As an essential branch of surveying and mapping science, underwater topographic surveys are closely related to human operations in oceans and lakes. For underwater topography detection in shallow-water areas, conventional acoustic methods face the hull stranding risk, and passive optical methods have low survey accuracy. The airborne laser sounding is a novel means for bathymetric surveys in shallow-water areas, and its application in offshore areas can fill the gap of underwater topography data in shallow-water areas. This study presents a brief introduction to the composition and principle of the airborne laser sounding system, followed by a description of laser sounding data acquisition. Furthermore, this study highlights the critical processing technologies for airborne laser sounding data, including waveform data processing, error correction, and point cloud data processing. Finally, this study summarizes the technical difficulties and developmental trends of airborne laser sounding.

Nearshore monitoring covers natural environments and human activities. High-accuracy identification of nearshore monitoring targets significantly influences the healthy development of the marine economy, the ecological protection of marine environments, and the prevention and mitigation of marine disasters. The nearshore monitoring targets feature multiple types, diverse sizes, and uncertainty. The existing identification models suffer low accuracy, low efficiency, and severe omission of small targets. This study proposed an identification model (Re-YOLOX) for nearshore monitoring targets by improving YOLOX using a learnable image resizer model (the Resizer model). First, the model training was intensified using the Resizer model to improve the feature learning and expression abilities and the recall rate of the Re-YOLOX model. Then, the feature pyramid fusion structure of the YOLOX algorithm was improved to reduce the omission of small targets in the identification. With the nearshore video data from UAV monitoring as the data set and cars, ships, and piles as monitoring targets, this study compared the Re-YOLOX model with other models, including CenterNet, Faster R-CNN, YOLOv3, and YOLOX. The results show that the Re-YOLOX model yielded a mean average precision of 94.23%, a mean recall of 91.99%, and a mean F1 score of 89.67%, all of which were higher than those of the other models. In summary, the Re-YOLOX model can improve the target identification accuracy while ensuring target identification efficiency, thus providing technical support for managing nearshore seas.

Conventional information extraction methods for aquacultural ponds frequently yield blurred boundaries and low accuracy due to the effect of different objects with the same spectrum in complex geographical environments of offshore and coastal areas. This study proposed a method for extracting information on coastal aquacultural ponds from remote sensing images based on the U²-Net deep learning model. First, an appropriate band combination method was selected to distinguish aquacultural ponds from other surface features through preprocessing of remote sensing images. Samples were then prepared through visual interpretation. Subsequently, the U²-Net model was trained, and information on coastal aquacultural ponds extracted. Finally, the scopes of aquacultural ponds were determined using the local optimum method. The experimental results show that the method proposed in this study yielded the average overall accuracy of 95.50%, with the average Kappa coefficient, recall, and F-value of 0.91, 91.45%, and 91.01%, respectively. Furthermore, 19 ponds were extracted, with a total area of 9.79 km². The average accuracies of the number and area of aquacultural ponds were 94.06% and 93.18%, respectively. The method proposed in this study allows for quick and accurate mapping of coastal aquacultural ponds, thus providing technical support for marine resource management and sustainable development.

Digging ponds to raise crabs is a non-grain behavior of cultivated land, endangering national food security. However, the intelligent interpretation of remote sensing images targeting this behavior faces challenges such as laborious manual interpretation and low verification efficiency. Based on a cooperative interpretation mechanism, this study proposed an intelligent method for detecting crab ponds using remote sensing images. This method, integrating the HRNet segmentation network and the Swin-Transformer classification network models and combining manual verification, improved the detection accuracy and work efficiency. The application results of this method to Gaochun District, Nanjing City, Jiangsu Province show that the method for intelligent detection can automatically determine 83.4% of the spots for detection, with final identification accuracy of 0.972. The method proposed in this study can significantly reduce the identification difficulty and manual verification workload while improving the detection accuracy. Therefore, this study will provide a reliable solution for the accurate and efficient detection of non-grain surface features such as crab ponds.

Accurate information about land use/land cover (LULC) can provide significant guidance for regional spatial planning and sustainable development. However, conventional methods for remote sensing image classification are challenging due to complex surface morphologies, diverse surface feature types, and nonlinear features of remote sensing images. Therefore, they fail to fully utilize the rich information in remote sensing images. This study developed a random forest-based classification method for remote sensing images to extract LULC information by integrating indices and principal components. First, the images covering the study area were selected to determine cloud cover and conduct median synthesis of images, obtaining interannual remote sensing images. Then, various calculated indices and the extracted principal components were integrated into the band stacks of remote sensing images. Furthermore, classifiers were constructed using different machine-learning algorithms. Finally, based on a confusion matrix, the classification results were evaluated using overall accuracy and the Kappa coefficient. The experimental results of the Hangzhouwan area show that the decision support based on vegetation, water, building indices, and principal components can improve the classification accuracy, yielding overall accuracy and Kappa coefficient of 91.42% and 0.894 2, respectively, which were higher than those of conventional methods such as random forest, classification and regression tree, and support vector machine. The method for remote sensing image classification proposed in this study, which integrates indices and principal components, can obtain high-accuracy land use classification results by accurately extracting land cover features in remote sensing images. This study will provide method support for fine-scale surface classification.

Urbanization has decreased the area of ecological land and deteriorated ecological environment in Zhanjiang City. Therefore, it is significant to quickly, comprehensively, and accurately monitor the changes the ecological environment quality in this city. Based on the Landsat images in 2000, 2005, 2009, 2015, and 2020, this study constructed the improved remote sensing ecological index (IRSEI) using six indicators, namely greenness (NDVI), humidity (WET), dryness (NDBSI), heatiness (LST), land use (LUI), and population distribution (POP). Using IRSEI, this study quantitatively analyzed the changes in the ecological environment quality in Zhanjiang during 2000—2020. The results are as follows: ① The mean IRSEI values of 2000, 2005, 2009, 2015, and 2020 are 0.18, 0.18, 0.35, 0.42, and 0.38, respectively, showing a first increasing and then decreasing trend. ② According to the difference processing on IRSEIs during 2000—2020, the proportions of ecological environment areas with significant improvement (dominant), improvement, no change, deterioration, and significant deterioration in the study area are 78.95%, 8.70%, 8.01%, 1.35%, and 2.99%, respectively. ③ The IRSEI can effectively reflect the poor urban environment along the coastal zone during 2000—2020, specifically manifested as a low IRSEI value of building land along the coastal zone. The results of this study can provide a theoretical and scientific basis for Zhanjiang’s ecological environment protection.

Coastal zones are the world’s most populated areas, with their ecosystems being strongly influenced by human activities. Tidal flats, shorelines, and aquacultural water bodies are critical elements in monitoring the health of coastal zone ecosystems. However, the dynamic changes in the waterlines between land and sea areas caused by tidal effects make it challenging to detect tidal flats and shorelines using the remote sensing technology. By integrating Landsat4/5/7/8 and Sentinel-2A/B satellite remote sensing images, this study conducted seven phases (1989—2021) of monitoring of tidal flats, shorelines, and aquacultural water bodies along coastal zones in China mainland. By taking advantage of the high frequency of multi-source satellite observations, this study identified tidal flats, shorelines, and aquacultural water bodies by detecting the waterlines at different tidal levels. The results are as follows: ① Seawater of different colors requires different combinations of water body indices. For clear or low-turbidity seawater, this study selected the modified normalized difference water index (mNDWI) and the normalized difference water index (NDWI) to detect the waterlines at high and low tidal levels, respectively. This improved the reliability of tidal flat detection, with the detected tidal flat area being 122% larger than that detected only using the mNDWI. For high-turbidity seawater (in Zhejiang, Jiangsu, and Shanghai), this study selected mNDWI to detect the waterlines at high and low tidal levels, avoiding misidentifying high-turbidity seawater as tidal flats using NDWI. Besides, this study selected NDWI to detect aquacultural water bodies. ② During 1989—2021, coastal zones in China mainland changed significantly, as evidenced by rapidly decreased tidal flats and increased aquacultural water bodies and shorelines. The decreased rate of tidal flats and the increased rates of shorelines and aquacultural water bodies along the coastal zones averaged 46.2%, 34.4%, and 149.3%, respectively. Correspondingly, the tidal flat area decreased by 7 173.2 km², while the the shoreline length and aquacultural water body area increased by 5 320.5 km and 9 046.5 km², respectively. Provinces or cities in northern China suffered more tidal flat losses than those in southern China. Based on the average decrease rate of tidal flats during 1989—2021, tidal flats in Liaoning, Hebei and Tianjin, and Shandong will disappear within 27 a, 10 a, and 22 a, respectively. ③ The area changes between tidal flats and aquacultural water bodies are highly negatively correlated, indicating that the expansion of aquacultural water bodies is a critical driving factor for the decrease in tidal flats.

Conventional pansharpening fusion methods suffer inaccurate extraction of details and low spectrum fusion accuracy. This study proposed a pansharpening algorithm of remote sensing images based on nonsubsampled Contourlet transform (NSCT) and pulse coupled neural networks (PCNN) by combining the multi-scale and -directional decomposition characteristics of NSCT and the pulse synchronous emission characteristics of PCNN. The process of this pansharpening algorithm is as follows: first, the details of panchromatic images were extracted through NSCT; then, the extracted detail features were injected into the irregular segmentation regions obtained using the PCNN model; finally, the sharpening fusion results of high-resolution multispectral remote-sensing images were obtained through statistical weighting. As corroborated by the experimental results of WorldView-2 and GF-2 data sets, the pansharpening algorithm outperforms other remote sensing image fusion algorithms in detail preservation and spectral consistency, verifying its effectiveness.

This study proposed a fast variational detection method for stripe noise based on interval sampling, aiming to improve the detection efficiency of multi-column strip noise during the pushbroom imaging of mainstream satellites. Based on the variational modeling of stripe noise components and the optimal solution, this method can quickly determine stripe noise components through interval sampling and establishing an estimation model of stripe noise components with interval sampling parameters. Then, this method can locate the stripe noise through the one-dimensional outlier detection and post-processing of the column mean values of stripe noise components. Owing to the interval sampling strategy, the method proposed in this study significantly improves the detection efficiency without impairing the stripe noise detection accuracy.

Deep semantic segmentation has been widely applied in land monitoring and interpretation based on remote sensing images. However, existing quality evaluation methods cannot reflect the preserved spatial geometric features of semantic segmentation results. Based on the practical demand for remote sensing image interpretation, surveying, and mapping, this study proposed a method for the performance evaluation of semantic segmentation models for remote sensing images considering geoscience features: the connectivity similarity index (CSIM). From the perspective of the connectivity similarity of surface feature spots in remote sensing images, the CSIM method embedded the surface features into the performance evaluation system of semantic segmentation models. The CSIM method allows for quantitatively evaluating the connectivity similarity of spots between the semantic segmentation results of remote sensing images and the actual sample labels, thus accurately describing the preserved spot integrity in the predicted classification results. Therefore, the CSIM method can objectively determine the applicability of a pre-training model to remote sensing image interpretation in surveying and mapping production. As substantiated by a lot of practice, the CSIM method can monitor and control the model training in real time, effectively guide the selection of the optimal pre-training model, and accurately evaluate the quality of remote sensing image interpretation results considering geoscience features. Therefore, the CSIM method is critical for deep-learning-enabled remote sensing image interpretation, surveying, and mapping.

Conventional land cover classification methods based on airborne multispectral light detection and ranging (MS-LiDAR) data have insufficient capability for the cooperative utilization of spatial-spectral information or too high dimensions of features in the joint utilization of various features. This study proposed a spatial-spectral joint segmentation algorithm for airborne MS-LiDAR point clouds based on the multivariate Gaussian mixture model (GMM). First, radiometric correction, anomaly removal, and data fusion were performed for the original multi-band independent point clouds, forming multispectral point clouds that presented spatial locations and their multi-band spectral information. Then, spatial-spectral feature vectors were constructed using the extracted multispectral and elevation features of laser points. Meanwhile, the unit and scale differences among different types of features were eliminated through feature normalization and discretization. Subsequently, a GMM was built to fit the multimodal distribution of objects in the spatial-spectral feature space. Accordingly, the response levels of laser points to various objects were obtained, and the classification of various objects was determined according to the principle of maximum responsiveness. Finally, a 3D majority voting method was designed to optimize the segmentation results. The effectiveness and feasibility of the proposed algorithm were verified through experiments based on surveyed Optech Titan MS-LiDAR data. The experimental results show that the multivariate GMM combined with multi-band intensity features and elevation features yielded an overall segmentation accuracy of 93.57% and a Kappa coefficient of 0.912. The results also indicate that the high-accuracy segmentation of MS-LiDAR point clouds can be achieved by only combining four-dimensional features. This study provides a new approach for comprehensively utilizing the multispectral and spatial information in MS-LiDAR data.

Acquiring the number of building floors can provide data support and decision-making services for urban safety and disaster hazards. The number is primarily acquired through manual investigation and statistics currently. Furthermore, the automatic inversion of building heights based on remote sensing images suffers from low algorithmic efficiency, incomplete extraction, and a low automation degree. To acquire the number of building floors quickly and extensively, this study designed an identification algorithm based on GF-7 satellite images. First, shadow lines were automatically extracted using the fishing net method based on preprocessing such as principal component analysis. Then, the building height was calculated based on the geometric relationship formed by the shadow, and the building height was then converted into the number of building floors. Finally, the error in the extraction results was corrected through support vector machine regression, aiming to eliminate the influence of the measurement error of the shadow length. With Chaoyang District in Beijing as the study area, this study conducted model training and testing of the identification algorithm. As shown by the experimental results with Zhengzhou City in Henan Province as the verification area, the overall identification accuracy was 90.21%, with an identification error of three floors at most for buildings with 6~50 floors. This study provides novel technical support and application service for automatically acquiring the number of building floors rapidly and extensively based on satellite data.

Consumer-grade unmanned aerial vehicles (UAVs) each have a single camera and high lens distortion. The accuracy of terrain modeling using UAVs is influenced by route design and control surveys. By designing different data collection schemes and Monte Carlo tests, this study investigated the influence of the camera’s tilt angle, flight height, and the number of ground control points (GCPs) on terrain modeling accuracy in three small river basins on the Loess Plateau. The results are as follows: ① Before the processing of UAV photogrammetry data, it is necessary to analyze the quality of GCPs through Monte Carlo tests to eliminate GCP errors. ② The effects of the tilt angles of cameras include: in the case of no available GCPs, tilt photogrammetry with tilt angles of cameras can both improve the overall accuracy of the sampling area and optimize the spatial distribution of errors, with these advantages related to the optimization of the camera distortion model; in the case of available GCPs, the camera tilt angle has minor influence on elevation accuracy but affects the saturation number of GCPs. Compared with vertical photogrammetry, tilt photogrammetry requires slightly more GCPs to achieve the optimal accuracy. ③ The effects of the flight height include: in the case of no available GCPs, tilt photogrammetry can reduce the sensitivity of elevation accuracy to flight height; in the case of available GCPs, flight heights of 60~160 m have no significant influence on elevation accuracy, and the change in flight height does not affect the saturation number of GCPs.

The thin cloud removal from remote sensing images with uneven thin cloud cover suffers from undercorrection or color distortion. This study proposed a high-fidelity end-to-end network method for thin cloud removal based on attentional feature fusion. First, this study designed an attentional feature fusion module integrating the attention mechanism and a fusion module. Through the cascade of three attentional feature fusion modules, the network focused on extracting the information on thin-cloud cover areas, reducing the impact of cloud-free areas. Furthermore, this study improved the color fidelity and detail clarity of images using the color and sharpening loss functions. The experimental results show that this method outperformed other methods in visual and quantitative evaluation indices (peak signal-to-noise ratio and structural similarity). This method yielded satisfactory effects of cloud removal in images with uneven thin cloud cover in various scenarios, producing images with actual colors, smooth brightness transition, and distinct detail contours.

Cloud cover tends to hinder information extraction from remote sensing images during image processing. However, complex and changeable surface backgrounds make it difficult to effectively extract the differences in features between cloud targets and backgrounds. Although existing methods exhibit satisfactory cloud detection effects under most backgrounds, they show significant misclassification and omission in some environments, failing to maintain encouraging performance due to poor stability and insufficient generalization ability. Given this, this study proposed a cloud detection method for multiple backgrounds. Based on Sentinel-2A data, this study analyzed the differences in spectral characteristics between cloud targets and backgrounds to assist in the selection of samples for detection. Based on this, this study introduced more effective detection indices HOT and CDI. Finally, this study obtained a random forest-based cloud detection model through training. Then, from the perspective of the influence of backgrounds and cloud target types on detection accuracy, this study compared the obtained cloud detection model with the Fmask algorithm using images with different backgrounds. The comparison results show that the method proposed in this study increased the overall accuracy and F1 score by 2.2% and 2.9%, respectively, with the standard deviations of them reducing by 29.6% and 72.5%, respectively. These findings indicated that this method can significantly improve the stability of cloud detection in different environments while maintaining high detection accuracy. Therefore, this method is effective in cloud detection in multi-backgrounds.

Change detection is the research focus of remote sensing. To overcome the shortcomings of the existing conditional random field (CRF)-based change detection, this study proposed a novel change detection method for synthetic aperture Radar (SAR) images based on an improved fully connected CRF (FCCRF). Firstly, this study summarized the comparative algorithms for generating differential images from SAR images, which were divided into three levels, namely pixel, neighborhood, and super-neighborhood. Then, this study selected three typical comparative algorithms-log ratio (LR), neighborhood ratio (NR), and improved non-local graph (INLG)-to produce three sets of complementary differential images. Finally, this study improved the FCCRF by extending the number of Gaussian kernels of the pairwise potential function of FCCRF and generated the change detection maps using the improved FCCRF model. The change detection method proposed in this study integrated the two-phase original SAR images, three sets of complementary differential images, and the global spatial information of images. In addition, this study presented a simple and effective parameter determination strategy, which allows the FCCRF to perform the change detection automatically. Experimental results on four sets of real SAR image data confirmed the effectiveness of the change detection method proposed in this study.

Surface subsidence caused by the exploitation of mineral resources must be considered during the development and utilization of land and space in mining areas. Furthermore, it serves as a significant indication of underground areas subjected to illicit mining. The exploitation of mineral resources is generally conducted in widespread, uneven, and dispersed areas, making it necessary to quickly and accurately identify and extract the spatial distribution of mining subsidence in large areas. This study determined the multitemporal differential interferometric phase diagram of mining areas using the differential interferometric synthetic aperture Radar (D-InSAR) technique. Furthermore, it trained networks for the intelligent identification of mining subsidence by employing deep-learning FCN-8s, PSPNet, Deeplabv3, and U-Net models. The results show that the U-Net model enjoys a high detection accuracy and a short detection time. To improve the semantic segmentation and extraction accuracy of information about mining subsidence, this study introduced the efficient channel attention (ECA) module into the conventional U-Net model during the training. Compared with the conventional model, the improved U-Net model increased the intersection over union (IOU) corresponding to mining subsidence by 2.54 percentage points.

As a principal instrument for observing and researching earthquake precursors, a borehole strain meter allows for point-based observation, high-accuracy observation, and the direct observation of shallow crustal stress-strain information. However, it fails to obtain information on spatial continuous deformation and is susceptible to interference by site environments such as pumping. The persistent scatterer interferometric synthetic aperture Radar (PS-InSAR) method can derive spatial continuous deformation fields and time-series deformation, with the cumulative deformation consistent with groundwater level changes. Using the PS-InSAR method, this study analyzed the land subsidence near the Huailai seismic station based on Sentinel-1 images, aiming to counteract surface deformation in the monitoring of borehole strain and to accurately analyze the anomalous information in the observational data. This study also investigated the mechanism of extension anomalies in the 2020—2021 observational data of the borehole strain. The results are as follows: The deformation center in the area near the Huailai seismic station was situated at the pumping well east of Yihebu Village. The time of the regional subsidence and that of the observed borehole strain anomalies were consistent with the pumping time of the pumping wells for heating in Yihebu Village. The extension anomalies in the borehole strain observational data of the Huailai seismic station shared consistent mechanisms with the surface changes caused by the pumping of the nearby pumping wells for heating. Therefore, the extension anomalies of borehole strain at the Huailai seismic station resulted from pumping the pumping wells for heating in Yihebu Village. This study shows that, in areas significantly affected by groundwater pumping, PS-InSAR plays a role of application demonstration in research on the anomaly mechanism through observation using observation instruments for earthquake precursors.

The leaf area index (LAI) is a critical parameter for the forest ecosystem. Improving the remote sensing estimation accuracy of the regional LAI of mountain forests at a low cost is of great significance for accurately determining the LAIs of forests and for further understanding the forest ecosystem. With spaceborne LiDAR ICESat-2/ATLAS data as a primary information source, this study investigated Shangri-La City in mountainous areas in southwestern China. Based on the remote sensing estimation model using random forest (RF) regression, RF hyperparameter optimization, and the data of 51 measured sample plots of LAI, this study analyzed the estimation effects of the model using accuracy evaluation indicators such as coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and median absolute error (MedAE). The results are as follows: The hyperparameter optimization of the RF regression model using a random surface search algorithm can significantly improve the estimation accuracy of LAI. The extracted characteristic parameters of ground spots showed high contribution and excellent effects in the LAI estimation of mountain forests. Therefore, they can be applied to the estimation of regional LAI of mountain forests. The RF regression model optimized using the random surface search algorithm yielded higher estimation accuracy. The estimation results were consistent with the forest distribution in the study area, indicating certain generality. Finally, this study determined that it is feasible to employ ICESat-2/ATLAS data products for LAI estimation, providing a reference for medium- to large-scale LAI estimation based on spaceborne LiDAR.

This study aims to determine the total nitrogen content (TNC) in soils in Tuoketuo County quickly and nondestructively, thus meeting the requirements of precision agriculture. With the soil TNC and hyperspectral data of 120 sampling sites in the study area as the data source, this study processed the hyperspectral data using the 1~2 orders fractional order differential (FOD) interval of 0.1 to screen the sensitive wavebands. Then, this study built 24 inversion models for soil TNC using the support vector machine (SVM) and the back propagation neural network (BPNN). The results are as follows: ① After FOD processing, the information at the wave crests and troughs of the spectra was amplified, and the reflectance of the remaining wavebands approached zero gradually with an increase in the decomposition scale. ② The Pearson correlation coefficient between original spectra and soil TNC was r = 0.61. This correlation coefficient was up to a maximum of 0.67 at 1.1- order after FOD processing, increasing by 0.06. ③ The BPNN prediction models outperformed the SVM prediction models. The optimal soil TNC prediction model was the BPNN model built after 1.1-order differential processing. This model yielded an R² of 0.75 and a root mean square error (RMSE) of 0.16 for the modeling set and an R² of 0.71 and an RMSE of 0.16 for the verification set, with a relative percent deviation (RPD) of 2.06. This model produced effective inversion results of the soil TNC in the study area, with a much higher accuracy than the BPNN model built using original spectra. Therefore, the BPNN model built using hyperspectral data through 1.1-order differential processing allows for the inversion-based prediction of soil TNC in the study area, providing a theoretical reference and technical support for local precision agriculture.

By introducing cultural, educational, and scientific research indices and optimizing gas regulation indices, this study proposed an index system to assess the gross ecosystem product (GEP) of natural resources in Hunan Province based on the existing theories and methods for ecosystem service. This study then built a grid-based GEP model with a grid scale of 30 m × 30 m to analyze the spatio-temporal evolution of the GEP of Hunan in 2000, 2010, and 2020. The results are as follows: ① In the temporal dimension, the GEP of the province increased by 3.34×10⁴ billion yuan over the past 20 years, with increased amplitude of 40.28%. The contribution of all ecosystems to the GEP was in the order of forest > farmland > grassland > wetland > city. ② In the spatial dimension, the GEP exhibited high values in western and southeastern regions and low values in central and northern regions. The GEP growth rate was higher in the Wulingshan area of western Hunan and lower in the Dongtinghu area of northern Hunan. ③ Compared with the existing research results on a grid scale of 10 km×10 km, the GEP results with a high spatial resolution showed more details of the spatial distribution of the aquatic and urban ecosystems. ④ The contribution degrees of ecosystem function values to the GEP of Hunan changed regularly over time. The contribution of the soil conservation function value dominated the changes in the GEP of the Wulingshan and Dongtinghu areas. The results of this study provide a basis for scientific decision-making in supervising natural resources and protecting the ecological environment in Hunan Province.

Economic development is frequently accompanied by the decreased quality and dysfunctional service capacity of the ecological environment. Clarifying the relationship and spatial differences between ecology and economy is a prerequisite for sustainable regional development. Based on the remote sensing images and socio-economic data of the Dongting Lake area in 2000, 2010, and 2020, this study calculated the ecosystem service values using the value equivalent method. Furthermore, it determined the coordination and consistency indices of ecosystem services and economy in 24 districts and counties in the Dongting Lake area. Then, this study comprehensively explored the relationship between ecological services and the economy, as well as their spatial aggregation, proposing targeted optimization measures for coordinating and balancing regional ecological services and economic development. The results are as follows: ① The total ecosystem service value of the Dongting Lake area decreased from 261.541 billion yuan in 2000 to 255.646 billion yuan in 2020. The spatial distribution of ecosystem service values presented a circular layer pattern, with high values occurring in the center, followed by the peripheral mountains, while the lowest values present in adjacent hills and plains. ② As for individual ecological service values, hydrological regulation contributed the most significantly, while the nutrient cycle maintenance contributed slightly. Additionally, the values of ecological services, except for biodiversity and aesthetic landscape, decreased to varying degrees during the study period. Among them, the hydrological regulation presented the most significant reduction in the ecological service values, accounting for more than 70% of the total reduction. ③ Regarding the spatio-temporal variations in the ecological services and economic development, the Dongting Lake area showed relatively stable ecological differences and expanded economic gaps. Districts and counties in the Dongting Lake area showed high coordination degrees but low consistency levels between ecology and economy. The ecological and economic spatial aggregations exhibited significant differences, with dominant ecological aggregation zones mainly distributed within and along Dongting Lake. ④ The critical driving factors in the spatial differences in ecological service values include human disturbance, elevation, slope, temperature, and precipitation. Therefore, to promote the harmonious development of ecology and economy in the Dongting Lake area and weaken the gap between both, it is necessary to propose feasible measures for strictly protecting the natural basement, strengthening human-guided utilization, and promoting the transformation from ecological resources into economic products.

Cities are core areas for human life and production. The ecological environment quality is a growing concern in cities, especially cities with fragile ecological environments in arid regions. This study selected 2 study areas from two typical oasis cities, namely Urumqi City in northern Xinjiang and Kashgar City in southern Xinjiang. It compared the spatio-temporal changes in the ecological environment quality of the two study areas in 2000, 2010, and 2020 using two urban remote sensing-based ecological indices (RSEIs) constructed based on the Google Earth Engine (GEE). Furthermore, it quantitatively analyzed the factors influencing the RESIs of the two cities using the random forest model. The results are as follows: ① Over the past 20 years, the ecological environment quality in study area 1 worsened but that in study area 2 improved overall. In study area 1, the ecological environment improved mainly in the old urban area and deteriorated in the newly built area at the periphery of the urban area. In study area 2, the ecological environment significantly improved in the northeastern part and deteriorated in the newly built area around the city center. ② The fractional vegetation cover is the most critical factor influencing RESIs of both study areas, followed by temperature and precipitation. These influencing factors had different influences on the RSEIs of the two study areas. ③ The primary reasons for the deterioration of the ecological environment in study area 1 included the expanded urban scale, the increased impervious surfaces, and the decreased fractional vegetation cover in the past 20 years are. In contrast, urbanization and green and healthy urban development pattern jointly played a significant role in improving the ecological environment quality in study area 2. The results of this study can provide a scientific basis for healthy urban development in both study areas.

The western Sichuan Plateau, with a fragile and sensitive ecological environment, acts as a critical ecological barrier between the Qinghai-Tibet Plateau and the Sichuan Basin. Research on the dynamic changes in the normalized difference vegetation index (NDVI) and their driving factors holds practical significance for monitoring the ecological environment quality of the western Sichuan Plateau. Based on 2001—2021 MODIS NDVI data, as well as meteorological data, surface factor data, and human activity data, this study analyzed the NDVI distribution of vegetation in the western Sichuan Plateau on a spatio-temporal scale using trend analysis, Hurst index, and geographical detector. Furthermore, this study determined the principal driving factors in NDVI changes. The results are as follows: During 2001—2021, the NDVI of 67.19% of regional vegetation in the western Sichuan Plateau showed a fluctuating upward trend. Elevation is the most critical factor influencing NDVIs, with an explanatory power of 0.529. The elevation is followed by accumulated temperature ≥0 ℃ and air temperature. The driving factors in interactions among NDVI exhibited nonlinear or double-factor enhancement, with q values between relative humidity and elevation being highest (0.623). 84% of factor combinations showed significantly different effects on the spatial NDVI distribution in the western Sichuan Plateau. The results of this study facilitate the research on the driving mechanism of vegetation growth, providing a reference for vegetation protection in the western Sichuan Plateau.

River levels serve as a critical parameter for understanding the changes in water cycles and water resources. An advanced Radar altimeter is a favorable tool for extracting the changes in river levels. This study aims to verify the ability of the Sentinel-3A/SRAL Radar altimeter to monitor river levels and improve the extraction accuracy of this Radar altimeter. With the main streams in the middle and lower reaches of the Yangtze River as the study area, this study conducted waveform retracking for the Sentinel-3A/SRAL L2 data using the center-of-gravity offset method, the primary peak threshold retracking algorithm (thresholds: 50% and 80%), the primary waveform centroid retracking algorithm, and the multiple-echo peak consistency retracking algorithm. Then, this study extracted the river levels during 2016—2021 in the study area and obtained the optimal retracking algorithm by comparing the accuracy of different algorithms. Based on the optimal retracking algorithm, this study extracted the water level changes in transit areas of 12 satellite orbits to analyze the water level change patterns. The results show that the center-of-gravity offset method is the optimal retracking algorithm for extracting river levels with the highest accuracy. Compared with the measured water levels, the water levels simulated using the center-of-gravity offset method exhibited the highest correlation coefficient (up to 0.968) and the smallest root mean square error (up to 0.680 m). During 2016—2021, the water levels in the study area generally showed an upward trend, with significant intra-annual seasonal changes.

This study investigated the spatial heterogeneity of the correlation between water quality and land use in the Chenjiang River basin of Chenzhou City using the water quality and high-resolution remote sensing data of the river basin in September 2021. Based on the division of river basin buffer zones by different types and scales, this study calculated land use components and their landscape configurations, such as the shape, size, and distribution of patches. Then, this study analyzed the spatial heterogeneity effect of regional land use on water quality through single-factor and multi-factor correlation analyses. The results are as follows: ① In the single-factor correlation analysis of spatial heterogeneity between water quality and land use, circular buffer zones showed higher universality than riparian buffer zones, which played a complementary role in analyzing the effect of built-up land occupation on water quality parameters. In the multi-factor correlation analysis of spatial heterogeneity, the land use presented generally higher interpretation rates for water quality parameters in riparian buffer zones than in circular buffer zones, with the maximum total interpretation rate of land use for water quality parameters in circular buffer zones occurring on a scale of a radius of 700 m. ② Different land use components had different effects on water quality. The proportions of cultivated land, grassland, forest land, and water areas correlated positively with water quality, while those of building land and bare land correlated negatively with water quality. Among them, cultivated land, grassland, and building land had the most significant effects on water quality. Land use planning should consider the influence of land use components and reasonably allocate the proportions of artificial and natural features. ③ Different landscape configurations had different effects on water quality. The landscape shape index (LSI) and the largest patch index (LPI), which reflect the shape and size of patches, had negative effects on water quality. Furthermore, the interspersion and juxtaposition index (IJI), which reflects the distribution of patches, correlated negatively with the ammonia nitrogen (NH3-N) content in water bodies on a small scale. Land use planning should consider the rationality of landscape configurations. For example, it is necessary to control the sizes of dominant land patches in the buffer zones. This study identified the buffer zone size that influences water quality the most and the land use components and landscape configurations that can explain the water quality of the Chenjiang River the best. Therefore, this study provides a basis for selecting scientific control measures for the deterioration of water quality in the Chenjiang River, showing certain significance for water environment protection.

This study constructed a monthly soil moisture content dataset of Henan Province during 1948—2021 by combining 1948—2014 GLDAS_2.0 data and 2000—2021 GLDAS_2.1 data. Through the Mann-Kendall (M-K) trend analysis, mutation test, wavelet analysis, and cross-correlation analysis, this study revealed the spatio-temporal distribution and critical influencing factors of soil moisture content in Henan Province. The results show that compared with the monthly soil moisture content data provided by GLDAS_2.1, the reconstructed monthly soil moisture content data during 2000—2014 showed an average deviation, average absolute error, and root mean square error of 2.09 mm, 13.01 mm, and 18.26 mm, respectively, indicating reliable data. According to the constructed soil moisture content data, the soil moisture content decreased at a rate of 0.301 0 mm/a during 1948—2021, with change rates of -0.236 8 mm/a in spring, -0.085 5 mm/a in summer, -0.380 5 mm/a in autumn, and -0.240 3 mm/a in winter in Henan Province. Spatially, the soil moisture content decreased from south to north, highly consistent with precipitation and evapotranspiration. The soil moisture content was 2.63 mm/cm in the vertical direction. The wavelet and cross-correlation analyses show that precipitation is a critical factor influencing soil moisture content. This study revealed the long-time-series spatio-temporal distribution of soil moisture content in Henan Province, providing a basis for the scientific management of surface water resources in Henan Province.

Research on the spatio-temporal evolution of urbanization is significant for optimizing the spatial structure of a city. Through the saturation and consistency correction of DMSP/OLS and NPP/VIIRS data, this study constructed a remote sensing dataset of night light in the Loess Plateau from 2000 to 2019. Then, this study calculated the compound night light index (CNLI) on different spatial scales and extracted the area of build-up areas in the Loess Plateau using a dichotomy method. Furthermore, this study analyzed the spatial evolution patterns using methods such as standard deviational ellipse (SDE). The results are as follows: ① The CNLI calculated from night light data correlated strongly with the urbanization development index (UDI) and various sub-indicators. ② The CNLI values of the Loess Plateau and five urban agglomerations showed significant upward trends during 2000—2019 and spatial downward trends from southeast to northwest. ③ The area of build-up areas in the Loess Plateau extracted using the dichotomy method had mean absolute and relative errors of 2.45 km² and 3.72%, respectively. ④ The focus of the built-up areas in the Loess Plateau showed a southeastward shifting trend during 2000—2019, with the SDE-covered area decreasing significantly (slope = 0.0107 km²/a; p < 0.01) and the azimuth angle changing from northeastern 83.33° to 88.37°. The results of this study can provide data support and a methodological reference for investigating the spatio-temporal patterns of urbanization in the Loess Plateau and other ecologically vulnerable areas.

As an integral component of the urban ecosystem, water bodies hold considerable ecological significance for mitigating the urban heat island effect and the thermal environment of human habitat. With multi-temporal Landsat and SPOT data as experimental data, this study proposed a method for determining surface emissivity for mixed pixels based on the principle behind the construction of the support vector machine (SVM) optimal endmember subset. Then, this study employed the surface emissivity determination method to analyze the coupling relationship of the water bodies and surrounding land of the Bahe River with the surface temperature using a mono-window algorithm. The results are as follows: ① The SVM optimal endmember subset construction method for mixed pixels yielded an error of surface emissivity less than 0.005 (R = 0.832) relative to the MODIS LSE product. This result indicates that the method has high accuracy and thus can be used to extract surface emissivity. ② Over the past 27 years, the land types and local surface temperature patterns on both sides of the Bahe River have changed significantly, with a sharp increase in construction land and a significant warming trend. The effects of land use types surrounding the Bahe River on surface temperature varied in different periods, with construction land, grassland, water bodies, and forest land being the principal land use types affecting the thermal environment on both sides of the Bahe River. The cooling effects of water bodies, forest land, grassland, and cultivated land are in the order of water bodies > forest land > grassland > cultivated land. ③ The effects of land use types on both sides of the Bahe River on local temperatures exhibited spatial differences during the same period. To the east of the Bahe River, the water bodies, forest land, grassland, and cultivated land show significant cooling effects. In contrast, to the west of the river, only water bodies, forest land, and grassland showed significant cooling effects. This study contributes to the proper understanding of the influence of urban rivers on the local thermal environment, providing a scientific reference for mitigating the local thermal environment of urban rivers and their surrounding areas.

Different moderate-resolution remote sensing satellites exhibit various effects in extracting rocky desertification information using the pixel unmixing method. Comparing these various effects can help further improve the extraction accuracy of rocky desertification information. By extracting information on rocky desertification in Puding County, Guizhou Province from GF-6 and Landsat8 using the pixel unmixing method, this study investigated the end member characteristics and desertification grade differences between GF-6 and Landsat8. Furthermore, this study explored the feasibility and effectiveness of GF-6 in extracting rocky desertification information. The results are as follows: ① Within the red-edge band of GF-6 data, the vegetation end member exhibited significantly different spectrum curves from bedrock and soil end members, making it easier to identify the vegetation end member. ② In terms of end member extraction accuracy of rocky desertification information, GF-6 and Landsat8 yielded overall accuracy (OA) of 0.63 and 0.45 in extracting the vegetation end member, respectively, corresponding to Kappa coefficients of 0.50 and 0.29 and RMSEs of 1.19 and 1.71, respectively. Moreover, GF-6 and Landsat8 yielded OA of 0.79 and 0.61 in extracting the bedrock end member, respectively, corresponding to Kappa coefficients of 0.63 and 0.42 and RMSEs of 0.54 and 0.88, respectively. ③ In the evaluation of rocky desertification grades, GF-6 and Landsat8 yielded OA of 0.76 and 0.59 in extracting rocky desertification grades, respectively, corresponding to Kappa coefficients of 0.56 and 0.38 and RMSEs of 0.64 and 1.27. Therefore, GF-6 outperforms Landsat8 in the accuracy of extracting rocky desertification information using the pixel unmixing method. In addition, the red-edge band of GF-6 data can effectively identify the vegetation information in areas with rocky desertification. In summary, the pixel unmixing method based on GF-6 data can be practically applied to rocky desertification monitoring.

Evaluating the remediation effect of heavy metal pollution in mines properly and rapidly holds considerable significance for ecological restoration and rehabilitation of mines. Based on the field-measured vegetation spectra, this study analyzed the typical spectral features of the main vegetation in the Dexing copper mining area. According to the heavy metal content in the leaves of vegetation tested in the laboratory, this study analyzed the relationship between heavy metal content and red edge position-a spectral characteristic parameter. This study calculated the red edge position of the vegetation in 2003 and 2009 using 2-scene Hyperion hyperspectral data, inferring the heavy metal enrichment in the vegetation of the mining area. Furthermore, this study evaluated the remediation effect of heavy metal pollution in the mining area. The results show that satisfactory results have been achieved from the remediation of heavy metal pollution around mine tailings nos. 1 and 2 in typical reclamation areas. Compared with 2003, 2009 witnessed generally satisfactory remediation effects of heavy metal pollution, with most areas being remedied and some newly polluted areas requiring remediation. The method proposed in this study can achieve a quick and reasonable evaluation of the remediation effect of large-scale heavy metal pollution in mining areas.

In the Pearl River Delta (PRD) region, widespread surface water and vegetation are liable to cause interferometric synthetic aperture Radar (InSAR) interference decoherence, and the cloudy, foggy, rainy, and humid climates frequently cause severe atmospheric delay noise in InSAR data. Accordingly, targeting the Longgang District of Shenzhen City in the southeastern PRD, this study generated the connection graph of interference image pairs using the small baseline subset and InSAR (SBAS InSAR) technique based on interference coherence optimization. This study also obtained the surface deformation information of Longgang District from September 2019 to November 2020 based on 35 scenes of Sentinel-1A images. It then compared the surface deformation information with the inversion results obtained using the persistent scatterer InSAR (PS InSAR) technique. Finally, this study deduced the causes of surface deformation. The results are as follows: ① The inversion results of SBAS InSAR and PS InSAR yielded almost the same surface deformation fields. SBAS InSAR exhibited a much higher coherent point density than PS InSAR in the region with high-amplitude deformation. This indicates that the SBAS InSAR based on the optimal interference coherence can yield accurate and reliable inversion results, enjoying more advantages in the inversion for a complete deformation field. ② The primary causes of surface deformation in Longgang District and its surrounding areas include unstable Karst collapse or slope triggered by continuous heavy rainfall, the changes in the underground hydrogeological environment caused by industrial mining and drainage, the subsidence of mining gob induced by underground construction, and static foundation load imposed by new high-rise buildings. The technical route of this study can provide a reference for the automation and engineering application of InSAR in the early identification of geological hazards in the PRD region.

This study aims to investigate the application of high-resolution remote sensing images in the supervision of river and lake sand mining in the Dongting Lake area. Based on the aerial and space high-resolution remote sensing images over the past 20 years, as well as human-computer interaction interpretation and field investigation verification, this study summarized the types and meanings of surface elements in river and lake sand mining, established the remote sensing interpretation symbols for river and lake sand mining, and analyzed representative typical cases. The results show that the interpretation symbols of remote sensing images for river and lake sand mining differ from those for onshore mining summarized previously. The river and lake sand mining was carried out using dredges as the mining equipment, sand carriers as the transport equipment, and sand yards and docks as transfer sites. The mining surfaces caused serrated bank lines during sandbar digging. Furthermore, surface cover changed near mining areas, including turbid water and shrinkage of sandbars and shoals. This study analyzed three typical cases, namely the evolution of the Hualong sand yard, the treatment of the Chenglingji wharf, and the illegal sand mining in Piaoweizhou of the eastern Dongting Lake. The analytical results indicate that high-resolution remote sensing can provide technical support for supervising river and lake sand mining.

Southwestern China suffers frequent geological disasters. The exploitation of mineral resources in southwestern China is highly liable to induce geological disasters and related secondary disasters. This study investigated the remote sensing-based dynamic monitoring technology for coal mine collapse areas in the coal mining concentration areas in Liupanshui City, Guizhou Province. Based on the high-resolution remote sensing images, this study established remote sensing geological interpretation symbols of coal mine collapse areas in the mountainous plateau of southwestern Guizhou and then dynamically monitored the geological disasters in Liupanshui from 2009 to 2018. Moreover, this study analyzed the present geological disasters in the study area. The remote sensing interpretation revealed that geological disasters in the study area were significantly aggravated over the years. Compared with 2009, 2018 witnessed an increase of 167% in the geological disasters, including 40% of new geological disaster areas and 34% of areas with deteriorated geological disasters. According to the geological disaster degrees in the study area, this study identified four geological disaster concentration areas, which were highly consistent with the mining concentration areas in the study area. Based on the remote sensing data, this study analyzed the types of land damaged by geological disasters in mines and investigated possible resulting damage to the people and the ecological environment in the study area. The results show that disasters that severely damaged land caused the largest damage area for forest and cultivated lands, which had a total number of 193 and a total area of about 333.55 hm². There are 360 areas with potential hazards in the study area, covering an area of 506.36 hm². They are dominated by 126 threats to roads, which cover an area of 110.04 hm². The results of this study can provide a reliable data reference and a critical research approach for restoring the local ecological environment and controlling geological disasters in mines. Moreover, based on the characteristics of the study area, this study further analyzed the causes of the geological disasters in mines, explored the geological disaster control schemes, and proposed countermeasures and suggestions.