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.

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.

Nonnegative matrix factorization (NMF) is commonly used in hyperspectral image (HSI) unmixing due to its high interpretability and computability. To effectively address HSI noise and improve unmixing efficiency, this study proposed a method for hyperspectral unmixing based on spectral-spatial total variation nonnegative matrix factorization (SSTVNMF) with feature space augmentation. First, the original data space was converted to the feature space through feature extraction, allowing the unmixing process to be performed in the feature space for enhanced unmixing efficiency. Second, to reduce the impact of noise, the spatial information was extracted using the bilateral filtering (BF) method for enhanced feature extraction, thereby ensuring the accuracy of extracted features. Third, to ensure the effectiveness of the unmixing method, total variation (TV) regularization that considers both spatial and spectral features was established based on the NMF method. The spatial TV promotes abundance smoothing by calculating the horizontal and vertical differences in abundance between neighboring pixels. Based on the minimum-volume TV, the spectral TV enhances endmember extraction by applying constraint forces between endmembers to minimize the volume. Finally, the proposed method was verified using the synthetic data from the USGS spectral library as simulated data and the Jasper Ridge, APEX, and Cuprite datasets as actual data. The experimental results demonstrate that the proposed method outperformed other improved NMF-based methods in terms of qualitative and quantitative assessments.

Hazes reduce the quality of remote sensing images while limiting the performance of back-end visual applications. Hence, this study proposed a multi-scale residual dehazing network based on dual attention. First, an atmospheric scattering model was constructed to combine the atmospheric light value and transmissivity to derive the atmospheric power of light. Second, an end-to-end deep learning model was used to clarify remote sensing images with hazes. The dehazing network consists of a shallow feature extraction module, a deep data extraction module, a dual mapping network, and a parallel convolution reconstruction module. Finally, the proposed dehazing network was compared with CARL-net, DFAD-net, SRBFP-net, and AMGP-net through subjective and objective comparison experiments. The results indicate that the proposed dehazing network obtained a visual state close to the original haze-free scene, exhibiting high contrast, bright chroma, corresponding saturation, and clear transmission map details. Moreover, it effectively removed image noise while maintaining the edge of the foreground part. Compared to the above four networks, the proposed dehazing network achieved superior peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM), higher algorithm processing efficiency, and stable algorithm processing time with the increase of image resolution.

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.

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.

Optical satellite remote sensing images of tropical and subtropical vegetation areas are often affected by cloud cover, leading to missing remote sensing information of surface features. Effectively detecting clouds, classifying clouds and objects, and extracting cloud cover information remain hot topics and challenges in remote sensing research. Many optical cameras in domestic satellites lack the short-wave and thermal infrared spectral bands, which are used in prevailing cloud detection algorithms, reducing the image data availability after cloud removal. Hence, this study suggested detecting the spatial distribution of cloud cover by utilizing only several spectral bands in the visible light - near-infrared range (400 nm to 1 000 nm). Based on the hyperspectral remote sensing images from the Zhuhai-1 satellite, this study constructed feature space scatter plots using spectral indices, including normalized difference vegetation index (NDVI) and normalized differential water index (NDWI), for cloud/object classification and detection. Moreover, this study extracted the cloud, water, and vegetation cover information from mixed pixels. The results demonstrate that compared to conventional cloud detection methods using spectral index thresholds, the cloud detection algorithm under the NDWI-NDVI feature space used in this study exhibited a superior cloud-water separation capability and simple operability. It can precisely describe the spatial distribution characteristics of cloud cover by suppressing the shadow effect on cloud cover. Overall, this study offers a novel technical approach for further developing cloud detection, cloud-water separation, and cloud cover information extraction algorithms for domestic optical satellite remote sensing data.

Compared to natural surfaces, urban surfaces have more complex geometric structures, leading to significant impacts of the multiple scattering effect within pixels and the neighborhood effect on the inversion results of urban land surface temperature (LST). This study proposed a novel urban LST inversion algorithm that integrates machine learning and an enhanced temperature and emissivity separation (TES) method. Finally, the proposed algorithm was applied to China’s SDGSAT-1 thermal infrared data. The algorithm comprises three key steps: First, the inversion of urban canopy brightness temperature from SDGSAT-1 data was conducted using the eXtreme Gradient Boosting (XGBoost) algorithm. Second, an enhanced TES algorithm based on the sky view factor (SVF) was developed to account for urban geometry, enabling high-precision urban LST inversion. Third, the accuracy of the inversion algorithm was assessed and applied to the urban area of Beijing. The results demonstrate that inversion using an XGBoost algorithm and a split-window algorithm yielded root mean squared errors (RMSEs) of approximately 0.2 K and 1.2 K, respectively. The LST RMSEs with and without available water vapor data were determined at 0.36 K and 0.73 K, respectively; and the LSE RMSEs under three bands were 0.020/0.026, 0.018/0.023, and 0.020/0.023, respectively. The differences in the LST inversion results derived using the original and improved TES algorithm ranged from 0 to 1.86 K.

The analysis of patches showing changes in coastal areas of the Chinese mainland tends to encounter challenges such as low image resolution, long time intervals, and limited spatial coverage. This study aims to obtain high-frequency, accurate information on changes in coastal areas nationwide. This will facilitate the dynamic monitoring of marine resources and the implementation of relevant protection policies for coastal areas in China. To this end, using domestic high-resolution remote sensing data of 15 days (i.e., one cycle), as well as the iteratively reweighted multivariate alteration detection (IR-MAD) algorithm combined with visual interpretation, this study extracted patches reflecting 2020—2023 changes along the coasts of 11 provinces and cities in the Chinese mainland. Accordingly, this study analyzed their spatiotemporal characteristics, landscape patterns, and spatial correlation. The results indicate distinct directional changes in the patches. The patches reflecting changes from sea enclosure to reclamation exhibited the largest areas across various investigated areas. Except for Hainan Province, the area of this type of patches exceeded 1 000 km². The proportions of patches reflecting different types of changes gradually tended to be balanced. In the winter of 2022, the proportion of patches showing changes in the reclamation dropped below 50% for the first time. The aggregation degree of patches reflecting various types of changes showed increasing trends, suggesting that patches reflecting various changes will become more concentrated in the future. The centroids of these patches of various regions shifted in varying directions, and these patches exhibited significant spatial correlation within a 20 km range.

This study investigated the coastal wetland of Jiangsu Province. Using methods such as satellite remote sensing and airborne multi-parameter remote sensing, this study estimated the biomass of dominant plants and estimated their carbon sequestration capacities. Based on fine-scale classification of surface features achieved using airborne hyperspectral data, this study extracted 11 land cover types. The vegetation cover was approximately 76%, and zones with human activities accounted for about 1.5% of the study area. The model for vegetation biomass inversion using the multi-parameter airborne remote sensing demonstrated higher accuracy than that based on satellite remote sensing, with a coefficient of determination (R²) greater than 0.8 and a root mean square error (RMSE) of 0.25. As calculated using the multi-parameter airborne remote sensing, Spartina alterniflora and reed within the study area exhibited aboveground carbon sequestration capacities of 0.41 kg/m² and 0.58 kg/m², respectively. This study demonstrates that the multi-parameter airborne remote sensing method can accurately determine vegetation types in wetlands and carbon sequestration capacity, thus providing crucial assessment parameters for research on the carbon cycle of the ecosystem and the current status of habitats within wetlands and precisely serving wetland resource surveys.

Resource development in mining areas alters land use patterns and causes ecological damage. This renders land use identification crucial to ecological restoration and management in mining areas. Although remote sensing imagery is widely used for land use classification, the use of a single data source has limitations in the classification for mining areas. Additionally, it is difficult for conventional machine learning algorithms to effectively perform the classification. To improve classification accuracy, this study investigated the eastern part of Dongsheng District, Ordos City as an example to conduct land use classification for mining areas using a convolutional neural network (CNN) combined with multi-source remote sensing data. First, a multi-source remote sensing time series feature set was developed using data from Sentinel-1/2, Luojia-1 01, and the NASA digital elevation model (DEM). Next, optimal features were selected using the Relief-F algorithm combined with a random forest algorithm. Finally, information on surface features was extracted using the ResNet50 CNN model. This facilitated land use classification in the mining area. The results show that the proposed method achieved an overall land use classification accuracy of 95.36% and a Kappa coefficient of 0.942 1, outperforming conventional methods such as the random forest approach. Furthermore, selecting optimal features using Relief-F combined with the random forest approach enhanced the classification accuracy of various classifiers. This study offers a methodological reference for land use classification of mining areas.

Understanding the characteristics of urban expansion and corresponding spatial changes serves as a prerequisite for optimizing urban spatial structure and resisting disorderly urban land expansion. This study focuses on the Chengdu-Chongqing economic circle. Using multi-source data fusion, this study extracted information on urban built-up areas from 2000 to 2020. From the aspects of urban expansion characteristics, spatial changes, and intercity spatial interaction intensity, this study analyzed the spatiotemporal evolution during urban expansion at both the regional and county scales. The results indicate that incorporating impervious surface information into multi-source data fusion improved the information extraction accuracy of built-up areas, achieving an overall classification accuracy of 98% and an average Kappa coefficient of 0.75. Urban expansion from 2000 to 2020 transitioned from low to medium-high speed and then to low speed. The dominant expansion type was edge expansion, accompanied by decreased spatial compactness. Within the Chengdu-Chongqing economic circle, the strongest spatial interaction intensity occurred between Chengdu and Chongqing. The urban spatial pattern exhibited a “dual cores with two wings” pattern, highlighting the pivotal role of Chengdu and Chongqing in driving the development of surrounding cities. These findings reveal the urban development patterns and spatial change characteristics within the Chengdu-Chongqing economic circle. They will facilitate the rational optimization of land use and territorial spatial patterns, promoting coordinated urban-rural development.

Exploring land use evolution and its impact on carbon storage is significant for mitigating climate change and promoting green and low-carbon development in metropolitan circles. Under the carbon peak and neutrality goals, this study implemented dual-constraint transition matrix optimization using point-of-interest (POI) data and the patch-generating land use simulation (PLUS) model, followed by the coupling with the integrated valuation of ecosystem services and trade-offs (InVEST) model. Based on the InVEST-PLUS coupled model, this study analyzed the land use evolution in the Jinan metropolitan circle from 2000 to 2020 and its impact on ecosystem carbon storage. Considering natural development, urban development, and ecological conservation as three distinct scenarios, this study simulated and predicted the land use change in the Jinan metropolitan circle in 2030 and 2060. Moreover, this study estimated the corresponding ecosystem carbon storage and analyzed the shift of the carbon storage center. Finally, this study explored the factors driving the spatial differentiation of carbon storage using the optimal parameters-based geographical detector (OPGD). The results indicate that from 2000 to 2020, the Jinan metropolitan circle saw a continued decrease in arable land, grassland, and unused land; a fluctuating increase in forest land; and a rapid increase in water area and construction land. The carbon storage and land use pattern in the Jinan metropolitan circle showed similar distributions characterized by higher values in the southeast and lower values in the northwest, with the main body of the Yellow River as the dividing line. The carbon storage in arable land served as the primary source of carbon storage in the Jinan metropolitan circle since it represented over 80 % of the total carbon storage. The simulation results reveal decreased carbon storage under the three scenarios, primarily due to the conversion from arable land in high carbon-density areas to construction land in low carbon-density areas. The ecological conservation scenario shows the highest total estimated carbon storage, which is 4 226.86×10⁶ t in 2030 and 3 967.94×10⁶ t in 2060. The carbon storage center in the Jinan metropolitan circle displays a certain shift in different development periods and scenarios due to land use change. However, the carbon storage center remains located in Licheng District, suggesting that the development of the Jinan metropolitan circle is relatively comprehensive and balanced. Various driving factors manifest significant impacts on the spatial distribution of carbon storage in the Jinan metropolitan circle. Notably, population density shows the greatest explanatory power for the spatial differentiation of carbon storage. Additionally, the interactions of various factors enhance their explanatory power for carbon storage.

The positive effects generated by large-scale underground mining in China's western semi-arid region have attracted increasing attention in recent years. Accurately understanding and scientifically utilizing these positive effects in mines plays a significant role in saving ecological restoration costs for mines. To reveal the perturbation characteristics of subsidence basins on precipitable water vapor (PWV), this study investigated the Daliuta mine in the Shendong mining area. Based on the simulation results of the atmospheric radiative transfer model and the ground-based GPS real-time observation data, this study constructed a water vapor inversion model using Sentinel-2 satellite images, obtaining the near-surface PWV content from 2017 to 2021 in the Daliuta mine. Furthermore, this study analyzed the near-surface PWV distributions in the single-mining-face subsidence basin and the mining-face-group subsidence area. By deploying HOBO temperature and humidity sensors on site, this study comparatively analyzed the near-surface relative humidity inside and outside the subsidence basin. The results indicate that subsidence basins showed positive convergence effects on PWV. Specifically, the near-surface PWV in the single-mining-face subsidence basin decreased gradually from the inside to the outside of the basin. The near-surface PWV in the mining-face-group subsidence area was significantly improved after mining. The relative humidity was significantly higher inside the subsidence basin compared to the outside. The differences in relative humidity in the vertical direction from the surface were 14.52, 13.53, 12.43, 10.60, and 10.33 percentege point, respectively, indicating gradually weakening water vapor convergence effects in the subsidence basin with an increase in elevation. The water vapor convergence effects were significant at nighttime but nonsignificant at daytime. Finally, based on vegetation surveys and previous studies, this study proposed a conceptual model for water vapor convergence effects in subsidence basins to explain the mechanism governing water vapor convergence. Additionally, subsidence basins somewhat contribute to the benign cycle of ecosystems in semi-arid mining areas.

Geological disasters, influenced by natural and human factors, directly threaten the safety of people’s lives and property. Exploring the spatiotemporal variations and development mechanisms of geological disaster risk can enhance disaster prevention and mitigation. This study examined 31 factors such as topography, rainfall, and social economy from the perspectives of nature and humanity. Based on the four-factor risk theory, this study investigated the variations of geological disaster risk in the western Sichuan region using methods like the analytic hierarchy process, principal component analysis, information value model, entropy weight method, and hot/cold spot analysis. Employing the obstacle degree model, this study explored the degrees of influence of various factors on geological disaster risk in the western Sichuan region. The results indicate that from 2007 to 2022, the geological disaster risk in the western Sichuan region was generally characterized by higher levels in the west and lower levels in the east. Kangding and Maerkang were the concentrated distribution areas of perennial cold spots. The area of extremely low and low risk levels increased by 8 871.1 km² and 12 478.6 km² respectively at growth rates of 1.056%/a and 1.485%/a respectively. The area of high and extremely high risk levels decreased by 10 127.8 km² and 9 880.1 km² respectively at growth rates of -0.02484 km²/a. The degrees of influence of various factors on risk levels exhibited temporal heterogeneity. The dominant obstacle factors (obstacle degree: above 5 %) were concentrated in risk and disaster prevention and mitigation indicators. Factors including rainfall, topography, and medical resources contributed significantly to geological disaster risk.

Rapid and uneven land subsidence severely threatens human life and production activities. Understanding the spatiotemporal evolutionary patterns of land subsidence is crucial for the precise prevention and control of geological disasters. Employing the persistent scatterer interferometric synthetic aperture Radar (PS-InSAR) technology, this study obtained the information of monthly surface deformation in Dezhou City to calculate the multi-scale subsidence vulnerability indices (SVI). Combining time series cluster analysis, space-time cube, spatiotemporal hot spot analysis, and spatiotemporal outlier analysis, this study explored the spatiotemporal distribution characteristics of multi-scale SVI in Dezhou City from July 2017 to December 2021. The time series cluster analysis reveals inconspicuous trend clustering on a monthly scale, and significant clustering characteristics on quarterly and semi-annual scales, with large subsidence fluctuations on a semi-annual scale. The space-time cube model presents poor continuity of SVI and subtle subsidence variations on a monthly scale. In contrast, the subsidence on quarterly and semi-annual scales exhibited relatively close occurrence time, showing a significant pattern of subsidence from March to August and rebound from September to February of the ensuing year. The spatiotemporal hot spot analysis of SVI in Dezhou City for 54 months shows that enhanced and continuous subsidences occurred primarily in the northwest of Wucheng County and Decheng District. Oscillatory subsidence and rebound occurred principally in Linyi, Yucheng, and Qihe counties in the southeast. The local outlier analysis of multi-scale SVI shows nonsignificant subsidence characteristics on a monthly scale but similar subsidence conditions on quarterly and semi-annual scales. Seasonal subsidence and semi-annual subsidence related to crop growth in Linyi and Qihe counties gradually weakened or even rebounded. Notably, the high-high clustering range on a semi-annual scale was broader, accompanied by a more significant rebound.

Scientifically and rationally demarcating urban and town areas is a fundamental task during China’s rapid urbanization stage. It serves as a critical basis for promoting the optimization and quality improvement of urban and rural spaces, scientifically coordinating urban and rural planning and construction management, and implementing territorial spatial planning. However, there is neither a unified concept nor a universal delimitation method for urban and town areas in China, hindering their planning, construction, development, and public management. Based on defining the relevant concepts of urban and town areas, this study proposed a people-centered method for determining town areas with no cities and counties set using geographic information system (GIS) technology, considering the characteristics and spatial relationships of land types. The data sources of this study include the results of the third national land resource survey, statistical bulletins, remote sensing image interpretation, and point-of-interest (POI) data. Finally, the proposed method was applied to demarcate the town areas in Xiapu County, Ningde City. The empirical study results demonstrate the effectiveness of the proposed method, which features a scientific and concise technology roadmap and strong operability. Therefore, the proposed method can provide a theoretical foundation for the rational territorial spatial planning.

Precisely extracting the information of industrial heat source activities serves as a significant prerequisite for the prevention and control of air pollution and the prediction of industrial economy in China. However, due to unclear heat source characteristics and inaccurate type determination, the remote sensing monitoring of industrial heat sources fails to be widely applied. This study investigated Hunan Province based on the Suomi-NPP VIIRS Nightfire data from 2015 to 2021. First, this study extracted nighttime industrial heat sources from the data using spatial filtering and the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm. Second, this study constructed temperature characteristic template functions for different industrial heat sources using the Gaussian mixture model. Third, this study determined the subcategories of industrial heat sources according to temperature similarity of the same categories, achieving an overall classification accuracy of 86.31 %. Finally, this study obtained the layout of industrial heat sources in Hunan Province. The results indicate that the industry in Hunan Province was dominated by petrochemical plants, with the smallest number of coal-to-chemical plants. Metallurgical enterprises, showing the highest heat radiation intensity, were primarily distributed in the Loudi-Xiangtan-Zhuzhou area. From 2015 to 2019, industrial heat sources in Hunan Province showed a decreasing trend, indicating that relevant government departments effectively rectified the scattered, non-compliant, and polluting factories in Hunan Province during the 13th Five-Year Plan period. During the COVID-19 pandemic, the number of heat sources changed slightly since work and production were gradually resumed under the effective regulation of the government. This study analyzed the grey relational degrees between the heat radiation emission intensity and the relevant indicators of energy consumption and industrial pollutant emissions. Based on the comprehensive industrial energy consumption, industrial sulfur dioxide emissions, and heat radiation emission intensity, this study explored the relevant situation of energy consumption, pollution, and heat emissions in Hunan Province, dividing the cities and prefectures into seven types accordingly. Overall, this study provides information sources and data support for local governments to dynamically monitor the production activities of local key industrial enterprises. Ascertaining the spatial distribution patterns and evolutionary trends of different industrial enterprises will contribute to the formulation of industrial transformation policies by the government and relevant departments and the practice of sustainable development.

Land use serves as the primary cause of global environmental changes. Therefore, investigating its spatiotemporal changes and corresponding driving factors is significant for promoting the sustainable development of regional socioeconomics and ecosystems. Based on nine stages of remote sensing monitoring data on land use/land cover from 1980 to 2020, this study analyzed the spatiotemporal changes in land use types in the Golmud River basin. By combining the analysis of significant correlations, this study explored the major factors driving changes in land use within the basin. The results indicate that over the past 40 years, unused land and grassland proved to be dominant land use types in the Golmud River basin. The areas of cultivated lands, water bodies, and construction lands exhibited an increasing trend, while those of forest lands, grasslands, and unused lands trended downward. The period from 2015 to 2020 witnessed significant changes in both the areas and the dynamic degrees of various land use types within the basin. During this period, spatial changes in land use transition predominately occurred in the central and northern parts of the basin. Between 1980 and 2020, the unused land showed significant fragmentation. Human activities, particularly total population and regional gross domestic product, were identified as the main factors driving changes in the land use type within the basin.

At present, the impacts of high-speed railways (HSRs) on cities along rail lines remain unclear. Previous analyses of these impacts based on remote sensing data focused primarily on qualitative assessment. Given this, this study investigated the Hebei section of the Beijing-Guangzhou HSR and introduced a remote sensing monitoring-based method that integrated qualitative and quantitative analyses for assessing the impacts of HSR on urban development. First, this study established a parameter index system used to characterize urban development changes. Then, multi-source and multi-scale remote sensing data were employed to monitor the spatiotemporal variations in these indices before and after the operation of the Beijing-Guangzhou HSR within this study area. Finally, four cities that were adjacent to the study area but lacked available HSRs were selected as a control group. Using the difference-in-differences (DID) model, this study quantified the impacts of HSRs on four cities along the Hebei section. The results indicate that the four cities along the Hebei section of the Beijing-Guangzhou HSR saw a rapid expansion in the construction land between 2005 and 2020, with an average annual expansion rate of 2.00%. The HSR construction exerted a significant impact on the direction of urban expansion, with the impact related to the spatial relationship between both. Compared to the four cities in the control group, the operation of the Beijing-Guangzhou HSR has slowed down the expansion rates of urban areas in the four cities along the line.

Wetlands, hailed as the "kidneys of the Earth", hold great significance for maintaining the stability of ecosystems. This study investigated 10 important wetland reserves along the Silk Road. Based on remote sensing data from the ZY3 satellite, it extracted the wetland types in 2015 and 2020 through interactions between object-oriented analysis and manual interpretation. As a result, a dataset of wetland distribution and its dynamic changes in the reserves was established. By combining topography, hydrological conditions, ecological importance, and wetland type transition, this study proposed a method for assessing the spatial potential of returning farmlands to wetlands. The results of wetland information extraction show that from 2015 to 2020, the wetland area in the 10 reserves exhibited a net increase of 238.04 km² thanks to both natural and anthropogenic factors. Such an increase was dominated by lacustrine wetlands, with the wetland rate rising by 0.58% generally. This demonstrates that the establishment of ecological reserves posed a positive impact on regional wetland protection. However, in local regions, wetlands still showed a trend of degradation, covering an area of 77.00 km². The potential analysis results of returning farmlands to wetlands indicate that a total of 325.13 km² of farmlands should be returned to wetlands, consisting of 10.63 km² requiring high-priority restoration, 167.02 km² subjected to medium-priority restoration, and 147.48 km² requiring low-priority restoration. The proposed region-specific scheme for ecological restoration in wetlands can provide decision-making support for wetland protection and management along the Silk Road.

Using level-1 (L1) brightness temperature data from the Microwave Radiation Imager (MWRI) on board Fengyun-3D (FY-3D) satellite and the corresponding Level-2 (L2) precipitation products, this study established a precipitation rate inversion model for land surface heavy precipitation in Hunan Province based the polarization corrected temperature (PCT) and scatter index (SI). The proposed model was validated using individual examples. The results indicate that the precipitation rates retrieved from the L1 brightness temperature data of the FY-3D satellite were generally consistent with the results obtained from the L2 precipitation products. Compared to actual data, the retrieved precipitation rates were slightly higher in low precipitation areas but smaller in centers of high precipitation areas. The ascending orbit-based inversion model exhibited a correlation coefficient, mean absolute error (MAE), and root mean square error (RMSE) of 0.876 1, 0.771 1, and 1.151 4 mm/h, respectively. Conversely, the descending orbit-based inversion model presented a correlation coefficient, MAE, and RMSE of 0.911 3, 1.130 4, and 1.832 2 mm/h, respectively. The inversion results showed a larger precipitation distribution range than that of L2 products. Compared to the measurements at ground meteorological stations, the inversion model demonstrated higher accuracy than L2 products. This study successfully determined the distribution of land surface heavy precipitation in Hunan through inversion. The results of this study can provide a reference for investigating the relationship between microwave brightness temperature and precipitation and estimating land surface heavy precipitation.

Investigating land-use-related carbon emissions (LCE) plays a vital role in achieving goals of peak carbon dioxide emissions and carbon neutrality (also referred to as the “dual carbon” goals). Research on the changes and prediction of LCE in Xiangxi Tujia and Miao Autonomous Prefecture (also referred to as the Xiangxi Prefecture) can provide a theoretical reference for the region to develop policies on the achievement of the “dual carbon” goals and for local balanced development and protection. Based on five sets of land use data from 2000 to 2020, this study analyzed the land use conditions and the spatiotemporal evolution of historical carbon emissions in Xiangxi Prefecture. The factors influencing LCE were determined using a decoupling model and a logarithmic mean Divisia index (LMDI) model. Furthermore, three land use scenarios were established: natural development, priority of cultivated land protection, and ecological protection priority. Using these scenarios, this study predicted the land use and carbon emissions in Xiangxi Prefecture in 2030. The results indicate that forest land represents the dominant land use type in Xiangxi Prefecture. Regarding land use transition, the period from 2000 to 2020 witnessed a significant increase in construction land, which encroached into substantial areas of forest land and cultivated land. Concurrently, water bodies and grassland decreased in area, being converted into forest land and cultivated land. From the perspective of carbon emissions, land use in the region exhibited a transformation from carbon sinks to carbon sources in general. During the 20-year span, the total LCE continued to increase. Construction land was identified as the primary land type as a carbon source, while forest land was the main land type as a carbon sink. Within the 20 years, carbon emissions decreased only in Huayuan County but increased in all other counties and cities. After 2010, the original regions with elevated carbon emissions showed a decrease in carbon emissions, while other regions witnessed growing carbon emissions to varying degrees. These regional changes in carbon emissions were largely attributed to the increased carbon emissions from construction land. Xiangxi Prefecture maintained a weak decoupling effect generally, with counties and cities fluctuating between weak decoupling and strong decoupling states. The economic output effect and energy efficiency effect served as the primary factors influencing carbon emissions. The overall land pattern remained relatively stable across the three scenarios. The carbon emissions of the three scenarios increased in the order of ecological protection priority, natural development, and priority of cultivated land protection. In the future, construction land will still represent the dominant factor causing overall changes in carbon emissions, while forest land will remain as the primary carbon sink.

Surface deformations pose significant threats to the normal operation of railways. Investigating the spatial distribution of surface deformations along the China-Laos railway holds great significance for disaster prevention and mitigation. Based on 36 scenes of ascending orbit and 50 scenes of descending orbit images from Sentinel-1A satellite from December 2021 to August 2023, this study conducted deformation inversion using the small baseline subset interferometric synthetic aperture Radar (SBAS-InSAR) technique. Besides, this study conducted spatial distribution statistics and analysis of surface deformations along the Jinghong section of the China-Laos railway. The results indicate that the overall deformation along the railway exhibits a heterogeneous distribution, with multiple potential hazards in the northern mountainous area. Among the selected typical deformation zones, the maximum subsidence rate in the northern mountainous area reaches -108.718 mm/a, whereas the southern plain area shows significant uplift with a rate of 227.315 mm/a. Along the railway, the surface deformation rates in the line of sight (LOS) direction ranged from -319.811 mm/a to 321.638 mm/a. Obvious subsidence occurred in Puwen Town and Dadugang Township. Conversely, minor subsidence was observed in urban areas like Mengyang town, Yunjinghong subdistrict, and Gasa town, with pronounced uplifts in the southeastern part of Menghan town. Along the railway, deformations in mountainous areas were primarily concentrated at elevations ranging from 800 m to 1400 m, with soft rocks prevailing in these deformed areas. InSAR-based analysis of the spatial distribution of the surface deformations along the railway is of significant value for the safe operation of the railway.

Canoeing is an important Olympic event; however, high-precision positioning and map data visualization technologies have not been widely adopted during the training phase of canoeing. To bridge this gap, this study proposed a domestically pioneering canoeing sport monitoring system based on real-scene 3D. This system integrates high-precision positioning, virtual reality (VR), and virtual-real fusion technologies, providing athletes and coaches with a straightforward, precise data analysis platform. First, this study presents an overview of the background and significance of the construction of the system. Then, it describes the architecture, major functions, and database design of the system. Finally, it introduces the software system development using technologies including the Cesium platform for 3D geospatial applications, ArcGIS Server, ArcGIS API for JavaScript, and WebSocket. The methods for developing key functions are also described. The core functions encompass 3D visualization of the training field, venue query and positioning, virtual-real integration of trajectories, real-time positioning and monitoring, trajectory playback, and data analysis. Therefore, this system enjoys the advances of high-precision positioning, 3D real scene visualization, and virtual-real fusion. The applications of this system will enhance the efficiency and quality of canoeing training and provide a valuable reference for related research fields.