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.

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.

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.

Rapid identification and detection of landslides can both meet the requirement of timely responses to disasters and hold great significance for loss assessment and rescue post-disaster. This study proposed a deep learning-based automatic information extraction method for landslides to improve their detection accuracy. Specifically, the model input of this method includes the remote sensing images of the target areas, data from digital elevation models, and variation characteristics extracted using robust change vector analysis (RCVA). Furthermore, a U-Net model integrating dense upsampling and asymmetric convolution is designed to improve the identification accuracy. Taking Wenchuan, Sichuan Province as the study area, this study designed experiments to test the pixel-level image segmentation accuracy of landslides using different data combinations and methods. The results indicate that the improved U-Net model proposed in the study can produce the optimal image segmentation results of landslides.

Understanding the spatiotemporal characteristics of vegetation growth in the Yellow River basin and their influencing factors is crucial for the conservation and development of the ecology. However, existing studies rarely focus on the latest spatiotemporal characteristics of different vegetation types in the basin and their relationships with their influencing factors. Using the 2000-2020 time series remote sensing data of MODIS normalized difference vegetation index (NDVI), along with methods including trend analysis, correlation analysis, partial correlation analysis, and residual analysis, this study investigated the spatiotemporal characteristics of various vegetation types in the Yellow River basin. Accordingly, this study clarified the mechanisms behind the impacts of temperature and precipitation on annual and monthly scales and explored the influence of human activities on the spatiotemporal characteristics of different vegetation types. The results indicate that from 2000 to 2020, the NDVI of different vegetation types in the Yellow River basin trended upward overall, particularly in cultivated land and forest land. However, the increasing trends trended downward at different degrees with increasing elevation. Over the 21 years, various vegetation types were improved in most areas in the basin. However, a few areas exhibited degraded vegetation types, primarily including grassland and cultivated land. The proportion of areas with anti-continuous future trends in various vegetation types notably increased. Temperature and precipitation produced positive impacts on the growth of various vegetation types in the Yellow River basin. Nevertheless, various vegetation types exhibited greater responses to precipitation than to temperature, and the responses featured notable time lags. Furthermore, grassland and shrub growth were more sensitive to precipitation and temperature. Human activities had positive impacts on the vegetation of the Yellow River basin overall. However, some negative effects were also observed in grassland and cultivated land, warranting attention in future planning. Overall, most areas exhibited improved vegetation in the Yellow River basin in the 20 years. Given that partial grassland and cultivated land experienced degradation, it is necessary to protect typical degradation areas. The findings of this study will provide scientific data and theoretical support for ecological construction and economic development in the Yellow River basin.

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.

Coalbed methane (CBM), a type of self-sourced unconventional clean energy, occurs in coal seams and their surrounding rocks. Conventional exploration methods for CBM enrichment areas are laborious, while remote sensing provides a new approach to the rapid exploration of such areas. The basic principle behind the remote sensing-based exploration of CBM enrichment areas is as follows: ① The extraction and comprehensive analysis of multi-source data are conducted based on the comparison between the spectral features of typical surface features and those of surface feature anomalies, including rock and mineral alterations, vegetation anomalies, and thermal anomalies, caused by hydrocarbon micro-seepage in CBM enrichment areas, along with data obtained using geophysical prospecting methods like geological, seismic, and magnetotelluric methods; ② The distribution range and gas-bearing properties of CBM enrichment areas are gradually delineated. This paper reviews the hydrocarbon seepage in the CBM enrichment areas and the response mechanisms of spectral anomalies of surface rocks, minerals, and vegetation. It covers the applications of various methods based on the inversion of spectral parameters of surface rocks, minerals, and vegetation, together with the inversion of the spectral anomalies of surface features, in the exploration of potential CBM enrichment areas. Additionally, this paper elucidates the different explanations for surface thermal anomalies caused by CBM-bearing strata, as well as major methods to improve the accuracy of surface temperature inversion and their applications. In the future, the main approach to achieving low-cost, rapid exploration of CBM enrichment areas will be the analysis and information extraction of three-dimensional, multi-source information based on the combination of remote sensing technology with the geological data, seismic exploration, and geomagnetic prospecting of coalfields.

Soil salinization is identified as a major cause of decreased soil fertility, productivity, vegetation coverage, and crop yield. Optical remote sensing monitoring enjoys advantages such as macro-scale, timeliness, dynamics, and low costs, rendering this technology significant for the dynamic monitoring of soil salinization. However, there is a lack of reviews of the systematic organization of multi-scale remote sensing data, multi-type remote sensing feature parameters, and inversion models. This study first organized the optical remote sensing data sources and summarized the remote sensing data sources and scale platforms utilized in current studies on saline soil monitoring. Accordingly, this study categorized multi-source remote sensing data into three different platforms: satellite, aerial, and ground. Second, this study organized the mainstream characteristic parameters for modeling and two typical inversion methods, i.e., statistical regression and machine learning, and analyzed the current status of research on both methods. Finally, this study explored the fusion of remote sensing data sources and compared the pros and cons of various modeling methods. Furthermore, in combination with current hot research topics, this study discussed the prospects for the application of data assimilation and deep learning to soil salinization monitoring.

The monitoring and assessment of urban ecological quality holds critical significance for sustainable urban development. To assess the ecological quality of developed coastal cities in China in recent years, this study investigated Nanjing City based on the Sentinel-2A remote sensing images obtained in 2021. It constructed a novel remote sensing green index (RSGI) model involving green spaces, blue spaces, buildings, and impervious surfaces for assessing the ecological quality of Nanjing. First, neural network supervised classification was applied to the Sentinel-2A remote sensing images, constructing the RSGI to assess the ecological quality of various districts in Nanjing. Then, the correlations between the RSGI and urban ecological factors were analyzed using the Pearson correlation coefficient. Finally, the ecological similarity between the districts was analyzed using the agglomerative hierarchical clustering method. The results of this study are as follows: (1) The ecological quality of Nanjing presented a pattern of low RSGI values in the central portion and high RSGI values in the surrounding areas, with the highest and lowest RSGI values (0.86 and 0.38) observed in Luhe and Qinhuai districts, respectively, differing by 0.48; (2) The RSGI exhibited a positive correlation with the density of green spaces and negative correlations with the densities of population, buildings, and impervious surfaces, all at the 0.01 level; (3) With the ecological similarity of 70% as the threshold, 11 districts in Nanjing were categorized into four clusters: Qinhuai, Gulou, and Jianye districts in the first cluster, Yuhuatai and Qixia districts in the second cluster, Xuanwu and Gaochun districts in the third cluster, and the rest four districts in the fourth cluster. The results of this study can provide a scientific basis for subsequent urban planning and sustainable development of Nanjing.

The geological exploration in Qinghai Province is conducted in typical highland desert areas characterized by significant terrain cutting, low vegetation cover, extensive bedrock outcrops, and challenging surface investigations. To investigate the application of hyperspectral data from a satellite in altered mineral mapping and ore prospecting in desert areas along the northern margin of the Qaidam Basin, this study utilized hyperspectral remote sensing images with high spatial and spectral resolutions obtained from the domestic ZY1-02D satellite. Following fine-scale radiometric calibration, atmospheric correction, data restoration, and orthorectification, high-quality hyperspectral remote sensing images were acquired for desert areas, such as Saibagou, along the northern margin of the Qinghai Province. After comprehensive field survey sampling and spectral testing using a FieldSpec Pro FR spectrometer, a standard laboratory spectral dataset of the surveyed areas was established, containing 13 major altered minerals such as chlorite. Finally, the extraction of altered minerals and ore prospecting were conducted. The results indicate that the primary altered minerals in the areas include 13 types: epidote, chlorite, albite, sericite, silicification, kaolinite, carbonate, limonite, actinolite, serpentine, hematite, pyrite, and malachite. The distribution of these altered minerals is closely associated with the strata, lithology of intrusions, and ductile shear structures. The analysis of the alteration characteristics of typical mineral deposits such as Saibagou reveals that malachitization, limonitization, pyritization, sericitization, and silicification are closely related to mineralization, being indicative of ore prospecting. Partial potassium feldspathization, chloritization, epidotization, and sodium feldspathization are also somewhat related to mineralization, serving as a valuable reference for ore prospecting. As a result of applying the above methods, combined with the 1:25 000 geochemical survey data, one copper-gold mineralized point was discovered in the Tuomoerrite area. Therefore, hyperspectral data-based alteration mineral mapping compensates for the limitations of traditional geological surveys and ore prospecting methods in the western desert areas of China with harsh natural conditions. This allows for rapid assessment of the regional geological setting and mineralization conditions, enabling the extraction of mineral spectral information. The hyperspectral data-based alteration mineral mapping provides abundant, fine-scale information for large-scale geological prospecting, offering broad application potential.

In the context of the growing maturity of on-road navigation, this study proposed a cross-disciplinary research direction-off-road navigation-based on the demands for navigation services in complex and unstructured environments. First, the development of on-road navigation and the demand scenarios of off-road navigation were introduced. Based on four specific aspects of vehicle trafficability, scientific issues in the transition from on-road to off-road navigation were presented, including refining remote sensing detection of geographical and geological trafficability elements, remote sensing-based retrieval of soft soil parameters in off-road areas, and quantitative mechanisms behind the impacts of climatic change on ground characteristics. Accordingly, the research direction of off-road navigation was clarified. Then, key technologies like vehicle trafficability calculation and characterization, digital road network construction in off-road areas, and intelligent path planning for off-road navigation were summarized. The technical approach of roadization for off-road areas and the concept of a digital road network for off-road areas were introduced, followed by the establishment of a comprehensive technology system for off-road navigation. Finally, in combination with practical applications, the potential of off-road navigation was confirmed. Research on off-road navigation will further enrich the connotation of navigation and expand its application boundaries.

This study aims to solve the challenges faced in the prediction of sugarcane yield in Guangxi, such as varied crops, complex investigations in the sugarcane planting areas, and difficult acquisition of remote-sensing images caused by the changeable weather. To this end, an improved semantic segmentation algorithm based on Sentinel-2 images was proposed to automatically identify sugarcane planting areas, and an extraction method for representative spectral features was developed to build a sugarcane yield prediction model based on multi-temporal Sentinel-2 and Landsat8 images. First, an ECA-BiseNetV2 identification model for sugarcane planting areas was constructed by introducing an efficient channel attention (ECA) module into the BiseNetV2 lightweight unstructured network. As a result, the overall pixel classification accuracy reached up to 91.54%, and the precision for sugarcane pixel identification was up to 95.57%. Then, multiple vegetation indices of different growth periods of the identified sugarcane planting areas were extracted, and the Landsat8 image-derived vegetation indices were converted into Sentinel-2 image-based ones using a linear regression model to reduce the differences of the indices derived using images from the two satellites. Subsequently, after the fitting of time-series data of the extracted vegetation indices using a cubic curve, the maximum indices were obtained as the representative spectral features. Finally, a yield prediction model was built using multiple machine learning algorithms. The results indicate that the test set of the decision tree model built using the fitted maximum values of the vegetation indices yielded R? of up to 0.759, 4.3%, higher than that (0.792) of the model built using the available actual maximum values. Therefore, this method can effectively resolve the difficulty in developing an accurate sugarcane yield prediction model caused by changeable weather-induced lack of remote sensing images of sugarcane of the key growth periods.

Land surface temperature (LST) proves to be an important parameter in surface processes on regional and global scales, and its spatiotemporal information can be obtained through thermal infrared remote sensing. However, the constraints of thermal infrared sensors (TIRSs) themselves and the inability of thermal infrared electromagnetic waves to penetrate clouds render it impossible to obtain LST with a high spatiotemporal resolution currently. This study presents a method for reconstructing hourly LST at 100-m resolution in all weathers. This method consists of three main steps: ① cloudy LST at four moments is reconstructed using a moderate resolution imaging spectroradiometer (MODIS) based on the conventional annual temperature cycle (ATC) model; ② the daily variation curve of LST is estimated based on the daily trend in the skin temperature (SKT); ③ with spectral indices as regressors, spatial downscaling is conducted for the hourly LST using Extreme Gradient Boosting (XGBoost). The results show that the proposed reconstruction method can obtain spatiotemporally continuous LST products, improve the spatial resolution of LST, and provide more details. The validation of the hourly 100-m-resolution LST using data from the surface radiation budget network (SURFRAD) developed by the U.S. indicates that the reconstructed hourly LST exhibits roughly the same trend as the measured values of the SURFRAD. The method for reconstructing all-weather hourly LST boasts high accuracy, with R² of 0.95, a root mean squared error (RMSE) of 3.75 K, and a bias of 0.75 K.

Crown volumes serve as a crucial factor for surface ecological monitoring. Laser point clouds can characterize the fine-scale spatial morphologies of individual trees, providing a data basis for crown volume extraction. However, existing laser point cloud-based methods for extracting crown volumes of individual trees are sensitive to parameters and exhibit low degrees of automation. Based on the analysis of the three-dimensional morphological structures of individual trees, this study proposed a spherical coordinate integration method for extracting crown volumes of individual trees based on the terrestrial laser scanning (TLS) point clouds. First, the crown points were obtained through visual elevation threshold-based segmentation according to the elevation distributions of TLS point clouds. Then, the TLS point clouds were projected onto the spherical coordinate space for infinitesimal segmentation into triangular pyramids. Finally, the crown volumes were determined through the three-dimensional spherical coordinate integration. Six types of TLS point cloud data for individual trees were selected for tests. As indicated by the test results, the proposed method effectively considers factors like crown morphology and point cloud density, achieving a maximum absolute error of 2.33 m³ and a maximum relative error of 3.40% in the crown volume extraction of individual trees. It manifests higher extraction accuracy and stability compared to the existing methods. Therefore, this study holds significant reference value for extracting tree parameters based on TLS point clouds.

On April 1, 2023, China’s first satellite constellation-L-band differential interferometric Synthetic Aperture Radar (L-SAR)-began to test the distribution of interferometric SAR data for natural resource applications. To evaluate the coherence and effectiveness of deformation monitoring using the L-SAR satellite for areas with high vegetation coverage, complex terrain, and long-term baseline, this study conducted potential landslide hazard identification in the Ankang area in the eastern Qinba Mountain. The deformation information of the study area was extracted using L-SAR data. Using such information, combined with high-resolution optical images for comprehensive remote sensing identification, this study identified seven potential landslide hazards in the study area through interpretation. Field investigation confirmed that the observed deformation signs in potential landslide hazard areas were consistent with the InSAR monitoring results. The study indicates that the L-SAR satellite enjoys a high interference imaging ability, high imaging quality, and effective deformation monitoring, meeting the demand for deformation monitoring in areas with high vegetation coverage. For mountainous areas with high vegetation coverage, the use of L-band SAR data through DInSAR technology, combined with comprehensive remote sensing identification using high-resolution optical imagery, allows for the effective identification of potential landslide hazards.

Hyperspectral technology has become the major means of coastal wetland monitoring. However, traditional hyperspectral classification methods usually face challenges such as insufficient feature extraction, the same surface features corresponding to different spectra, and fragmented scenes. To solve these problems, this study proposed a new classification method by applying Transformer to hyperspectral classification. This vision Transformer (ViT)-based method expanded the receptive field by learning global spatial features using non-local technology, thus overcoming the insufficient extraction of discriminant features. Meanwhile, this method enhanced the cross-layer information interchange through cross-layer adaptive residual connection, thus eliminating information loss. This study, taking NC16 and NC13 wetland datasets of the Yellow River Delta as experimental data, compared the classification method proposed in this study to support vector machine (SVM), one-dimensional convolution neural network (1DCNN), contextual deep convolution neural network (CDCNN), spectral-spatial residual network (SSRN), hybrid spectral network (HybridSN), and ViT. The comparison results show that the new method yielded significantly elevated overall accuracy (OA) of up to 96.24% and 73.84%, average accuracy (AA) reaching 83.42% and 74.87%, and Kappa coefficients of up to 94.80% and 68.94%, respectively for the two datasets.

Extracting information about buildings from a large and complex set of remote sensing images has always been a hot research topic in the intelligent applications of remote sensing. To address issues such as inaccurate information extraction of buildings and the tendency to ignore small buildings within a complex environment in remote sensing images, this study proposed the SC-deep network-a semantic segmentation algorithm for remote sensing images based on a hybrid attention mechanism and Deeplabv3+. Utilizing an encoder-decoder structure, this network employs a backbone residual attention network to extract deep- and shallow-layer features. Meanwhile, this network aggregates the spatial and channel information weights in remote sensing images using a dilated space pyramid pool module and a channel-space attention module. These allow for effectively utilizing the multi-scale information of building structures in remote sensing images, thereby reducing the loss of image details during training. The experimental results indicate that the proposed method outperforms other mainstream segmentation networks on the Aerial imagery dataset. Overall, this method can effectively identify and extract the edges of complex buildings and small structures, exhibiting superior building extraction performance.

The rapid assessment of building damage following destructive earthquakes serves as a critical foundation for decision-making and technical guarantee in post-earthquake scientific evaluations, holding great significance in humanitarian aid and emergency response. This study aims to overcome the challenge in rapidly quantifying the number of buildings affected. Considering that most existing post-earthquake building damage assessments based on remote sensing images rely on pre- and post-disaster image segmentation, this study, by using the U-Net deep convolutional neural network as the main model, introduced a three-stage convolutional neural network for building damage assessment (BDANet) framework that integrates assessment and prediction for post-earthquake building damage information. First, the encoder-decoder network structure of U-Net was used to extract building location information. Second, building damage was assessed using pre- and post-disaster images to localize and grade damage in the image segmentation results. Finally, the number of buildings damaged at various levels was predicted to support post-disaster rescue and reconstruction efforts. The study evaluated and quantified the levels of post-earthquake building damage in the M7.1 earthquake in Morelos State, central Mexico in 2017 and the M7.8 earthquake in Türkiye in 2023, confirming the accuracy and reliability of the proposed method. The experimental findings provide timely and precise data and technical support for post-disaster risk assessment.

To capture information about the 3D geometric structures of trees more effectively and address the challenge of high-precision, high-fidelity tree reconstruction, this study proposed a method for 3D modeling of trees based on terrestrial LiDAR point cloud. To overcome the occlusion caused by leaf gaps in TLS, this method fully considered the aggregation of leaves, as well as the morphological characteristics of both leaves and branches. By conducting the model fitting and reconstruction of tree leaves and branches using Delaunay triangulation and Alpha-shape algorithm, respectively, the proposed method effectively addressed previous issues such as unrealistic tree structures and imprecise organ modeling, thus achieving the 3D reconstruction of individual tree leaves and small branches efficiently. This study holds great significance for determining forest structural parameters and managing resources, while also offering a valuable reference for component-level real scene 3D modeling of typical trees.

Building heights are necessary for urban informatics, providing a significant basis for the planning and early warning of risks for urban construction. The shadow method, which can measure the heights of urban buildings on a large scale at a low cost, faces challenges such as low efficiency, accuracy, and robustness in building height inversion in complex shadow scenes. This study proposed a measurement method for these scenes. First, the shadows were measured and delineated using the fishing net method combined with multiple constraints. Second, the shadow lengths of all the zones divided were obtained, and the optimal values were determined using the quartile method and the bidirectional approximation strategy. Third, the shadow lengths were determined through a comprehensive assessment of the optimal values of all zones. The results show that 90.6% of building heights calculated using the new method exhibited absolute errors ranging from 0 to 5 m. Therefore, this method features elevated accuracy of building height inversion for various complex shadow scenes, laying a basis for research into the inversion and expansion of urban building heights.

The concentrations of negative oxygen ions and particulate matter 2.5 (PM_2.5) serve as important indicators in the assessment of the degrees of air freshness and cleanliness. Based on 2018-2022 data from 50 negative oxygen ion observation stations affiliated with the Fujian meteorological departments, along with the ecological parameters such as aerosol, vegetation index, and surface brightness temperature obtained by satellite-based remote sensing inversion, this study built estimation models for the concentrations of negative oxygen ions and PM_2.5 using the Cubist machine learning method. Accordingly, it developed an air freshness index (AFI), and the fine-scale mesh-based monitoring of regional air freshness was achieved. The results show that the estimation model for the negative oxygen ion concentration yielded goodness of fit of 0.838 and 0.526 for the training and test sets, respectively. In comparison, the estimation model for the PM_2.5 concentration exhibited goodness of fit of 0.968 and 0.867 for the training and test sets, respectively. Then, this study developed the AFI by comprehensively considering negative oxygen ions and PM_2.5. Then, this study graded the AFI using the frequency quartiles of the statistical data series combined with the spatiotemporal changes in negative oxygen ions. The results indicate that the AFI monitoring results based on meteorology, remote sensing, and machine learning algorithms are consistent with the actual conditions.

Landslides may cause the loss of lives and property, and an accurate and complete map showing the spatial distribution of landslides and the determination of landslide susceptibility areas assist in guiding the optimization of the production, living, and ecological spaces. However, landslide investigations are complicated by dense vegetation. LiDAR technology enables the presentation of actual terrain features, thereby achieving landslide identification in densely vegetated areas. This study obtained the LiDAR point cloud data of the study area through ground-imitating flight and then built a digital elevation model (DEM) through data processing. Then, based on mountain shadow analysis, color-enhanced presentation, and 3D scene simulation, the locations and scales of existing landslides in the study area were identified. The field verification revealed an interpretation accuracy of landslides of up to 86.4%. For the assessment of landslide susceptibility areas, this study, with existing landslides as samples, delineated landslide susceptibility areas through remote sensing classification for the first time. Specifically, images were synthesized using the landslide-related elevations, slopes, and surface undulations, and then landslide susceptibility areas were determined using the support vector machine (SVM) classification method. The analysis of the inspection samples reveals a landslide identification accuracy of 81.91%. The results show that the image identification based on high-accuracy LiDAR data and visually enhanced images allows for the delineation of small landslides and that the SVM classification method enables the accurate location of landslide susceptibility areas. This study provides a basis for the future planning and optimization of the production, living, and ecological spaces.

This study aims to evaluate and analyze the geology of mines in Ili Valley and investigate the countermeasures for ecological restoration therein. Utilizing the mining development status derived from remote sensing data and the remote sensing survey results of geological environment, as well as multi-source geological, socio-economic, and meteorological data, this study built a hierarchy structural model using analytic hierarchy process (AHP) and assessed the geological environment of mines in the Ili Valley, The results indicate that the severely affected areas are relatively concentrated, accounting for 4.61% of the total area of Ili Valley. The moderately severely affected areas present a continuous distribution. These areas overlap with each other, exhibiting indistinct boundaries. The generally affected areas are primarily distributed in extremely high mountain areas, medium to high mountain areas, and low mountain and hilly areas. The unaffected areas are primarily distributed in the alluvial plain area in the central part Ili River Valley and the plain area of the Zhaosu Basin. The areas with high ecological carrying capacity are mainly concentrated in the central region except for the south of Zhaosu County and Tekes County, the eastern edge of Nilka County, and the northern area of Khorgos. This study proposed corresponding ecological restoration and management measures and countermeasures against major geological issues. The findings of this study can provide basic data and technical support for the sustainable development of the ecology and the rational exploitation of mine resources in the Ili Valley. Additionally, these findings can serve as a case study for monitoring and assessing the geology of mines in arid and semi-arid areas.

Accurately extracting building information from high-resolution remote sensing images faces challenges due to complex background transformations and the diversity of building shapes. This study developed a high-resolution building semantic segmentation network-building mining net (BMNet), which integrated a hybrid attention mechanism with multi-scale feature enhancement. First, the encoder utilized VGG-16 as the backbone network to extract features, obtaining four layers of feature representations. Then, a decoder was designed to address the issue of detail loss in high-layer features within multi-scale information. Specifically, a series attention module (SAM), which combined channel attention and spatial attention, was introduced to enhance the representation capabilities of high-layer features. Additionally, the building mining module(BMM) with progressive feature enhancement was designed to further improve the accuracy of building segmentation. With the upsampled feature mapping, the feature mapping post-processed using SAM, and initial prediction results as input, the BMM output background noise information and then filtered out background information using the context information exploration module designed in this study. Optimal prediction results were achieved after multiple processing using the BMM. Comparative experiment results indicate that the BMNet outperformed U-Net, with accuracy and intersection over union (IoU) increasing by 4.6% and 4.8%, respectively on the WHU Building dataset, by 7.9% and 8.9%, respectively on the Massachusetts buildings dataset, and by 6.7% and 11.0%, respectively on the Inria Aerial Image Labeling Dataset. These results validate the effectiveness and practicality of the proposed model.

The Xuyong-Gulin (Xugu) Expressway, located along the southern margin of the Sichuan Basin, faces complex geological conditions, with its safe operation threatened by geologic hazards. Therefore, the identification and analysis of geologic hazards along the expressway holds great significance. Interferometric synthetic aperture Radar (InSAR) technique enjoys the advantages of all-weather, all-time observation capabilities, wide coverage, and mm-scale surface deformation monitoring, playing an important role in wide-field landslide detection and monitoring. Based on this, this study processed the Sentinel-1 ascending and descending datasets from February 2017 to September 2020 using the small baselines subset (SBAS) InSAR technique. As a result, the surface deformation rates along the expressway were determined, and 18 landslides were identified. The analysis indicates that the deformations of landslides are related to anthropogenic activities. The analytical results also reveal that the combination of ascending and descending datasets allows for more accurate identification of landslide distribution. With the continuous data accumulation and technological development, InSAR is expected to play an increasingly important role in the prevention and control of geologic disasters.

The rapid development of hyperspectral remote sensing technology in China fully ensures the effective application of large-scale surface feature classification. However, achieving high-precision classification under few-spot conditions while fully leveraging hyperspectral spatial-spectral information remains challenging. This study developed a 3D convolutional autoencoder (3D-CAE) network guided by physical constraints from mixed pixel decomposition. This approach enables accurate estimation of endmember abundance while effectively expressing regularized spatial-spectral features of hyperspectral data. In combination with a support vector machine (SVM) classifier, the method achieves hyperspectral classification under few-spot conditions. The classification performance of various models was evaluated at different sampling rates. To validate the proposed method, this study conducted experiments including comparisons with traditional hyperspectral feature extraction and classification methods, such as supervised classification approaches. The classification performance of various models was also evaluated at different sampling rates. The experimental results demonstrate that the proposed hyperspectral classification method has a significant advantage of accuracy, achieving a mean intersection over union (mIoU) of 0.829, which was close to 0.8 even at a low sampling rate of 1/200, surpassing its counterparts. These results confirm that the proposed method exhibits robustness under few-spot conditions. This study provides a valuable technical reference for addressing hyperspectral classification challenges under few-spot conditions.

To address the accuracy reduction in the semantic segmentation of remote sensing images due to insufficient extraction of contextual dependencies and loss of spatial details, this study proposed a semantic segmentation method based on context- and class-aware feature fusion. With ResNet-50 as the backbone network for feature extraction, the proposed method incorporates the attention module during downsampling to enhance feature representation and contextual dependency extraction. It constructs a large receptive field block on skip connections to extract rich multiscale contextual information, thereby mitigating the impacts of scale variations between targets. Furthermore, it connects a scene feature association and fusion module in parallel behind the block to guide local feature fusion based on global features. Finally, it constructs a class prediction module and a class-aware feature fusion module in the decoder part to accurately fuse the low-level advanced semantic information with high-level detailed information. The proposed method was validated on the Potsdam and Vaihingen datasets and compared with six commonly used methods, including DeepLabv3+ and BuildFormer, to verify its effectiveness. Experimental results demonstrate that the proposed method outperformed other methods in terms of recall, F1-score, and accuracy. Particularly, it yielded intersection over union (IoU) values of 90.44% and 86.74% for building segmentation, achieving improvements of 1.55% and 2.41%, respectively, compared to suboptimal networks DeepLabv3+ and A2FPN.

Ecological quality is an important indicator of a regional development level. Objective, quantitative dynamic monitoring and analysis of long-term ecological quality can provide a scientific basis for urban sustainable development and ecological construction. Based on Landsat remote sensing images, this study constructed the remote sensing ecological index (RSEI) as an evaluation index using principal component analysis. Accordingly, this study explored the spatiotemporal change characteristics of ecological quality in Zhengzhou from 2001 to 2020, as well as the extent of influence of various driving factors, using the Sen+Mann-Kendall trend analysis, the Hurst index, and geographical detectors. The results indicate that from 2001 to 2020, Zhengzhou maintained moderate ecological quality overall. The RSEI showed downward, upward, and then downward trends sequentially. Spatially, the eastern plains showed lower ecological quality, whereas the southwestern mountainous and hilly areas exhibited higher ecological quality. The regional ecological quality remained unchanged predominantly or saw slight improvements over these years except for 2010, when the area of zones with ecological quality deteriorating significantly increased due to high temperature. From 2001 to 2020, the ecological quality in Zhengzhou exhibited significant trends, with 56.34% of areas showing an upward trend and 42.26% exhibiting a downward trend. These results, along with the Hurst index, reveal that the downward trend in ecological quality in the eastern part is primarily characterized by sustainable changes in the future, while the upward trend in ecological quality in the southwestern partition is primarily characterized by anti-sustainable changes in the future. Driving force analysis indicates that over the 20 years, primary factors influencing changes in ecological quality in Zhengzhou included land use type and population density, whose explanatory power is significantly stronger than other factors. The impact of natural factors, such as elevation and average annual precipitation, has gradually diminished, while the influence of the night light index, which reflects the urbanization level, has progressively increased. The results of this study will provide a scientific basis for the evaluation and preservation of ecosystems in Zhengzhou.

During the melt season, supraglacial lakes are widely distributed across polar ice sheets, storing large amounts of surface meltwater. When some of these supraglacial lakes rupture at the bottom, the released meltwater infiltrates ice sheets, affecting their movement and stability. Therefore, accurate bathymetry retrieval of supraglacial lakes and estimating the volume of supraglacial lakes are significant for understanding the hydrological processes of polar ice sheets. However, field measurement of supraglacial lake depth is difficult, costly, and small-scale. Meanwhile, the bathymetry models derived from optical satellite images with low to medium spatial resolutions are insufficiently accurate. Given these, this study conducted the bathymetry retrieval of supraglacial lakes based on eight-band remote sensing images from the small-size optical satellite PlanetScope SuperDove (spatial resolution: 3 m) and ICESat-2 laser altimetry data. First, the ICESat-2 laser altimetry point clouds data for the lake surface and bottom were separated and modeled using adaptive kernel density estimation to derive lake depth observations. Second, Optimal Band Ratio Analysis (OBRA) was used to examine the correlations between various bands of PlanetScope images (and combinations thereof) and ICESat-2 bathymetry data, leading to the development of four kinds of empirical formulas for the bathymetry retrieval of supraglacial lakes: quadratic, exponential, power, and logarithmic functions. Then, four supraglacial lakes covered by concurrent PlanetScope and ICESat-2 data were selected to test the retrieval accuracy. The results indicate that the Green I band of PlanetScope is the most favorable for the bathymetry retrieval, demonstrating the strongest correlation with the ICESat-2 derived depths (R²=0.94) and the highest inversion accuracy (RMSE=1.0 m, RRMSE=0.15). The study reveals that integrating active and passive satellite data has great potential for analyzing hydrological processes in polar ice sheets.

Assessing regional habitat quality holds great significance for maintaining regional biodiversity, enhancing human well-being, and achieving regional sustainable development. Based on the land use data of 2000, 2010, and 2020, this study analyzed the spatiotemporal characteristics of the habitat quality in the ecological green integrated demonstration area in the Yangtze River Delta using the InVEST model and the habitat quality index method. Furthermore, this study explored the relationship between regional habitat quality and land use. Key findings are as follows: ① From 2000 to 2020, the study area exhibited moderate habitat quality, with the habitat quality index trending downward and the habitat degradation gradually mitigating. Regarding the districts and counties in this area, Qingpu District of Shanghai, Wujiang District of Suzhou, Jiangsu, and Jiashan County of Jiaxing, Zhejiang (the two districts and one county) showed a downward trend in the habitat quality from 2000 to 2010. In contrast, from 2010 to 2020, Qingpu District and Jiashan County exhibited improved habitat quality, while Wujiang District still maintained a downward trend in the habitat quality; ② From 2000 to 2020, the study area primarily featured moderate habitat, with major land types including cultivated land and grassland. During this period, the water and wetland areas in the north and center, respectively exhibited the highest habitat quality, while the construction land in the study area displayed poor and inferior habitat; ③ There is a strong correlation between the habitat quality and land use structure in the study area. Specifically, areas with more intense changes in land use feature more significant variations in habitat quality. The results of this study will provide a reference for biodiversity conservation and land use management in the study area.

In recent years, target detection, as an important branch of computer vision technology, has been widely applied in fields such as medicine, military affairs, and urban rail transit. As satellite and remote sensing technologies advance, images obtained using these technologies contain abundant information. This makes it crucial to conduct automatic target detection and understanding of these images. However, due to the random directions and dense distribution of targets in remote sensing images, conventional methods are prone to lead to missing or incorrect detection. In response, this study proposes a multi-convolution kernel feature combination-based adaptive region proposal network (MFCARPN) algorithm for multi-directional detection. This algorithm introduces multiple convolution kernel features for target extraction. The weight parameters of these convolution kernel features can be determined through adaptive learning according to the differences between the targets, yielding the characteristic patterns that match better with targets. Meanwhile, in combination with the original features of the targets, the parameters of the classification and regression model vary dynamically according to the difference between targets. Thus, the RPN’s adaptive ability can be improved. The experimental results indicate that the mAP of the standard dataset DOTA reached up to 75.52%, which is 0.5 percentages higher than that of the baseline algorithm GV. Therefore, the MFCARPN algorithm proposed in this study proves effective.

Synthetic aperture radar (SAR), allowing for all-weather and all-day imaging, can provide essential data for large-scale flood inundation monitoring. However, limitations such as the revisit period of SAR images make it challenging for single-source SAR data to meet the high temporal requirements for dynamic flood inundation monitoring, which is crucial for disaster relief and decision-making support. Combining multi-temporal SAR data for dynamic flood inundation monitoring is of significant practical value. Nevertheless, SAR images from different sensors exhibit significant spatiotemporal heterogeneity, rendering direct comparisons difficult. Additionally, previous studies frequently extracted flood inundation extents using single-pixel or local spatial neighborhood features while neglecting the application of spatiotemporal non-local features pre- and post-flooding. Therefore, this study first proposed a feature space alignment method for multi-source SAR data based on backscatter characteristics. Then, differential information pre- and post-flooding was extracted using the progressive non-local theory, and flood inundation maps were prepared. Finally, dynamic flood inundation monitoring results were obtained through logical operations of the time-series flood inundation maps. This method was validated using the flood disaster in August 2023 in Zhuozhou, during which five multi-source SAR datasets were acquired from Sentinel-1, Gaofen-3 (GF-3), and Fucheng-1. The results indicate that compared to six commonly used flood monitoring methods, the proposed method exhibited the optimal performance, yielding a Kappa coefficient and F1 score of 0.85 and 0.88, respectively. The dynamic monitoring results of the flood inundation in Zhuozhou reveal that the floodwater in the main urban area largely receded by August 3, and the water levels then gradually decreased, with the inundated areas shifting to the Baigou River in the lower reaches.

To resolve the limitations of traditional economic data such as the lack of spatial information and the difficulty in capturing the spatial disparities and dynamic patterns of regional economic development, this study integrated nighttime light data with land use and socio-economic data to develop a spatialized gross domestic product (GDP) model for the Chengdu-Chongqing region. Using trend analysis and a modified gravity model, this work analyzes the economic development characteristics of the region at the pixel level and in terms of inter-city economic relationships. The results indicate that the spatialized GDP model, constructed from multiple data sources, demonstrated high accuracy, with errors not exceeding 1.1%. The areas with the fastest GDP density growth in the Chengdu-Chongqing region are mainly concentrated around the core urban areas of Chengdu and Chongqing, accounting for approximately 73.9% of the total. These areas also show pronounced economic agglomeration characteristics. The inter-city economic relationships in the Chengdu-Chongqing region are continually strengthening, and the overall quality of urban development is steadily improving. Chengdu, in particular, has the closest economic ties with its neighboring cities. Overall, the Chengdu-Chongqing regional economy exhibits a spatial pattern of “dual-core driven development”, with the intensity of inter-city economic relationships continuing to strengthen. This study will provide valuable data support and methodological insights for promoting the high-quality economic development of the Chengdu-Chongqing urban agglomeration.

Extracting accurate water body information holds great significance for water resources protection and urban planning. However, due to numerous surface features and complex environments, along with different morphologies, scales, and spectral characteristics of different water bodies, remote sensing images inevitably exhibit heterogeneity, spectral similarities, and inter-class similarities between water bodies and other surface features. Existing methods fail to fully exploit boundary cues, the semantic correlation between different layers, and multi-scale representations, rendering the accurate information extraction of water bodies from remote sensing images still challenging. This study proposed a boundary guidance and cross-scale information interaction network (BGCIINet) for information extraction of water bodies from remote sensing images. First, this study proposed a boundary guidance (BG) module for the first time by combing the Sobel operator. This module can be used to effectively capture boundary cues in low-level features and efficiently embed these cues into a decoder to produce rich boundary information. Second, a cross-scale information interaction (CII) module was introduced to enhance the multi-scale representation capability of the network and facilitate information exchange between layers. Extensive experiments on two datasets demonstrate that the proposed method outperforms four state-of-the-art methods, offering rich boundary details and completeness under challenging scenarios. Therefore, the proposed method is more effective in extracting water body information from remote sensing images. This study will provide a valuable reference of methods for future research.

In the change detection for high-resolution remote sensing images, non-subsampled contourlet transform (NSCT) and change vector analysis (CVA) cannot ensure high detection accuracies under single thresholds due to significantly different changes in surface features. Hence, under the framework of change vector analysis in posterior probability space (CVAPS), this study proposed a NSCT-based change detection method combining fuzzy C-means (FCM) clustering and a simple Bayesian network (SBN): the FCM-SBN-CVAPS-NSCT method. First, the proposed method coupled FCM with an SBN to generate a change intensity map in posterior probability space. Then, the change intensity map was decomposed into submaps of different scales and directions through NSCT. The reconstructed change intensity map was optimized by preserving the details and eliminating noise in the high-frequency submaps. Finally, the multi-scale and multi-directional change detection in posterior probability space was achieved, enhancing the change detection accuracy. As indicated by the experimental results, the Kappa values obtained by the proposed method for three study areas were 0.100 9, 0.056 6, and 0.067 4 higher than those derived from the FCM-SBN-CVAPS method, demonstrating certain superiority.

Deep learning-based methods for information extraction of roads from high-resolution remote sensing images face challenges in extracting information about both global context and edge details. This study proposed a cascaded neural network for road segmentation in remote sensing images, allowing both types of information to be simultaneously learned. First, the input feature images were sent to encoders CNN and Transformer. Then, the characteristics learned by both branch encoders were effectively combined using the shuffle attention dual branch fusion (SA-DBF) module, thus achieving the fusion of global and local information. Using the SA-DBF module, the model of the features learned from both branches was established through fine-grained interaction, during which channel and spatial information in the feature images were efficiently extracted and invalid noise was suppressed using multiple attention mechanisms. The proposed network was evaluated using the Massachusetts Road dataset, yielding an overall accuracy rate (OA) of 98.04%, an intersection over union (IoU) of 88.03%, and an F1 score of 65.13%. Compared to that of mainstream methodsU-Net and TransRoadNet, the IoU of the proposed network increased by 2.01 and 1.42 percentage points, respectively. Experimental results indicate that the proposed method outperforms all the methods compared and can effectively improve the accuracy of road segmentation.

The pansharpening of remote sensing images is a process that extracts the spectral information of multispectral images and the structural information of panchromatic images and then fuse the information to form high-resolution multispectral remote sensing images, which, however, suffer a lack of spectral or structural information. To mitigate this problem, this study proposed a pansharpening algorithm for remote sensing images based on a multi-depth neural network. This algorithm includes a structural protection module and a spectral protection module. The structural protection module extracts the high-frequency information of panchromatic and multispectral images through filtering and then extracts the multi-scale information of the images using a multi-depth neural network. The purpose is to improve the spatial information extraction ability of the model and to reduce the risk of overfitting. The spectral protection module connects the upsampled multispectral images with the structural protection module through a skip connection. To validate the effectiveness of the new algorithm, this study, under consistent experimental conditions, compared the new algorithm with multiple pansharpening algorithms for remote sensing images and assessed these algorithms from the angles of subjective visual effects and objective evaluation. The results indicate that the algorithm proposed in this study can effectively protect the spectral information of multispectral images and the structural information of panchromatic images in solving the lack of structural information in current algorithms for image fusion.

Net ecosystem productivity (NEP) is recognized as an important characteristic quantity of ecosystems and a physical quantity of carbon exchange between terrestrial ecosystems and the atmosphere. Utilizing MODIS NPP and meteorological data, this study estimated the vegetation NEP in Hunan Province from 2000 to 2020 using a soil microbial respiration model. Furthermore, this study analyzed the dynamic characteristics of vegetation carbon sink through trend analysis, variation coefficient, and standard deviation ellipse methods, followed by a quantitative assessment of the impacts of natural factors on vegetation carbon sink using geographical detectors and correlation analysis. The results indicate that the annual multiyear average of vegetation carbon sink in Hunan Province was 603.01 gC·m^-2·a^-1. The vegetation carbon sink presented a spatial distribution pattern of higher values in the south and west and lower values in the north and east, decreasing gradually from southwest to northeast. From 2000 to 2020, the average trend coefficient of vegetation carbon sink was 2.97 gC·m^-2·a^-1, trending upward overall. The coefficient of variation was primarily characterized by small fluctuations and fairly small fluctuations, while areas of great fluctuations are mainly scattered around certain cities, which are more susceptible to natural or anthropogenic disturbances. The variations in vegetation carbon sink in Hunan Province result from multiple factors, with the explanatory power of various factors decreased in the order of altitude, slope, temperature, precipitation, and slope. Both altitude and slope exhibited strong explanatory power regarding the spatiotemporal distribution of vegetation carbon sink in Hunan Province, while temperature and precipitation demonstrated weaker explanatory power. Areas where vegetation carbon sink was positively correlated with temperature and precipitation accounted for 75.13% and 73.11% of the total vegetation area, respectively.

Shanxi Province is recognized as a significant coal base in China. The extensive and intensive coal mining activities have adversely affected the local ecology, rendering the research on ecological evolution in Shanxi Province highly significant. Utilizing the Google Earth Engine (GEE) platform, this study calculated the 2000-2022 remote sensing ecological index (RSEI) of Shanxi Province using images from Moderate Resolution Imaging Spectroradiometer (MODIS). The Mann-Kendall trend analysis method was employed to analyze the evolutionary trends of RSEI, while Pettitt mutation tests were conducted to identify RSEI mutations. Furthermore, the Pearson correlation coefficient was used to analyze the correlation between RSEI and climatic factors. The results indicate that Shanxi Province exhibited relatively high ecological quality on average during the period. However, the coal mining areas in the province displayed moderate ecological quality overall. The spatial distribution of precipitation and temperature can effectively account for the spatial distribution of RSEI. Most regions in Shanxi Province showed an upward trend in RSEI, with areas with reduced RSEI predominantly located in coal mining areas and basin areas with high population density and a developed economy. During the 23 years, the ecological quality in Shanxi Province has evolved from poor to moderate and then to relatively good, increased while fluctuating from 2000 to 2006, kept stable from 2006 to 2012, regressed after increase from 2012 to 2019, and continuously increased from 2019 to 2022. Similarly, the ecological quality in coal mining areas has shifted from relatively poor to relatively good, increased while fluctuating from 2000 to 2006, kept stable from 2006 to 2012, decreased while fluctuating from 2012 to 2019, and continuously increased from 2019 to 2022. The year 2010 is identified as a pivotal point for the ecological quality of Shanxi Province, with the ecological quality trending upward from 2000 to 2010 and comprehensively improving after 2010 across the province and its coal mining areas. The interannual variations in precipitation generally produce positive impacts on the ecological quality, while the variations in interannual temperature exert insignificant impacts.

High-resolution remote sensing technology can greatly enhance the efficiency of investigations into active faults due to its high ability to identify the fine structures of microlandforms. This study presents a systematic summary of the symbols of remote sensing images for active faults. By comprehensively utilizing data from the Landsat8 and GF-2 satellites, as well as previous results and field geological surveys, this study analyzed and examined the active faults in the Maoyaba area of western Sichuan Province through the interpretation of remote sensing images of both macro- and microlandforms. The results show that, besides the Yidun-Litang fault zone, several nearly-W-E-trending normal active faults occur in the study area. Based on this finding, as well as the analysis of the regional geological setting, it can be concluded that crustal materials along the southeastern margin of the Qinghai-Tibet Plateau were continuously squeezed out laterally under the background of the intense collision and compression between the Indian and Eurasian plates, leading to the formation of two conjugate faults: the dextral Batang strike-slip fault and the sinistral Litang strike-slip fault. The joint control of both faults resulted in the local extension of the study area and the formation of nearly-W-E-trending fault structures, which govern the development and evolution of the Damaoyaba Basin, the Xiaomaoyaba Basin, and the Cuopu Basin in the north.

As marine geological surveys continue to deepen, there is an urgent need to develop new-generation information technologies to accelerate the transformation of marine geological survey pattern. In recent years, the digital marine geological project has developed a comprehensive framework of trinity that integrates geological cloud, big data, and intellectualization based on the practical needs of marine geological surveys. Furthermore, the planning of three major systems, i.e., the support, core, and key systems, has been proposed for marine geological informatization. These suggest significant progress in the construction of marine geological cloud platform, marine geological big data infrastructure, and intelligent applications in marine geology. The progress also includes the building of professional marine geological nodes and network systems, the formation of a national marine geological data resource system, and the advancement in the intelligent application of marine geological operations. Information-based construction have played a full role in promoting the transformation and upgrading of geological surveys, while also serving natural resources management.

Shaanxi Province, serving as both one of China’s initial pilot areas for the returning farmland to forestland/grassland project and an important energy supply base in the Yellow River basin, has made substantial investments in mineral resource development and ecological environment protection and restoration in recent years. Based on trend analysis and correlation analysis conducted using MATLAB, this study examined the spatiotemporal differentiation pattern of vegetation ecology and its responses to the dual disturbances of climate conditions and mining activities. The results indicate that from 2001 to 2022, the normalized difference vegetation index (NDVI) of Shaanxi Province exhibited an upward trend while fluctuating, with an average annual increase of 0.006. The lowest NDVI value occurred in 2015. Precipitation acted as the major factor affecting the NDVI of Shaanxi Province. In most areas, NDVI exhibited a significant positive correlation with both precipitation and humidity. The correlation between NDVI and mining activities was increasingly significant with an increase in the mining area. In some energy-based cities, NDVI decreased initially and then increased, exhibiting a V-shaped trend. Overall, mining activity made more positive than negative contributions to changes in NDVI of Shaanxi Province. The results of this study will provide foundational data and a scientific reference for ecological protection and mine restoration and management in Shaanxi Province.

The key to the high performance of semantic segmentation models for high-resolution remote sensing images lies in the high domain consistency between the training and testing datasets. The domain discrepancies between different datasets, including differences in geographic locations, sensors’ imaging patterns, and weather conditions, lead to significantly decreased accuracy when a model trained on one dataset is applied to another. Domain adaptation is an effective strategy to address the aforementioned issue. From the perspective of a domain adaptation model, this study developed an adversarial learning-based unsupervised domain adaptation framework for the semantic segmentation of high-resolution remote sensing images. This framework fused the entropy-weighted attention and class-wise domain feature aggregation mechanism into the global and local domain alignment modules, respectively, alleviating the domain discrepancies between the source and target. Additionally, the object context representation (OCR) and Atrous spatial pyramid pooling (ASPP) modules were incorporated to fully leverage spatial- and object-level contextual information in the images. Furthermore, the OCR and ASPP combination strategy was employed to improve segmentation accuracy and precision. The experimental results indicate that the proposed method allows for superior cross-domain segmentation on two publicly available datasets, outperforming other methods of the same type.

In recent years, geological hazard identification based on integrated remote sensing has been widely carried out, featuring wide surveyed areas, high time pressure, and heavy tasks. To meet the demand for the effective utilization of multi-source remote sensing data, multi-person collaboration, and quick result integration, along with the requirements of geological hazard identification tools, this study established a 3D geological hazard identification information platform. This platform, adopting a C/S architecture, allows for the effective organization and management of multi-source optical and radar remote sensing data, vector data, and 3D models and possesses functions such as the loading of multi-source data, multi-person collaboration, quick interpretations and identification, and result expression and output. This platform has successively supported fine-scale identification of hidden hazards and multiple emergency security efforts in national key areas with high geologic hazard susceptibility, such as Gansu, Yunnan, and Sichuan provinces. The application results indicate that this platform allows for rapid data supply and assists in improving work efficiency.

Remote sensing data with a high spatial resolution can be used to quickly ascertain the current status of the geological environment of mines in China, yielding objective and accurate results. By comparing 2020—2021 remote sensing images of 169 districts, counties, and cities in Hebei Province, 728 patches signaling suspected lands destroyed in mines were identified through interpretation. In 2021, the newly rehabilitated areas of the mining environment in Hebei Province reached 1 305.51 hm², while the newly increased lands destroyed by mining were 932.13 hm², resulting in a net increase of 373.38 hm² in lands attributed to the geological environment rehabilitation. These results indicate the general achievement of mining while rehabilitating. Based on a preliminary analysis of the current status of the geological environment of mines in Hebei Province and existing primary issues, this study proposed countermeasures and recommendations for future rehabilitation efforts. The results of this study will provide foundational data and technical support for managing the geological environment of mines in Hebei Province and evaluating the effectiveness of mine greening initiatives.

The rapid survey and accurate mapping of the spatial distribution of crops using remote sensing are fundamental to modern precision agriculture. However, limitations in the acquisition, processing, and analysis of remote sensing images impact the mapping accuracy of traditional crop planting structures. Therefore, there is an urgent need to conduct spatial modeling and feature analysis for the uncertainty in crop classification. Using the Ningxia Yellow River irrigation area as a trial area and farmland parcels as the basic spatial units, this study classified crops on a parcel scale utilizing multi-source remote sensing data and machine learning algorithms. Then, an uncertainty calculation model was constructed based on mixed entropy, yielding the spatial distribution of the uncertainty of crop types in farmland parcels. Afterward, multi-source auxiliary data were employed to build a regression model for the uncertainty, and the potential impacts of related geographical variables on the uncertainty were explored. The experiment results indicate that 1.49 million vector units were constructed for the farmland parcels during the farmland extraction and classification session, yielding an overall crop classification accuracy of 0.80. The mapping results aligned well with the actual agricultural management units, and the classification results proved more better than the traditional pixel-based methods. The uncertainty in the parcel-scale crop classification was generally lower, with significant differences among crop types. The uncertainty was low for rice, vegetable plots, and alfalfa, relatively higher for wheat of single- and double-cropping patterns, and moderate for maize. The uncertainty in parcel-scale crop classification is influenced by various environmental factors such as planting structure and resource conditions, exhibiting the most significant correlations with crop type and water accessibility.

This study constructed an assessment index system for ecological vulnerability based on the vulnerability scoping diagram (VSD) model. It dynamically assessed the three-phase ecological vulnerability of Kangding City from 2011 to 2019 using the analytic hierarchy process - principal component analysis - entropy weight method (AHP-PCA-EWM). Through the analysis of spatio-temporal variations, spatial correlations, and driving factors, it revealed the spatio-temporal differentiation characteristics and driving mechanism of Kangding’s ecological vulnerability, aiming to provide suggestions for the ecological restoration, conservation, and sustainable development of Kangding. The results of this study are as follows: ① Throughout the study period, Kangding exhibited an overall moderate ecological vulnerability, with increased potentially- and slightly-vulnerable areas and a decreased severely vulnerable area, suggesting a promising ecological evolutionary trend. Moreover, the ecological vulnerability of Kangding manifested spatial distributions characterized by high-high clusters in the western and southeastern portions and low-low clusters in the northeastern and middle portion; ② The spatial distributions of ecological vulnerability in Kangding were subjected to various internal and external factors, with natural driving factors like vegetation cover, biological abundance, soil and water conservation, and meteorology being the dominant ones.

Drought is identified as one type of the most serious natural disasters affecting agricultural production, and developing a comprehensive drought index holds practical significance for the assessment of drought in various districts and counties in Chongqing. By coupling the soil moisture and precipitation Z-index data downscaled using the XGBoost algorithm and using the Copula function, this study developed a gridded comprehensive drought index CSDIM-A on a kilometer scale with the boundaries of various districts and counties of Chongqing as the spatial division criteria and decades of a month as time units. Using this index, this study assessed the spatiotemporal characteristics of drought and performed an experimental demonstration of the index using Chongqing as the study area. The results indicate that the downscaling enhanced the spatial continuity of remote sensing-based products, thus providing support for the subsequent construction of a comprehensive drought index on a kilometer scale. The generalized extreme value distribution and the t location-scale distribution applied to the fitting of the data distributions of soil moisture and precipitation in most districts and counties of Chongqing, respectively, while the Frank-copula function suited for the fitting of the joint distribution of binary variables on a scale of a month decade. As validated based on soil moisture content, CSDI_M-A can more effectively reflect drought than the precipitation Z-index, with its spatial distribution in various districts and counties consistent with the actual drought data. This indicates that the CSDI_M-A can be used as a reference for drought assessment.

Based on 1985-2020 Landsat data, this study estimated eight phases of annual vegetation fractional cover (VFC) of the Beijing-Tianjin-Hebei region. Using the Theil-Sen Median and Mann-Kendall trend analyses, this study comprehensively analyzed the spatiotemporal variation characteristics of VFC in four major functional areas for the coordinated development of the Beijing-Tianjin-Hebei region. Furthermore, employing geodetectors, this study explored the degrees and mechanisms of the impacts of climatic, natural, and anthropogenic factors, along with their interactions, on the regional VFC from both static and dynamic perspectives. The results indicate that from 1985 to 2020, the Beijing-Tianjin-Hebei region exhibited sound vegetation coverage overall, which decreased in the order of the southern functional expansion area (SFEA), the northwestern ecological conservation area (NECA), the central core functional area (CCFA), the eastern coastal development area (ECDA). The VFC of the Beijing-Tianjin-Hebei region trended upward while fluctuating, with an increasing rate of 0.097%/10a. The VFC exhibited a spatial distribution pattern of high values in the west and low values in the east. Specifically, areas with elevated VFC were primarily distributed in the Yanshan, Damaqun, and Taihang mountains within the NECA, while those with reduced VFC were principally found in the built-up areas and their surrounding areas of cities and counties in the CCFA, ECDA, and SFEA. At the single-factor level, the primary and secondary factors controlling VFC across the four functional areas differed greatly, with land-use and soil types exhibiting higher interpretability. Regarding the influencing elements, the main factors driving spatial differentiation of VFC in the CCFA and SFEA included anthropogenic factors, those in ECDA comprised anthropogenic and natural factors, and those in NECA were dominated by climatic and natural factors. For the VFC of the four functional areas in all these years, the land use type manifested high interpretability, which trended upward overall. The q values of soil types were higher in ECDA and NECA, trending downward in the NECA. Secondary factors controlling the VFC exhibited different interannual interpretability in various functional areas. All influencing factors exhibited enhanced influence to varying extents, with no mutual independence or weakened influence observed. Additionally, the meteorological factor emerged as the primary interacting variable.

Analyzing the carbon stock in a terrestrial ecosystem is a key link for research on the global and regional carbon cycle. Assessing the long-time-series carbon stock in the West Dongting Lake National Nature Reserve will provide scientific data for regional ecological monitoring and management. Based on the land use data from 2000 to 2020, this study explored the spatiotemporal changes in the carbon stock of the nature reserve based on the carbon stock estimated using the InVEST model and identified key areas of carbon stock changes. The results indicate that in the past two decades, the carbon stock in the nature reserve exhibited a fluctuating upward trend, ranging from 113.5×10⁴ to 125.7×10⁴ tons. The carbon stock presented relative changing rates of less than 2% during this period, except for 2003, when the changing rate was 3.2%. Over the past two decades, the core zone of the nature reserve ranked first in carbon stock among subregions every year, followed by the pilot zones. The carbon stock in most areas of the nature reserve remained unchanged or changed slightly. Nevertheless, there still existed some areas with significant changes in the carbon stock. The key areas of carbon stock changes featured diverse spatial distribution patterns of carbon stock, such as concentrated, linear, and scattered patterns, with land use types in these areas exhibiting corresponding change intensities of carbon stock. The changes in the carbon stock in the pilot zones were greatly affected by human interference, while those in the core area were primarily related to precipitation. The results of this study will assist in scientifically promoting carbon neutrality and peak carbon dioxide emissions in the West Dongting Lake National Nature Reserve.