A timely and accurate understanding of the spatial extents and distributions of industrial solid waste and open pits in mining areas is significant for the precise control of solid waste contamination and the ecosystem conservation. Remote sensing technology is an effective monitoring method. However, single-scale sample sets fail to fully represent the features of industrial solid waste yards and open pits with different shapes and sizes. Constructing multiscale sample sets may be effective in solving the problem of incomplete feature representation for different industrial solid waste yards and open pits, thereby enhancing the identification accuracy and generalization capability of models. By fully considering the differences in the shape and size of different industrial solid waste yards and open pits, this study proposed a remote sensing identification method for industrial solid waste and open pits based on the multiscale sample set optimization strategy. In the proposed method, a multiscale sample set was prepared based on the preprocessed data of the GF-1B, GF-1C, and GF6 satellite remote sensing images. Subsequently, a U-Net deep learning network model was constructed to identify industrial solid waste and open pits. Finally, the identification accuracy was compared with that of the single-scale sample set model. The results show that the U-Net deep learning network model based on the multiscale sample set achieved identification accuracy of 81.23 %, recall of 66.88 %, F1-score of 73.36 %, and average intersection over union of 73.55 %, suggesting improvements by 6.02, 1.02, 3.12, and 9.86 percentage points, respectively, compared to the single-scale sample set model. Overall, this study provides a reliable approach for precisely monitoring industrial solid waste and open pits.

The performance of remote sensing image processing software directly influences the effectiveness and efficiency of teaching and research activities conducted by related workers. Focusing on land use classification in a complex mine scene, this study comparatively investigated the performance of popular remote sensing software including Pixel Information Expert (PIE), Environment for Visualizing Images (ENVI), ERDAS IMAGINE (ERDAS), and eCognition Developer (eCognition), and the self-developed deep learning algorithm. The results show that ENVI yielded the highest overall accuracy (OA) and Kappa coefficient but the lowest classification efficiency in conventional pixel-oriented classification. In contrast, ERDAS exhibited the highest operational efficiency while maintaining relatively high accuracy. eCognition achieved the optimal OA and Kappa coefficient and relatively high operational efficiency in conventional object-oriented classification. The deep convolutional neural network algorithm demonstrated superior accuracy over the classification results of conventional methods. Overall, this study quantitatively revealed the performance of various software on different strategies and methods, providing a scientific basis for related workers to choose appropriate image processing software and improve teaching effect and research efficiency.

Timely and accurate monitoring and analysis of land for mine development and associated restoration and management is a principal task in remote sensing survey and monitoring of mine environments. Hence, using high-spatial-resolution remote sensing data as primary data source, this study delineated the new mining-destroyed land area (558.07 hm²) and land restoration and management area (1 019.07 hm², including artificially restored 975.29 hm² and naturally restored 43.78 hm²) in Shandong Province in 2021. Additionally, there were 26.59 hm² of destroyed land due to infrastructure and road construction and 77.03 hm² of tailings pond management. By preliminarily analyzing the remote sensing work concerning the new mining-destroyed land and land restoration and management in Shandong Province in 2021, this study proposed countermeasures and suggestions for existing problems. The investigation and monitoring results can effectively support the scientific decision-making of land management departments in Shandong Province.

Remote sensing technology plays a significant role in mining and geological environment monitoring. Based on the ArcGIS platform, and using the preprocessed data of high-spatial-resolution satellite remote sensing images for Henan Province obtained in 2020 and 2021 as the information source, this study derived the information of the new mining-destroyed land and associated restoration and management through remote sensing interpretation marks and visual interpretation. Through statistical analysis, this study summarized the mining-destroyed land characteristics and the restoration and management status throughout Henan Province, followed by suggestions for existing problems. Overall, this study provides a reference for government departments to make decisions and for protecting geological environments in mines.

The Yangtze River economic belt (YREB), spanning the east, middle, and west regions of China, serves as a pioneering demonstration belt for the construction of ecological civilization. Hubei Province, a member of the YREB, holds abundant mineral resources. Its long-term exploitation of mineral resources has caused ecosystem damage and social stability disruption, necessitating ecological restoration of abandoned open-pit mines. However, few studies concern the systematic tracking, monitoring, and analysis of the ecological restoration of abandoned open-pit mines in the YREB within Hubei Province. Based on the remote sensing data from the domestic high-resolution satellite from 2017 to 2022, and utilizing human-computer interaction interpretation, this study monitored the distributions and ecological restoration of abandoned open-pit mines within 10 km on both sides of the mainstems and major tributaries (Hanjiang River and Qingjiang River) in the YREB within Hubei Province. The results indicate that by the end of 2017, 537 patches of abandoned open-pit mines had been identified in the study area, involving 283 mines with a total area of 2 225.19 hm². The monitoring results of dynamic changes from 2018 to 2022 show that the ecological restoration and treatment area of abandoned open-pit mines increased from 291.01 hm² in 2018 to 1 741.19 hm² in 2022, manifesting a continuously rising treatment rate, suggesting overall improved ecological restoration and treatment results. Using the remote sensing ecological index (RSEI) values from 2017 to 2022, this study assessed the ecological restoration effects of abandoned open-pit mines. The assessment results reveal that the average RSEI value for the patches increased from 0.397 7 in 2017 to 0.423 9 in 2022, with a growth rate of 6.59 %, suggesting significantly improved ecological conditions and restoration effects. Overall, the monitoring and assessment results of the dynamic changes in ecological restoration of abandoned open-pit mines in the study area provide valuable data and methodological insights for monitoring abandoned open-pit mines in Hubei Province and other regions in China, highlighting the significance of this study.

Addressing mining-induced challenges in geological environments is currently a priority for ecosystem conservation and restoration. A prerequisite for formulating effective conservation and restoration strategic planning is to thoroughly ascertain the ecological status and challenges. Based on high-resolution remote sensing image data from 2020 and 2021, and field investigations, this study dynamically monitored the geological environments in mines in west-central Inner Mongolia. The results indicate that in 2021, mining activities in west-central Inner Mongolia led to an additional 3 380.91 hm² of destroyed land, while the newly restored and managed area reached 1 801.31 hm², suggesting an overall imbalance between mining and management. This study analyzed the status and challenges of geological environments in mines from the perspectives of the spatial distribution and type of destroyed land, mineral species, and restoration and management. Furthermore, this study proposed recommendations for subsequent remote sensing monitoring, restoration, and management of geological environments in mines. Overall, the results and recommendations of this study can serve as a reference for local ecological environment restoration and mineral resource planning.

To investigate the ecological and environment effects of land-use changes under different mining modes, this study utilized the Google Earth Engine (GEE) cloud computing platform to construct a remote sensing ecological index (RSEI) by integrating the greenness, heat, dryness, and wetness indicators. The RSEI was utilized to assess the ecological quality of two mining areas with different mining modes: the Guqiao Coal Mine in Huainan City (underground mining) and the Nanshan Iron Mine in Ma’anshan City (open-pit mining). Through a comparative analysis of relevant data from 2000 to 2020, this study analyzed the dynamic evolutionary patterns between land use changes and ecological quality in the two mining areas. The results indicate that cultivated land occupied the largest proportion in both mining areas. The underground mining area was characterized by a significantly expanded water area, whereas the open-pit mining area featured reduced cultivated and forest lands and increased construction land. Both mining areas exhibited overall good-to-fair ecological quality. Specifically, the RSEI values for the Guqiao Coal Mine were 0.60, 0.82, 0.71, 0.65, and 0.68, while those for the Nanshan Iron Mine were 0.58, 0.59, 0.59, 0.63, and 0.64. Among various land use types, construction land and water bodies displayed relatively poor ecological conditions, whereas forest and cultivated lands exhibited more favorable conditions. The underground mining area showed surface subsidence and the transition of cultivated land to water areas, leading to deteriorating ecological quality. In contrast, the open-pit mining area showed soil stripping, shrinking forest and cultivated lands, and construction land expansion, contributing significantly to the declining ecological quality.

The Shendong mining area is a significant coal-producing area in China. Against the backdrop of climatic amelioration and large-scale coal mining, vegetation in the Shendong mining area has been influenced negatively by coal mining and positively by climatic amelioration and ecological restoration. Long time-series quantitative monitoring and assessment of vegetation cover in the mining area using remote sensing techniques play a significant role in local ecological quality management and ecological conservation. Based on Landsat satellite imagery data, this study conducted a long time-series monitoring of the normalized difference vegetation index (NDVI) in the Shendong mining area over a nearly 40-year period from 1986 to 2023. This monitoring focused on the interannual variations, variation trends, stability, and future variations of vegetation cover in the mining area. Moreover, this study performed a segmented quantitative analysis, taking 2008 (the onset of large-scale coal mining) as a demarcation point. The results indicate that climatic amelioration over the past nearly four decades has facilitated vegetation growth in the Shendong mining area. Despite the negative impacts of large-scale coal mining on surface vegetation, more favorable climatic conditions and ecological restoration efforts in the mining area have ensured a continuous improvement in vegetation cover, with a higher restoration rate observed locally. The Shendong mining area was characterized by improved vegetation cover across different stages,with the improved area exceeding 80 %. Large-scale coal mining caused limited vegetation deterioration, predominantly occurring in the open-pit mining area. In contrast, the vegetation restoration project in the underground mining area effectively ensured a favorable environment for vegetation growth. The vegetation cover in the Shendong mining area remained relatively stable at different stages. During large-scale coal mining, significant vegetation cover fluctuations occurred primarily in the stopes and waste dumps of the open-pit mining area. The underground mining area exhibited relatively stable vegetation cover overall, except for the land used for industrial and mining construction. Concerning future variations of vegetation cover, the Shendong mining area exhibited a relatively limited capability to maintain its current state. Due to large-scale mining activities, 3.92 % of the area underwent continuous degradation, which was primarily observed in the stopes of the open-pit mining area. This highlighted the urgent need for artificial ecological restoration in the stopes. The results of this study provide a reliable data reference for the supervision of ecological quality in the Shendong mining area, facilitating the more scientific and efficient establishment of a comprehensive ecological prevention and control system.

To address the issue of spatial and spectral information fusion during pansharpening, this study proposed a sharpening model for panchromatic and multispectral images based on the multiscale weighted pulse-coupled neural network (PCNN) and low-rank and sparse decomposition in the non-subsampled shearlet transform (NSST) domain. The sharpening model consists of low- and high-frequency processing modules. For high-frequency subbands, a method for weighting high-frequency subbands in various scales and directions was proposed, accompanied by an adaptive PCNN model tailored to their characteristics in different directions. In contrast, low-frequency subbands were decomposed into low-rank and sparse parts, with corresponding fusion rules created according to their characteristics. The fused image was then obtained through inverse NSST. The experiments on the sharpening model were conducted using GeoEye,QuickBird, and Pléiades datasets. Moreover, an ablation experiment was designed for the multiscale weighting module for high-frequency information. Compared to suboptimal models, the sharpening model in this study increased the peak signal-to-noise ratio (PSNR) value by approximately 1 dB, 1.6 dB, and 2.2 dB, respectively. The experimental results demonstrate that the sharpening model outperformed other algorithms in index assessment, effectively resolving the challenge of extracting high-frequency information.

Change detection based on remote sensing images holds significant application potential in land source survey updating, and urban development monitoring and planning. Concerning the challenges of change detection based on remote sensing images in practical applications, this study proposed a lightweight change detection method combining the siameseinverted residual structure with self-attention enhancement. Instead of the traditional convolutional neural network structure, the siamese improved inverted residual structure was used as the backbone network to fully extract the feature information and significantly reduce the network complexity. The self-attention enhancement module was employed to improve the network's ability to pay attention to global information. Edge weights were added to the loss function to precisely optimize the details of the extraction results. The multilevel hopping residual connections were applied to fully integrate the global and local features. Finally, the performance of the proposed method was tested on the public and prepared datasets of remote sensing images for change detection, respectively. The results indicate that compared to other change detection methods, the proposed method significantly reduced network parameters and computational complexity while maintaining the detection accuracy, contributing to lightweight models of change detection based on remote sensing images.

The high computational complexity and large model scales of existing cloud detection algorithms render their deployment on edge devices almost infeasible. To address this challenge, this study proposed a MobileNet-based lightweight cloud detection model. In the downsampling stage, a residual module based on the attention mechanism was employed to reduce model parameters through group convolution. The channel shuffling mechanism and the squeeze-and-excitation (SE) channel attention were integrated to enhance the information exchange between channels. These approaches reduced parameters and computational complexity while maintaining the ability to extract significant features. In the upsampling stage, the RepConv module and the improved atrous spatial pyramid pooling (ASPP) module were used to enhance the network’s learning capability and its ability to capture image details and spatial information. Experimental results demonstrate that the proposed model can achieve higher cloud detection accuracy while reducing parameters and model complexity, substantiating its practicality and feasibility.

The abundant contextual information in synthetic aperture radar (SAR) images remains underutilized in deep learning-based ship detection. Hence, this study proposed a novel method for detecting ships from SAR images based on high-dimensional contextual attention and dual receptive field enhancement. The dual receptive field enhancement was employed to extract multi-dimensional feature information from SAR images, thereby guiding the dynamic attention matrix to learn rich contextual information during the coarse-to-fine extraction of high-dimensional features. Based on YOLOv7, a YOLO-HD network was constructed by incorporating a lightweight convolutional module, a lightweight asymmetric multi-level compression detection head, and a new loss function,XIoU. A comparative experiment was conducted on the E-HRSID and SSDD datasets. The proposed method achieved average detection accuracy of 91.36 % and 97.64 %, respectively, representing improvements by 4.56 and 9.83 percentage points compared to the original model, and outperforming other classical models.

The classification of hyperspectral images faces challenges like ineffective extraction of multi-scale features and easy loss of pose information. Considering these challenges, this study proposed a classification network of hyperspectral images with multi-scale feature fusion-the hierarchical multi-scale concatenation net (HMC-Net). Initially, multi-scale convolution kernels were applied for parallel computing to extract multi-level features. Meanwhile, the 1×1 convolutional kernels were employed to reduce input-output dimensions, balancing computational complexity. These operations enabled efficient feature extraction without significantly increasing the overall computational burden. Subsequently, independent capsule networks were used for parallel processing of features at various scales. The max pooling was improved via dynamic routing to enhance the translation invariance of features, thereby reducing the loss of pose information. Finally, the concatenate operation integrated feature maps of different scales, thereby achieving a precise analysis of multi-level information in the classification of hyperspectral images. Comparative experimental results demonstrate that the HMC-Net achieved an overall accuracy of 94%, 98%, and 99% on the Kennedy Space Center, University of Pavia, and Salinas datasets, respectively. Compared to the latest classification model of hyperspectral images, the HMC-Net exhibited significant performance advantages, validating its effectiveness.

Forest aboveground biomass (AGB) serves as a significant indicator for monitoring forest resources and a crucial part of forest carbon stock. AGB estimation methods, characterized by simple models and physical significance, play a significant role in improving the monitoring efficiency of forest resources. Based on previous studies, this study proposed an allometric model method for AGB estimation by integrating the height features and forest canopy closure derived from the airborne light detection and ranging (LiDAR), and the vegetation indice derived from satellite multispectral data (also referred to as ModelB_N). This study investigated Genhe City in Inner Mongolia using LiDAR data and Sentinel-2A multispectral data acquired in 2022, combined with sample plot data obtained around this period. By comparatively analyzing the correlations of LiDAR-derived height features and vegetation indices with AGB, this study applied optimal LiDAR-derived height features and vegetation indices to ModelB_N. Finally, this model was compared with models using only height features (ModelB), integrating both height features and vegetation indices (ModelB_Y), and combining height features and canopy closure (ModelB_HC). The results indicate that among the LiDAR-derived height features, the 90th height percentile (H90) exhibited the highest correlation with AGB in the study area. Among the vegetation indices, the kernel normalized difference vegetation index manifested the highest correlation with AGB. Among the four models, the ModelB_N achieved the highest adjusted R-square value (R_{\mathrm{a}\mathrm{d}\mathrm{j}}^2, 0.78), the highest estimation accuracy (EA, 83.25 %), and the lowest root mean square error (RMSE, 15.87 t/m²). The ModelB_N outperformed the ModelB_HC, with improvements in R_{\mathrm{a}\mathrm{d}\mathrm{j}}^2 value and EA by 0.05 and 1.75 %, respectively, and a reduction in RMSE by 1.66 t/hm². The ModelB_Y outperformed the ModelB, with improvements in R_{\mathrm{a}\mathrm{d}\mathrm{j}}^2 value and EA by 0.03 and 1.19 %, respectively, and a reduction in RMSE by 1.12 t/hm². These results demonstrate the rationality of using vegetation indices as an exponential power. Despite the failure to possess the lowest uncertainty in all pixels, the ModelB_N showed the optimal performance. Overall, the ModelB_N demonstrates the highest accuracy, a simple and efficient process, and certain physical significance. Therefore, the ModelB_N can function as a novel technique for AGB estimation to provide technical support for forest resource monitoring.

Water bodies serve as one of the three major elements in maintaining wetlands. Their dynamic monitoring can effectively protect wetland ecosystems. Conventional methods for monitoring water bodies in wetlands employ field surveys or manual interpretation of remote sensing images, which are costly and inefficient, and inapplicable to continuous dynamic monitoring. In recent years, using methods like machine and deep learning to extract water body features from satellite remote sensing images has developed into an effective means for monitoring water bodies in wetlands. Based on the hyperspectral images from the ZY1-02D satellite, this study classified the water bodies in the Baiyangdian wetland using machine learning, convolutional and transformer neural networks. The accuracy and computational efficiency of water body classification under different spectral preprocessing methods and different image neighborhood sizes in training were compared to explore the optimal data preprocessing method and classification model for water bodies in wetlands. The results indicate that deep learning significantly outperformed machine learning in classification accuracy and computational efficiency. In particular, the spectral-spatial residual network (SSRN) model based on the convolutional neural network achieved the highest classification accuracy (OA: 99.09 %, Recall: 99.62 %, F1-score: 0.99) under conditions of all spectral bands and a 9×9 neighborhood size. Besides, despite a low signal-to-noise ratio, the atmospheric water vapor absorption band contained significant information, assisting in improving the classification accuracy of water bodies in the wetland during model training and prediction. The results of this study are expected to provide methodological support for the business operation of water body classification of wetlands.

The AMS-3000 wide-field three-linear-array stereoscopic aerial survey system (hereafter referred to as the AMS-3000 system) is China’s first independently developed airborne linear-array aerial survey system. It can obtain panchromatic and R-, G-, and B-band multispectral images. However, the data quality of these images lacks quantitative assessments and analyses. Focusing on the area along the Jinsha River within western Panzhihua City, Sichuan Province, this study assessed the spectral quality of the data obtained from the AMS-3000 system in terms of grayscale, texture, and energy features, and noise level. Moreover, this study compared the AMS-3000 system with the internationally recognized ADS100 aerial photography system and assessed the geometric accuracy of the AMS-3000 system using the 1∶2 000-scale terrain data. Additionally, this study analyzed the effectiveness of the AMS-3000 system in the geological industry by applying it to the investigation of the mineral resource exploitation status and geologic hazards. Overall, this study serves as a reference for the application promotion and improvement of the AMS-3000 system.

By objectively assessing the response of forest fires to factors like terrain, vegetation, and human activities, this study aims to provide technical guidance for forest fire prevention and risk zoning in Chongqing City, China. In this study, 1 206 historical forest fire data of Chongqing City from 2000 to 2022 were used as dependent variables. The height, slope, terrain ruggedness, vegetation cover, land cover classification, and road network distance data were utilized as forest fire risk factors. With these data, a piecewise function was established to obtain the single-factor risk probabilities of forest fires. Based on the criteria importance through intercriteria correlation (CRITIC), the weights of the single-factor risk probabilities of forest fires were calculated to derive the spatial distribution of weighted forest fire risk probabilities in Chongqing City. Finally, according to the risk probabilities of forest fires, the forest fire risk in Chongqing City was divided into the low, relatively low, relatively high, high, and extremely high levels. The results indicate that among nine forest fire risk factors, the contributions of forest land, dry land, and vegetation cover to forest fire risk ranked top three, whereas the slope, height, and terrain ruggedness contributed little to forest fire risk. The forest fire risk levels of Chongqing City based on the weights of single-factor risk probabilities demonstrated satisfactory verification effects. Forest fires falling in zones at relatively high and above risk levels represented 83 %. In contrast, forest fires falling in zones at low and relatively low risk levels represented 8.33 % and 8.67 %, respectively. The forest fire risk in Chongqing City was intimately associated with the terrain trend, land use, and human activities. The high-risk and extremely high-risk zones were primarily distributed in low to middle mountain forest areas subjected to frequent human activities. Additionally, the areas surrounding forest land, located near farmland, rural roads, residential areas, and cemeteries, were also classified into high-risk zones since the frequent use of fire for production and daily life was prone to induce forest fires. The low-risk zones included primarily low and flat non-forest areas and steep forest areas, along with building land, water bodies, and paddy and dry lands that are far from forest land. Overall, the results of this study can be used to assess the spatial distribution of forest fire risk, providing scientific guidance for forest fire prevention.

In satellite image-based law enforcement for minerals, using unmanned aerial vehicles (UAVs) for investigation and aerial photography in mining areas with complex geographic conditions and unknown staffing situations, and photogrammetry for three-dimensional modeling of mining areas serves as an effective means to ensure the personal safety of field workers and improve work efficiency and accuracy. Lightweight UAVs are more applicable to satellite image-based law enforcement for highly mobile minerals due to their flexible take-off and landing conditions and high maneuverability while ensuring shooting clarity and modeling data requirements. Based on the UAV aerial survey results of several mining faces in Liaoning Province, this study demonstrates that satellite image-based law enforcement for minerals assisted by lightweight UAVs can significantly improve the efficiency and safety of fieldwork. Moreover, the modeling results provide data support for subsequent accurate survey and analysis and multi-temporal monitoring in mining areas.

Socioeconomic development and intensified urbanization have influenced ecosystems essential for human survival. In particular, the ecological quality of the Yangtze River economic belt (YREB) within Jiangsu Province has been significantly challenged due to urbanization and land development, establishing ecological vulnerability assessment as a prominent focus. This study investigated the ecological vulnerability in the YREB within Jiangsu Province across four periods from 2005 to 2020, based on the sensitivity-resilience-pressure (SRP) model that involves 16 indicators in three categories: ecological resilience, pressure, and sensitivity. Using the analytic hierarchy process-selective principal component analysis (AHP-SPCA) weighting method and geodetector, this study delved into the characteristics and drivers of ecological vulnerability. The results indicate that the ecological vulnerability in the study area increased gradually from Nanjing to Nantong cities. Ecological vulnerability levels shift primarily between adjacent levels, characterized by decreased moderate/severe vulnerability and increased mild/slight/potential vulnerability. Primary drivers of ecological vulnerability include the proportion of arable land, population density, and biodiversity, with the interaction between vegetation cover and the proportion of arable land showing the highest explanatory power. Overall, the results of this study provide a significant reference for ecosystem conservation and sustainable development along the Yangtze River within Jiangsu Province.

Under severe global climate change, achieving carbon peak and neutrality goals is of great significance. Exploring the influence of a specific factor on carbon dioxide (CO₂) emission intensity while controlling other driver variables remains a challenge. With CO₂ emission intensity data at the prefecture-level city scale as a data source, this study analyzed the spatial heterogeneity and spatial correlation of CO₂ emission intensity using the geodetector model and the spatial autocorrelation method, respectively. Using the constructed Bayesian causal forest model, and controlling other drivers, this study obtained the causal effects of China’s urban population size on CO₂ emission intensity from 2005 to 2020, presenting a U-shaped curve. Accordingly, this study explored the influence mechanism of China’s urban population size on CO₂ emission intensity. Based on the above analysis, this study proposed reasonable emission reduction policy recommendations for different regions, serving as a reference to enhance urban sustainable development.

Xichuan County serves as a primary water source area for the middle route of the South-to-North Water Diversion Project. Investigating the spatiotemporal variations and driving mechanism of vegetation cover in Xichuan County is significant for the ecological restoration of the county and the environmental protection of the water source area for the middle route. Based on available Landsat and MODIS data, this study constructed long time-series fractional vegetation cover (FVC) data for Xichuan County from 2002 to 2022 using the spatiotemporal adaptive reflection fusion model (STARFM) and the dimidiate pixel model. In combination with regression and trend analyses, the geodetector model, and correlation analysis, this study explored the spatiotemporal variations and driving mechanism of vegetation cover in Xichuan County during the study period. The results indicate that the coefficient of determination (R²) between the STARFM-reconstructed and real annual-scale FVC reached 0.914, an improvement of 0.05 compared to 0.864 under conditions of data missing. Therefore, the STARFM can provide a reliable data basis for more accurately investigating the dynamic evolution of vegetation cover in Xichuan County. From 2002 to 2022, the vegetation cover in Xichuan County was ordinary, with an average FVC value of 0.516, characterized by higher vegetation cover in the northwest compared to the southeast. The vegetation cover in Xichuan County showed an overall improvement trend, with an improved area representing 76.02 %, primarily covering the northwestern and southeastern portions of Xichuan County. In contrast, the degraded area represented 23.98 %, primarily covering the areas surrounding the Danjiangkou reservoir, Danjiang River, and Xishui branch. The spatial heterogeneity of vegetation cover in Xichuan County was predominantly influenced by elevation and slope, followed by soil type and average temperature, with minimal impacts from soil texture and average rainfall. The improvement and degradation of vegetation cover in Xichuan County were principally caused by anthropogenic factors, with minimal influence from climate factors. The primary anthropogenic factor denotes the middle route of the South-to-North Water Diversion Project, which contributed significantly to vegetation growth rather than inhibitory effects.

The Baige landslide occurred twice in October and November 2018, causing huge economic losses and extensive social impact. Monitoring the activity characteristics of the Baige landslide in various stages based on multisource data is significant for understanding the failure mechanism of this landslide. With Sentinel-1, ALOS-2, and Landsat8 data as data sources, this study derived the deformation characteristics of the Baige landslide before, during, and after two slide events using techniques, such as small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR), SAR offset tracking, and optical offset tracking. The optical offset calculation results show that from November 2014 to March 29, 2018 in the pre-sliding stage, the cumulative displacement of the Baige landslide reached 40 m, with deformation concentrated in the middle of the landslide. The SAR offset results indicate that the cumulative displacement of the landslide reached 6.4 m in May and July 2018 in the pre-sliding stage, with deformation also concentrated in the middle of the landslide. The InSAR-based monitoring results reveal that after the two failures of the Baige landslide in October and November 2018, significant residual deformation remained in the trailing edge and upper left side of the landslide. From November 2018 to November 2021 in the post-sliding stage, the Baige landslide exhibited a deformation rate of -140 mm/y at the high trailing edge of the landslide, and the deformation range on the upper left side continued to expand. All the results of this study reconstructed the whole sliding process of the Baige landslide subjected to large displacements, providing a valuable reference for the monitoring and early warning of landslides.

Net ecosystem productivity (NEP) serves as a significant index that quantitatively represents the carbon sequestration capacity of ecosystems. This study aims to explore the carbon source/sink status of the Inner Mongolian grassland ecosystem to support the efforts for low carbon and emission reduction. Based on MODIS NPP and meteorological data, and applying the trend analysis, coefficient of variation, Hurst index, and path analysis, this study explored the spatiotemporal variations of the NEP index in the Inner Mongolian grassland ecosystem from 2001 to 2020 and its relationship with influential factors. The results indicate that the overall spatial distribution pattern of average NEP in the Inner Mongolian grassland ecosystem was characterized by a gradual increase from northwest to southeast, and a gradual decrease from the Great Xing’an Range to the eastern and western foothills. The average annual NEP over the past 20 years was 210.65 gC·m^-2·a^-1, showing a fluctuating increase at a rate of 3.81 gC·m^-2·a^-1. The areas with increased NEP represented 99.33 % of the total grassland area, suggesting relatively stable changes in carbon sink. However, 69.08 % of NEP in the grassland system is expected to show weak anti-persistence in the near future, suggesting that carbon sink might be weakened. The selected influential factors, dominated by rainfall and minimum temperature, comprehensively explained 83.7 % of NEP variations. The results of this study assist in understanding the carbon sequestration characteristics of the Inner Mongolian grassland ecosystem while holding critical significance for achieving the carbon peak and neutrality goals.

The space-air-ground investigation system has achieved remarkable results in the identification and monitoring demonstration work of geohazards. Leshan City in Sichuan Province, China, is a key zone for preventing and controlling geohazards within the demonstration area. The high vegetation cover and concentrated rainfall lead to the high concealment and sudden occurrence of geohazards in Leshan City, necessitating the identification and monitoring of geohazards in this city. Hence, under the guidance of the space-air-ground investigation system, this study explored the identification and monitoring of geohazards in the highly vegetation-covered mountain areas within Leshan City. The results indicate that 75 geohazards were identified in the study areas, with 51 confirmed through field verification, suggesting an identification accuracy rate of 68 %. Among them, 36 geohazards were newly identified. The geohazards were primarily concentrated in two areas, where 37 were identified, representing 72.5 % of the total geohazards in the study areas. Concerning techniques for identifying geohazards at different deformation stages of slopes, stacking-interferometric synthetic aperture radar (InSAR) can be employed to detect geohazards at the initial deformation stage of slopes. For slopes experiencing significant deformation within the detection range of InSAR, techniques like stacking-InSAR, high-resolution optical satellite imagery, and light detection and ranging (LiDAR) can all be used for geohazard identification. For highly vegetation-covered mountain areas, the LiDAR technique, which can be utilized to remove the effects of surface vegetation, combined with expert knowledge, can be used for geohazard identification. Additionally, remote sensing techniques face challenges in effectively identifying concealed geohazards.

Under the influence of climate change and human activities, the increasing risk of ecological service degradation poses a significant challenge to regional ecological security. Exploring ecologic zones based on ecosystem service value (ESV) and landscape ecological risk index (ERI) enables an intuitive identification of the regional ecological function status, thereby providing a basis for regional ecosystem conservation. This study investigated Qinhuangdao City in Hebei Province, China, by integrating landscape ecology, equivalent factor, GIS grid, and spatial autocorrelation methods. Under the whole-process dynamic control framework, this study analyzed the spatiotemporal dynamics of ESV and ERI from 2001 to 2021, revealing their evolutionary patterns. Moreover, based on the value-risk spatial aggregation patterns, this study determined the ecologic zones in Qinhuangdao City. The results indicate that over the past two decades, the ESV in Qinhuangdao City exhibited overall slight variations, with an increase of about 0.073 billion yuan, presenting a spatial pattern characterized by alternatively distributed large dispersion and small aggregation. The average ERI value showed a fluctuating downward trend, gradually shifting toward lower risk, with higher ERI values in the north compared to the south. The evolutionary types of ESV and ERI in Qinhuangdao City can be classified into maintenance, upgrading, mitigation, and fluctuation types, with the maintenance type representing the largest proportion. A positive correlation was observed between ESV and ERI per unit area in Qinhuangdao City, dominated by zones with high ESV and ERI in the northern part of Qinhuangdao City.

Interferometric synthetic aperture Radar (InSAR) technology serves as a significant approach for analyzing surface subsidence in large-scale mining areas. Accurately predicting surface subsidence plays a significant role in preventing geological disasters. The long short-term memory (LSTM) network model faces challenges in parameter selection, while the rime optimization algorithm (RIME) is susceptible to local optimum and dependence on the initial solution. Considering these challenges, this study proposed a surface subsidence prediction model with LSTM optimized by the improved rime optimization algorithm (IRIME). The IRIME incorporated chaotic mapping, the improved Lévy flight mechanism, and the global exploration strategy of the hunter-prey optimizer (HPO). The proposed model is also referred to as the IRIME-LSTM model. With the Honghui coal mine as the study area, this study obtained the subsidence time series of highly coherent points in the mining area using small baseline subset (SBAS)-InSAR technology. Subsequently, this study conducted multi-step predictions of these highly coherent points using the IRIME-LSTM model, with the prediction results compared with the InSAR monitoring data. The results of this study indicate that the IRIME-LSTM model yielded a root mean square error (RMSE) of 2.65 mm, a mean absolute error (MAE) of 1.59 mm, and a mean absolute percentage error (MAPE) of 3.92 % in the overall test set. Compared to the RIME-LSTM and GS-LSTM models, the IRIME-LSTM model reduced the RMSE by 37.20 % and 51.73 %, the MAE by 42.60 % and 56.32 %, and the MAPE by 35.63 % and 50.51 %, respectively, demonstrating its high reliability and feasibility.

Global warming has exacerbated drought conditions, posing a significant threat to ecosystem structures and functions. Analyzing the spatiotemporal evolution of ecological vulnerability and its response to drought plays a significant role in achieving regional high-quality and sustainable development. With Xinjiang as the study area, this study constructed an assessment index system for ecological vulnerability based on the ecological sensitivity-resilience-pressure (SRP) model. Using methods like local spatial autocorrelation, coefficient of variation, slope trend analysis, and Hurst exponent, this study assessed the ecological vulnerability in Xinjiang from 2000 to 2020, followed by future trend prediction. Moreover, this study explored the impacts of drought on ecological vulnerability using the standardized precipitation evapotranspiration index (SPEI). The results indicate that the overall ecological vulnerability was relatively high in Xinjiang, with its spatial distribution characterized by significant regional differences and spatial aggregation. The SPEI value showed a downward trend at an average annual rate of 0.093 9, suggesting a significant worsening trend of regional aridification. The area featuring a negative correlation between drought and ecological vulnerability represented 54.1 %, indicating that ecological vulnerability in most areas decreased with improved regional moisture conditions. The stable distribution area of ecological vulnerability constituted 77.8 %, dominated by severely and extremely vulnerable areas. In the future, the majority of Xinjiang (61.3 %) is projected to witness decreased ecological vulnerability and enhanced ecological quality. Overall, the results of this study deepen the understanding of the status and driving mechanism of ecological vulnerability in Xinjiang, providing a scientific reference and decision-making basis for enhancing the adaptability of regional ecosystems to environmental changes.