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.

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 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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

Hills cutting and land reclaiming (HCLR) serves as the most direct way for cities that are constrained by terrain to overcome land scarcity and facilitate urban spatial expansion. Obtaining the range of HCLR quickly and accurately using remote sensing technology is significant for assessing the regional ecosystem and new city development planning scientifically. Based on the Google Earth Engine (GEE) cloud computing platform for remote sensing, as well as multi-temporal data from Sentinel-1 SAR and Sentinel-2 multispectral imager (MSI), this study determined the spatiotemporal distribution of the 2017—2022 HCLR range in the study area using multi-temporal change monitoring and a random forest algorithm. Using a combination of Sentinel-1 ascending and descending images and based on noise filtering and multi-temporal image synthesis, this study calculated the difference in the backscattering intensity before and after HCLR. Then, the excavation range was determined using a threshold determined using the percentile threshold method combined with sample data. The results demonstrate that this method exhibited high operability, with an overall classification accuracy and Kappa coefficient of 85% and 0.83, respectively. By monitoring multi-temporal changes in the VH polarization band of Sentinel-1 and using a combination of Sentinel-1 ascending and descending images, this study acquired the spatial distribution of excavation areas within the study area from 2017 to 2022. Before 2019, the excavation areas were primarily concentrated in Jiuzhou Development Zone, Country Garden, and Poly Lingxiu Mountain. After 2020, new excavation areas, such as Liujiagou and Shuiyuan Station, emerged, with the scope and intensity of excavation gradually increasing. By combining the spectral, texture, and topographic features of SAR and optical data and based on feature optimization combined with a random forest algorithm, this study determined the spatial distribution of yearly HCLR from 2017 to 2022. Before 2018, the HCLR scale was small, with an area of 2.655 km². After 2019, the scale increased each year, especially in 2021, when the area reached 12.607 km², accounting for 34.56% of the total land reclamation area during the monitoring period. In 2022, the reclamation area obtained through further excavation on previously reclaimed land was estimated at only 2.686 km² due to the increasing slope and excavation volume. The method developed in this study for monitoring excavation areas and extracting land reclaiming ranges enables effective monitoring and extraction of the HCLR range.

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.

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.

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.

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.

Arsenic (As) is a metalloid element with high carcinogenicity, rendering it particularly important to detect As content in soils in a swift and accurate manner. The study focused on the topsoil in Urumqi City, where 84 soil samples were collected and tested for their As content and original spectral reflectance. This study examined the relationships of As content in the soils with the spectral reflectance under the original spectra and 12 spectral transformations using the Pearson correlation analysis, followed by screening characteristic bands. Hyperspectral models for the inversion of As content in soils were developed using partial least squares regression (PLSR), random forest regression (RFR), and support vector machine regression (SVMR). Finally, the prediction performance of the hyperspectral models was elevated based on the coefficients of determination (R²), root-mean-square errors (RMSEs), and mean absolute errors (MAEs). The results indicated that applying differential transformations to the original spectral data can effectively enhance the spectral features and improve the correlation between spectral reflectance and As content in soils. The prediction performance of the hyperspectral models decreased in the order of RFR, SVMR, and PLSR. The RFR model based on root-mean-square second order differentiation (RMSSD-RFR) exhibited the best fitting effects and the highest prediction stability, with R² of 0.821, a RMSE of 0.143 mg/kg, and a MAE of 0.523 mg/kg. This study provides a scientific basis for developing hyperspectral models for the inversion of As content in soils in an oasis city.

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.

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.

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.

Based on the standardized precipitation index (SPI) and vegetation condition index (VCI) from 2000 to 2020, this study analyzed the trends in meteorological drought across different vegetation types in Shanxi Province using methods such as variational mode decomposition (VMD), Mann-Kendall trend analysis, and Pearson correlation coefficient. Accordingly, this study quantified the response time of vegetation growth conditions to meteorological drought. The results indicate that from the beginning of the 2000s, the overall meteorological drought in Shanxi Province has gradually eased. However, on a seasonal scale, areas with increasingly aggravated drought continuously expand from spring to winter. Meteorological drought has alleviated across various vegetation types, with the alleviation becoming increasingly significant with an increase in the time scale. In contrast, on a seasonal scale, the drought relief gradually weakens from spring to winter, during which drought aggravation progressively strengthens. Vegetation growth conditions are significantly influenced by meteorological drought. On the annual scale, there is a predominantly positive correlation between both. On the seasonal scale, areas with a strong correlation between both gradually contract from spring to winter, when such areas are dominated by the northwestern and northeastern areas of the province. Additionally, the response time of vegetation to drought is longer in spring and winter compared to autumn and summer. Across different vegetation types, the responses of vegetation growth conditions to meteorological drought prove the most rapid during the summer, and cultivated lands are identified as the most sensitive land type to meteorological drought.

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.

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.