, on the basis of which the evolvement trends of clouds can be analyzed properly.

As a development in geography and geographic information science, virtual geographic environment (VGE) embodies abstract concepts, makes people immerge in the virtual world, studies the geography problems, and carries out geographic examinations. VGE has plenty of multi-kinds of data, such as digital elevation model data, data of objects under and on the Earth surface, image data, audio data, video data, avatar movement data, interaction data, environment data, and so on. VGE data can be classified as dynamic data and static data according to data changing. At the same time, VGE data can also be classified into global data and local data with an eye to data using range. According to VGE data attributes and its classifications, we design a VGE database, which fits VGE's three main contents: virtual geographic environment construction, entities construction and the interaction between environment and entities. There has a global database to store global data and several local databases to store their region data. The database is also distributed, so we study a method and an architecture to solve the database collaboration. We call the method "pre-data-preparing". In the end, we give an example of DEM database collaboration of GuanTing lake located in the west of Beijing.

Abstract Realistic image synthesis of highly complex, natural scenes has been a challenging topic in computer graphics over the years. Generation of snowy scenery could be even more difficult due to the difficulty of using common modelling primitives such as polygons and curved surfaces to express the snow‐like shapes. In this paper, we propose a hybrid multi‐mapping method to tackle the snowy scenery problem, in which a displacement map is utilized to model snowy blocks on near objects based on proximate polygons, and a volumetric texture map is combined to handle distant objects such as bushes and trees. Our experimental results showed that the hybrid method is viable in producing realistic snowy scenery with comprehensive complex environments, and in a favourable requirement of storage usage and rendering computation. Copyright © 2003 John Wiley & Sons, Ltd.

Concepts of fuzzy objects have been put forward by various authors (Burrough and Frank 1996) to represent objects with indeterminate boundaries. In most of these proposals the uncertainties in thematic aspects and the geometric aspects are treated separately. Furthermore little attention is paid to methods for object identification, whereas it is generally in this stage that the uncertainty aspects of objects become manifest. When objects are to be extracted from image data then the uncertainty of image classes will directly effect the uncertainty of the determination of the spatial extent of objects. Therefore a complete and formalized description of fuzzy objects is needed to integrate these two aspects and analyse their mutual effects. The syntax for fuzzy objects (Molenaar 1998), was developed as a generalization of the formal syntax model for conventional crisp objects by incorporating uncertainties. This provides the basic framework for the approach presented in this paper. However, the model still needs further development in order to represent objects for different application contexts. Moreover, the model needs to be tested in practice. This paper proposes three fuzzy object models to represent objects with fuzzy spatial extents for different situations. The Fuzzy-Fuzzy object (FF-object) model represents objects that have an uncertain thematic description and an uncertain spatial extent, these objects may spatially overlap each other. The Fuzzy-Crisp object (FC-object) model represents objects with an uncertain spatial extent but a determined thematic content and the Crisp-Fuzzy object (CF-object) model represents objects with a crisp boundary but uncertain content. The latter two models are suitable for representing fuzzy objects that are spatially disjoint. The procedure and criteria for identifying the conditional spatial extent and boundaries based upon fuzzy classification result are discussed and are formalized based upon the syntactic representation. The identification of objects by these models is illustrated by two cases: one from coastal geomorphology of Ameland, The Netherlands and one from land cover classification of Hong Kong.

An important trend of current GIS development is to provide easy and effective access to spatial analysis functionalities for supporting decision making based on geo-referenced data. Within the framework of the ongoing SQL standards for spatial extensions, a spatial query language, called SQV/SDA, has been designed to meet such a requirement. Since the language needs to incorporate the important derivation functions (e.g., map-overlay and feature-fusion) as well as the spatial relationship and metric functions, the functionality of the FROM clause in SQL is developed in addition to the SELECT and WHERE clauses. By restructuring the FROM clause via a subquery, SQL/SDA is well-adapted to the general spatial analysis procedures using current GIS packages. Such an extended SQL, therefore, stretches the capabilities of previous ones. The implementation of SQL/SDA on the Internet adopts a hybrid model, which takes advantage of the Web GIS design methods in both the client side and server side. The client side of SQL/SDA, programmed in the Java language, provides a query interface by introducing visual constructs such as icons, listboxes, and comboboxes to assist in the composition of queries, thereby enhancing the usability of the language. The server side of SQL/SDA, which is composed of a query processor and Spatial Database Engine (SDE), carries out query processing on spatial databases after receiving user requests.

Investment environment (IE) analysis is a spatially oriented problem. Although geographical information systems play an important role in dealing with spatial problems, few efforts have used GIS for IE analysis. The traditional standalone GIS limits the information flow between the end users and the information providers, especially the feedback information from users. For local government it is more important to understand the interests of the investors and their expectations so that they can improve their local IE and attract more investment. The Internet provides a means of direct and mutual communication for both sides. This paper deals with issues related to the Internet-based investment environment information system (IEIS). A case study on the IE of cities along the recently completed Beijing-Kowloon Railway (BKR) is given to illustrate the methodology of an Internet-based GIS approach.