Mangrove forests are recognized as one of the most biodiverse and productive marine ecosystems globally. This study investigated Beibu Gulf, Guangxi Province. Using Landsat, Sentinel, and PALSAR SAR images from 1985 to 2019 as data sources, as well as the Google Earth Engine (GEE) cloud platform, this study established a multisource dataset by integrating spectral bands, spectral indices, texture features, digital elevation models (DEMs), and backscatter coefficients. Furthermore, 14 classification schemes were developed, and a mangrove remote sensing recognition model was built using an object-based random forest (RF) algorithm. Accordingly, the long-time-series spatiotemporal dynamics of mangrove forests in Beibu Gulf were monitored. The monitoring results show that the object-based RF algorithm demonstrates a high ability to identify mangrove forests. Specifically, Scheme 3 combined with data from 2019 yielded the highest overall accuracy (96.3%) and a kappa coefficient of 0.956, which are 16.3% and 0.195 higher than those of Scheme 1 combined data from 1995, respectively. The classification schemes differed in the producer’s and user’s accuracy of different surface features in the Beibu Gulf. Specifically, these schemes yielded the highest user’s and producer’s accuracy of mangrove forests exceeding 94.6% and 92.0%, respectively. From 1985 to 2019, the area of mangrove forests in Beibu Gulf showed an increasing trend, with an annual changing rate of 6.63%, and the area expanded from inland to coastal areas. The results of this study provide a reference for the protection and sustainable management of mangrove forests while also verifying the feasibility of monitoring long-term spatiotemporal dynamics of mangrove forests based on the GEE platform.

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.

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.

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.

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 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.

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.

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.

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.

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.

To address the issues of low detection accuracy caused by significant scale variations, complex scenes, and limited feature information of small targets in remote sensing images, as well as low detection efficiency resulting from the large parameter size and high complexity of current object detection models, this study proposes a lightweight YOLOv7-tiny model for remote sensing image detection. First, the network neck was improved by incorporating group shuffle convolution (GSConv) and VoV-GSCSP modules. This allows for sufficient detection accuracy while reducing computational costs and network complexity. Second, a dynamic head (DyHead) combined with an attention mechanism was adopted during prediction. The performance of the detection head was enhanced using multi-head self-attention across scale-aware feature layers, spatially-aware positions, and task-aware output channels. Finally, the loss function of the original model was optimized by integrating the normalized Wasserstein distance (NWD) metric for small-target assessment and a bounding box regression loss function based on the minimum point distance IoU (MPDIoU). This assists in enhancing robustness for small target detection. The experimental results demonstrate that the proposed algorithm achieved mAP@50 scores of 87.7% and 94.7% on the DIOR and RSOD datasets, respectively, indicating increases of 2.7 and 5.1 percentage points compared to the original YOLOv7-tiny model. Furthermore, the frames per second (FPS) increased by 12.2% and 11.9%, respectively. Therefore, the proposed algorithm can effectively enhance both the accuracy and real-time performance of small target detection from remote sensing images.

Field verification of natural resources is a vital part of natural resource surveys. To address issues such as low efficiency and security risks encountered in traditional field verification methods, this study developed an application scheme for field verification utilizing UAV-based geographic information video technology. First, this study examined the characteristics of UAV-based geographic information video technology. Based on these characteristics, as well as the requirements of field verification, the features for the field verification were categorized into two types: land use classification and measurement assessment. Subsequently, the UAV-based geographic information video acquisition was designed for each type. The collected videos were then combined with a geographic information system (GIS) platform for feature evaluation and measurement. The application scheme was tested based on production practices. The test results indicate that the proposed scheme can improve the efficiency of the field inspection, with the measurement accuracy meeting the demand for actual production needs. Furthermore, the scheme can overcome the limitations of ground-based photography and reduce safety risks.

The ecological security pattern serves as an indicator of ecosystem health and sustainability, playing a crucial role in enhancing human well-being. This study identified ecological source areas in the Guanzhong Plain based on three ecosystem services, including water conservation, soil conservation, and habitat provision. Considering regional characteristics, this study selected soil erosion sensitivity index, normalized difference vegetation index (NDVI), and nighttime lighting as disturbance factors to correct the basic resistance surface and identify ecological corridors. The results indicate that the primary and secondary ecological source areas in the Guanzhong Plain cover 3 011.85 km² and 8 434.51 km², respectively, corresponding to 5.22% and 14.62% of the total area. These areas, characterized by mountainous terrain and high vegetation cover, are primarily distributed in the Qinling Mountains in the south, the hilly and gully regions in northern Baoji City, and the junctions of Xianyang, Tongchuan, and Weinan cities. The resistance surface correction for Guanzhong Plain reduced 61 ecological corridors (total length: 1 613.4 km), leading to significant changes in their distribution, and ultimately rationalizing corridor identification. Overall, this study provides a novel case for constructing regional ecological security patterns and data support for ecological conservation and urban planning in the Guanzhong Plain.

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 increasing dependence on groundwater in the Hexi region has led to a significant drop in the groundwater table, which has induced land subsidence in some areas. Studying the relationship between groundwater changes and land subsidence hysteresis in the Hexi region holds great significance for local water resource management, land use planning, and agricultural development. This study determined the changing rate of groundwater in the study area from 2010 to 2017 using the GRACE and GLDAS data and verified the reliability of the inverted groundwater changes by combining measured data from monitoring wells. Then, this study derived the surface deformation rate of the local subsidence areas from October 2014 to June 2017 using the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique, as well as comparing and validating the results using the persistent scatterer interferometric synthetic aperture radar (PS-InSAR) technique. Finally, this study analyzed the relationship between groundwater changes and surface subsidence data using fast Fourier transform and time-delay correlation analysis. The results indicate that the time lags between land subsidence and groundwater changes were 74~86 d, 61~80 d, 80~99 d, and 74~99 d, respectively in the Linze, Ganzhou, Liangzhou, and Jinchuan subsidence areas, with respective correlation coefficients ranging from 0.541 to 0.593, from 0.589 to 0.689, from 0.600 to 0.750, and 0.543 to 0.630, respectively. The results of this study will provide a scientific basis for water resource management, land use planning, and agricultural development in the Hexi region.

The Liaohe estuary of China boasts the largest red beach landscape in the world. Monitoring the spatiotemporal dynamics of Suaeda salsa in this region is of great significance for revealing the performance of conservation measures such as returning aquaculture to wetlands. Currently, satellite remote sensing technology has been widely applied to the mapping and identification of coastal vegetation including Suaeda salsa. However, existing classification methods rely on black-box models, which are difficult to interpret, while overlooking exploring identification mechanisms. This has hindered the improvement and development of related methods. Fortunately, the advancement in explainable artificial intelligence (XAI) has provided new directions for analyzing the black-box models. Considering that the decision rules in random forests are interpretable, this study developed a new method to extract the optimal decision rules from trained random forest models. Using this method, this study ultimately reconstructed the optimal decision rules used to identify Suaeda salsa, i.e., B3/B4<0.90 & B5/B3≥1.46, with an overall data accuracy exceeding 90%. Using annual Sentinel-2 images from 2017 to 2022 as a data source, the study successfully extracted the annual dynamics of Suaeda salsa in the Liaohe Estuary. Accordingly, by combining the centroid migration method, this study analyzed the spatiotemporal changes in the Suaeda salsa following the implementation of returning aquaculture to wetlands, revealing the current status that the Suaeda salsa in this region is undergoing rapid restoration.

In recent years, the rapid expansion of the aquaculture pond industry has given rise to a series of ecological and environmental issues. The Jianghan Plain is recognized as one of the most important freshwater aquaculture bases in China, and investigating changes in its aquaculture ponds is crucial for China’s ecological conservation. Focusing on the Jianghan Plain, this study proposed a method for extracting and monitoring changes in aquaculture ponds using Google Earth Engine (GEE) and Sentinel-2 dense time-series images. Using this method, which combined K-means clustering and a hierarchical decision tree classification algorithm, this study achieved accurate information extraction and spatiotemporal pattern analyses of aquaculture ponds in the plain in each year from 2016 to 2022. The results indicate that the combination of K-means and the hierarchical decision tree algorithm that integrated time-varying features allowed for accurate classification of aquaculture ponds, with an overall classification accuracy of 91.90% and a Kappa coefficient exceeding 0.84. In 2022, the aquaculture pond area of Jianghan Plain is 2 126.43 km². Among these area of aquaculture ponds, 43.24% were concentrated in Jingzhou City, while Yichang City had the fewest area of aquaculture ponds, accounting for only 0.76%. From 2016 to 2022, aquaculture ponds in the Jianghan Plain exhibited an upward trend overall and dynamics with pronounced spatial heterogeneity. Specifically, the total area increased to 2 126.43 km² from 1 947.43 km², increasing by 9.19%. The proposed methodology provides an important reference for the precise monitoring of aquaculture ponds, and the resulting dataset serves as a valuable reference and holds great practical significance for the ecological conservation and the assessment of sustainable development goals in the Jianghan Plain.

Constructing a regional green infrastructure (GI) network can alleviate the contradiction between land use and ecological development in the process of rapid urbanization, playing a significant role in future urban planning. This study investigated Lincang City, a typical mountain city in Southwest China. Employing the patch-generating land use simulation (PLUS) model, this study predicted the land use and land cover (LULC) in Lincang City in 2030 under the ecological priority scenario. Furthermore, this study extracted information about the ecological source areas and corridors by integrating the morphological spatial pattern analysis (MSPA), minimum cumulative resistance (MCR) model, and circuit theory. Finally, this study constructed an optimized GI network for 2030 adapted to the sustainable expansion of Lincang City. The results show that from 2020 to 2030, the construction land area in Lincang City is projected to expand by about 23 %, while forest land and grassland will decrease by 0.2 % and 1.3 %, respectively. The water area is expected to increase by 46.9 % under reasonable management and protection. The core zone of GI landscape elements will represent 56.12% of the total area, while the edges will make up 21.3%. The spurs, bridging zones, islets, perforations, and circuits will constitute the rest 22.6%. Under the sustainable urban expansion scenario, the GI scale remains overall stable, with a relatively scattered distribution in built-up areas. The optimized GI network will involve 12 ecological source areas and 24 ecological corridors. The GI network of Lincang City in 2030 constructed based on the MSPA-PLUS model strengthens the understanding of the GI network for the sustainable development of a mountain city, adapting to future urban development. This study provides novel insights into the urban planning of mountain cities including Lincang and critical implications for GI protection and regional ecological security maintenance.

The change in carbon stocks is recognized as an important indicator of the carbon pool function. The effective, accurate assessment of carbon stocks is of great significance for research on regional carbon cycle and carbon source/sink dynamics, climate change mitigation, and regional sustainable development. Based on multi-time series remote sensing images (Sentinel-1 and Sentinel-2) and the Google Earth Engine (GEE) cloud computing platform, this study matched the photon point data of ICESat-2-derived vegetation canopy for the inversion of mangrove forest heights. Then, the inversion of the biomass of mangrove forests was conducted using a large-scale tree height-biomass model. Consequently, the heights, above-ground biomass, and carbon stocks of mangrove forests in Hainan Island were obtained, and their distribution and variations were further analyzed. The results indicate that in 2016, 2019, and 2022, mangrove forests in Hainan Island exhibited average heights of 6.99 m, 7.26 m, and 7.71 m, respectively, with an increasing trend observed in the highlights across all regions in the three years. Their total above-ground biomass was 400 939.48 t, 411 928.64 t, and 458 759.02 t, respectively, with average densities of 110.23 t/hm², 114.61 t/hm², and 120.02 t/hm², respectively. The above-ground biomass of Dongzhai Port and the Bamenwan area, both located in the northeastern part of Hainan, accounted for about 80% of the total. The carbon stocks of mangrove forests exhibited rates of increase ranging from 1% to 4.45% over the three years, with the top two growth rates occurring in Dongzhai Port and the Bamenwan area, respectively (4.45% and 3.17%). The results of this study can provide foundational data and a methodological reference for assessing carbon stocks of large-scale mangrove forests and serve as important parameters for mangrove forest management and protection in Hainan Island, holding THE value of widespread applications.

To address the issues of missing small-scale surface features and incomplete continuous features in segmentation results, this study proposed a densely connected multiscale semantic segmentation network (DMS-Net) model for land cover segmentation. The model integrates a multiscale densely connected atrous spatial convolution pyramid pooling module and strip pooling to extract multiscale and spatially continuous features. A position paralleling Channel attention module (PPCA) is employed to assess feature weights for high-efficiency expression. A cascade low-level feature fusion (CLFF) module is applied to capture neglected low-level features, further complementing details. Experimental results demonstrate that the DMS-Net model achieved an overall accuracy (OA) of 89.97 % and a mean intersection over union (mIoU) of 75.59 % on an iteratively extended dataset, outperforming traditional machine learning methods and deep learning models like U-Net, PSPNet, and Deeplabv3+. The segmentation results of the DMS-Net model reveal structurally complete surface features with clear boundaries, underscoring its practical value in multiscale extraction and analysis of remote sensing information for land cover.

This study investigated the temperature distribution in the sea area around the Qinshan Nuclear Power Plant using Landsat thermal infrared remote sensing data. The results indicate a strong correlation between the inversion results of temperature and the measured data, suggesting reliable inversion results. Before the operation of the nuclear power plant, the surrounding sea area exhibited relatively uniform temperature, with no significant temperature difference except for natural warming. Furthermore, the temperature along the coast remained almost unchanged in the north-south direction and displayed slight temperature gradients in the east-west direction, with temperature variation not exceeding 0.6 ℃ within 10 km from the coast. After the operation of the nuclear power, the surrounding sea area showed temperature differentiation. The distribution characteristic of thermal discharge was closely related to tides and seasons. In the same season, the increased amplitude of the temperature during ebb tides generally exceeded that during flood tide. Under the same tidal condition, the increased amplitude of the temperature in summer typically exceeded that in winter. At a certain water intake of the first plant, the surface seawater manifested a temperature rise of over 1.0 ℃ during flood tide. Landsat data generally meet the demand for research on temperature distribution in the surrounding sea area of the Qinshan Nuclear Power Plant, and the distribution of thermal discharge under specific tidal conditions can be investigated using aerial remote sensing monitoring.

This study aims to develop a machine learning algorithm based on the characteristics of AGRI data to generate an aerosol dataset with a high spatiotemporal resolution. Using aerosol data from 67 aerosol robotic network (AERONET) sites in China and its surrounding areas in 2021, this study selected data of factors such as apparent reflectance, observation angles, elevation, and MODIS surface reflectance acquired from FY-4A advanced geostationary radiation imager (AGRI)-a new generation geostationary meteorological satellite of China. Then, this study performed the inversion of aerosol optical depth (AOD) using four machine learning methods-random forest (RF), gradient boosting regression tree (GBRT), extreme gradient boosting (XGBoost), and back propagation neural network (BPNN). Using the optimal model determined based on evaluation metrics, this study achieved the AOD inversion with a spatial resolution of 4 km × 4 km based on FY-4A data. Then, this study compared the inversion results with MODIS aerosol products of roughly the same periods. The results indicate that the AOD inversion models based on the four machine learning algorithms yielded correlation coefficients (R) exceeding 0.90, mean absolute errors (MAEs) of less than 0.09, and root mean square errors (RMSE) below 0.14. This indicates that it is feasible to conduct AOD inversion using machine learning-based models. The GBRT-based model exhibited the highest inversion accuracy among the four methods, with a correlation coefficient of 0.82, MAE of 0.16, and RMSE of 0.25, as indicated by the verification results. Additionally, 47% of the inversion results fell within the expected error ranges, indicating that the results of AOD inversion from FY-4A data using the GBRT-based model were generally consistent with observed values. The comparison between the GBRT model-derived AOD inversion results and the results of MODIS aerosol products shows that the former exhibited high consistency with the latter in terms of spatial distribution, with 83.57% of grid deviations falling within the range from -1.0 to 0 and the former slightly higher than the latter.

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.

Accurately identifying plant communities in coastal wetlands is critical for strengthening the ecological quality monitoring and enhancing the ecosystem functions of coastal wetlands. With the Yellow River Delta as the study area, this study constructed a feature vector set including phenological, optical, red-edge, and radar features based on Sentinel-1/2 image data using the Google Earth Engine (GEE) platform. It classified the wetland plant communities in the Yellow River Delta in 2021 using the random forest algorithm. Moreover, it explored the effects of phenological features in classification. The results reveal an overall classification accuracy of 97.91 % and a Kappa coefficient of 0.97. In 2021, the distribution areas of Phragmites australis, Suaeda glauca, Spartina alterniflora, and Tamarix chinensis were 49.91 km², 39.91 km², 79.36 km², and 20.86 km², respectively. The phenological features of typical plant communities in the Yellow River Delta wetlands were effectively extracted based on the normalized difference vegetation index (NDVI) time-series fitting curves. The highly distinguishable features included the maximum value date, base value, growth amplitude, beginning-of-season growth rate, and end-of-season decline rate. Compared to other feature variables, phenological features contributed more significantly to the overall classification accuracy, suggesting their prominent role in classification. The results of this study provide a methodological reference and scientific basis for the plant community monitoring and ecological conservation of coastal wetlands in the Yellow River Delta.

The rapid social and economic development and the trend of human migration to large- and mid-size cities, especially provincial capitals, have significantly intensified land use conflicts. The coordination among production, living, and ecological spaces is significant for sustainable, regional social and economic development. This study created a multi-purpose suitability assessment model from the perspective of the production, living, and ecological functions, identified the production, living, and ecological suitability, as well as the intensity of potential land use conflicts, in Nanchang City, China while considering land space background and planning objectives for differentiated regional regulation. The results indicate that over 65% of areas in the city are suitable for production and living. Areas with ecological, productive, and living suitability differ in spatial distribution and structural composition and exhibit pronounced overlaps. This indicates potential land use conflicts. The conflict identification results reveal that the areas with severe, strong, moderate, and weak land use conflicts account for 0.53%, 18.81%, 5.77%, and 5.67%, respectively. Given the different spatial distributions, area proportions, and characteristics of areas with potential land use conflicts, differentiated regulations are required. Based on comprehensive considerations of the conflict identification results and the functional zoning stated in the Nanchang City Land and Space Master Plan (2021—2035), this study determined nine major zones for differentiated regulation. This study made some preliminary attempts in zoning regulation against land use conflicts while considering both land use suitability and the requirements for social and economic development. The results of this study will provide a scientific basis for identifying land use conflicts and optimizing land space layout in other similar cities.

Vegetation, the main body of a terrestrial ecosystem, serves as an important indicator of environmental changes in a regional ecosystem. The Tuojiang River basin is an economically and industrially developed area in Sichuan. Dynamic vegetation monitoring and the analysis of factors driving its changes hold great significance for ecological change assessment and ecological protection. This study investigated the Tuojiang River basin. Based on MODIS data of normalized difference vegetation index (NDVI) from 2000 to 2021, this study detected, analyzed, and compared linear and nonlinear characteristics of the data, including mutation types and years, using linear regression Slope and an improved BFAST01 model. Additionally, this study explored the factors influencing the NDVI using the Optimal Parameters-based Geographic Detector (OPGD) model. The results indicate that more than 95% of the Tuojiang River basin exhibited NDVI values exceeding 0.6. The linear regression analysis for NDVI trends revealed that regions with significantly improved and significantly degraded vegetation coverage accounted for 18.07% and 10.60%, respectively, of the total area of the river basin, as indicated by image pixels. The BFAST01 nonlinear mutation analysis showed that the NDVI trends in the Tuojiang River basin over the 22 years can be categorized into eight mutation types, with the proportion of regions exhibiting improved vegetation coverage (58.62%) exceeding that of regions with degraded vegetation coverage (41.38%). These findings were consistent with the linear regression analysis, suggesting that the vegetation in the river basin was well protected in the 22 years. Mutations were concentrated between 2002 and 2018, with “interruption-+” and “reversal+-” representing the most common mutation types, accounting for 14.83% and 13.19%, respectively. In contrast, other mutation types exhibited a relatively even distribution across different stages. The results of the OPGD model revealed slight variations in the factors influencing NDVI across different years. Generally, the most influential factors included land use/land cover (LULC), elevation, and terrain and landforms, followed by meteorological factors such as temperature and precipitation. In contrast, other factors produced relatively minor impacts. Overall, despite some impacts, human factors like population and GDP exerted less influence on vegetation than natural factors in the Tuojiang River basin. Therefore, vegetation protection and restoration should consider the combined effects of both natural factors and anthropogenic activities.

The Corona Virus Disease 2019 (COVID-19) pandemic significantly affected China’s economy. This study investigated China’s five cities that witnessed large-scale COVID-19 outbreaks based on NPP-VIIRS night light (NTL) data. A fitting model between the NTL index and GDP statistics was established. This model can reflect the monthly economic variations, yielding the spatial distribution of GPD. Finally, this study analyzed the trend in the spatial variations of the economy in the five cities during the COVID-19 pandemic by analyzing the differences in monthly GDP density. The results indicate that the GDP predicted using the GDP spatialization based on the NTL index exhibited relatively small errors and can reflect the impacts of the COVID-19 pandemic on the urban economy in an intuitive and clear manner. Under the influence of mobility policies, the marginal areas of most of the cities experienced economic recession in the early and late stages of the pandemic, with economic growth observed in the middle stage of the pandemic. In contrast, the central areas of the cities experienced economic recession in the middle stage of the pandemic, were subjected to minor impacts in its early stage, and witnessed a rapid economic recovery in its late stage. Additionally, the economy in the central areas of the cities was more resistant to the impacts of the pandemic than that in their marginal areas.

Remote sensing monitoring in national-level nature reserves covers a land area of approximately 1.7 million km². This process involves the delineation of numerous features that indicate variations in the nature reserves, requiring specialized expertise. As a result, ensuring the accuracy and normalization of mapping is challenging even using substantial human and material resources. This affects the quality and effectiveness of monitoring result applications and relevant services. To address this issue, employing geometric techniques like the Sutherland-Hodgman clipping algorithm based on the ArcPy package, along with the customized ArcToolbox tools for encapsulating automated mapping scripts, this study automatically extracted the information and images of features from a geographic database. Subsequently, this study automatically generated the distribution maps of features that reflected variations in national-level nature reserves. Over 50000 maps were plotted using the proposed method, with an accuracy of 100%. Practical application demonstrates that the automatic mapping for a single map can be completed within 29.06 s on average, significantly less than manual mapping. The proposed method can meet practical production needs, with the automated mapping scripts proving stable, reliable, and widely applicable. The proposed method can significantly enhance the efficiency of the applications of the monitoring results reflecting variations in the national-level nature reserves, holding great practical significance.

Change detection using synthetic aperture radar (SAR) images based on deep learning has been a significant research topic in the field of remote sensing. However, it is limited by unreliable training samples and highly time-consuming training. Hence, this study proposed a novel unsupervised change detection method using SAR images based on the broad learning system (BLS). First, a reliable pre-classification method is presented by incorporating neighborhood information into similarity operators, adaptive dual-threshold segmentation, superpixel correction, and visual saliency analysis. This pre-classification method generates a pre-classification map and corresponding training samples. Second, the BLS network is trained using the training samples to generate the BLS-based prediction map for change detection. Third, the pre-classification map and the BLS-based prediction map are fused through two-stage voting to generate the final change detection map. The experimental results of five real SAR image datasets show that the proposed method can produce more reliable training samples and achieve higher accuracy in change detection. Moreover, its efficiency is significantly higher than the change detection model using SAR images based on deep learning.

The grassland ecosystem is one of the most important and widely distributed terrestrial ecosystems. Analyzing the grassland degradation and its influential factors holds great significance for guiding the conservation and sustainable use of grassland resources, as well as the restoration and reconstruction of degraded ecosystems. This study extracted information on the distribution of grassland in western Songnen Plain using an object-oriented classification method and a multi-layer decision tree while comprehensively considering the degradation of vegetation and soils. Using Landsat TM image data, this study constructed a comprehensive grassland degradation index (GDI) for 11 even years from 2000 to 2020, followed by the assessment of the spatiotemporal dynamics of grassland degradation. Using the standardized precipitation evapotranspiration index (SPEI) as an indicator of drought, this study analyzed the responses of grassland degradation to the spatiotemporal changes in climate-induced drought. The results indicate that from 2000 to 2020, grassland in the western Songnen Plain decreased to 1 024 700 hm² from 1 051 700 hm², with an annual decreasing rate of 0.1%. The grassland degradation showed a nonsignificant downward trend, with 81.7% of the grassland exhibiting a stable or downward degradation trend. The SPEI exhibited an increasing trend in both spring and summer, representing a downward drought trend with significant regional differences. Besides, there was a nonsignificant weak positive correlation between GDI and SPEI in both spring and summer. The results of this study will provide data support for the conservation and sustainable use of grasslands in the western Songnen Plain, while also holding active significance for managing and controlling the ecological and economic benefits of grasslands in this region.

Coal gangue mountains are key areas for the ecological restoration of coal mines. Understanding their geographical distribution holds great significance for regional environmental management. This study focused on part of Xinluo District, Longyan City, Fujian Province. Using GF-2 remote sensing images and data from the ASTER GDEM digital elevation model, this study extracted spectral, texture, and topographic features and then optimized these features using the sequential forward selection method. Subsequently, this study developed a model for surface feature classification using a random forest algorithm. Using this model, this study categorized surface features by integrating multi-source data and comprehensive feature combinations and then achieved the identification and information extraction of coal gangue mountains. The results indicate that the classification accuracy did not necessarily increase with the number of features. After feature selection, the number of features was reduced from 17 to 9, and the overall extraction accuracy of coal gangue mountains reached 94.07%, with a Kappa coefficient of 0.819. Factors playing an important role in the identification and information extraction of coal gangue deposit areas included elevation, slope, aspect, multi-spectral bands B1, B2, and B4 in the spectral characteristics, normalized vegetation index, and grayscale value of images. In contrast, texture features merely improved the accuracy of surface feature types with distinct textural variations, while producing limited effects on the information extraction of coal gangue mountains. For the study area, only the mean texture feature produced significant effects on the information extraction accuracy of coal gangue mountains. The combination of random forest and feature optimization algorithm can effectively enhance the information extraction accuracy of coal gangue mountain, efficiently integrate multi-source feature data, and accelerate model calculation, serving as a practically feasible method for the information extraction of coal gangue mountains.

The rapid and accurate acquisition of forest disturbances using advanced technological methods is of great significance for maintaining forest ecological security. In this study, all Landsat images of Lishui City, China from June to August from 1992 to 2022 were acquired. Based on the LandTrendr algorithm on the Google Earth Engine (GEE) platform, this study analyzed the characteristics of forest disturbances in the city. A spatiotemporal analysis of forest disturbances across various counties and cities within Lishui was conducted, and the influence patterns of natural factors including slope, elevation, and precipitation on forest disturbances were also explored. The results indicate that vegetation disturbances in Lishui City generally decreased over the 30 years. Spatially, the most severe forest disturbances occurred in Longquan City and Suichang County located in northwestern Lishui City. Temporally, 2008 witnessed the most severe forest disturbances. In addition, areas with gentle slopes and high elevations, as well as years with reduced precipitation, were more sensitive to forest disturbance over the 30 years. This study will provide a scientific basis and reference for the preservation and management of forest resources in Lishui City.

To explore the spatiotemporal evolution of the bottomland in Dongting Lake since the middle stage of the Republic of China, this study examined the historical topographic maps and aerospace remote sensing data concerning the study area for over 10 time periods since the 1930s. Based on remote sensing image interpretation, statistical data analysis, and historical comparison, this study analyzed the temporal variations in the bottomland area of Dongting Lake in various periods to infer the corresponding spatial distributions of the bottomland. The results show that the spatial development of the bottomland in Dongting Lake was primarily characterized by the rapidly advancing delta at the mouth of the east branch of the Ouchi River and Piaowei Islet in East Dongting Lake, the alluvial deposits along the Caowei and Songzhu rivers in the north of South Dongting Lake, and the “Jiangnan Grassland” landscape formed by the bottom uplift of Qili and Muping lakes. The bottomland area in Dongting Lake expanded from 1 622.17 km² in 1 938 to 1 962.28 km² in 2018, coupled with the 980.96 km² of reclaimed high bottomland, suggesting a net increase of 1 321.07 km². In terms of spatial distribution, the bottomland area exhibited an undulating trend rather than a continuous increase. It manifested a significant expansion from 1938 to 1948 and 1958 to 1998 but a slow shrinkage from 1948 to 1958 and 1998 to 2018. Overall, the results of this study provide objective data for preserving lakeshore ecosystems and biodiversity and serving ecological restoration and environmental conservation in the Yangtze River basin.

Over the past few decades, excessive groundwater exploitation has led to a significant decrease in the groundwater level and serious land subsidence in Taiyuan City. In recent years, Taiyuan has vigorously implemented strict groundwater management measures and the project of water diversion into Shanxi from the Yellow River, substantially alleviating groundwater overexploitation and gradually recovering groundwater levels in the city. Therefore, it is necessary to scientifically assess the effect of groundwater level revovery on land subsidence. Based on 2003—2010 synthetic aperture radar (SAR) data from ENVISAT and 2017—2021 SAR data from Sentinel-1, this study extracted the land subsidence information of Taiyuan City of both periods using persistent scatterer interferometric SAR (PS-INSAR). Accordingly, this study compared and analyzed the temporal evolution of land subsidence during the two periods by combining the groundwater extraction volumes, water volumes diverted from the water diversion project, and data on groundwater levels. The results show that the land subsidence in Taiyuan City has been significantly mitigated, with the urban area having shifted from subsidence to uplift. In the Xiaodian area, which underwent the most serious land subsidence, the subsidence area expanded. Nevertheless, the overall land subsidence rate decreased, and the subsidence center has moved southward. The main cause for the slowdown of the land subsidence and even the land uplift in Taiyuan is the continuous groundwater level recovery attributed to the reduced groundwater exploitation and the water diversion project. The results of this study provide a scientific basis for fine-scale land subsidence prevention and groundwater management in Taiyuan City under conditions of groundwater level recovery.

To improve the efficiency of UAV aerial surveys in complex banded areas, this study explored and proposed a design method for curved flight paths. This method included planning algorithms for both horizontal and variable-altitude curved flight paths for banded areas, as well as a detection algorithm for flight path safety based on a digital elevation model (DEM). First, a simulation system for UAV aerial surveys was constructed, and the method was tested for planar aerial surveys, variable altitude aerial surveys, and safety detection through simulation experiments. Then, the quality of the aerial photography production data was verified using actual aerial photography experiments. The results indicate that design algorithms for horizontal and variable-altitude flight paths can automatically generate reasonable flight paths for complex banded areas and that the detection algorithm for flight path safety can ensure route safety. Compared to conventional flight paths, the quality of aerial photography data from curved flight paths can also meet the requirements of existing regulations. In other words, for aerial surveys in complex banded areas, the method presented in this study allows for the automatic design of reasonable, safe flight paths and, thus, can effectively improve the operational efficiency of UAV aerial photography.

Land use demands vary under different development objectives. Scientifically and rationally regulating changes in land use are crucial to efficient land resource utilization and achieving ecological, developmental, and economic coordination in the Pearl River Delta urban agglomeration. Based on the land use data of the urban agglomeration of 1990, 2000, 2010, and 2020 and using the FLUS-Markov model, this study predicted the quantity and spatial changes in land use in the Pearl River Delta urban agglomeration by 2035 under three scenarios: natural development, ecological protection, and development priority. Furthermore, this study determined the differences in land use change under the three scenarios. Additionally, a simulation analysis of the land use in 2035 was conducted to facilitate the optimized land and space allocation under varying developmental objectives. The results indicate significant changes in the use of construction land in the Pearl River Delta urban agglomeration. From 1990 to 2020, the area of construction land, including urban land and infrastructure land increased by 4 945.25 km², representing an increase of 2.8 times. The simulations and predictions under three land use scenarios reveal that the urban land area will trend upward by the end of 2034, with its expansion speed being restricted under the ecological protection scenario, while the ecological land, such as forest land, grassland, and water area, will maintain an increasing trend until 2035. From 1990 to 2020, the arable land area decreased by 3 759.5 km². Under the three land use scenarios, the trend of arable land reduction will continuously decrease until 2035, with the decreasing trends slowing down from 2020 to 2035. Especially, under the development scenario, the area of construction land will continue to increase, the decreasing trend of the arable land area will be somewhat curbed, while the area of grassland and forest land will undergo a more serious decrease. Although dominant factors affecting arable land protection in the Pearl River Delta urban agglomeration vary across different development stages, the main factor is infrastructure construction such as rail transit roads.

Existing residual trend methods utilize a pixel-by-pixel modeling strategy, in which the ordinary least squares method is employed. These methods suffer certain limitations. On the one hand, the pixel-by-pixel modeling strategy causes each model to contain signal interference from human activities in local space. On the other hand, the ordinary least squares method is unfavorable for simulating commonly observed nonlinear characteristics. This study proposed an entirely new residual trend method based on regional modeling and machine learning. Besides, this study compared two types of environmental variables used to express spatial heterogeneity: ①direct-environmental variables (DEVs) such as terrain, hydrology, and land use; and ②proxy-environmental variables (PEVs) that combine the spatiotemporal series of vegetation and climate. First, a regional modeling strategy was adopted. After DEVs and PEVs were introduced individually, models for the vegetation-climate relationship were built using machine learning. Second, residuals were determined based on the definition of the residual trend method. Finally, the contributions of anthropogenic and climatic factors to vegetation change were assessed. The results indicate that compared to the previous pixel-by-pixel residual trend method that utilizes ordinary least squares, the new residual trend method can simulate the nonlinear features of the vegetation-climate relationship and exhibits enhanced resistance to human signal interference. For the new method, significantly higher performance can be achieved using PEVs compared to DEVs. PEVs can fully utilize the original modeling data, without increasing difficulties with data acquisition and avoiding additional data errors. The residual trend method based on regional modeling and machine learning proposed in this study allows for more effective attribution of vegetation changes.

In 2021, China launched the third comprehensive scientific expedition in Xinjiang to establish a natural protected area system centered around national parks and to achieve the goal of the declaration and protection of world natural heritage. Based on the natural landscape identification using the space-ground integrated technology, this study constructed an EWM-CRITIC-TOPSIS model, followed by the elevation of 460 typical natural landscapes of 15 categories in Xinjiang. The results indicate that compared to traditional multi-index evaluation methods, the EWM-CRITIC-TOPSIS model can reduce the limitations of a single weighting approach by comprehensively considering various evaluation indicators, proving highly applicable to landscape assessment. The assessment of landscapes by categories reveals that grade I, II, III, and IV geological and geomorphological landscapes account for 2.9%, 30.5%, 44.7%, and 21.9%, respectively; grade I, II, III, and IV terrestrial biological landscape represent 1.7%, 24.6%, 40.0%, and 33.7%, respectively, and grade I, II, III, and IV wetland landscapes account for 12.2%, 26.7%, 52.2%, and 8.9%, respectively. This study will provide an important foundation and reference for the protection, utilization, and management of natural landscape resources in Xinjiang.

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.

Landslide identification has always been a research topic in the study of geological disasters, playing a significant role in emergency rescue and command. To address the limitations in landslide identification, such as missed/false detection, and low identification accuracy, this study proposed an improved YOLOv7 model that enables simultaneous object detection and image segmentation for landslides. The improved model optimized its core network by integrating data, adding the convolutional block attention module (CBAM), and changing the intersection over union (IoU) loss function. Its effectiveness was verified using the landslide dataset of Bijie City, Guizhou Province, and the 0.2 m high-resolution digital orthophoto map (DOM) of historical landslides in Sichuan Province. The results indicate that the improved model performed well in landslide detection and segmentation, achieving more efficient and accurate landslide identification compared to the conventional YOLOv7 model, and other prevailing models like Fast RCNN and Mask RCNN. Taking the Baige area in Sichuan Province as an example, this model can effectively enhance the automation level of landslide disaster information acquisition while improving accuracy.

This study designed a one-stop platform for automatically extracting patches from multisource domestic satellite images based on a deep learning framework. The platform focuses primarily on critical techniques including semantic segmentation of ground objects, swarm intelligence algorithms for patch extraction, and deep feature interpretation. To address challenges in remote sensing image interpretation, such as significant color differences, vast data volumes of single images, diverse multi-channel image representations, and considerable differences in the sizes of remote sensing targets, the platform incorporates intelligent semantic segmentation and swarm intelligence algorithms for automatic patch extraction into the framework. It offers a range of customizable general and specialized models while supporting the self-training of models. With functions including large-scale data management, data annotation, model training, model testing, patch extraction, and application analysis, the platform has been successfully applied to the intelligent semantic segmentation and patch extraction of ground objects like buildings, vegetation, farmland, industrial zones, and water bodies in Taiyuan City, Shanxi Province based on multisource domestic satellite images.

Remote sensing image fusion technology can combine and enhance information from two or more multi-source remote sensing images, making the fused image more accurate and comprehensive. The nonsubsampled contourlet transform (NSCT) is effective in extracting details from high-resolution remote sensing images through multi-scale and multi-directional decomposition, thus achieving image sharpening with high spatial resolution. However, traditional NSCT produces limited high-frequency details and is prone to introduce artifacts such as “ghosting” in fused images. To address this issue, the study proposed a new panchromatic sharpening fusion algorithm for remote sensing images by combining NSCT with a guided filter (GF). Specifically, the promoted algorithm extracted the detail components from histogram-matched images using the multi-scale, multi-direction decomposition and reconstruction properties of the NSCT. Meanwhile, it extracted multi-spectral detail components with panchromatic detail features using GF. Finally, the fused images with high-spatial and high-spectral resolutions were obtained by sharpening based on weighted detail components. The proposed algorithm was proved effective through both subjective and objective evaluations using multiple high-resolution remote sensing datasets.

Plastic greenhouses have gained extensive application in modern agriculture. This, however, gives rise to ecological issues. Remote sensing data enable effective extraction and identification of plastic greenhouses on a large scale. Existing studies largely focus on plastic greenhouse extraction using either classification or spectral indices methods. However, there exists a lack of the combination and comparative analysis of both methods. This study proposed a method for plastic greenhouse extraction that integrates multiple Sentinel-2 spectral indices and a one-class classification method (improved one-class random forest). Furthermore, this study extracted information on plastic greenhouses using an improved one-class random forest method, as well as six spectral indices of plastic greenhouses as classification features. The extraction results were then compared with those of the proposed method to demonstrate the effectiveness of the latter. The results indicate that the proposed method yielded an overall accuracy of above 97% across four seasons, with kappa coefficients exceeding 0.82 and F1 scores of over 0.84. These metrics all were better than those yielded using the six spectral indices. Furthermore, the proposed method exhibited differences in the overall accuracy, kappa coefficient, and F1 score across four seasons of less than 1%, under 0.1, and below 0.1 respectively. This suggests the high seasonal stability of the method, outperforming the extraction results obtained by using spectral indices alone. This study provides a method for accurately monitoring the spatial distribution of plastic greenhouses.

Forest carbon sink, an important factor in maintaining the ecological balance of the earth and coping with climate change, plays a key role in the global carbon cycle. It absorbs large amounts of carbon dioxide and stores carbon element, helping mitigate climate change. Additionally, forest carbon sink provides essential ecological services, such as biodiversity conservation, water resource regulation, and soil conservation. Therefore, the estimation of forest carbon sink is critical. Based on solar-induced chlorophyll fluorescence (SIF) and using the gross primary productivity (GPP) as an intermediate variable, this study estimated forest carbon sink in the forest region of Northeast China during the vegetation growth period (i.e., from June to September) between 2011 and 2020. The results reveal a strong spatial correlation between forest carbon sink and SIF in this region. The similar distributions of SIF values and carbon sink in the forest region of Northeast China indicate that the Changbai Mountains and the Da Hinggan Mountains had high and low carbon sink capacities, respectively. Over the vegetation growth period from June to September, the carbon sink capacity in the region showed a gradual upward trend initially, followed by a gradual downward trend. Overall, it is highly feasible to estimate carbon sink using SIF in the forest region of Northeast China.

To determine the origin of surface substrate for soil salinization and alkalization in the Songnen Plain, this study investigated Songyuan City based on Sentinel-2 multispectral images. Considering various commonly used indices like the soil salinity index (SSI), soil water index (SWI), and vegetation index (VI), this study constructed the optimal 3D spectral feature model to calculate the soil salinization-alkalization index (SSAI) for inversion of the soil salinization-alkalization status. Surface water and groundwater in areas subjected to soil salinization and alkalization were sampled to test their salt ion concentrations, followed by the analysis of salt ion sources according to the groundwater levels. The surface substrate was explored through planar grid layout and vertical stratified sampling. A total of 2 362 soil samples were collected in various layers within a depth of 5 m to test their pH and texture for the construction of a 3D surface substrate model. The results of this study reveal a positive linear correlation between the inversion result of remote sensing data and the topsoil salt content (coefficient of determination: R²=0.74). The study area was characterized by alkalization of sodium bicarbonate, with soil salt ions originating primarily from groundwater. The deep multilayer argillaceous soils acted as an aquiclude to prevent the downward infiltration, migration, and dilution of salt ions along with water. This surface substrate condition serves as the objective cause of soil salinization and alkalization in the study area.

In recent decades, the surface mass balance (SMB) and the calving of outlet glaciers have accelerated the mass loss of the Greenland Ice Sheet (GrIS), with SMB’s contribution continuing to increase. Therefore, determining SMB’s spatiotemporal distribution is critical for understanding the mass balance of the GrIS. Currently, the regional climate model and the remote sensing observation of outlet glacier flux gates serve as two primary calculation methods for the GrIS’s SMB. However, the former method results in large uncertainties in the SMB simulation. The latter method can only indirectly estimate the overall SMB value for the upper reaches of the flux gate, failing to reflect the spatial distribution of SMB. This study proposed a method for estimating the GrIS’s SMB based on remote sensing data and ice flux divergence, obtaining the relatively accurate spatial distribution of SMB. First, the interannual variation in the elevation of the GrIS was derived from ICESat-2 laser altimetry data. Second, based on MEaSUREs-derived glacier flow velocity data and BedMachine-derived ice thickness data, the ice flux divergence was calculated using the pixel-based finite difference method to estimate the GrIS’s elevation changes caused by glacier flow. The GrIS’s elevation changes caused by SMB were then obtained by subtracting the elevation changes caused by glacier flow from the ICESat-2 elevation data. Third, through the firn densification model, the elevation changes caused by SMB were converted into mass changes to reflect the interannual spatial distribution of the GrIS’s SMB. The proposed method was applied to estimate the spatial distribution of the GrIS’s SMB in 2019 and 2020, yielding relatively high accuracy (RMSE=0.519 m w.e.) in comparison with the measured SMB from the observation station, and outperforming the regional climate model (RMSE=0.565 m to 0.877 m w.e.), ultimately demonstrating its reliability.

Riparian zones have been extensively used in non-point source pollution control projects worldwide, and remote sensing has gradually become a significant means of non-point source pollution research. However, combining remote sensing technology with riparian zones for efficient pollution interception effects is still a challenge. With the Xingyun Lake basin in Yunnan Province as the study area, this study established a soil and water assessment tool (SWAT) model by coupling with remote sensing. It created a riparian zone by changing the land use type for scenario simulation, investigating the different effects of various widths and vegetation types on pollutant reduction. The key findings are as follows: ①The created riparian zone exhibited better interception effects for nitrogen compared to phosphorus; ② Concerning different vegetation types in the riparian zone, forest land manifested significantly better pollution interception effects than grassland. Moreover, the pollutant reduction rate gradually increased with an increase in the width of the riparian zone; ③A riparian zone consisting of 30-m-wide forest land and 30-m-wide grassland can reduce total nitrogen production by 5.20% and total phosphorus production by 6.03% while intercepting 19.83% of organic nitrogen and 21.30% of organic phosphorus into the lake, demonstrating the optimal pollution interception effects.

Interferometric Synthetic Aperture Radar (InSAR) faces the challenges of the insufficient number of monitoring points and low monitoring accuracy when applied to complex environments with dense vegetation and large-gradient surface deformation in a mining area. To address these challenges, this study proposed an improved distributed scatterer InSAR (DS-InSAR) method assisted by stacking technology. This method identified statistically homogenous pixels using a confidence interval hypothesis test and achieved phase optimization utilizing a phase triangulation algorithm. Subsequently, the residual phases were derived by removing the linear deformation phases determined via stacking-based simulation. This step contributed to sparse deformation phase fringes, thereby enhancing the accuracy of spatiotemporal filtering and three-dimensional phase unwrapping within the subsequent DS-InSAR processing framework. Finally, the simulated phases were compensated, and thus complete deformation fields were determined. By processing Sentinel-1A SAR images covering the Xinjulong Coal Mine from October 2015 to March 2016, this study interpreted the time-series surface deformation characteristics in the mining area during this period. The findings revealed three significant deformation sites in the mining area, with a maximum cumulative radar line-of-sight (LOS) deformation of up to -313 mm. Compared to conventional small Baseline Subset (SBAS) InSAR, the proposed method yielded more uniformly distributed monitoring points via inversion, with a density approximately 12.9 times higher. The root mean squared error (RMSE) of the inversion was approximately 6.82 mm relative to leveling data, representing an accuracy improvement of about 3.0 mm compared to the SBAS results.

Precipitation products, including the Global Precipitation Measurement (GPM) mission, have been widely used in river basin studies due to their advantages like continuous distributions and broad spatial ranges. However, they are limited by insufficient accuracy and low spatial resolution. Based on the random forest (RF), this study integrated multisource influencing factors to generate two daily precipitation products with high spatial resolution: RF1 and RF2. The two daily precipitation products were input to the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) model to simulate daily runoff changes in the Xinjiang River basin. Finally, this study assessed the contributions of RF1 and RF2 to the improvement of GPM’s hydrologic applicability. The results show that both RF1 and RF2 improved the accuracy and distribution details of GPM data. RF2 exhibited a higher correlation and lower error, whereas RF1 manifested superior performance in detecting precipitation events. The RF1-simulated runoff curves resembled GPM-derived curves, showing significant improvements. RF2 corrected partial GPM’s overestimates and more accurately revealed the peak values of real flow curves in some periods. However, the uneven distribution of monitoring stations affected RF2’s prediction in complex terrain areas, limiting its simulation accuracy. Overall, both RF1 and RF2 can effectively reflect daily precipitation changes in the Xinjiang River basin, improving GPM’s hydrologic applicability to varying degrees.

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.