The development of emerging technologies poses some risks while improving urban construction and human life, thus further causing urban safety problems. Tianjin is a coastal city in China, where the coastal sea level keeps increasing, water cycling is changed by the water supply of the South-to-North Water Diversion Project, and the underground space is subject to development and utilization. These factors, coupled with land subsidence, are all critical for the assessment of emerging risks in Tianjin. This study extracted information on the land subsidence of the southern plain in Tianjin and then predicted the retreat of the natural coastline in Tianjin by combining the sea level rise rate. Accordingly, this study predicted the high-risk factors brought by relative sea level rise in Tianjin using a machine learning method (XGBoost). In addition, this study analyzed the emerging risks caused by the South-to-North Water Diversion Project and the development and utilization of underground space and revealed the response patterns of the water supply and the construction and operation of subways to the urban safety of Tianjin. The study on the emerging risks brought about by the combination of land subsidence and modern human activities will provide a scientific basis for regional disaster prevention and mitigation and improve cities’ ability to resist disasters.

In the context of achieving peak carbon dioxide emissions and carbon neutrality, conducting a remote sensing-based ecological assessment and monitoring analysis is greatly significant for ascertaining the ecological condition in time and formulating scientific and reasonable ecological protection policies. The early remote sensing-based ecological assessment indices, simple and involving complex processes, are difficult to find wide applications. In contrast, the remote sensing ecological index (RSEI), contributing to elevated assessment efficiency, has been extensively used. To gain a deeper understanding of RSEI, this study describes its background, calculation method, and research status and provides a summary of the current issues and regional adjustments. Furthermore, it analyzes the main application directions of RSEI, namely the in-depth analyses of regional ecological assessment and change monitoring. Finally, the study proposes that despite a broad space for RSEI development, it is necessary to conduct research into the spatiotemporal scales of images, storage and batch processing capabilities, model adaption, and intelligentization.

With the fast development and wide application of remote sensing technology, remote sensing images with higher quality are needed. However, it is difficult to directly acquire high-resolution, multispectral remote sensing images. To obtain high-quality images by integrating the information from different imaging sensors, pansharpening technology emerged. Pansharpening is an effective method used to obtain multispectral images with high spatial resolution. Many scholars have studied this method and obtained fruitful achievements. In recent years, deep learning theory has developed rapidly and has been widely applied in pansharpening. This study aims to systematically introduce the progress in pansharpening and promote its development. To this end, this study first introduced the traditional, classical pansharpening methods, followed by commonly used remote sensing satellites. Then, this study elaborated on the pansharpening methods based on deep learning from the perspective of supervised learning, unsupervised learning, and semi-supervised learning. After that, it described and analyzed loss functions. To demonstrate the superiority of the pansharpening methods based on deep learning and analyze the effects of loss functions, this study conducted remote sensing image fusion experiments. Finally, this study presented the future prospects of the pansharpening methods based on deep learning.

Inland surface water bodies, including rivers, lakes, and reservoirs, are significant freshwater resources for human beings and ecology, and their monitoring and control are greatly significant. Optical remote sensing provides great convenience for the monitoring of surface water resources, proving to be an important means for the information extraction and dynamic monitoring of inland surface water bodies. This study reviews the basic principles, remote sensing data sources, methods, existing issues, and prospects of the information extraction of water bodies. Owing to the unique characteristics of the remote sensing images of inland surface water bodies, their information can be extracted in an accurate, scientific, and effective manner using remote sensing. Multiple remote sensing data resources can be applied to the information extraction, and the optical remote sensing-based extraction methods include the threshold value method, classifier method, object orientation method, and deep learning method. Given that different methods have unique advantages, disadvantages, and applicable conditions, selecting appropriate multi-source data and varying methods based on the conditions of study areas tend to improve the information extraction accuracy. Nevertheless, there still exist some issues in the optical remote sensing-based water body information extraction, such as the balance of spatiotemporal resolution of remote sensing data, the information mining of water body characteristics, the generalization ability of water body models, and the uniformity of criteria for accuracy evaluation.

The rapid and accurate estimation of soil water content at different spatial and temporal scales is key research content in the fields of hydrology, environment, geology, agriculture, and climate change. However, it is still a challenge to obtain accurate soil water content presently. In the past, the traditional point-based soil sampling and analysis methods were time-consuming and laborious. By contrast, retrieving soil water content using remote sensing images has the advantages of a wide range, high timeliness, low cost, and strong dynamic contrast. In hyperspectral remote sensing, soil water content is related to the wavelength range of soil reflectance. So far, many methods have been used to describe the relationships between soil water content and hyperspectral remote sensing. This paper summarized existing methods for estimating soil water content based on hyperspectral reflectance and divided them into four categories: spectral reflectance methods, function methods, model methods, and machine learning methods. Moreover, this paper compared and analyzed the potential and limitations of different methods in terms of accuracy, complexity, auxiliary data requirements, operability under different modes, and the dependence on soil types. Finally, this study put forward corresponding suggestions for future research on the relationships between soil water content and hyperspectral reflectance.

Atmospheric correction is an important preprocessing step for hyperspectral remote sensing images. The atmospheric correction quality determines the application degree of hyperspectral remote sensing to a certain extent. First, this study analyzed the influence of the atmosphere on radiative transfer and summarized the inversion methods of aerosol optical thickness and water vapor in the atmosphere, indicating the main atmospheric factors affecting the quality of hyperspectral remote sensing images. Then, the influence of the atmosphere was demonstrated theoretically by clarifying the derivation process of the radiative transfer equation and the action mechanism of relevant parameters, indicating the main aspects of hyperspectral atmospheric correction. Furthermore, this study summarized the hyperspectral atmospheric correction methods formed in recent years, including methods based on empirical statistics and radiative transfer, and analyzed the study advances and development trends of hyperspectral atmospheric correction. Finally, this study forecasted the development of atmospheric correction of hyperspectral remote sensing images. This study will provide a certain reference for the engineering application and study of hyperspectral remote sensing.

To scientifically evaluate the land suitability of urban functional areas and to accurately assess the intensive urban land use (IULU) in Hohhot City, this study built an indicator system by integrating the industry survey data and the features extracted from remote sensing images. Then, it assessed the urban function zoning and the IULU in Hohhot through quantification and integration based on land. The results show that 93.0% of the functional areas share common multivariate quantitative characteristics, indicating suitable functional orientation and land use. Moreover, this study built a high-precision multivariate regression model using remote sensing factors (i.e., the principal components of images and the proportions of the shadow and vegetation areas) and survey data (i.e., carbon stock, building density, and the land prices of residential and commercial functional areas). Then, the floor area ratio was calculated based on the model, thus achieving the quantitative assessment of the IULU. The results of this study show that the assessment of IULU based on remote sensing images and industry survey data is feasible and has value in popularization and applications.

Since the availability of global runoff data decrease year by year, the inversion algorithms, as substitutes for the river discharge measured at hydrological stations, have become increasingly important. With the continuous development of satellite remote sensing technology, the methods for estimating river discharge have increased in number. This study systematically summarized the remote sensing-based inversion methods for river discharge, as well as the inversion methods for hydraulic remote sensing elements that are closely related to the estimation of river discharge and the progress made in them. Moreover, this study reviewed the methods, principles, and application status of two types of algorithms based on hydrological models and empirical regression equations and summarized the applicable conditions and shortcomings of different methods. Finally, this study predicted the worldwide development trends of the river discharge inversion based on the satellite remote sensing technology, including ① actively developing the advanced data assimilation technology for satellite remote sensing data; ② integrating new sensor products; ③ optimizing and innovating algorithms.

The remote sensing information extraction of vegetation is the basis and key link for remote sensing investigation and dynamic monitoring of vegetation coverage, which is of great significance for regional ecological environment protection and sustainable development. For this purpose, the research progress on the remote sensing information extraction methods of vegetation was reviewed from prior knowledge, expert knowledge and related auxiliary information, extraction of vegetation phenological features, the fusion of multi-source remote sensing data, machine learning, and other methods. Then, the main problems and challenges existing at the present stage were pointed out, and the future development trend was put forward. The research shows that there are many methods to extract remote sensing information about vegetation, and different methods have their own advantages and disadvantages in the application. However, the research on remote sensing information extraction methods of vegetation is currently facing many challenges, such as the lack of openness of high-resolution remote sensing data, the poor stability of parameter settings in vegetation information extraction models, the prominent phenomenon of same objects with different spectra and different objects with the same spectrum, the difficulties in automatic extraction of vegetation remote sensing information based on an expert knowledge base, and the need in further research on the multiple-method fusion. Therefore, making more breakthroughs in integrating multi-source data, multiple methods and new features of multi-temporal remote sensing images will be necessary to promote the refined, automated, and intelligent development of remote sensing information extraction of vegetation.

The principal component analysis is a widely used method for dimensionality reduction of hyperspectral remote sensing images. In task-oriented work, the principal component selection method based on cumulative variance contribution rate is not ideal. To address the problem of principal component selection after principal component analysis transformation, a method of principal component selection based on spatial statistics is proposed. The selection of principal components is performed by calculating the values of the semi-variogram parameter range and partial sill/sill of each principal component. The magnitude of a range is used to judge the range of spatial correlation of each principal component, and the partial sill/sill is used to judge the strength of spatial correlation of each principal component. The simulation proves that the variable range and partial sill/sill can effectively express the range and strength of spatial correlation of hyperspectral remote sensing images. Based on the experiment of real hyperspectral remote sensing images, the empirical threshold of principal component selection is determined from subjective and objective aspects, that is, the range is 2.5, and the partial sill/sill is 0.2. According to the classification results based on the support vector machine algorithm, compared with traditional methods, the principal components with better image quality can be screened by using variable range and partial sill/sill, which can not only achieve the purpose of dimensionality reduction, but also ensure high classification accuracy.

To obtain the distribution of nighttime light pollution on a city scale, this study monitors the nighttime light pollution in Nanjing City based on Luojia 1-01 nighttime light remote sensing images. The apparent radiance of the remote sensing images was converted into the surface incident luminance according to surface reflectance and building coverage ratio. Based on this and the illuminance values observed in the field, various empirical models were established to calculate the nighttime illuminance of Nanjing City. Finally, the distribution of nighttime light pollution in Nanjing City was analyzed according to the calculated nighttime illuminance. The results show that the third-order polynomial regression model had the highest accuracy, with a determination coefficient of 0.87 and a mean absolute error (MAE) of 4.71 lx. The nighttime illuminance in Nanjing City varied in the range of 0~55 lx, with obvious spatial distribution differences. In general, the areas with high illuminance were mainly concentrated in the main urban area and the illuminance showed a decreasing trend from the main urban area to the surrounding area. Light pollution was the most serious in Gulou and Qinhuai districts, where light pollution covered more than 70% in terms of area. The light pollution in the suburb was relatively weak, and the three districts with the weakest light pollution included Gaochun, Lishui, and Liuhe districts successively, where light pollution covered less than 4% in terms of area. Some areas in Nanjing City showed extremely high illuminance (> 30 lx), including large shopping malls, large factories, traffic hubs, roads, and some residential areas. It should be noted that there are many residential areas near these places except for traffic hubs and large factories. This study explored a method of monitoring urban light pollution at night based on Luojia 1-01 remote sensing data. It will provide data support for the light pollution control and management in Nanjing City and a scientific reference for the light pollution monitoring in other areas.

The exploitation of rich and unique mineral resources in Hainan Island has promoted economic growth but has also caused serious ecological environment problems. Analyzing the impacts of mining in Hainan Island and proposing suggestions on ecological restoration facilitate the protection and management of the ecological environment in Hainan Island. To this end, this study obtained the information on land destruction and ecological restoration of mines in Hainan Island using 2018 remote sensing images with high spatial resolution through image preprocessing, establishing interpretation indicators, and man-machine interactive interpretation. Specifically, with the information on land destruction and ecological restoration of mines as input, the assessment indicator system for mine geological environment was established based on 13 assessment factors of four categories, namely physical geography, basic geology, resource damage, and geological environment. Then, this study analyzed and assessed the effects of the geological environment of mines based on the analytic hierarchy process, obtaining the following results. The severely affected areas account for 0.22% of the total land area of Hainan Province and are mainly distributed in Wenchang City, Ledong Li Autonomous County, Xiuying District of Haikou City, Chengmai County, Lin’gao County, and Changjiang Li Autonomous County. The mine geological environment problems in these areas mainly include secondary geological disasters such as mining collapse of goaves and landslides caused by the mining of large-scale iron ore mines, as well as soil erosion and ecosystem degradation caused by the mining of coastal zirconium-titanium placers. The moderately severely affected areas account for 1.68% of the total land area of Hainan Province and are mainly distributed in Wenchang City, Danzhou City, Chengmai County, Qionghai City, Lin’gao County, Haikou City, and Dongfang City. The mine geological environment problems mainly include land damage caused by landslides induced by the mining of small- and medium-sized iron ore mines, as well as severe impacts on original terrain and landforms and the natural ecological environment caused by mining. The generally affected areas account for 4.93% of the total land area of Hainan Province and are mainly distributed in the coastal areas in the eastern part, the economically developed areas in the middle and northern parts, and the area with rich metallic minerals in the western part in Hainan Province. The mine geological environment problems in these areas mainly include the destruction of the surface landforms and natural vegetation caused by the mining of the scattered small nonmetal mines of building materials. This study proposed ecological restoration countermeasures targeting the different geological environment problems. For metal mines, it is suggested to primarily restore the ecosystem by natural restoration methods, supplemented by artificial restoration methods based on the elimination of geological hazards, soil improvement, and water environment management. For zirconium-titanium placers and nonmetal mines of building materials, it is recommended to restore vegetation to prevent water and soil erosion. For the coastal mine areas with severe desertification, it is recommended to gradually restore the ecosystem of the mining areas by growing crops such as watermelons and peanuts to improve soil and planting trees such as casuarina and Vatican hainanensis.

This study performed a multi-scale error analysis of the mean surface downward shortwave radiation flux product ERA5 (0.25° × 0.25°) of the European Centre for Medium-Range Weather Forecasts (ECMWF) using 93 pieces of solar radiation hourly data in 2013 of China. Subsequently, this study revised and analyzed the total radiation product ERA5 by training the random forest model using various relevant elements such as meteorological and geographic ones. Finally, the model was used to obtain the map of revised hourly radiation spatial distribution. As a result, the reanalyzed data can be better applied in industries such as agriculture, electric power, and urban construction. The results are as follows. ① The MAE, RMSE, and R values between the ERA5 solar radiation and the measured values of stations in 2013 were 27.60 W/m², 29.87 W/m², and 0.97 respectively. Moreover, the ERA5 values were higher than the measured values of stations. ② The accuracy was improved after the revision using the random forest model. After revision, the MAE, RMSE, and R values between the ERA5 solar radiation and the measured values of stations were 3.34 W/m², 3.85 W/m², and 1.00, respectively, indicating that correlation was significantly improved. ③ The spatial macroscopic distribution patterns of radiation before and after revision were consistent, but the ERA5 radiation value significantly decreased in local areas.

The vegetation optical depth (VOD) serves as a microwave-based method for estimating vegetation water content and biomass. Compared to optical remote sensing, the satellite-based VOD, exhibiting a lower sensitivity to atmospheric disturbances, can measure the characteristics and information of vegetation in various aspects, thus providing an independent and complementary data source for global vegetation monitoring. It has been extensively applied to investigate the effects of global climate and environmental changes on vegetation. Discerning the research advances of VOD application in the dynamic monitoring of global vegetation is critical for VOD’s further development and application. Hence, this study first presented the primary methods for obtaining the VOD through inversion of passive and active microwave data, comparatively analyzing the principal characteristics of various sensor VOD products. Then, this study generalized the current research advances of VOD in the dynamic monitoring of vegetation in terms of vegetation characteristic monitoring (like vegetation water content and biomass), carbon balance analysis, drought monitoring, and phenological analysis. Finally, this study expounded the advantages, limitations, and improvement approaches of VOD products, envisioning the application prospect of VOD in the dynamic monitoring of vegetation.

Predicting the subsequent subsidence in mining areas according to the law of mining subsidence is the key to assessing mining risks and adjusting mining planning. This study determined the available conditions of the logistic model for mining subsidence prediction through analysis and simulation experiments and proposed a method for the dynamic prediction of mining subsidence based on small baseline subset (SBAS)-interferometric synthetic aperture radar (InSAR) technology and the logistic model. Firstly, the time-series subsidence data of a mining area was obtained using the SBAS-InSAR technology. Then, taking the time series subsidence data as the data for fitting, the parameters of the logistic model were calculated pixel by pixel by using the trust region algorithm. Then, the pixel range in which the subsequent subsidence can be predicted was determined according to the available conditions of the logistic model. Finally, according to the Logistic model, the subsequent subsidence within the predictable range was predicted. This method was applied to a certain mining area in Erdos City, Inner Mongolia for tests, and the prediction results were verified using the InSAR monitoring results of corresponding dates. The predicted results after 36 days and 108 days of ming had the root mean square error (RMSE) of 0.010 1 m and 0.023 6 m, respectively, and their proportion with prediction errors of less than 0.03 m reached 98.9% and 89.3%, respectively. These results indicate that the method for dynamic prediction proposed in this study has high prediction accuracy.

Determining the present distribution of historically abandoned mines nationwide and carrying out orderly ecological rehabilitation of these mines are important parts in the preparation of mine ecological rehabilitation planning and serve as the main bases for the deployment of ecological rehabilitation engineering. This study proposed the technical process and method for determining the historically abandoned mines according to the definition of historically abandoned mines and the public management requirements. This technical method was proven effective through tests.

In order to realize the refined line inspection management of transmission lines, improve its operation and maintenance efficiency, realize satellite intelligent inspection, and accurately find the defects and hidden dangers of towers and transmission lines, the paper took the coordinates of transmission line towers in Kunming City, Yunnan Province as an example and proposed a method to calibrate the coordinates of transmission towers using satellite images. The method first uses the reference base-map data as the basis to match the control points and uses the digital elevation model (DEM) to perform geometric correction on the original remote sensing image. Then combined with such technologies as shadow detection and edge detection and visual interpretation, the calibrated tower coordinates are obtained. The experiment verified the geometric correction accuracy of the SuperView-1 (SV1) and Gaofen-2(GF2) satellite images in the Kunming area, and the errors in the plane after correction were 0.931 and 1.387 m, respectively. In addition, the experiment verified the calibration accuracy of the old tower coordinates on the two lines. The results show that the plane accuracy of the tower has increased from 13.811 m and 8.256 m to 5.970 m and 5.104 m, respectively, which meets the basic power grid requirements. This method can realize the calibration of the tower coordinates, reduce the workload of manual inspection, and improve the efficiency of line inspection. With the explosive growth of remote sensing image data, multi-source images from the space and ground will continue to be combined, and the technology for the positioning of transmission towers based on satellite remote sensing images will have a broader development prospect.

China is one of the countries with frequent landslide disasters. In recent years. In recent years, more than 70% of the catastrophic geological hazards have occurred not within the scope of known hidden danger points of geological hazards in China. Therefore, there is an urgent need for investigating large-scale landslide disasters using automatic and efficient technologies and methods for landslide identification. To quickly identify the location of landslides from massive remote sensing images, it is necessary to determine the key areas of landslides to support subsequent interpretation and research. This study investigated loess landslide identification based on GF-1 images and digital elevation model (DEM) data. First, a database of remote sensing images and DEM landslide samples was constructed. Second, the landslide samples were classified using the channel fusion convolutional neural network model. Finally, the classification results were restored to the remote sensing images according to the location information. Experimental results showed that the model yielded landslide identification accuracy of 95.7% and a recall rate of 100.0%. The model used in this study has a small number of network layers, a high convergence speed, and higher efficiency and identification accuracy. As a result, it allows for the quick identification of key landslide areas from remote sensing images in the case of a limited number of samples, thus supporting the investigation of large-scale landslide disasters.

Water information extraction is an important study direction in the application of high spatial resolution remote sensing images. Conventional recognition methods only focus on the shallow features of water. Therefore, to further improve the robustness of water information extraction algorithms and increase the segmentation precision by extracting more deep information from remote sensing images, this study proposed a water classification method using the semantic segmentation model based on deep learning. First, deep neural networks were used to mine the information from high-resolution remote sensing images. Then, attention modules were used to integrate the deep information with the shallow features such as shape, structure, texture, and hue. Based on the integrated information, a new deep semantic segmentation model with higher precision and prediction efficiency than existent models was built. Finally, the ablation experiment was conducted to compare with conventional recognition methods and common semantic segmentation models. The experiment demonstrates that the proposed algorithm model yields higher overall precision and efficiency than previous methods and that the algorithm parameters are easy to set and less human intervention is required in the model. This study proved the accuracy and efficiency of deep learning and attention mechanism on water information extraction from high-resolution remote sensing images. Moreover, this study provided a possible solution for the segmentation of high-resolution remote sensing images using the deep learning method and explored the future prospect of the solution.

Snow proves to be both an important factor in characterizing the surface cryosphere and a critical parameter for weather and hydrological phenomena. Employing remote sensing to conduct long-term and large-scale monitoring of snow morphologies and their changes plays a vital role in research into global climate change, investigations into hydrology and water resources, and geological disaster prevention. After decades of development, significant progress has been made in the field of remote sensing-based snow monitoring technology both in China and abroad. Accordingly, the products for remote sensing-based snow monitoring have become increasingly abundant, and the snow-orientated inversion algorithms have been continuously improved. This paper provides a summary of the existing, widely applied products after categorizing them into three types: snow-cover extent (SEC), snow coverage, and snow depth/snow water equivalent (SWE) products. Furthermore, this study organizes the commercialized remote sensing inversion algorithms used in existing, typical SEC and SWE products. The review of advances in the relevant scientific research reveals that, with the constant presence of sensors with high temporal and spatial resolutions in China and abroad and the support of both novel optical and microwave data sources and new technologies, researchers have gradually improved the accuracy of snow-orientated inversion algorithms by optimizing these algorithms based on regional characteristics. This will provide more support for continuously improving remote sensing-based snow monitoring products in the future.

The typical crop cotton in the Ugan-Kuqa River Delta Oasis was used as the research object to study the applicability and optimization process of the deep learning method in the identification of cotton distribution in arid areas. Based on the domestic GF-2 images and the field survey data, the Unet deep learning method was adopted, in which the characteristics of the Unet network’s multiple convolution operations were fully utilized to explore the deep-level characteristics of cotton in remote sensing images, thereby improving the precision of cotton extraction. The results show that the recognition effect of the Unet model to extract cotton, corn, and peppers in the study area is better than the classification results of the object-oriented method and the traditional machine learning algorithms. The overall precision is 84.22%, and the Kappa coefficient is 0.804 7. Compared with the object-oriented method and the traditional machine learning algorithms SVM and RF, the overall precision has increased by 7.94 percentage points,11.93 percentage points, and 11.73 percentage points, respectively, and the Kappa coefficient has increased by 10.13%, 14.72%, and 14.60%, respectively. In the classification results of the Unet model, both the mapping precision and the user precision of cotton are higher than those of the other three methods, which are 94.95% and 89.07%, respectively. Therefore, it is feasible and reliable to use the Unet model to extract high-precision cotton spatial distribution information of arid areas on GF-2 high-resolution remote sensing images.

Tropical forests play a vital role in biodiversity conservation and research on global climate change. However, the complexity and diversity of vegetation types pose challenges to the fine remote sensing-based classification of tropical forests. The classification of tropical forests in the Jianfengling area, Hainan Province was analyzed using the multi-temporal Landsat8 data of the Google Earth Engine (GEE) platform. Based on the analysis of the impacts of the size and combination of multi-temporal data on the classification accuracy, this study proposed a classification method based on multi-temporal Landsat8 images for the vegetation type groups of tropical natural forests, such as typical tropical rain forest, tropical monsoon forest, and evergreen broad-leaved forests. The results are as follows. ① The classification accuracy of tropical natural forests was significantly improved as the size of multi-temporal data increased. The classification accuracy of the vegetation type groups of natural forests in Hainan Island reached 91%. ② When the multi-temporal data reached a certain size, the classification accuracy tended to be stable. Different combinations of multi-temporal data can improve the classification accuracy of tropical forests, especially when the classification accuracy of individual data involved was low. This finding reflects the broadness of the selection of temporal data. The proposed method, taking advantage of the temporal changes in remote sensing data, provides an effective reference for the remote sensing-based classification of tropical natural forests in Hainan Island.

Fast extraction of buildings with high accuracy from remote sensing images is an important research of remote sensing intelligent application services. To address the problems of imprecise segmentation of building edge in remote sensing images, holes in large-scale target segmentation, and a large amount of network parameters in the DeepLab model, a lightweight DeepLabv3+ model for building extraction from remote sensing images is proposed. In this method, the lightweight network MobileNetv2 is used to replace Xception, the backbone network of DeepLabv3+, so as to reduce the number of parameters and improve the training speed; The hole rate of hole convolution in ASPP is optimized to improve the effect of multi-scale semantic feature extraction. The improved model has been tested on WHU and Massachusetts data sets. The results show that the IOU and F1 score in WHU dataset are 82.37% and 92.89%, respectively, 2.71 percentage points and 2.14 percentage points higher than those of DeepLabv3+, 2.04 percentage points, and 2.32 percentage points higher than those of DeepLabv3+ in Massachusetts data set. The number of training parameters and training time is reduced, and the accuracy of the building extraction is effectively improved, which can meet the requirements of fast extraction of high-precision buildings.

With the trend towards the precise and digital planting management of orchards, apple cultivation relies more heavily on the planting management supporting technologies of orchards. In recent years, continuous breakthroughs made in spatial resolution and revisiting frequency have made remote sensing technology a major supporting technology for the precise planting management of apple orchards. However, there is an absence of reviews of the application status and prospect of this technology in the planting management of orchards. Based on the analysis of primary applications of remote sensing technology in the precise planting management of apple orchards, this study classified the applications into three major categories, namely the surveys of basic orchard information, inversions of orchard parameters, and the planting management support of orchards. Furthermore, this study reviewed the methods and performance of the applications of remote sensing technology in various fields and explored the application potential. Finally, it identified three types of problems with current research and application of remote sensing technology, namely insufficient studies on mechanisms and in some application fields, low-degree integration of multiple technologies, and the lack of large-scale application models. In addition, this study proposed four hot research and application topics in the future, namely models used to simulate the growth mechanisms of apple trees, the integrated support system for the planting management of apple trees, the single-tree monitoring based on satellite data, and the diversified services of remote sensing-based monitoring products.

Impervious surface is a key factor to measure the urban ecological environment. It is of great significance for urban development planning to grasp the dynamic changes of impervious surfaces timely and accurately. Taking the Chenggong District of Kunming City as an example, based on the Landsat images in 2007, 2011, 2015, and 2019, the comparative study of normalized difference impervious surface index (NDISI) and modified soil adjusted vegetation index(MSAVI) was carried out to analyze the spatial and temporal evolution characteristics of impervious surface. The results showed that: ①As the extraction accuracy and Kappa coefficient of NDISI were 87.01% and 0.81, respectively, which were better than MSAVI’s 81.78% and 0.75, this paper selected the NDISI method to extract impervious surfaces in the Chenggong District;② the impervious surface area extracted in this paper increased from 46.12 km² in 2007 to 72.64 km² in 2011, 146.94 km² in 2015 and 164.42 km² in 2019, especially from 2011 to 2015, the impervious surface area had the fastest growth rate and nearly doubled. The changes to the impervious surface in Chenggong District are mainly influenced by such factors as national policies, urban planning, topographic factors, and traffic development. The impervious surface area along the Dianchi Lake in the west of Chenggong District and several administrative regions in the middle of Chenggong District developed rapidly, which brings certain pressure on the prevention and control of waterlogging in urban areas and the Dianchi Lake area. In the process of future urban planning, the expansion scope and speed of impervious surfaces should be well controlled to avoid ecological and environmental problems caused by the unreasonable spatial patterns of impervious surfaces.

The remote sensing-based feature extraction of opencast mining areas is a hot topic in research on the monitoring of mining activities. However, there is a lack of systematic reviews and summaries of relevant studies. Therefore, this study first defined the features of an opencast mining area, divided the feature extraction into single- and multi-feature extractions according to feature types, and briefly described the differences between the feature extraction of opencast mining areas and general surface feature extraction and land use classification. Then, this study briefly summarized the sources and data processing platforms of remote sensing images available in relevant studies. Subsequently, this study divided the remote sensing-based methods for the feature extraction of opencast mining areas into three categories, namely visual interpretation, traditional feature-based approach, and deep learning. Then, it summarized the research status of these methods and analyzed their advantages, disadvantages, and applicability. Finally, this study proposed the future research direction of the remote sensing-based feature extraction of opencast mining areas, holding that the future developmental trend is to further promote the intelligent, fine-scale, and robust feature extraction of mining areas by effectively utilizing multi-source and multi-temporal data, networks with a stronger feature extraction capacity, and methods for the optimization of complex scenes. The results of this study can be used as a reference for the study and application of remote sensing-based feature extraction of opencast mining areas.

The mariculture industry occupies an important position in the marine economy of Guangdong Province. Timely and accurate knowledge of the spatial distribution and area changing trends of mariculture areas can greatly promote the sustainable development of the mariculture industry. Conventional interpretation methods for remote sensing images have problems of poor repeatability, low applicability, and high subjective arbitrariness. By contrast, the U-Net convolutional neural network, which belongs to the deep learning network model, can better extract the features of the object with higher extraction precision. Therefore, based on the multi-temporal Landsat TM/OLI remote sensing images, this study identified the mariculture areas (enclosed-sea and open-cage aquaculture areas) in Guangdong from 1998 to 2021 using the U-Net model as the interpretation model. The area trend analysis of mariculture areas was made. The changing characteristics of mariculture areas in terms of spatial distribution patterns were studied. The results are as follows. Compared with network models such as K-Means cluster analysis and DBN, the U-Net model with higher interpretation precision is more suitable for the interpretation of mariculture areas in Guangdong. The mariculture areas in Guangdong are mainly distributed in the western portion of Guangdong, such as Zhanjiang, Jiangmen, and Yangjiang. The mariculture areas in Guangdong can be classified into three levels in terms of area. They have small changes and keep a relatively stable state. The mariculture areas in Guangdong showed a spatial trend of outward expansion from 1998 to 2014 and inward contraction from 2014 to 2021. This study will provide data and technical support for the scientific management of the mariculture areas in Guangdong.

Net ecosystem productivity (NEP) represents the carbon sequestration capacity of a regional ecosystem. Based on the Google Earth Engine (GEE) platform, this study analyzed the temporal and spatial variations in the NEP of the Three-River Headwaters Region (TRHR) from 2001 to 2020 based on the Moderate Resolution Imaging Spectrometer (MODIS) and meteorological data and revealed their relationships with climate factors. The results are as follows: ① The TRHR had an important carbon sink function, with carbon sink areas accounting for 99.89%; The carbon source areas in the TRHR were primarily distributed in the northwest, accounting for only 0.11%. The NEP of the TRHR decreased gradually from the southeast to the northwest and differed significantly among different ecological areas; ② The NEP of the TRHR showed an upward trend overall in the past 20 years, with an annual increasing rate of 1.13 gC/(m²·a), indicating huge carbon sequestration potential; ③ The area of zones whose NEP showed an upward trend accounted for 95.05% of the total area. Ecological engineering construction significantly improved the NEP of vegetation. As a result, the carbon sink function gradually increased and was highly stable; ④ The TRHR had an annual average NEP of 120.93 gC/(m²·a), and the NEP was positively correlated with the annual precipitation but negatively correlated with average annual temperature and annual solar radiation. The warm, humid climate and the ecological engineering construction contributed to the carbon sink function of vegetation in the TRHR. This is of great significance for improving the carbon sink value of the terrestrial ecosystem and achieving the peak carbon dioxide emissions and carbon neutrality of China.

As a typical and important ground target, the bridge is the vital passage between transportation lines, so automatic detection of a bridge is of great social and economic significance. Deep learning has become a new way of bridge detection, but the detection accuracy for bridges obscured by cloud and mist is low. In order to solve this problem, an automatic bridge target detection method combining Random erase (RE) data enhancement and the YOLOv4 model is proposed: firstly, the scale range of the target in the data set is determined, and the candidate frame size is obtained by K-means clustering; secondly, the cloud obscuration is simulated by a combination of RE and mosaic data enhancement; thirdly, the enhanced data set is trained by YOLOv4 network; and finally, the mean Average Precision (mAP) is used to evaluate the experimental results. The experimental results show that the detection accuracy obtained by mAP is 97.06%, which is 2.99% higher than that of traditional YOLOv4, and the average detection accuracy of bridges obscured by a cloud is improved by 12%, which verifies the effectiveness and practicability of the proposed method.

Water is an important factor in the formation and maintenance of wetland ecosystems. Monitoring the changes in the water area of wetlands is of great significance for wetland conservation. Taking Sentinel-1A data from 2018 to 2019 as the data source, this study calculated the intra- and inter-annual synthetic aperture radar (SAR) backscattering coefficient (σ⁰) and coherence coefficient (μ⁰) images of the Zhalong Wetland. Then, this study assigned weights according to the proximity to water bodies of color optical images and extracted the weighted images of σ⁰ and μ⁰. Finally, this study extracted the wetland water bodies using the threshold segmentation method and random forest algorithm. The purpose is to monitor the dynamic variations in the wetland water area and explore the intra- and inter-annual variation rules of the wetland water body. The results are as follows. The random forest algorithm yielded the highest extraction accuracy of water bodies, with an absolute value of the mean difference of representative months was 6.69 km². The threshold segmentation method based on μ⁰ images yielded the lowest classification accuracy of water bodies, with an absolute value of the mean difference of 13.07 km². Overall, the intra-annual water area of the Zhalong Wetland showed significant seasonal variations during 2018—2019. The water area fluctuated in the ranges of 1 300~1 600 km² during late spring and early summer and 700~900 km² during late summer and early autumn. The inter-annual water area varied with conditions such as climate and temperature. In particular, the wetland water area in October and November 2019 was approximately 1 050 km² greater than that in 2018 due to large amounts of rainfall. As shown by the calculation based on effective data, the water area in 2019 was about 550 km² greater than that in 2018 in the Zhalong Wetland.

In order to explore the development trend of border cities in China and assess the city’s border defense capability, the D-LinkNet34 deep learning algorithm is used to automate the extraction of buildings and roads in Tuolin, Shiquanhe and Pulan towns in Tibet Autonomous Region, and to analyze the development trend and border defense capability of border towns based on landscape index and population size. Analysis results show that: ① The extraction method based on D-LinkNet deep learning network can effectively further classify urban construction land, with average total progress of more than 80% and IOU above 70%.② The distribution of plaques in the towns of Pulan and Shiquanhe shows a trend of aggregation, and the trend of urban expansion weakened. The distribution of plaques in Tuolin Town shows a scattered trend, and the trend of urban expansion is obvious. ③ The building area is linearly related to the resident population, and the building area of Tuolin Town increased by about 68.75%from 2002 to 2018, and the resident population increased by about 39.00%. The building area of Shiquanhe Town increased by about 70.75% from 2004 to 2020, while the resident population increased by about 68.44%. The building area of Pulan Town increased by about 68.36% from 2005 to 2018, while the resident population increased by about 25.04%. This study provides a new method for quantitative evaluation of the expansion characteristics and border defense capability of border cities, as well as a reference for building China’s border defense capability.

Vehicle detection is a hot research topic in the fields of computer vision, photogrammetry, and remote sensing. With the continuous development of deep learning technology, vehicle detection based on remote sensing images has been applied in fields such as smart city construction and intelligent transportation. This study systematically summarized existent vehicle detection algorithms based on remote sensing images and deep learning models and highlighted the classification, analysis, and comparison of one-stage and two-stage vehicle detection algorithms. Moreover, this study summarized the key technologies of vehicle detection in large-scale and complex backgrounds and analyzed the advantages and disadvantages of mainstream deep learning models of vehicle detection based on remote sensing images. Experiments were conducted to evaluate the YOLOv5, Faster-RCNN, FCOS, and SSD algorithms using DOTA and DIOR datasets. The vehicle detection precision based on the DOTA dataset was 0.695, 0.410, 0.370, and 0.251, respectively and that based on the DIOR dataset was 0.566, 0.243, 0.231, and 0.154, respectively. The experimental results show that the small target scale is still the main factor restricting the vehicle detection performance based on remote sensing images and that the application of deep learning models to the detection of small targets is to be further improved. Finally, based on public datasets and the analysis of existing algorithms, this study proposed the solution and development trend of vehicle detection based on remote sensing images in large-scale and complex backgrounds.

The remote sensing image fusion technology can combine multi-source images containing complementary information to obtain images with richer content and higher spectral quality, thus it is the key and foundation of remote sensing applications. Aiming at the problems of spectral distortion and spatial structure distortion that are prone to occur in the process of remote sensing image fusion, the knowledge-based remote sensing image FuseNet (RSFuseNet) was constructed based on the attention mechanism and using normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) as prior knowledge. Firstly, considering that the high-pass filtering can fully extract edge texture details, a high-pass filtering module was constructed to extract high-frequency details of panchromatic images. Secondly, NDVI and NDWI were extracted from multi-spectral images. Then, an adaptive squeeze-and-excitation (SE) module was constructed to recalibrate the input features. Finally, the adaptive SE module was combined with the convolution unit to perform fusion processing on the input features. The experiment was conducted using Gaofen 6 remote sensing image as the data source, and selecting Gram-Schmidt (GS) transformation, principal component analysis (PCA), a deep network architecture for pan-sharpening (PanNet), and pansharpening by convolutional neural networks (PNN) models as comparative models. The experimental results show that the peak signal to noise ratio (PSNR) index (40.5) and the structural similarity (SSIM) index (0.98) of the RSFuseNet model are better than those of comparative models, indicating that the method in this study has obvious advantages in remote sensing image fusion.

Compared with common dual-temporal satellite images, satellite time series images contain richer surface information and can alleviate the impact of foreign objects with the same spectrum and the same object with different spectra. Therefore, they play an important role in change detection. However, the change detection methods for satellite time series images are mostly based on pixels and ignore the spatial relationship between pixels and their surroundings. This causes noise in the change detection result. Accordingly, this study proposed a method of change detection based on spatial-temporal-spectral features(CDSTS) for satellite time series images. First, the temporal, spatial (textural and statistical), and spectral features of each pixel were extracted from Landsat time series images using a gray-level co-occurrence matrix and local statistical calculation methods. Then, anomalies of time series features were automatically screened according to the time series performance regularity of each pixel in different bands. These anomalies were then fused with the detection results of the continuous change detection and classification method (CCDC) to obtain high-precision changed/unchanged training sample points. Finally, the SVM classifier was trained using the training sample points and their corresponding spatial-temporal-spectral features for full graph classification. The results show that the CDSTS algorithm significantly outperforms the commonly used time series change detection algorithms CCDC and COLD (continuous monitoring of land disturbance) in terms of change detection precision, with the overall precision improved by 4.8 to 11.7 percentage points.

The Axi mining area in Xinjiang has a complex geographical environment. The long-term exploitation of mineral resources has caused severe ground subsidence and deformation in the mining area, as well as safety hazards of mining and production and the destruction of the surrounding ecological environment. This study aims to further investigate and analyze the spatial-temporal variation characteristics of the ground subsidence and the patterns of surface deformation in the Axi mining area. To this end, this study first calculated the land subsidence using the small baseline subset-interferometric synthetic aperture Radar (SBAS-InSAR) technique based on the 127 scenes descending Sentinel-1A images acquired from February 9, 2017 to April 25, 2021. Then, it compared the subsidence monitoring results obtained using the InSAR technique with the leveling results for verification. Finally, this study analyzed the spatial-temporal variation characteristics of land subsidence in the Axi mining area in recent five years and investigated the driving factors for the land subsidence. The results show that the surface deformation of the Axi mining area showed a roughly stable trend and significant local subsidence throughout the monitoring period. The main factors affecting the ground subsidence included mineral exploitation, geological structure, precipitation, and the impoundment of open-pit mines. This study will provide a scientific basis for ground subsidence monitoring and the future proper exploitation of underground minerals in the Axi mining area.

Water conservation is one of the most important functions of an ecosystem and can maintain and provide water resources for the ecosystem and humans. According to the physical meaning of water conservation, this study used leaf area index, vegetation coverage, and evapotranspiration to represent the water conservation of the vegetation layer and used surface temperature, soil moisture content, and slope to represent the water conservation capacity of the soil layer. Then, this study developed a remote sensing monitoring and evaluation model for water conservation through principal component analysis to explore the spatial-temporal distribution characteristics of the water conservation capacity in the Three Gorges reservoir area. The results show that the water conservation index (WCI) contained the objective information of various indices, could be used to quickly and conveniently assess the water conservation function in the Three Gorges Reservoir area, and properly represented the water conservation capacity there. In 2019, the water conservation capacity was unevenly distributed in the Three Gorges reservoir area and was high downstream and low upstream. The northeastern part of Chongqing was dominated by forest ecosystems and had the strongest water conservation function. From 2013 to 2019, the WCI slightly increased in most areas, especially in some parts of Fengdu, Kaizhou, and Yunyang areas.

The evaluation of ecosystem service value (ESV) is an important basis for formulating policies regarding ecological protection, ecological compensation, and the accounting for natural resource assets. An in-depth study of the characteristics and driving factors of the spatiotemporal changes in the ESV in Xiangxi Tujia and Miao Autonomous Prefecture, Hunan Province, China is greatly significant for ecological control and protection. This study analyzed the changes in land use based on seven phases of 1990—2018 land use data of Xiangxi and evaluated the ESV in Xiangxi using the equivalence factor method. Moreover, it analyzed the spatiotemporal characteristics of the ESV by combining a spatial statistical model and further explored the driving factors of the ESV. The results are as follows. The main land type in Xiangxi is forest land. In the past 28 years, the area of forest land and grassland decreased due to occupation by construction land, the area of construction land, wetland, and unused land increased, and the water area roughly remained unchanged. Overall, land use in Xiangxi had a moderate or low degree of activity. The total ESV successively increased, decreased, increased, and decreased, forming an M-shaped trend. Moreover, it declined overall. Spatially, the total ESV was higher in the southeast than in the northwest. The spatial self correlation analysis indicated that the ESV in the study area showed positive spatial aggregation, and the ecological spatial pattern in Xiangxi had not changed significantly over the past 28 years. The driving factors leading to the spatiotemporal differences in the ESV main included urbanization rate, population density, the gross output by forestry, and area of forest land. This study will provide a theoretical reference for the rational utilization of land resources and ecological protection in Xiangxi.

Snow depth and snow water equivalent are critical elements for snow cover observation and are greatly significant in fields such as cryosphere, global climate change, and water resource surveys. Microwave remote sensing is superior to both visible-light and near-infrared remote sensing in snow cover observation. This study systematically summarized the research results of the passive microwave remote sensing in the inversion of snow depth and snow water equivalent. It organized three types of snow cover observation methods, i.e., field surveys, long-term observations at ground stations, and regional observations based on satellite remote sensing, as well as major snow cover parameters to be observed. Furthermore, it summarized and evaluated three inversion algorithms, i.e., semi-empirical method, physical model, and machine learning. Finally, this study presented the results of the snow cover in the Qinghai-Tibet Plateau observed using passive microwave remote sensing, predicted the future development trend of remote sensing-based inversion of snow cover parameters, and put forward scientific suggestions for the in-depth implementation of the inversion of snow depth and snow water equivalent passive microwave remote sensing.

Aquaculture is an important way for humans to obtain food, and aquaculture ponds are a major production mode of aquaculture. The Pearl River Delta, as an important aquaculture base in southern China, has undergone great changes in its spatial distribution in the past 30 years. This study investigated Zhongshan City and its adjacent areas. First, the mixed pixels of Landsat and Sentinel-2 remote sensing data were decomposed using the linear mixed pixel decomposition method. Then, the NDWI threshold range corresponding to the water abundance of 70% and above was selected through visual comparison and analysis. Finally, the spatio-temporal distribution of typical aquaculture ponds from 1990 to 2021 was obtained. The study results show that the aquaculture ponds in Zhongshan City and its adjacent areas have experienced a process of first increasing and then decreasing since 1990. Specifically, the area of aquaculture ponds nearly doubled from 1990 to 2000, tended to be stable from 2000 to 2010, but decreased by nearly 50% from 2010 to 2021. This study can reduce the impact of mixed pixels on the monitoring of aquaculture ponds and support the scientific aquaculture and sustainable development of fisheries in the Greater Bay Area.

Mining collapse has caused damage to soil, vegetation, and water resources. With the implementation of the national ecological restoration strategy, it is significant to effectively identify and monitor collapse areas. For this purpose, based on multi-source high-resolution remote sensing images and Sentinel-1 SAR radar images, this study identified and monitored the mining collapses of a coal mine in Baiyin City, Gansu Province using the two technologies, namely the Stacking-InSAR method for extracting ground subsidence data and the human-computer interactive interpretation of optical images of mining collapse. Moreover, this study comprehensively compared the characteristics of both techniques and explored the application prospects of both techniques in the deployment of ecological restoration engineering. The results are as follows: ① The Stacking-InSAR radar monitoring technology can better reflect the deformation during the monitoring period and can effectively identify the mining collapse areas in shallow, middle, and deep coal seams. ② The high-resolution optical image technology can better identify the mining collapse areas in shallow and middle coal seams, more accurately identify the damaged land, and can well identify the historically formed mining collapse areas and damaged land whose collapse deformation has stopped. ③ The collapse deformation and land damage of various stages can be obtained by combining the InSAR monitoring technology and the recognition method base on high-resolution remote sensing images, thus providing detailed and reliable basic data for ecological restoration engineering.

As a major form of soil degradation, soil salinization can greatly harm agricultural production and ecological environment. Remote sensing methods can acquire soil spectral characteristics in a rapid, macroscopic, and timely manner. Based on this, remote sensing monitoring models can be built for a wide range of soil salinization monitoring and assessment. Thus, summarizing and discussing the building methods for remote sensing monitoring models of soil salinization is of great significance to improve the precision of remote sensing monitoring of soil salinization and to monitor and control salinized soil. This study reviewed the recent literature related to remote sensing studies concerning soil salinization at home and abroad. Then, it summarized the steps such as factor selection, model building, and precision verification in the building of remote sensing monitoring models of soil salinization. Focusing on the current hot research topic, this study discussed the limitations and development trends. The main conclusions are as follows. The remote sensing monitoring models of soil salinization are important means for monitoring and forecasting salinized soil. In recent years, the hot research topic in this field is to improve the model precision using new data sources and models. Differences exist in the use of remote sensing data sources among different studies, but the modeling factors are all optimized from spectral sensitive bands, prior spectral indices, and remote sensing-derived data. The remote sensing monitoring models of soil salinization mainly include the linear regression model and the machine learning model. The remote sensing models built for different regions have different precision and applicability.

To control the negative effects of the disorderly development of aquaculture ponds and promote the further development of the aquaculture industry, the top priority is to realize rapid and accurate identification and extraction of information on aquaculture ponds. Aquaculture ponds are special net-like water bodies divided by complex roads and dikes. Simple spectral features or spatial texture features are not enough for accurate information extraction. Moreover, the mixed feature rule set gets more demanding on computer performance. Therefore, based on the Landsat image sequence and the Google Earth Engine (GEE) platform, this study proposed an automatic extraction method for coastal aquaculture ponds, which combined the image spectral information, spatial features, and morphological operation. In this method, dual characteristic water spectral indices, that is, the modified combined index for water identification (MCIWI) and the modified normalized difference water index (MNDWI), were employed to highlight the grid characteristics of large water bodies and aquaculture ponds. Then, the low-frequency filtering spatial convolution operation was used to stretch the differences between aquaculture and non-aquaculture water bodies. Finally, the information on aquaculture pond areas as a whole was identified and extracted accurately and quickly. The results are as follows. ① This method has overall precision of 93% and a Kappa coefficient of 0.86. According to the test process verification of typical area superposition comparison, the overlapping proportions between the extraction results and the actual results were all more than 90%, averaging 92.5%, reflecting the high precision and reliability of this extraction method. ② In 2020, the coastal aquaculture ponds in Fujian Province had a total area of 511.73 km²and were mainly distributed in Zhangzhou, Fuzhou, and Ningde cities. ③ The kernel density analysis suggested that Zhangzhou had a high concentration of aquaculture ponds and thus had high pressure in the management of aquaculture ponds. This method can realize automatic information extraction of coastal aquaculture ponds. Thus, it is of great significance to promote the orderly management and scientific development of fishery aquaculture.

Ecological evaluation plays an important role in supporting urban development planning and using a remote sensing index to carry out ecological evaluation is a feasible method. Today, with the development of cloud computing, this paper explores a time-series calculation method of remote sensing ecological index suitable for Google Earth Engine, to address the problem that the calculation results of different sensors differ greatly in the process of big data calculation. Firstly, by taking Kuitun City, Xinjiang Uygur Autonomous Region, as the study area, this paper performs the de-clouded fusion process on Landsat images from 1989 to 2019. Secondly, this paper calculates the four major components of the fused images and makes preferences in the calculation of the humidity component and temperature component. Finally, this paper proposes the normalization method of the overall optimum and calculates the remotely sensed ecological index for each year on this basis. The analysis of the obtained results shows that the first principal component under the calculation by this method has a higher contribution rate, and the time series results on this basis have a higher polynomial fitting effect. It indicates that the method can specify uniform standards for different sensors, enhance the comparability of calculated results between different sensors, optimize the calculated results of remote sensing ecological indices, and ensure the interpretability of ecological evaluation grading results.

Timely and accurate detection and statistical analysis of the spatial distributions and time-series variations of water bodies like rivers and lakes holds critical significance and application value. It has become a significant interest in current remote sensing surface observation research. Conventional water body extraction methods rely on empirically designed index models for threshold-based segmentation or classification of water bodies. They are susceptible to shadows of surface features like vegetation and buildings, and physicochemical characteristics like sediment content and saline-alkali concentration in water bodies, thus failing to maintain robustness under different spatio-temporal scales. With the rapid acquisition of massive multi-source and multi-resolution remote sensing images, deep learning algorithms have gradually exhibited prominent advantages in water body extraction, garnering considerable attention both domestically and internationally. Thanks to the powerful learning abilities and flexible convolutional structure design schemes of deep neural network models, researchers have successively proposed various models and learning strategies to enhance the robustness and accuracy of water body extraction. However, there lacks a comprehensive review and problem analysis of research advances in this regard. Therefore, this study summarized the relevant research results published domestically and internationally in recent years, especially the advantages, limitations, and existing problems of different algorithms in the water body extraction from remote sensing images. Moreover, this study proposed suggestions and prospects for the advancement of deep learning-based methods for extracting water bodies from remote sensing images.

As an essential branch of surveying and mapping science, underwater topographic surveys are closely related to human operations in oceans and lakes. For underwater topography detection in shallow-water areas, conventional acoustic methods face the hull stranding risk, and passive optical methods have low survey accuracy. The airborne laser sounding is a novel means for bathymetric surveys in shallow-water areas, and its application in offshore areas can fill the gap of underwater topography data in shallow-water areas. This study presents a brief introduction to the composition and principle of the airborne laser sounding system, followed by a description of laser sounding data acquisition. Furthermore, this study highlights the critical processing technologies for airborne laser sounding data, including waveform data processing, error correction, and point cloud data processing. Finally, this study summarizes the technical difficulties and developmental trends of airborne laser sounding.

The rapid detection of soil salinity using remote sensing technology can scientifically guide the soil salinization control and the rational development of oasis agriculture. Based on 95 soil samples from the oasis of the Weigan-Kuqa River delta, this study established four soil salinity estimation models of multiple linear regression, partial least squares regression (PLSR), support vector machine regression (SVR), and random forest regression using the spectral index, band reflectance, and the measured soil salinity. Then, it conducted the remote sensing inversion for the spatial distribution pattern of the soil salinity in the study area using the optimal estimation results. The results are as follows: ① Nine spectral factors that were significantly related to soil salinity were screened using the all-subsets regression method, with correlation coefficients of all above 0.5 (P < 0.01). Among them, the correlation coefficient between salinity index SI-T and the soil salinity was the highest (0.648); ② The comparison of estimation precision show that the fitting effect of the four inversion models was in the order of random forest regression > SVR > PLSR > multiple linear regression. Among these models, the random forest model had the best fitting precision. Its training and validation sets had coefficients of determination(R²) of 0.870 and 0.766, respectively, with relative percent deviation (RPD) of 2.792 and 2.105, respectively, both of which were greater than 2. These results indicate that the random forest model had a good inversion effect and stable estimation capacity; ③ According to the inversion results of the random forest model, grade I and II zones account for 41.62% and are distributed in the cultivated land area inside the oasis; grade III, IV, and V zones account for 56.41% and are primarily distributed in the desert and the desert-oasis ecotones. Therefore, compared with conventional statistical models, the random forest modeling method can yield significantly better estimation effects in the inversion of soil salinity. This study can be used as a reference for the monitoring of soil salinization in oases in arid areas.

The ground subsidence caused by continuous mining in mining areas will seriously destroy the environment. There is an urgent need to quickly identify the locations and surface deformation of large-scope mining areas in the mining area monitoring. Given this, this study carried out large-scale detection and monitoring of the subsidence of mining areas in Linfen City using the synthetic aperture Radar interferometry (InSAR) technique. Firstly, by processing and analyzing 12 scenes of Sentinel 1A ascending data using the differential interferometric synthetic aperture Radar (D-InSAR) technique, this study conducted large-scale detection of subsidence disasters in mining areas in the study area. Then, this study processed 432 scenes of Sentinel 1A ascending data from different orbits using the small baseline subset InSAR (SBAS-InSAR) and monitored the obtained key areas. The results of this study show that there are a total of 105 subsidence areas in Linfen City, all of which are located in the mountains on both sides of the faulted Linfen basin. Further time-series deformation monitoring of key subsidence areas shows that many subsidence areas are continuously deforming, with high deformation amplitude and the deformation rate up to a maximum of -381 mm/a, and have caused huge damage to the ecological environment and infrastructure on the surface. The mining points near the subsidence area were identified according to optical images, thus verifying the reliability of the large-scale detection and monitoring method based on the InSAR technology. The results of this study will provide an important basis for the prevention and control of subsidence disasters in the mining areas of Linfen.

Flat landslides, typically characterized by crack grooves, are a common type of special disasters in southwestern China. However, the dense vegetation and complex terrain in disaster-developed areas limit the efficiency of conventional ground or remote sensing (RS) survey methods in the identification and extraction of disaster information. As one of the emerging remote sensing technologies, the airborne LiDAR technology and its data visualization analysis methods provide a new solution for the accurate identification of flat landslides. First, the high resolution digital elevation model (HRDEM) can be obtained using the UAV airborne LiDAR. Then, the HRDEM can be combined with visualization methods including sky view factor (SVF), hillshades, and 3D morphology simulation for the effective identification of flat landslides and their crack grooves. This study investigated the newly identified landslide hazard in the southern part of Nuoguzhai Village, Chunzai Town, Tongjiang County, northern Sichuan Province. The comprehensive RS identification method was used to realize the construction of landslide identification signs, the determination of the landslide boundary, the identification of crack groove position, and information extraction based on airborne LiDAR data. Combined with the results of field surveys, the effectiveness of the airborne LiDAR technology for the identification of flat landslides and their crack grooves in highly vegetation-covered areas was verified from both qualitative and quantitative aspects. The related study results can be used as a reference for the early identification, monitoring, and prevention of flat landslides.

This study aims to understand the variation trend of the geological environment and the ecological restoration prospect of mines in Jilin Province. Using the 2015—2019 high-resolution remote sensing data from a domestic satellite and other multi-source information, this study carried out the remote sensing-based dynamic monitoring of the geological environment and ecological restoration of mines in Jilin Province by means of automatic information extraction, human-machine interactive interpretation, in-door comprehensive research, and field surveys and verification. The analysis of the changes in the geological environment and ecological restoration of the mines based on the monitoring results allowed for basically ascertaining the current situations and variation trend of the occupation of land resources by mines, damage to land resources by mines, and the geological disasters, environmental pollution, and ecological restoration of mines in Jilin Province. The analysis results are objective and accurate, indicating good application results of remote sensing-based monitoring. The results of this study can provide references and bases for further promoting the ecological protection and restoration engineering of mountains, rivers, forests, lakes, grass, and sand in Jilin Province.

The cloud removal of remote sensing images is very important in the processing and analysis of remote sensing images and plays a crucial role in the subsequent image information extraction and other operations. Aiming at the high-quality requirements and low applicability of the reconstructed images in the cloud removal of multi-temporal remote sensing image fusion, a thick cloud removal algorithm based on one or more reference images was proposed, mainly including a selection of reference image, radiometric normalization, multi-temporal image fusion, and Poisson image editing. Firstly, the reference image was selected according to the image masking and the principal component information, and the radiometric normalization of the multi-source remote sensing image was carried out to preserve the change of ground feature information. Then, the image was fused based on the selective multi-source total variation model, and the boundary gradient discontinuity after image fusion was reduced by Poisson image editing. The experimental results show that the proposed method can effectively remove clouds from multi-source remote sensing images with thick clouds and different quality, and obtain higher image detail precision than traditional methods.