The spectral analysis method can be used to qualitatively and quantitatively research water quality parameters using the characteristics that the molecules or ions of substances in the solution can absorb the full spectrum of ultraviolet-visible light. It enjoys the advantages such as high detection speed, low cost, in-situ measurement, no secondary pollution, and the simultaneous online monitoring of multiple water quality parameters. Based on the statement of the theoretical basis of water quality spectrum analysis, this paper systematically analyzes the principles and characteristics of various measurement methods. By comparing domestic and foreign full-spectrum water quality online monitoring devices, this paper points out the key technological difficulties in the establishment of high-precision online inversion models of water quality parameters and further proposes the development trends of multi-parameter online monitoring systems of water quality using the spectral analysis method. Therefore, this paper will provide a reference for the research on water environment monitoring technologies and the development of instruments for water quality parameter detection based on the theories of spectral analysis.

Soil moisture (SM) plays an irreplaceable role in agricultural production, and agricultural water use, yield estimation, and drought monitoring are all closely related to SM. Therefore, it is of great significance to monitor the changes in SM. At present, the remote sensing technique is an effective tool for the monitoring of the changes in SM in large areas. Optical remote sensing is sensitive to the composition of surface vegetation, while microwaves can penetrate vegetation to obtain the information of SM under vegetation. Meanwhile, the sensitivity of synthetic aperture Radar (SAR) backscattering to the changes in SM is greatly affected by the vegetation canopy. In areas covered by vegetation, microwave remote sensing will be affected by both surface roughness and vegetation. Therefore, the joint application of optical and SAR remote sensing can well remove the impacts of vegetation and surface roughness, thus improving the inversion accuracy of SM. This paper summarizes the remote sensing models and retrieval methods commonly used in the research on the cooperative inversion of SM using optical and SAR remote sensing. Meanwhile, it proposes the difficulties in the research and the future development of the cooperative inversion.

Vegetation phenology reflects the interactions between the physiological and ecological processes of vegetation and environmental changes and thus it is practically significant to research and develop the software used to extract the vegetation phenological information based on time series remote sensing data. The existing pieces of software mainly include those developed by foreign R&D staff based on specific remote sensing data. They integrate different methods for data smoothing and reconstruction and serve different users. The analysis and comparison of the functions and characteristics of the existing pieces of software will assist users to select more targeted software and can also provide references for the R&D of the software for vegetation phenology monitoring. This paper first briefly introduces the monitoring principles of vegetation phenological information using remote sensing data and commonly used data smoothing methods for the reconstruction of time series remote sensing data. Then it summarizes multiple pieces of professional software for vegetation phenology monitoring that integrate the reconstruction methods and phenological information extraction methods. Most especially, it introduces the software TIMESAT, SPIRITS, and DATimeS in detail and compares and analyzes their functions and characteristics. Finally, it puts forward the prospect of developing localization application software with user-friendly graphical user interfaces according to the development of remote sensing big data and the demand for vegetation phenology monitoring.

Urban land subsidence is a kind of slowly developing geological disaster and has sustained negative impacts on the social economy and human life. Therefore, it is of great significance to carry out effective and wide-area urban subsidence monitoring. With 34 high-resolution TerraSAR-X SAR images obtained from April 07, 2009 to December 14, 2010 as data sources, the land subsidence in Tianjin City was monitored using the differential interferometry of time series based on interferometric point target analysis (IPTA) in this study. Then the monitoring precision was verified using the precise leveling data, and a verification method of subsidence time series based on least-squares fitting was adopted. Finally, subsidence analysis and interpretation were carried out based on the verification results. Compared to the leveling data, the root mean square errors of the subsidence rates obtained using IPTA and that using the least squares-fitting of time series were 3.15 mm/a and -3.25 mm/a, respectively. According to the analysis of subsidence results, the overall subsidence of the study area is significantly uneven, the maximum subsidence rate is -128.41 mm/a, and the spatial-temporal distribution of the land subsidence correlates highly with surface cover types and groundwater exploitation.

Land subsidence is an environmental geological phenomenon caused by many factors, and it can reduce the smoothness of high-speed railways and thus affects the safe operation of high-speed railways. Traditional rardom forest models do not take account of the internal complexity of time series data in the prediction of time series data. Therefore, this paper constructs a wavelet transform-random forest (WT-RF) prediction model, predicts the land subsidence along the Tianjin-Baoding high-speed railway using the model, and assesses the impacts of land subsidence on the changes in the slope of the high-speed railway. The results are as follows: ① From 2016 to 2018, the change range of the slope of the Tianjin-Baoding high-speed railway was 0~0.16‰ due to the cumulative land subsidence. ② The WT-RF model showed high prediction accuracy of the land subsidence. ③ From 2018 to 2020, the land subsidence still showed an increasing trend, although the change range of the slope along the Tianjin-Baoding high-speed railway was 0~0.2 ‰. It can be concluded that the land subsidence has an impact on the changes in the slope of the Tianjin-Baoding high-speed railway. Therefore, it is necessary to control the land subsidence to ensure the safe operation of the high-speed railway.

The time-series interferometric synthetic aperture Radar (InSAR) technology has been widely used since it allows for the safe and efficient obtainment of large-scale high-precision ground subsidence data. It is still a hot topic to efficiently obtain accurate land subsidence data of mining areas at different mining states using this technology to provide data support for the ecological governance of the mining areas. Based on Sentinel-1A images (58 scenes per complete orbit), this paper conducts time series monitoring of six mining areas in Xuzhou City using the multiple master-image coherent target small-baseline interferometric SAR (MCTSB-InSAR) technique and obtains land subsidence results during 2016—2018. Meanwhile, it verifies the accuracy of the obtained subsidence rate using the measured data in a similar period, yielding a difference in root mean square error of 4.0 mm/a. Therefore, the monitoring requirements can be satisfied. The monitoring results are as follows. The Zhangshuanglou and Sanhejian coal mines suffered serious land subsidence, with the maximum average annual subsidence rate exceeding 100 mm/a and the maximum cumulative subsidence exceeding 300 mm. In comparison, the Qishan, Shitun, Quantai, and Zhangji coal mines experienced light subsidence, which all occurred within the mining areas and did not show a notable expansion trend during the monitoring period. Based on these results and the monitoring data of the basic geographical state of Jiangsu Province in 2016, there were 2 844 and 672 high-coherence points falling in houses and roads, respectively for the Sanhejian and Zhangshuanglou coal mines, which accounted for 73.66% and 63.33% of the total high coherence points of the mines, respectively. For the mining areas except for the Quantai coal mine, there was a roughly linear relationship between the subsidence amount and time, which was stronger in the mines under mining than in the mines where mining had stopped. In contrast, the relationship between the subsidence amount in the Quantai coal mine and time presented a nonlinear law. The experiment results show that Sentinel-1A images and the MCTSB-InSAR technique have good application prospects in the monitoring and analysis of land subsidence in mining areas.

The land subsidence in the Beijing-Tianjin-Hebei (BTH) region has developed the most rapidly and affects the largest area in China and it has become an unnegligible geological problem in the coordinated development of the BTH region. In this study, the multi-track Sentinel-1A data from January 2016 to October 2018 that cover the whole BTH plain was processed using the multi-temporal InSAR (MT-InSAR) technique. After the verification using leveling data and the cross-validation using the data from adjacent tracks, the land subsidence in the BTH region during 2016—2018 were obtained by integrating multi-track SAR data results. The InSAR monitoring results show that the maximum subsidence rate in the BTH region reached 164 mm/a and the land subsidence was widely and unevenly distributed in space in the study area during the monitoring period. According to the analysis of the spatial-temporal change characteristics of the land subsidence in the BTH region, the land subsidence showed an increasing trend in the Tangshan-Qinhuangdao area but stably developed in the remaining areas in the BTH region during 2016—2018. This paper demonstrates that the reliability of the InSAR technique in the monitoring of land subsidence in large regions. The results of this study will provide an important basis for the prevention and mitigation of regional subsidence and will provide a scientific guarantee for the construction of the BTH urban agglomeration.

The extraction of mosaic lines is an important step in the mosaic of remote sensing images. To address the problems related to mosaic line extraction existing in current mosaic techniques of high-resolution remote sensing images, the authors propose a mosaic line extraction method based on multi-scale segmentation and the A* algorithm and the steps are as follows. First, pre-segment the overlapping regions of images using the simple linear iterative cluster (SLIC) algorithm, and conduct the clustering of regions with notable surface features to generate compact superpixels to obtain and extract the texture information of the surface features in the images. Then, merge the adjacent regions by continuously increasing the regional dissimilarity threshold while recording the region merging process using a scale set model. Meanwhile, determine the optimal segmentation scale according to the local variance of spectral characteristics and the Moran index to solve the problem of over-segmentation. Finally, find out the best mosaic lines on the segmentation paths using the A* algorithm. Experimental results prove that this method can effectively solve the problem that mosaic lines pass through distinct areas such as buildings, farmlands, and rivers, thus reducing splicing traces. Meanwhile, the optimal segmentation scale can be effectively selected by recording the merging process using a scale set model. Therefore, the mosaic line extraction method proposed in this study can be widely applied in the mosaic of high-resolution remote sensing images and is practically significant for the automatic mosaic of remote sensing images.

The scene recognition of shipbuilding enterprises is of practical significance for the restoration of the coastal ecological environment, the protection of water environment, and the promotion of the coordinated development of shipbuilding enterprises. However, it is difficult to realize the automatic recognition of shipbuilding enterprises from satellite remote sensing images based on traditional medium- and low-level features. Therefore, this paper proposes a multi-model multi-scale scene recognition method of shipbuilding enterprises based on a convolutional neural network with spatial constraints and the steps are as follows. Firstly, train multiple convolutional neural network models using the samples of global-scale shipbuilding enterprise scenes and local-scale docks (slipways), workshops, and ships individually, and conduct multi-model multi-scale detection. Then, locate local-scale objects at a pixel level and calculate the spatial distance of the objects. Finally, conduct comprehensive judgment and extraction of the shipbuilding enterprise scenes according to the multi-scale detection results, the combination method of object tags, and the spatial distance of objects. The method was applied to five typical shipbuilding intensive areas in Jiangsu Province, China, the surrounding areas of Nagasaki and Ehime prefectures, Japan, and Mokpo and Geoje cities, South Korea. As a result, the overall recognition accuracy and recall rate were 87% and 85%, respectively in Jiangsu Province, were 91% and 87%, respectively in the study area in Japanese, and were 85% and 92%, respectively in the study area in South Korean. The experimental results show that this method can realize the effective recognition of the complex scenes of shipbuilding enterprises based on remote sensing images.

The change vector analysis in posterior probability space (CVAPS) method has been widely used in the change detection of remote sensing images owing to its many advantages. It uses the support vector machine (SVM) to estimate the posterior probability vector. However, in the classification of low and medium resolution remote sensing images, SVM cannot effectively deal with the problems of the same object with the different spectra, different objects with the same spectrum, and mixed pixels and thus cannot guarantee the accuracy of the final detection results. Therefore, this paper adopts the fuzzy c-means (FCM) clustering for modeling and couples the FCM with a simple Bayesian network (SBN) to solve the problem of mixed pixels and estimate the posterior probability vector, thus achieving a new posterior probability space change vector analysis method. The experimental results indicate that, compared to the SVM-based CVAPS algorithm, the algorithm proposed in this study shows higher overall accuracy, higher Kappa coefficient, more reliable performance that is less affected by the number of training samples, simpler parameter setting, and lower time consumption. Therefore, the algorithm proposed in this paper helps to improve the accuracy and efficiency of the change detection of remote sensing images.

The scientific and reasonable delineation of an urban area serves as an important basis for making urban and rural statistics, formulating urban and rural policies, and implementing land space planning. At present, a unified concept system and unified delimitation methods of urban areas are yet to be established, which brings a lot of uncertainty to the formulation and implementation of land space planning. Therefore, aiming at the problems of the fuzzy concept, inconsistent demarcation methods, unclear spatial boundaries, this paper defined the spatial connotation of urban areas based on extensive research and proposed the spatial delimitation method of urban areas using surveying and mapping geographic information and the techniques such as remote sensing. Based on the data of the Third National Land Survey, this study determined the surface features incorporated into urban areas according to urban land use characteristics and spatial connection successively, in order to extract the spatial scopes of urban areas. Finally, taking Bazhong City as a case study, the rationality and feasibility of the process were verified, indicating that the method can be used in the extraction of the spatial scopes of urban areas and has the value of widespread applications.

The classification of synthetic aperture Radar (SAR) images is one of the key technologies in the field of remote sensing applications. To address the problem that regional class labels affect the classification accuracy in the object-based Markov random field (OMRF) model, this paper proposes the concept of regional category fuzzy probability (RCFP) label field, which can effectively avoid the misclassification caused by wrong class labels by fully considering the possible categories of a single object. The RCFP of every region can be obtained using the regional edge information and posterior probability according to the features of the region and its adjacent regions. Then it is included in the calculation of feature field parameters to make the feature field parameters highly close to the real conditions of objects. The study area is located in the eastern part of Kaifeng City, Henan Province, covering an area of about 1 400 km². Sentinel-1 SAR images were used for the classification experiment of farmlands, buildings, and water in the study area, and the performance of the improved method in this study was compared with that of the method of K-means, fuzzy C-means (FCM), MRF, and OGMRF-RC. The experimental results show that the overall accuracy (OA) and the Kappa coefficient of the proposed method are 94.16% and 0.8957 respectively, which are higher than those of other methods.

The study on the information extraction methods of coastal wetlands is highly significant for accurately grasping the distribution status of coastal wetlands and for protecting and managing the rare resources in coastal wetlands. To improve the information extraction precision of surface features in coastal wetland conservation areas, this paper screens the polarimetric decomposition features using the separability index and classifies fully polarimetric SAR images using the random forest method. The details are as follows. Based on the domestic GF-3 fully polarimetric radar images of the Liaohe River Estuary National Nature Reserve in Liaoning Province, five polarimetric target decomposition methods were used to extract polarimetric features, the separable index was adopted to optimize feature selection, and finally the random forest method was utilized to conduct the classification and accuracy assessment of surface features in the study area. The experiment results show that the classification accuracy of surface features in wetlands based on optimized polarimetric features was up to 75.47%. Meanwhile, the optimized polarimetric feature parameters can effectively avoid information redundancy and improve the information extraction accuracy of surface features in coastal wetland conservation areas.

To improve the spatial resolution and expand the application scopes of GPM precipitation products, the downscaling study of GPM precipitation products was conducted based on the precipitation data of Guizhou Province by establishing multiple spatial downscaling models. Firstly, with the topographic factors including longitude, latitude, elevation, slope, and aspect as explanatory variables and the original GPM precipitation data as target variables, multiple downscaling models were established based on the methods of multivariate linear regression, geographically weighted regression, extreme learning machine, support vector machine, and random forest regression. Then multiyear average precipitation data were applied and assessed, and the optimal model was selected to conduct the spatial downscaling study of the annual and monthly precipitation amount in typical years in Guizhou Province. According to the results, the downscaling models except for the random forest regression model all performed well. Most especially, the multivariate linear regression model performed the most stably and effectively and yielded the highly improved downscaling results in terms of observation accuracy and spatial correlation. This study will provide a set of high-resolution gridded precipitation products for Guizhou Province and provide support for regional hydrometeorological research.

The iron and steel industry is a very important part in economic development. Obtaining the knowledge of the monthly production of steel companies is conducive to the macro control of the economy and the rational allocation of resources. In this paper, a monthly production estimation model for steel companies was proposed based on the grading results of the surface temperature obtained from the inversion of satellite thermal infrared data as well as the theory and method of landscape pattern indices. The surface temperature anomalous values and the thermal landscape distribution parameters of steel companies can be calculated according to the vector data of the spatial framework of steel companies. Based on this and the actual monthly production data of two typical steel companies in Central China and North China, the estimation model was established through the least-squares fitting, and the coefficient of determination (R²) of the model was greater than 0.9. According to the posterior variance test results, the accuracy of the estimation model proposed in this study is level 2. Meanwhile, the actual production values all fall within the 95% confidence interval of the estimation values. All these comprehensively reflect the monthly production model proposed in this paper are highly accurate.

The remote sensing-based information recognition of Mars dunes has important significance for the exploration of the interactions between the Martian atmosphere and the dune surface. Aiming at the low accuracy of the automatic information extraction of Mars dunes using the traditional machine learning method, this paper designs a method combining texture feature extraction and deep learning to automatically identify the information of Mars dunes. In detail, this method conducts information extraction based on the texture feature extraction of Mars remote sensing images combined with a deep learning model, thus realizing the end-to-end semantic segmentation of the remote sensing images. According to experiment results, the U-Net method can fully utilize the rich texture information in the remote sensing images and the extraction accuracy of dunes of this method was 96.7%, which was 3.2 percentage points higher than that of the traditional random forest method. Furthermore, compared to the traditional random forest method, the U-net method extracted more accurate and clearer contours of Mars dunes and yielded better extraction effects of highly fragmented dunes. Therefore, the U-net method can be used for accurate and automatic information extraction of Mars dunes.

In recent years, coral bleaching events have frequently occurred globally due to global warming and other factors. However, the Coral Reef Watch (CRW) program established by the National Oceanic and Atmospheric Administration (NOAA) has underestimated the actual situation of coral bleaching in the South China Sea. Based on 180 cases of coral bleaching in the South China Sea and its surrounding waters since 1985, this paper obtains the optimum threshold combination by calculating the false negative rate (FNR), the false positive rate (FPR), and the accuracy (ACC) of different threshold combinations, thus improving the detection accuracy of coral bleaching events in the South China Sea. The results are as follows: (1) The FNR of the bleaching detecting results obtained using the NOAA threshold was 70.70%, indicating the long-term underestimation of the coral bleaching; (2) With the optimized critical threshold (CT) and alert threshold (AT), the ACC was improved from 58.13% to 73.90%, meanwhile the FNR and FPR were both less than 30%. As revealed by the coral bleaching event in the Nansha Islands in June 2007, the optimized thermal stress index can be used to effectively detect the event and mark the bleaching alarm level in time compared to the past underestimation. Therefore, the improved method for thermal threshold detection can improve the monitoring level of coral bleaching and are conducive to the management and protection of coral reefs in the South China Sea.

This paper aims to explore the optimal inversion model of regional heavy metal content in soil. With Longhai City taken as the study area, this study preprocessed the original spectral data of soil using the methods of Savizky Golay (SG), wavelet transform (WT), gaussian filter (GF), and multiple scatter correction (MSC) individually, then eliminated the interference and wavelength bearing no information using the wavelength selection algorithms developed based on model population analysis (MPA), including the competitive adaptive reweighted sampling (CARS), variable iterative space shrinkage approach (VISSA), iteratively variable subset optimization (IVSO), and interval combination optimization (ICO), and finally predicted the lead content in soil using the linear partial least squares regression (PLSR) model, nonlinear support vector machine (SVM) model, and extreme learning machine (ELM) based on neural network. The results are as follows. ① Among the inversion models of lead content in soil established using various preprocessing methods, the model built based on reconstructed spectral data of level 7th by wavelet transform had the most optimal prediction accuracy, with R²=0.736, RMSE=5.426, RPD=1.976, and RPIQ=2.560. ② The CARS, VISSA, IVSO, and ICO algorithms developed based on MPA significantly improved the performance of model interpretation and generalization and improved modeling efficiency. ③ In terms of overall prediction results, the three regression models were in the order of ELM>PLSR>SVM. Among them, the ICO-ELM had the highest prediction accuracy, with R²=0.863, RMSE=3.953, RPD=2.712,and RPIQ=3.514. Therefore, the optimal model established in this study can provide a new theoretical reference for the rapid monitoring of regional land quality and ecological indicators.

The recognition and extraction of mine tailing information serve as an important step in the dynamic monitoring of the mine environment. The classification of surface features using medium-low spatial resolution images is mostly conducted based on spectral information. However, some roads and tailings have similar spectral reflectance due to the special environment in mining areas. As a result, it is liable to misclassify tailings as roads in the surface feature classification based on spectral information only, which affects the structural integrity and area statistics of tailing ponds. Given this, this paper comprehensively analyzes the spectral, shape-related, and texture characteristics of iron mine tailings in the Qianxi area, Hebei Province based on high spatial resolution images obtained from the Beijing-2 satellite and proposes an object-oriented classification method based on multiple features. The steps of the method are as follows. Firstly, perform multi-scale segmentation of Beijing-2 images and the reflectance and take the spectral differences of surface features in each band as the spectral characteristic values of surface features. Secondly, extract the values of length-to-width ratio of objects using a covariance matrix and object boundaries and take them as the characteristic values of surface feature shapes. Then, calculate the gray-level co-occurrence matrix using principal component bands, and select the contrast, correlation, and entropy values that can effectively distinguish the texture characteristics between tailings and other surface features as the texture characteristic values of remote sensing images. Finally, conduct object-oriented classification and precision assessment using the nearest neighbor method according to the characteristic information of surface features. The results indicate that the object-oriented classification method can effectively avoid the misclassification of the roads in tailing ponds and thus provide a research basis for the implementation of large-scope and high-precision identification and dynamic monitoring of mine tailings.

This paper aims to study the coupling and coordination relationships between the urbanization and ecological environment in the areas along the Beijing-Hangzhou Grand Canal from 1992 to 2018. To this end, it builds the remote sensing ecological index (RSEI) based on Landsat data to characterize the quality of the ecological environment and establishes the compounded night light index (CNLI) based on DMSP/OLS and NPP/VIIRS nighttime light (NTL) data to characterize the urbanization level. Meanwhile, it determines the coordinated development pattern between the urbanization and ecological environment in the area by applying a coupling and coordination degree model and its classification principles. The results are as follows. ① The urban development level along the Beijing-Hangzhou Grand Canal is characterized by spatial imbalance. It is high in the southern and northern areas but low in the middle areas. During 1992—2018, the number of cities with a high urbanization level increased year by year. The periods from 1992 to 2002, from 2002 to 2013, and from 2013 to 2018 witnessed the slow development, the accelerated development, and the steady improvement of urbanization, respectively. ② During 1992—2018, the RSEI values were all greater than 0.4, indicating a high-quality ecological environment. The ecological environment in the areas along the canal was relatively stable from 1992 to 2002, improved from 2002 to 2007, and deteriorated from 2007 to 2018 to a certain degree. ③ During 1992—2018, the coupling and coordination degree between urbanization and the ecological environment increased first and then decreased. In terms of the coordinated development type, urbanization lagged behind the ecological environment firstly and then the latter gradually lagged behind the former, indicating that the quality of the ecological environment needs further improving. That is, it is necessary to strengthen the protection of the ecological environment while developing the economy along the Beijing-Hangzhou Grand Canal.

Conventional methods for regional bathymetry mainly use shipborne acoustic detection technologies. However, since the hull cannot access the coastal shallow waters and the areas with dense islands and coral reefs, there is no available data of near-coastal areas. These problems can be effectively solved with the emergence and development of airborne lidar bathymetric technology, which has become a fast and efficient method for water-depth and submarine topographic exploration. Based on the airborne laser sounder CZMIL Nova, this paper introduces the characteristics and influencing factors of the land-sea integrated technologic surveying and its preliminary application in the land-sea integrated topographic surveying of islands.

The eco-geological vulnerability assessment of Xichang City, Sichuan Province was performed in this study to provide bases for the ecological protection and restoration of the city. Firstly, an assessment indicator system was constructed, for which 10 major influencing factors of the eco-geological vulnerability in Xichang City were selected according to the eco-geological survey and comprehensive research. Then the eco-geological vulnerability assessment of Xichang City was conducted using the improved analytic hierarchy process (AHP) and the geographic information system (GIS). The results are as follows. Xichang City suffers from vulnerable eco-geology. The areas with moderate-high eco-geological vulnerability account for 50.14%, although no areas suffer extreme eco-geological vulnerability. The areas not suffering eco-geological vulnerability and those with slight eco-geological vulnerability are concentrated in the Anning River valley and the Qionghai Lake Basin, while the areas with moderate-high eco-geological vulnerability are mainly distributed in the Maoniu Mountain area in the western part of Xichang City and the Luoji Mountain area in the southeastern part of the city. Overall, the whole city includes five areas with eco-geological vulnerability, namely two areas with slight eco-geological vulnerability in the Anning River Valley (I) and Daqing (II), one area with moderate eco-geological vulnerability in the Baru-Ma’anshan area (III), and two areas with high eco-geological vulnerability on the western slope of the Luoji mountain (Ⅳ) and in the Maoniu Mountain (Ⅴ). Different ecological protection and restoration schemes and different development and construction measures should be implemented for different eco-geologically vulnerable areas in the city.

A rainstorm struck northeast Chongqing from August 31 to September 2, 2014. It triggered extensive landslides and resulted in casualties and serious economic losses. To learn the condition of the landslides induced by the rainstorm and analyze the relationship between the landslides and rainfall, this study obtains the distribution of the landslides through the interpretation of high-resolution satellite remote sensing images before and after the rainstorm using RS and GIS techniques. It can be concluded that complicated geological tectonic conditions and corresponding unique tectonic erosion landform pattern make northeast Chongqing become the center of the rainstorm and also lead to the frequent occurrence of rainfall-triggered landslides in this area. Landslides will continuously occur when the maximum daily rainfall and accumulated rainfall exceed 80 mm and 160 mm, respectively, and extensive landslides will occur when the maximum daily rainfall and accumulated rainfall exceed 100 mm and 210 mm, respectively. Furthermore, landslides are the most liable to occur in windward slopes with a gradient of about 25°. Therefore, the regional topography should be taken into account in the analysis and prediction of rainfall-induced geological disasters to improve the accuracy of spatial and temporal prediction and analysis of geological hazards.

Megacities have formed due to rapid urbanization. As a result, the surface cover has rapidly changed, which changes the heat balance of Earth's surface and induces drastic changes in the thermal environment in megacities. With six typical megacities (Beijing, Shanghai, Guangzhou, London, New York, and Tokyo) across the world as study objects and multi-temporal Landsat remote-sensing images of the 1990s, the 2000s, and 2015 as the main data sources, this study compares the changes in the thermal environment among the six megacities and analyzes their causes. For each of the megacities, the surface temperature was determined through reversion using the universal single-channel algorithm and the urban heat island ratio index (URI) was calculated to quantitatively compare the spatial-temporal changes in the heat island effect during the study period. The results are as follows. From the 1990s to 2015, the URI values of Beijing, Shanghai, and Tokyo showed an overall upward trend, and while that of Guangzhou, London, and New York showed an overall downward trend. In 2015, Tokyo suffered the most serious urban heat island effect (URI=0.630), followed by Beijing, Shanghai, New York, and Guangzhou successively, of which the URI values were 0.617, 0.594, 0.555, and 0.530, respectively. In contrast, London had the smallest URI of 0.433. The megacities such as Beijing, Shanghai, Guangzhou, and Tokyo all considerably expanded throughout the study period. In these cities, the built-up areas and impervious surfaces increased by more than 500 km² and more than 370 km² on average, respectively in terms of area. They continuously spread outwards and occupied ecological land. Furthermore, green belts can not be formed between urban clusters. All these caused a significant increase in urban surface temperature and especially the significant aggravation of the heat island effect in new urban areas. In comparison, the thermal environment in the old urban areas was significantly improved through urban reconstruction. London and New York were not significantly expanded, where the surface temperature slightly changed. Therefore, it is necessary to pay attention to ecological philosophy, optimize the pattern of urban surface space, and improve the efficiency of ecological land in future urban construction.

To meet the demand of various industries for high-resolution and high-precision precipitation data, this study establishes the downscaling models of the TRMM precipitation data of the Xiangjiang River basin based on the methods of multivariate linear regression (MLR) and geographically weighted regression (GWR). The leave-one-out cross-validation method was adopted to select the optimal model, and a satellite-ground fusion precipitation product with a resolution of 0.05° was obtained through inversion. On this basis, the spatial-temporal change characteristics of the precipitation in the Xiangjiang River basin were analyzed. The results are as follows. The spatial resolution of the TRMM precipitation data was greatly improved after downscaling. As verified using the precipitation observed at meteorological stations, the coefficient of determination of the TRMM precipitation data increased by more than 0.27, and the root mean square error and average relative error of the TRMM precipitation data decreased by more than 28.42 mm and 29.88 percentage points, respectively on average after downscaling. All these indicate that the regression downscaling model that takes account of vegetation, terrain, and geographic elements can accurately describe the spatial distribution characteristics of precipitation. According to the verification using the precipitation observed at meteorological stations, the coefficient of determination of the GWR downscaling model increased by 0.06 compared to the MLR downscaling model. Meanwhile, the root mean square error and average relative error of the precipitation data obtained using the GWR downscaling model decreased by 14.88 mm and 8.83 percentage points, respectively on average compared to precipitation data obtained using the MLR downscaling model. These indicate better effects of the GWR downscaling model. The spatial-temporal change characteristics of the precipitation in the Xiangjiang River basin during 2006—2017 are greatly different on different time scales, which is reflected in the changing trend and its significance and the locations and area of corresponding zones.

As the first railway project crossing the Himalayas in the world, the construction of the China-Nepal railway is confronted with many environmental and geological problems such as high elevation, a big difference in elevation, alpine climate, seismic activity zones, soft-rock deformation, and geological disasters. Since the design and selection of route schemes of the railway line are notably restricted by geological conditions, it is necessary to thoroughly understand various geological problems in the study area. This study gives full play to the remote sensing technique to overcome the limitations of surface surveys, reduce the workload of field surveys, and improve work efficiency. Based on the analyses of existing data on basic geology, engineering geology, and geological environment, this study uses the multi-source remote sensing technique to conduct a detailed interpretation and analysis of the adverse geological elements in the study area, including terrain, landform, stratigraphic lithology, geological structures, hydrogeology, landslides, debris flow, and wind-blown sand. In this way, it provides detailed, comprehensive, and reliable remote sensing data for the engineering geological survey and the route design and selection of the China-Nepal railway and plays an important role in technical support.

It is significant for maintaining ecological security to study the impacts of the Three Gorges Dam Project on the surrounding ecological environment. At present, massive studies have revealed the impacts from the construction and water impoundment of the Three Gorges Dam on meteorology, vegetation, land use, and the occurrence of disasters. However, the impacts of the project on surface water-an important part of the Earth’s water resources-are still unclear, especially in the upper reaches of the Yangtze River. Based on multi-source data and the Google Earth Engine platform, this study analyzes the changes in permanent surface water, vegetation, and meteorological factors in the Chongqing area before (1990—2002), during (2003—2012) and after (2013—2019) the water impoundment of Three Gorges Dam Project. The results show: ① Both surface water and vegetation in the study area showed an increasing trend during 1990—2019 with different growth patterns and notably response to the water impoundment. In contrast, the temperature and precipitation continuously fluctuated but did not significantly respond to the water impoundment process during that period. ② The area of the permanent surface water in the study area increased at an annual rate of 18.32 km² during the water impoundment but did not greatly change before and after the water impoundment. The newly added permanent surface water was mainly distributed along the Yangtze River and its tributaries, especially in the middle part of the Chongqing section of the Yangtze River. Besides, a minority of it was distributed in some lakes and reservoirs. For example, the area of the Changshou lake increased by more than 20% during the water impoundment. ③ The normalized difference vegetation index (NDVI) increased by 18.55% in a stepwise way before, during, and after the water impoundment, which is attributable to joint effects of the increase in surface water and the restoration projects of degraded ecosystem. This study indicates that the water impoundment of the Three Gorges Dam Project has notable impacts on the spatial-temporal dynamics of the water resources in the Chongqing area. Meanwhile, it reveals effective evidence that the water conservancy projects can change the coverage and water resource distribution on the ground surface. All these will provide scientific basis for water resource management in the Chongqing area and even the whole Yangtze River Basin.

Soil is the largest potential reservoir of carbon, and the content of soil organic matter (SOM) is the key influencing factor of soil carbon storage. Therefore, SOM is an important index in the analysis of the changes in soil carbon storage. This paper aims to understand the optimal response bands in spectra to the SOM content in the process of coal mining and the changes in the temporal-spatial dynamic patterns of the SOM in a whole coal mining area. Based on the linear regression analysis of measured SOM, near-earth hyperspectral reflectance, and satellite multispectral reflectance, the SOM changes in the study area on June 1, July 4, and September 21, 2019 were quantitatively analyzed, and the SOM changes in underground coal mines (named Dahaize, Balasu, Nalinhe 2, and Yingpanhao) and their surrounding river basins were monitored. The SOM inversion results obtained using the first-order differential transformation of the near-earth hyperspectral reflectance were the closest to the measured SOM. A regression inversion model was established based on the extracted hyperspectral and multispectral characteristic bands and their correlation with the SOM. As indicated by the precision verification results, the correlation between the values predicted through SOM reversion and measured SOM values reached 0.90. Meanwhile, the SOM content in the study area was high in the east and low in the west and it gradually decreased along the upper, middle, and lower reaches of rivers and estuaries. The SOM content obtained through pre-mining simulation was 5% higher than that acquired via remote sensing-based estimation, indicating that coal mining affects the SOM content to a certain extent. It is also proven that the linear regression model of SOM inversion has the prospect of wide application. The above results will provide bases for quantitative research, management, and sustainable development of soil resources and ecological environment in the study area.

The identification of land-use conflicts is of great significance for maintaining the balance between ecological protection and the socio-economic development in oases in arid areas. Based on the data of land use status in 2000, 2010, and 2018 of Urumqi City-a valley oasis in the Urumqi River Basin, this paper constructs a comprehensive measurement model of land-use conflicts and further analyzes the distribution characteristics of the spatial pattern of land-use conflicts using the methods of spatial autocorrelation and cold/hot spot analysis. The results are as follows. ① The total area of land-use conflicts has increased year by year, and the area of lands with different land-use conflict levels is in the order of no conflict > moderate conflicts > mild conflicts > severe conflicts. Meanwhile, land-use conflicts are more liable to occur in grassland, cultivated land, construction land, and woodland. ② There is a significant spatial positive correlation between land-use conflicts in the study area, and the land-use conflicts are highly concentrated in terms of space. According to the analysis of cold and hot spots, it is found that from 2000 to 2018, the hot spots of land-use conflicts migrated from the northern part of the central urban area and southwestern parts to the eastern and southern parts of the urban area, with the distribution scope narrowing. In comparison, the cold spots of the land-use conflicts mainly concentrated inside the central urban area and the eastern and southern mountainous areas. ③ There is a positive spatial correlation between the land use degree and the land-use conflicts in the study area. C₀/(C₀+C) increased from 0.21 to 0.49 during 2000—2018, indicating that the spatial correlation weakened and the land-use conflicts are increasingly affected by random factors.

It is of great significance for the monitoring and supervision of tailing ponds in China to realize the rapid recognition of the spatial scopes of tailing ponds using the remote sensing technique. Based on the U-Net framework, this paper proposes a deep learning-based intelligent recognition method of the spatial ranges of tailing ponds using the remote sensing technique. The method proposed was verified in Honghe Hani and Yi Autonomous Prefecture in Yunnan Province using Chinese GF-6 satellite images. The results show that the precision, recall rate, and F1-score of the method were 0.874, 0.843, and 0.858, respectively, which were significantly better than those obtained using the methods of random forest, support vector machine, and maximum likelihood. Furthermore, the time consumption of the new method kept the same order of magnitude as that of the three methods. Therefore, the method proposed in this study has a broad application prospect in the rapid monitoring of the spatial scopes of tailing ponds in China.

This study investigates the spatial-temporal changes in the fractional vegetation cover in North Shanxi using the methods of inverse distance weighting and Pearson correlation analysis based on the GIS, RS, and SPSS22.0 platforms and the MODIS data of North Shanxi from 2000 to 2015. Meanwhile, it researches the driving factors of the vegetation changes according to the data of precipitation, air temperature, and social economy. The results are as follows. During 2000-2015, the overall fractional vegetation cover in the study area varied in the range of 0.35~0.55. Meanwhile, the spatial-temporal differences in the distribution of climatic factors produced different effects on the fractional vegetation cover. According to the results of principal component analysis, the contribution rates of GDP, rural population, total population, cultivated land area, precipitation, and air temperature were 41.4%, -38.3%, 35.7%, 32.8%, 21.3%, and 7.1%, respectively for the changes in the fractional vegetation cover. The study on the driving factors of vegetation cover will provide a scientific basis for future ecological protection.

With the in-depth development of the national fitness movement in China, the public’s health awareness has significantly increased, but sports and fitness facilities have gradually been unable to meet the people’s growing need for fitness. Based on the review and summary of the historical development, application scope, and application means of the geographic information system (GIS) technology in national fitness, this paper concludes that the GIS is primarily applied to the spatial data analysis, the resource allocation of fitness facilities, and the query and retrieval of fitness information in the field of national fitness by means of the spatial distribution characteristics analysis, accessibility assessment, and correlation verification of national fitness facilities. The GIS technology can efficiently address the issues concerning the layout of sports and fitness facilities, thus facilitating physical exercise activities of the public and significantly improving the effects of national fitness activities. Furthermore, the GIS technology will provide more effective technical support for national fitness activities in the scientific and technological progress and the development of multidisciplinary research.