In the context of achieving peak carbon dioxide emissions and carbon neutrality, conducting a remote sensing-based ecological assessment and monitoring analysis is greatly significant for ascertaining the ecological condition in time and formulating scientific and reasonable ecological protection policies. The early remote sensing-based ecological assessment indices, simple and involving complex processes, are difficult to find wide applications. In contrast, the remote sensing ecological index (RSEI), contributing to elevated assessment efficiency, has been extensively used. To gain a deeper understanding of RSEI, this study describes its background, calculation method, and research status and provides a summary of the current issues and regional adjustments. Furthermore, it analyzes the main application directions of RSEI, namely the in-depth analyses of regional ecological assessment and change monitoring. Finally, the study proposes that despite a broad space for RSEI development, it is necessary to conduct research into the spatiotemporal scales of images, storage and batch processing capabilities, model adaption, and intelligentization.

Timely and accurate detection and statistical analysis of the spatial distributions and time-series variations of water bodies like rivers and lakes holds critical significance and application value. It has become a significant interest in current remote sensing surface observation research. Conventional water body extraction methods rely on empirically designed index models for threshold-based segmentation or classification of water bodies. They are susceptible to shadows of surface features like vegetation and buildings, and physicochemical characteristics like sediment content and saline-alkali concentration in water bodies, thus failing to maintain robustness under different spatio-temporal scales. With the rapid acquisition of massive multi-source and multi-resolution remote sensing images, deep learning algorithms have gradually exhibited prominent advantages in water body extraction, garnering considerable attention both domestically and internationally. Thanks to the powerful learning abilities and flexible convolutional structure design schemes of deep neural network models, researchers have successively proposed various models and learning strategies to enhance the robustness and accuracy of water body extraction. However, there lacks a comprehensive review and problem analysis of research advances in this regard. Therefore, this study summarized the relevant research results published domestically and internationally in recent years, especially the advantages, limitations, and existing problems of different algorithms in the water body extraction from remote sensing images. Moreover, this study proposed suggestions and prospects for the advancement of deep learning-based methods for extracting water bodies from remote sensing images.

Since the availability of global runoff data decrease year by year, the inversion algorithms, as substitutes for the river discharge measured at hydrological stations, have become increasingly important. With the continuous development of satellite remote sensing technology, the methods for estimating river discharge have increased in number. This study systematically summarized the remote sensing-based inversion methods for river discharge, as well as the inversion methods for hydraulic remote sensing elements that are closely related to the estimation of river discharge and the progress made in them. Moreover, this study reviewed the methods, principles, and application status of two types of algorithms based on hydrological models and empirical regression equations and summarized the applicable conditions and shortcomings of different methods. Finally, this study predicted the worldwide development trends of the river discharge inversion based on the satellite remote sensing technology, including ① actively developing the advanced data assimilation technology for satellite remote sensing data; ② integrating new sensor products; ③ optimizing and innovating algorithms.

Soil moisture is closely associated with global climate change, the carbon cycle, and the water cycle, as well as agricultural production and ecological conservation and restoration. The detection of soil moisture has shifted from ground survey to remote sensing detection, achieving global- and regional-scale survey and monitoring. Given differences in data spectrum segments, radiative transfer mechanisms, and inversion algorithms, it is necessary to comprehensively analyze the mechanisms, advantages, and limitations of algorithms, with the purpose of laying a foundation for accuracy and algorithm improvement. From the aspects of optical remote sensing, microwave remote sensing, and optic-microwave cooperation, this study systematically analyzed the features and challenges of the following inversion techniques: inversion based on the T_s-VI spatial and T_s-NSSR temporal characteristics of optical remote sensing data, inversion using passive and active microwave data, joint inversion using active and passive microwave data and remote sensing data, and optical-microwave cooperative inversion based on accuracy improvement and spatio-temporal transformation. At present, the joint inversion of soil moisture using multi-source remote sensing data faces the following challenges: ① The data suffer missing and spatio-temporal mismatching; ② Different data sources exhibit varying degrees of surface penetration; ③ The joint inversion model relies on empirical parameters and numerous auxiliary parameters. These challenges can be addressed with the improvement in the satellite monitoring network, the increase in the surface detection depths of data sources, the clarification of the physical mechanisms of joint inversion, and the establishment of spatio-temporal continuous datasets of auxiliary parameters.

The vegetation optical depth (VOD) serves as a microwave-based method for estimating vegetation water content and biomass. Compared to optical remote sensing, the satellite-based VOD, exhibiting a lower sensitivity to atmospheric disturbances, can measure the characteristics and information of vegetation in various aspects, thus providing an independent and complementary data source for global vegetation monitoring. It has been extensively applied to investigate the effects of global climate and environmental changes on vegetation. Discerning the research advances of VOD application in the dynamic monitoring of global vegetation is critical for VOD’s further development and application. Hence, this study first presented the primary methods for obtaining the VOD through inversion of passive and active microwave data, comparatively analyzing the principal characteristics of various sensor VOD products. Then, this study generalized the current research advances of VOD in the dynamic monitoring of vegetation in terms of vegetation characteristic monitoring (like vegetation water content and biomass), carbon balance analysis, drought monitoring, and phenological analysis. Finally, this study expounded the advantages, limitations, and improvement approaches of VOD products, envisioning the application prospect of VOD in the dynamic monitoring of vegetation.

The continuous monitoring of the dynamic changes in coastlines is crucial to ascertaining the change patterns and evolution characteristics of coastlines. Long-time-series coastline datasets allow for the detailed description of the dynamic changes in coastlines from the spatio-temporal dimensions and further reflect the effects of human activities and natural factors on coastal areas. Therefore, they are conducive to the scientific management and sustainable development of the spatial resources in coastal wetlands. Based on the Google Earth Engine (GEE), this study analyzed the change in the coastline of Hangzhou Bay during 1990—2019 based on long-time-series Landsat TM/ETM+/OLI images. Using the pixel-level modified normalized difference water index (MNDWI) time series reconstruction technology, this study achieved the automatic information extraction of long-time-series coastlines and the analysis of spatio-temporal changes by combining the Otsu algorithm threshold segmentation and the Digital Shoreline Analysis System. The results show that the total coastline length of Hangzhou Bay increased by about 20.69 km during 1990—2019, corresponding to an increase in the land area by about 764.81 km², with an average annual increase rate of 0.35%. In addition, the average end point rate (EPR) and linear regression rate (LRR) of the coastline were 110.07 m/a and 119.06 m/a, respectively. The analysis of the spatio-temporal evolution of the coastline in Hangzhou Bay over 30 years will provide a basis for the sustainable development and comprehensive management of resources along the coastline in Hangzhou Bay.

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.

With the trend towards the precise and digital planting management of orchards, apple cultivation relies more heavily on the planting management supporting technologies of orchards. In recent years, continuous breakthroughs made in spatial resolution and revisiting frequency have made remote sensing technology a major supporting technology for the precise planting management of apple orchards. However, there is an absence of reviews of the application status and prospect of this technology in the planting management of orchards. Based on the analysis of primary applications of remote sensing technology in the precise planting management of apple orchards, this study classified the applications into three major categories, namely the surveys of basic orchard information, inversions of orchard parameters, and the planting management support of orchards. Furthermore, this study reviewed the methods and performance of the applications of remote sensing technology in various fields and explored the application potential. Finally, it identified three types of problems with current research and application of remote sensing technology, namely insufficient studies on mechanisms and in some application fields, low-degree integration of multiple technologies, and the lack of large-scale application models. In addition, this study proposed four hot research and application topics in the future, namely models used to simulate the growth mechanisms of apple trees, the integrated support system for the planting management of apple trees, the single-tree monitoring based on satellite data, and the diversified services of remote sensing-based monitoring products.

The remote sensing-based feature extraction of opencast mining areas is a hot topic in research on the monitoring of mining activities. However, there is a lack of systematic reviews and summaries of relevant studies. Therefore, this study first defined the features of an opencast mining area, divided the feature extraction into single- and multi-feature extractions according to feature types, and briefly described the differences between the feature extraction of opencast mining areas and general surface feature extraction and land use classification. Then, this study briefly summarized the sources and data processing platforms of remote sensing images available in relevant studies. Subsequently, this study divided the remote sensing-based methods for the feature extraction of opencast mining areas into three categories, namely visual interpretation, traditional feature-based approach, and deep learning. Then, it summarized the research status of these methods and analyzed their advantages, disadvantages, and applicability. Finally, this study proposed the future research direction of the remote sensing-based feature extraction of opencast mining areas, holding that the future developmental trend is to further promote the intelligent, fine-scale, and robust feature extraction of mining areas by effectively utilizing multi-source and multi-temporal data, networks with a stronger feature extraction capacity, and methods for the optimization of complex scenes. The results of this study can be used as a reference for the study and application of remote sensing-based feature extraction of opencast mining areas.

The Beijing Daxing International Airport, located in the Yufa—Lixian area of Daxing District, is one of Beijing’s five major land subsidence areas. Differential deformations pose risks to the airport’s safe and stable operation. By applying the time-series interferometric synthetic aperture Radar (InSAR) technique, this study obtained the spatio-temporal characteristics of the airport’s deformations from 39 scenes of high-resolution COSMO-SkyMed (CSK) SAR images taken from September 2019 to November 2021. The monitoring results, with high accuracy, are roughly consistent with level monitoring results. Findings indicate that the airport’s subsidence lasted from 2019 to 2021, with the highest subsidence rate measured at -47.5 mm/a and a maximum cumulative subsidence amount of -103.84 mm. Notably, all four runways exhibited varying degrees of differential subsidence. Furthermore, this study delved into the spatio-temporal characteristics of deformations in the runways, as well as deformations in other high-deformation zones such as terminal buildings, maintenance aprons, oil tank areas, and the business jet apron. By combining the foundation treatment, this study analyzed the factors influencing the airport’s subsidence, providing a reference for the airport’s safe and stable operation.

To obtain the fundamental data of mine environments objectively, this study monitored the damaged mining land and the ecological restoration land in abandoned open-pit mines in China by combining remote sensing data with multi-source data, computer automated information extraction with human-computer interactive interpretation, and comprehensive laboratory research with field investigation. The remote sensing monitoring in 2022 shows that the mining land of abandoned open-pit mines in China covered an area of 82.74×10⁴ hm², representing 0.86‰ of the national land area, primarily distributed in Inner Mongolia and Xinjiang Uygur autonomous regions as well as Hebei, Shandong, and Heilongjiang provinces. Among them, the damaged mining land and the ecological restoration land accounted for 50.74×10⁴ hm² and 32.00×10⁴ hm², respectively, with an ecological restoration rate of 38.68%. The mining land of abandoned open-pit mines occupied primary farmland of 2.63×10⁴ hm², representing 3.18% of the total mining area. The mining land of nationwide abandoned open-pit mines within the ecological red line accounted for 8.09×10⁴ hm², representing 9.77% of the total mining area. The mining land of nationwide abandoned open-pit mines, coinciding with the result of the third national land resource survey (mining land), totaled 30.13×10⁴ hm², representing 36.42% of the total mining area. This study preliminarily analyzed the present situation and existing problems of remote sensing work involving the mining land of nationwide abandoned open-pit mines, the occupation of primary farmland, the mining land of such mines within the ecological red line, and corresponding environmental restoration and governance. Finally, this study proposed countermeasures and suggestions in this regard.

Understanding the spatiotemporal characteristics of vegetation growth in the Yellow River basin and their influencing factors is crucial for the conservation and development of the ecology. However, existing studies rarely focus on the latest spatiotemporal characteristics of different vegetation types in the basin and their relationships with their influencing factors. Using the 2000-2020 time series remote sensing data of MODIS normalized difference vegetation index (NDVI), along with methods including trend analysis, correlation analysis, partial correlation analysis, and residual analysis, this study investigated the spatiotemporal characteristics of various vegetation types in the Yellow River basin. Accordingly, this study clarified the mechanisms behind the impacts of temperature and precipitation on annual and monthly scales and explored the influence of human activities on the spatiotemporal characteristics of different vegetation types. The results indicate that from 2000 to 2020, the NDVI of different vegetation types in the Yellow River basin trended upward overall, particularly in cultivated land and forest land. However, the increasing trends trended downward at different degrees with increasing elevation. Over the 21 years, various vegetation types were improved in most areas in the basin. However, a few areas exhibited degraded vegetation types, primarily including grassland and cultivated land. The proportion of areas with anti-continuous future trends in various vegetation types notably increased. Temperature and precipitation produced positive impacts on the growth of various vegetation types in the Yellow River basin. Nevertheless, various vegetation types exhibited greater responses to precipitation than to temperature, and the responses featured notable time lags. Furthermore, grassland and shrub growth were more sensitive to precipitation and temperature. Human activities had positive impacts on the vegetation of the Yellow River basin overall. However, some negative effects were also observed in grassland and cultivated land, warranting attention in future planning. Overall, most areas exhibited improved vegetation in the Yellow River basin in the 20 years. Given that partial grassland and cultivated land experienced degradation, it is necessary to protect typical degradation areas. The findings of this study will provide scientific data and theoretical support for ecological construction and economic development in the Yellow River basin.

Inland surface water bodies, including rivers, lakes, and reservoirs, are significant freshwater resources for human beings and ecology, and their monitoring and control are greatly significant. Optical remote sensing provides great convenience for the monitoring of surface water resources, proving to be an important means for the information extraction and dynamic monitoring of inland surface water bodies. This study reviews the basic principles, remote sensing data sources, methods, existing issues, and prospects of the information extraction of water bodies. Owing to the unique characteristics of the remote sensing images of inland surface water bodies, their information can be extracted in an accurate, scientific, and effective manner using remote sensing. Multiple remote sensing data resources can be applied to the information extraction, and the optical remote sensing-based extraction methods include the threshold value method, classifier method, object orientation method, and deep learning method. Given that different methods have unique advantages, disadvantages, and applicable conditions, selecting appropriate multi-source data and varying methods based on the conditions of study areas tend to improve the information extraction accuracy. Nevertheless, there still exist some issues in the optical remote sensing-based water body information extraction, such as the balance of spatiotemporal resolution of remote sensing data, the information mining of water body characteristics, the generalization ability of water body models, and the uniformity of criteria for accuracy evaluation.

Cloud detection, as a crucial step in preprocessing optical satellite images, plays a significant role in the subsequent application analysis. The increasingly enriched optical satellite remote sensing images pose a challenge in achieving quick cloud detection of numerous multi-source satellite remote sensing images. Given that conventional cloud detection exhibits low accuracy and limited universality, this study proposed a multi-scale feature fusion neural network model, i.e., the multi-source remote sensing cloud detection network (MCDNet). The MCDNet comprises a U-shaped architecture and a lightweight backbone network, and its decoder integrates multi-scale feature fusion and a channel attention mechanism to enhance model performance. The MCDNet model was trained using tens of thousands of globally distributed multi-source satellite images, covering commonly used satellite data like Google and Landsat data and domestic satellite data like GF-1, GF-2, and GF-5 data. Several classic semantic segmentation models were used for comparison with the MCDNet model in the experiment. The experimental results indicate that the MCDNet model exhibited superior performance in cloud detection, achieving detection accuracy of over 90% for all types of satellite data. Additionally, the MCDNet model was tested on the Sentinel data that were not used in training, yielding satisfactory cloud detection effects. This demonstrates the MCDNet model’s robustness and potential for use as a general model for cloud detection of medium- to high-resolution satellite images.

Quantitative post-flood recovery assessment based on vegetation and lighting indices is critical for assessing economic reconstruction and ecological restoration in afflicted areas. This study investigated the “7.20” rainstorm disaster area in Henan. Based on the daily and monthly NPP-VIIRS data, Sentinel-NDVI and MODIS-EVI data, and statistical yearbook data, this study characterized the spatial intricacies within urban areas by constructing a normalized difference urban index (NDUI). Then, it simulated the population and GDP distributions by employing a regression model. Finally, this study assessed the post-flood recovery from two distinct aspects: nighttime light data and vegetation cover data. The results are as follows: ① High- and medium-risk zones covered an area of 1 429.04 km², accounting for 6.06% of the total study area. High-risk zones were primarily distributed in western Zhengzhou, eastern Xinxiang, eastern Anyang, and northern Hebi, with Zhengzhou suffering the most severe impact; ② In terms of the vegetation cover recovery rate (VCRR), low overall vegetation recovery was observed in Weihui and Linzhou cities and Qixian and Huaxian counties, with VCRRs mostly below 0. This indicates a deteriorating vegetation cover trend; ③ The fitting between NDUI and socio-economic statistical data yielded accuracy exceeding 0.8, suggesting that the NDUI can be applied to precise location-based rescue and targeted post-disaster reconstruction in the aftermath of floods. Additionally, the assessment results based on NPP-VIIRS and MODIS-EVI data were highly complementary, implying that the flood research based on the integration of the two types of data enjoys high application value for post-disaster rescue and recovery assessment.

Tailings ponds are considerable hazard sources with high potential energy. Ascertaining the number and distribution of tailings ponds in a timely manner through rapid identification and monitoring of their spatial extents is critical for the environmental supervision and governance of tailings ponds in China. Due to the lack of pertinence for potential targets, identifying tailings ponds based on solely remote sensing images is prone to produce confusion between tailings ponds and exposed surfaces, resulting in significant errors in practical applications. This study proposed an extraction method for tailings ponds, which integrated enterprise directory, multi-source geographic data (e.g., data from spatial distribution points, digital elevation model (DEM), and road networks), and high-resolution remote sensing images. The application of this method in Gejiu City, Yunnan Province indicates that integrating multi-source geographic data can effectively exclude the interferential areas without tailings ponds, with the precision and recall rates of the extraction results reaching 83.9% and 72.4%, respectively. The method proposed in this study boasts significant application prospects in high-frequency and automated monitoring of tailings ponds nationwide.

Focusing on Quanjiao County in Chuzhou City, this study determined 90 features, including spectral, traditional vegetation index, red-edge vegetation index, and texture features, from Sentinel-2 satellite data on the GEE platform. This study examined the effects of diverse feature optimization algorithms combined with a random forest classifier on identifying crop planting types in the study area. These algorithms included the random forest-recursive feature elimination (RF_RFE) algorithm, the Relief F algorithm based on Relief expansion, and the correlation-based feature selection (CFS) algorithm. On this basis, this study further analyzed the classification effects of the optimal feature optimization algorithm in various machine learning classification approaches. The study demonstrates that: ① Spectral features proved to be the most crucial for crop identification, followed by red-edge index features, and texture features manifested minimal effects; ② RF_RFE-based remote sensing identification results exhibited the highest accuracy, with overall accuracy of 92% and a Kappa coefficient of 0.89; ③ Under the RF_RFE feature optimization method, the RF’s Kappa coefficient was 0.01 and 0.41 higher than that of the support vector machine (SVM) and the minimum distance classification (MDC), respectively. This indicates that the RF_RFE feature optimization method based on multiple features, combined with the RF algorithm, can effectively enhance the accuracy and efficiency of remote sensing identification of crops.

Rapid identification and detection of landslides can both meet the requirement of timely responses to disasters and hold great significance for loss assessment and rescue post-disaster. This study proposed a deep learning-based automatic information extraction method for landslides to improve their detection accuracy. Specifically, the model input of this method includes the remote sensing images of the target areas, data from digital elevation models, and variation characteristics extracted using robust change vector analysis (RCVA). Furthermore, a U-Net model integrating dense upsampling and asymmetric convolution is designed to improve the identification accuracy. Taking Wenchuan, Sichuan Province as the study area, this study designed experiments to test the pixel-level image segmentation accuracy of landslides using different data combinations and methods. The results indicate that the improved U-Net model proposed in the study can produce the optimal image segmentation results of landslides.

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.

Inland water bodies, as irreplaceable resources in ecosystems, play a vital role in climate change and regional water circulation. Scientifically and accurately monitoring the distribution and dynamic changes of water bodies is critical for ecosystem balance maintenance, sustainable human development, and early warning of floods and droughts. However, current research primarily focuses on the static monitoring of inland water bodies, lacking high-resolution monitoring of dynamic changes in water bodies. Hence, relying on the Google Earth Engine (GEE) cloud computing platform, this study monitored the dynamic changes of water bodies at a spatial resolution of 10 m, with the Sentinel-2 surface reflectance data in 2020 as the data source. First, the optimal water body monitoring features were selected by examining the features of typical land cover types in Sentinel-2 spectral bands and water indices. Then, an automatic extraction method for water body training datasets was proposed in conjunction with priori water body products, obtaining high-confidence water body training samples. Furthermore, the spectral angle (SA) and Euclidean distance (ED) methods were integrated based on the Dempster-Shafer (D-S) evidence theory model, and a SA-ED dynamic monitoring model for water bodies was developed combined with the extracted optimal water body monitoring features. Finally, the stability of the SA-ED model was tested with Henan Province as a study area, demonstrating that the SA-ED model can effectively monitor the dynamic changes in water bodies. The SA-ED model yielded an overall monitoring accuracy of 97.03% for water bodies in Henan Province, with user accuracy of 95.85% and producer accuracy of 95.17% for permanent water bodies, user and producer accuracies of 96.21% and 93.82% for seasonal water bodies, respectively. The results of this study provide a novel approach for the fine-resolution monitoring of dynamic changes in water bodies.

The video synthetic aperture radar (VideoSAR) technology is widely used in military reconnaissance, geological exploration, and disaster prediction, among other fields. Owing to multiple interference factors in SAR videos, such as speckle noise, specular reflection, and overlay effect, moving targets are easily mixed with background or other targets. Therefore, this study proposed an effective VideoSAR target detection and tracking algorithm. Firstly, several features of VideoSAR were extracted to construct multichannel feature maps. Then, deeper features were extracted using the improved lightweight EfficientDet network, thus improving the accuracy of SAR target detection while considering algorithm efficiency. Finally, the trajectory association strategy based on bounding boxes was employed to associate the same target in VideoSAR. The experimental results show that the method proposed in this study is effective for SAR shadow target detection and tracking.

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.

Snow proves to be both an important factor in characterizing the surface cryosphere and a critical parameter for weather and hydrological phenomena. Employing remote sensing to conduct long-term and large-scale monitoring of snow morphologies and their changes plays a vital role in research into global climate change, investigations into hydrology and water resources, and geological disaster prevention. After decades of development, significant progress has been made in the field of remote sensing-based snow monitoring technology both in China and abroad. Accordingly, the products for remote sensing-based snow monitoring have become increasingly abundant, and the snow-orientated inversion algorithms have been continuously improved. This paper provides a summary of the existing, widely applied products after categorizing them into three types: snow-cover extent (SEC), snow coverage, and snow depth/snow water equivalent (SWE) products. Furthermore, this study organizes the commercialized remote sensing inversion algorithms used in existing, typical SEC and SWE products. The review of advances in the relevant scientific research reveals that, with the constant presence of sensors with high temporal and spatial resolutions in China and abroad and the support of both novel optical and microwave data sources and new technologies, researchers have gradually improved the accuracy of snow-orientated inversion algorithms by optimizing these algorithms based on regional characteristics. This will provide more support for continuously improving remote sensing-based snow monitoring products in the future.

The one-class classification (OCC) of land use in image interpretation is a hot research topic of remote sensing. Many novel algorithms of OCC were introduced and developed. The maximum entropy model (MaxEnt)-the most promising OCC algorithm as evaluated-is widely used in the OCC study of land use. However, it is unclear about the applicability of these algorithms (including MaxEnt) in multi-class classification (MCC) of land use. Thus, this study established a procedure for MaxEnt-based land-use MCC in remote sensing image interpretation and applied the procedure to the land-use MCC of the Yunyan River basin. The overall classification effect of MaxEnt and the performance of MaxEnt in the prediction of various land were evaluated using overall classification accuracy, Kappa coefficient, sensitivity, and specificity. Moreover, the Kappa coefficient was also used to evaluate the consistency between MaxEnt and random forest (RF), maximum likelihood classification (MLC), and support vector machine (SVM) in the prediction of land use maps. The results are as follows: ① MaxEnt showed the best classification effect, with overall classification accuracy of 84% and a Kappa coefficient of 0.8; ② MaxEnt showed no worst performance in any land type, and even performed the best in some land types; ③ MaxEnt showed high classification consistency with RF and SVM, and the consistency evaluation of the land use maps obtained using the three algorithms yielded Kappa coefficients of greater than 0.6; ④ Compared with the other the three algorithms, MLC yielded a significantly different land use map, with a Kappa coefficient of less than 0.4. This result indicates that MLC is not applicable to the interpretation of land use of the study area. The procedure established in this study only depends on the occurrence probability of land use rather than the threshold selected. As a result, the OCC algorithms represented by MaxEnt have great potential for application to the land-use MCC in remote sensing image interpretation. In addition, the introduction of parallel computing into large-scale land use interpretation will help improve the efficiency of solving MCC problems using MaxEnt.

In view of the drastic changes in the ocean-atmosphere environment, the accurate and efficient identification of coral reef substrate information is essential for the dynamic monitoring of coral reefs. Based on the Landsat8 satellite data of the Yongle Atoll in the Xisha Islands of four periods during 2013—2021, this study proposed a decision tree classification model using spectral and texture indices according to the spectral and texture differences between different substrates. Then, the coral information was extracted using object-oriented and pixel-based classification methods. In addition, the changes in the substrate of the Yongle Atoll were quantitatively analyzed. The results are as follows: ① The results of the object-oriented classification are superior to those of pixel-based classification overall. Moreover, the decision tree classification results yielded Kappa coefficients of 0.63~0.68, with classification accuracy about 7~10 percentage points higher than that of conventional supervised classification; ② Coral thickets are mostly distributed in the central, weakly-hydrodynamic parts of islands and reefs. The corals in the Yinyu Reef and the Jinyin Island exhibit a planar distribution pattern, while those in other islands and reefs mostly show a zonal distribution pattern; ③ The areas of coral thickets and sandbanks in the Yongle Atoll changed significantly overall. Although the total area of coral thickets increased by 1.689 km², the coral thickets in the Shiyu, Jinqing, Quanfu, and Shanhu islands and the Lingyang reef were severely degraded, with areas decreasing by 0.107~0.892 km². This study verified that the substrate index established using medium spatial resolution images is reliable and can be applied to remote sensing information extraction of corals. Therefore, this study will provide technical support for the investigation and scientific management of coral reef resources.

Monitoring the spatio-temporal dynamic changes in macroalgae aquaculture is crucial to its environmental management. However, few studies have been reported on the comparative monitoring of different macroalgae species. Based on images of the Sentinel-2 satellite and using the normalized difference vegetation index (NDVI) and the support vector machine (SVM), this study monitored the dynamic characteristics of both the Porphyra aquaculture area in the sea area of southern Wendeng District, Weihai City, Shandong Province and the kelp aquaculture area in the sea area of southern Rongcheng City, Weihai City. The results show that: ① The Porphyra aquaculture in Wendeng District was first captured in the satellite images of 2016, which is the same as the first year of Porphyra aquaculture in this city; the extraction method used in this study performed well in extracting the information about both the Porphyra and the kelp aquaculture areas overall, with the overall accuracy of 84% and above; ② During 2017—2021, the Porphyra aquaculture area monitored through remote sensing increased year by year and showed a trend far away from the shore; ③ The Porphyra and kelp aquaculture areas monitored both showed seasonal variations (high in winter and low in summer) of cold-water macroalgae aquaculture, but the minimum and maximum values of the Porphyra aquaculture area appeared 1~2 months earlier than those of the kelp aquaculture area. Compared with statistical yearbooks, satellite remote sensing can provide more accurate spatio-temporal information on macroalgae aquaculture. This study can be used as a reference in terms of monitoring technology and data for the management of macroalgae aquaculture in coastal areas of northern China.

With the rapid socio-economic development and the increasing demand for natural resources in China, the protection of natural reserves is facing increasing difficulties. The remote sensing-based research on monitoring the disturbance and the restoration of mangrove forests through time series analysis is still in its initial stage. Moreover, time series algorithms are highly complex. Based on the LandTrendr time segmentation algorithm of Google Earth Engine (GEE) and the Landsat image time-series data, this study investigated the disturbance to mangrove forests in the Dongzhaigang Mangrove Nature Reserve during 1990—2020. The results are as follows: ① A total of 42.39 hm² of mangrove forests were disturbed during 1990—2020, among which the largest disturbance area of 12.78 hm² occurred in 2014; ② During 1990—2020, minor, moderate, and severe disturbances accounted for 65.39%, 30.78%, and 3.83%, respectively; ③ The overall identification accuracy of the pixels of mangrove forests subject to changes was 89.50%, and the overall detection accuracy of years witnessing disturbance was 88%, with a Kappa coefficient of 0.79. This study analyzed the years and areas of the disturbance to mangrove forests in the Dongzhaigang Mangrove Nature Reserve over 30 years based on LandTrendr. Moreover, this study analyzed the disturbance factors according to the actual situation and concluded that human activities are the main disturbance factor, followed by natural factors, such as diseases, pests, and extreme weather events. This study will provide a scientific basis and a decision reference for the management of the mangrove forest reserve.

High-intensity coal mining leads to significant surface deformation and secondary geological disasters. Synthetic aperture Radar interferometry (InSAR), exhibiting high deformation monitoring capability, fails to detect enough target pixels in the mining core and surrounding low-coherence areas. This study intends to increase the density and coverage of deformation monitoring points in mining areas by combining distributed targets (DTs) and slowly-decorrelating filtered phase targets (SDFPTs). First, DT and SDFPT candidate pixels were selected using the fast statistically homogenous pixel selection (FaSHPS) method and the amplitude dispersion index method, respectively for phase optimization and stability analysis. Then, qualified DT and SDFPT pixels were screened out to constitute a fused pixel set, which was subjected to three-dimensional phase unwrapping, phase time series recovery, and spatio-temporal filtering. Consequently, the deformation time series and the annual average deformation rate were determined based on the fused pixel set. Finally, the method proposed in this study was applied to monitor the deformation in the Buertai coal mine using 60 scenes of Sentinel-1 images covering the coal mine from April 2018 to April 2020. The results reveal a significant increase in the density and coverage of deformation points through the integration of DT and SDFPT, thus allowing for the monitoring of higher levels of maximum deformation. Within the experimental area, five deformation cones were identified, with the maximum cumulative deformation amplitude reaching -309.76 mm. The influencing range of the deformations and the difference in the deformation amplitude of the time series in different years are closely related to mining activities.

Earthquake-induced surface deformations are characterized by large scales and extensive coverage, and the resultant secondary geological disasters significantly impact local infrastructure and engineering construction. Investigating the surface deformations caused by the Menyuan earthquake is critical for understanding the seismic deformation movement and identifying potential geological disasters. This study obtained the coseismic deformation field of the Menyuan earthquake using the differential interferometric synthetic aperture Radar (D-InSAR) technique. Based on the geometric relationships between the ascending descending passes, this study extracted the two-dimensional information of surface deformations induced by the Menyuan earthquake. The results show that the coseismic deformations occurred primarily at the intersection of Lenglongling and Tuolaishan faults. The line-of-sight (LOS) surface deformations from ascending and descending passes exhibited uplift of 0.40 m and 0.80 m and subsidence of -0.65 m and -0.70 m, respectively. As indicated by the analysis of two-dimensional deformation based on the ascending and descending LOS surface deformation results, the maximum amplitude of vertical deformations dominated by subsidence was -0.32 m and the maximum amplitude of horizontal deformation dominated by eastward motion was 0.87 m, suggesting significant horizontal seismic deformations and fault activity dominated by left-lateral strike-slip process. Based on the 21 scenes of Sentinel-1A SAR images covering the study area taken from the ascending pass, this study extracted the information on the surface deformations after the Mengyuan earthquake using the small baseline subset-interferometric synthetic aperture Radar (SBAS-InSAR) technique, determining the LOS time series and average deformation rates. The results show that from January 17, 2022 to September 26, 2022, the study area experienced relatively stable overall deformations and significant local deformations. The fault activity was identified as the primary factor affecting the surface deformations, with a maximum average deformation rate of 53 mm/a and a maximum deformation amplitude of 77 mm. The results of this study will provide technical support for earthquake disaster mitigation, emergency management, and sustainable socio-economic development.

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.

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.

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.

Multi-label classification of remote sensing images plays a fundamental role in remote sensing analysis. Parsing given remote sensing images to identify semantic labels can provide a significant technical basis for downstream computer vision tasks. With the continuously improved spatial resolution of remote sensing images, many remote sensing objects with different scales, colors, and shapes are distributed in various zones of images, posing high challenges to the multi-label classification task of remote sensing images. This study focuses on the multi-label classification of images in the field of remote sensing, summarizing and analyzing the frontier research advances in this regard. First of all, this study expounded the problem definition for the multi-label classification task of remote sensing images while generalizing the commonly used multi-label image datasets and model evaluation indicators. Furthermore, by systematically presenting the frontier progress in this field, this study delved into two key tasks in the multi-label classification of remote sensing images: feature extraction of remote sensing images and label feature extraction. Finally, based on the characteristics of remote sensing images, this study analyzed the current challenges of multi-label classification as well as subsequent research orientation.

Zhejiang Province is both the birthplace of the theory that both the mountain of gold and silver and the lushmountain with lucid waters are required (also known as the Two Mountains theory) and the first ecological province in China. The study on the vegetation ecological quality of Zhejiang can be used as an important reference for the construction of ecological civilization. Based on multi-source remote sensing data and meteorological observation data, this study investigated the spatio-temporal variations of vegetation ecological quality in Zhejiang during 2000—2020, as well as their response to climate factors and human activities. The results show that: ① Both the fractional vegetation cover (FVC) and the net primary production (NPP) in Zhejiang showed an upward trend during 2000—2020, with significantly increased vegetation greenness; ② The vegetation eco-environmental quality in Zhejiang showed a fluctuating upward trend during 2000—2020, with the vegetation ecological quality indices (VEQIs) of mountainous areas significantly higher than those of basin and plain areas; ③ The dominant factor driving the VEQI variations in Zhejiang during 2000—2020 is human activities, while climate factors occupied a dominant position only in some areas of southwestern Zhejiang. This study deepens the understanding of the spatio-temporal variations of vegetation ecological quality in Zhejiang and their driving factors and, thus, is of great significance for the construction of ecological civilization in Zhejiang and even other regions in China.

Coalbed methane (CBM), a type of self-sourced unconventional clean energy, occurs in coal seams and their surrounding rocks. Conventional exploration methods for CBM enrichment areas are laborious, while remote sensing provides a new approach to the rapid exploration of such areas. The basic principle behind the remote sensing-based exploration of CBM enrichment areas is as follows: ① The extraction and comprehensive analysis of multi-source data are conducted based on the comparison between the spectral features of typical surface features and those of surface feature anomalies, including rock and mineral alterations, vegetation anomalies, and thermal anomalies, caused by hydrocarbon micro-seepage in CBM enrichment areas, along with data obtained using geophysical prospecting methods like geological, seismic, and magnetotelluric methods; ② The distribution range and gas-bearing properties of CBM enrichment areas are gradually delineated. This paper reviews the hydrocarbon seepage in the CBM enrichment areas and the response mechanisms of spectral anomalies of surface rocks, minerals, and vegetation. It covers the applications of various methods based on the inversion of spectral parameters of surface rocks, minerals, and vegetation, together with the inversion of the spectral anomalies of surface features, in the exploration of potential CBM enrichment areas. Additionally, this paper elucidates the different explanations for surface thermal anomalies caused by CBM-bearing strata, as well as major methods to improve the accuracy of surface temperature inversion and their applications. In the future, the main approach to achieving low-cost, rapid exploration of CBM enrichment areas will be the analysis and information extraction of three-dimensional, multi-source information based on the combination of remote sensing technology with the geological data, seismic exploration, and geomagnetic prospecting of coalfields.

This study aims to analyze the changes in the land use and vegetation cover in the Xiong'an New Area from 1991 to 2021. To this end, this study explored the characteristics of the land use changes in the area over the 30 years based on the Landsat TM\OLI data of five periods using the GIS technology and map fusion method. Then, it extracted the vegetation cover information using the dimidiate pixel model and analyzed the changes in the vegetation cover. Furthermore, this study explored the potential factors driving the vegetation cover change in the area using the geographic detector model and analyzed the impact of land use change on vegetation cover change by referencing the existing map fusion method. The results show that: ① From 1991 to 2021, the construction land in Xiong’an New Area increased by 108.09 km², primarily transformed from farmland and other types of land; other types of land reduced by 108.17 km², predominantly transformed to farmland; forestland and grassland increased by 11.56 km², mainly transformed from water areas and other types of land; the water area decreased by 38.76 km², mainly transformed to farmland and other types of land; and the area of farmland roughly remained unchanged; ② Over the 30 years, the Xiong’an New Area generally exhibited high vegetation coverage, and the area with moderate and high vegetation coverage and above accounted for more than 50.00%. The vegetation coverage in the Xiong’an New Area presented an overall spatial distribution pattern characterized by high in Anxin County, moderate in Rongcheng County, and low in Xiong County. Regarding the phased changes, this area showed a degradation trend from 1991 to 2001, and the area with degraded vegetation cover accounted for 39.15%. From 2001 to 2021, this area exhibited an improvement trend, the area with improved vegetation cover accounted for up to 47.55%; ③ The vegetation cover change showed spatial differentiation, significantly affected by the population density, GDP, soil type, and soil quality but slightly affected by the elevation and slope. The transformation of construction land and other types of land to farmland acted as an important reason for the improvement in vegetation cover, while the transformation of farmland to construction land and other types of land served as an important reason for vegetation degradation. The results of this study can, to some extent, provide a scientific basis and suggestions for the sustainable development of Xiong’an New Area.

The accurate acquisition of land cover/use changes and their types is critical to territorial space planning, ecological environment monitoring, and disaster assessment. However, most current studies on the change detection focus on binary change detection. This study proposed a multi-class change detection method using a multi-task Siamese network of remote sensing images. First, an object-oriented unsupervised change detection method was employed to select areas that were most/least prone to change in the new and old temporal images. These areas were used as samples for the multi-task Siamese network. Subsequently, the multi-task Siamese network model was used to learn and predict the new and old temporal land-use maps and binary change maps. Finally, the final multi-class change detection results were derived from these maps. The multi-task Siamese network was tested based on the images from the Third National Land Survey and corresponding land-use maps. The results demonstrate that the method proposed in this study is applicable to the change detection cases where changed and unchanged samples lack but there are available historical thematic maps.

High-resolution remote sensing images contain rich data and information, which reduce the difference between the target and the background, resulting in substandard detection accuracy and reduced target detection performance. Based on the deep learning algorithm You Only Look Once (YOLO), this study designed a lightweight network model GC-YOLOv5 by combining end-to-end coordinate attention (CA) and the lightweight network module GhostConv. The CA was employed to encode channels along the horizontal and vertical directions, enabling the attention mechanism module to simultaneously capture remote spatial interactions with precise location information and helping the network locate targets of interest more accurately. The original ordinary convolutional module convolutional-batchnormal-SiLu (CBS) was replaced by the GhostConv module, reducing the number of parameters in the feature channel fusion process and the size of the optimal model. Experiments were conducted on the GC-YOLOv5 using the publicly available NWPU-VHR-10 dataset, with the robustness of the model verified on the RSOD dataset. The results show that GC-YOLOv5 yielded a detection accuracy of 96.5% on the NWPU-VHR-10 dataset, with a recall rate of 96.4% and mAP of 97.7%. Moreover, GC-YOLOv5 achieved satisfactory results on the RSOD dataset.

The emergence of geographic big data provides a new data source for the study of urban spatial structures. Identifying the polycentric urban structure based on geographic big data is currently a hot research topic in academic communities. This study proposed a method for identifying the polycentric urban structure based on multi-source geographic big data. First, the spatial units in the study area were determined using a region segmentation algorithm based on drainage divides. Then, the urban centers and subcenters were identified using the two-stage algorithm for urban center identification. Finally, the identification results were compared and verified. The results of this study are as follows: ① The region segmentation algorithm based on drainage divides can effectively identify the spatial features of nighttime light data, and the basic spatial units acquired using this algorithm can be used to identify urban spatial structures; ② The urban centers identified based on the Weibo (MicroBlog) check-in data, which can effectively reflect urban human activities, and the two-stage algorithm for urban center identification are roughly consistent with those set in the urban planning. Therefore, the method proposed in this study is of great significance for expanding the application scope of geographic big data and enriching the existing research methods for urban spatial structures.

Conventional remote sensing monitoring techniques, constrained by data availability and computational capacity, often fall short of the research requirements of extensive landslide disaster monitoring. This study established a dynamic assessment model for landslide hazards in the Three Gorges Reservoir area based on cloud computing platform Google Earth Engine (GEE), achieving dynamic assessment of landslide hazards in the area under the support of the massive data storage and robust computational capabilities of GEE. First, based on factors such as slope, slope aspect, normalized difference vegetation index (NDVI), normalized differential water index (NDWI), and geological structures, a landslide susceptibility zone map was established using a weighted gradient boosting decision tree (WGBDT) model. Then, the rainfall threshold inducing landslides in the Three Gorges Reservoir area was determined based on the Global Precipitation Measurement (GPM) data from the National Aeronautics and Space Administration (NASA). Subsequently, the rainfall classification criteria and a landslide hazard assessment model were established by combining rainfall and landslide susceptibility. Finally, focusing on the rainfall on August 31 in the Three Gorges Reservoir area, the daily distribution maps of landslide hazards in the Three Gorges Reservoir area were plotted, yielding the spatio-temporal variation trend of landslide hazards. In sum, the data processing and analysis tools of GEE allow for the analysis of landslide-related data of the Three Gorges Reservoir area, thus providing nearly real-time monitoring and early warning information for landslide hazards and offering a basis for the formulation of disaster prevention and mitigation policies.

Accurate mangrove forest distribution information is critical to the conservation and management of mangrove forests. Despite extensive studies on the remote sensing mapping of mangrove forests, it is necessary to improve their mapping accuracy by effectively utilizing multi-source remote sensing features. First, this study designed 15 feature associations using temporal features, including spectral, scattering, texture, and terrain features, which were extracted from multi-source remote sensing data. Then, using a random forest model, it analyzed the accuracy of different feature associations in mangrove forest identification, obtaining the optimal feature association. Finally, this study mapped the 10-m-resolution mangrove forest distribution of Guangdong Province in 2021 based on platform Google Earth Engine (GEE). The results show that spectral features in winter exhibited the highest importance, with richer feature types corresponding to higher mapping accuracy. The optimal feature association yielded overall accuracy of 92.25% and a Kappa value of 0.91. Overall, this study extracted information on mangrove forests in Guangdong based on multi-feature parameters and the optimal feature association. The results of this study will provide a scientific reference for accurate mapping of mangrove forests on a large scale.

Daily mean temperatures, as a primary indicator reflecting climatic characteristics, play a decisive role in monitoring urban heat island effects and agroecological environments. However, daily mean temperatures measured at meteorological stations lack spatial representativeness in regional-scale models. By contrast, the inversion results of daily mean temperatures using remote sensing data can better accommodate the large-scale monitoring needs, but with insufficient accuracy and quality. This study presented several common estimation methods for daily mean temperatures using remote sensing data, including multiple linear regression, machine learning, and feature space-based extrapolation. Then, based on the principle and process for estimation of daily mean temperatures using remote sensing data, this study systematically analyzed the effects of uncertainties such as clouds and aerosols and offered corresponding solutions. Finally, this study predicted the development trend of such estimation methods. Additionally, this study posited that image fusion and multi-source data fusion at different transit times can significantly improve the estimation accuracy under cloud interference.

Forest fires are one of the most significant disturbance factors affecting forest ecosystems. Exploring their spatio-temporal variations and forest restoration holds certain sociological and ecological significance. The Great Xing’an Range, possessing the largest primitive area in China, is a key area suffering frequent forest fires. Hence, this study extracted the distribution information of burned zones in the Great Xing’an Range from 2002 to 2021 from the MODIS time series products involving burned zones, land cover, and gross primary productivity (GPP). Moreover, it statistically analyzed the post-fire forest restoration. The results show that: ① Fires in the forest area of the Great Xing’an Range showed an overall downward trend from 2002 to 2021, but the burned areas showed fluctuating changes. Both the burned area and fire frequency were the highest in 2003, followed by 2008, with the lowest burned area seen in 2019; ② Forest fires occurred primarily in spring and autumn, with the highest burned area and fire frequency in March and the second highest fire frequency in September; ③ Forest fires manifested an uneven spatial distribution from northeast to southwest, predominantly in the Great Xing’an Range within Heilongjiang and Hulunbuir City of Inner Mongolia. Moreover, the forest fire area in Inner Mongolia far exceeded that in Heilongjiang. The analysis of forest types in burned zones reveals that the burned areas decreased in the order of broad-leaved, mixed, and needle-leaved forests. According to the time series analysis of GPP in burned zones, GPP values recovered the fastest in the first year post-fire, but it took nearly seven years to recover to the pre-fire growth level. Different forest types manifested significantly distinct post-fire restoration rates, which decreased in the order of broad-leaved, needle-leaved, and mixed forests. Overall, ascertaining the spatio-temporal distribution of forest fires can provide data support for the arrangement and adjustment of fire prevention and control efforts, while investigating the post-fire forest restoration can provide a scientific basis for the rehabilitation and sustainable development of forests.

The monitoring and assessment of urban ecological quality holds critical significance for sustainable urban development. To assess the ecological quality of developed coastal cities in China in recent years, this study investigated Nanjing City based on the Sentinel-2A remote sensing images obtained in 2021. It constructed a novel remote sensing green index (RSGI) model involving green spaces, blue spaces, buildings, and impervious surfaces for assessing the ecological quality of Nanjing. First, neural network supervised classification was applied to the Sentinel-2A remote sensing images, constructing the RSGI to assess the ecological quality of various districts in Nanjing. Then, the correlations between the RSGI and urban ecological factors were analyzed using the Pearson correlation coefficient. Finally, the ecological similarity between the districts was analyzed using the agglomerative hierarchical clustering method. The results of this study are as follows: (1) The ecological quality of Nanjing presented a pattern of low RSGI values in the central portion and high RSGI values in the surrounding areas, with the highest and lowest RSGI values (0.86 and 0.38) observed in Luhe and Qinhuai districts, respectively, differing by 0.48; (2) The RSGI exhibited a positive correlation with the density of green spaces and negative correlations with the densities of population, buildings, and impervious surfaces, all at the 0.01 level; (3) With the ecological similarity of 70% as the threshold, 11 districts in Nanjing were categorized into four clusters: Qinhuai, Gulou, and Jianye districts in the first cluster, Yuhuatai and Qixia districts in the second cluster, Xuanwu and Gaochun districts in the third cluster, and the rest four districts in the fourth cluster. The results of this study can provide a scientific basis for subsequent urban planning and sustainable development of Nanjing.

Conventional processing methods for remote sensing data are inefficient and time-consuming. Using the object-oriented classification method this study extracted the distribution of mangrove forests of 2000, 2010, and 2020 in the Shankou Mangrove Nature Reserve in Guangxi based on the GEE cloud platform and Landsat TM/OLI remote sensing data. Then, this study monitored the spatio-temporal variations in mangrove forests in the study area in combination with the landscape analysis method and revealed their driving factors. The results are as follows: ① During 2000—2020, the mangrove forests in the study area increased by about 63 hm², including a significant increase of about 40 hm² during 2010—2020; ② Compared with other land use types, the mangrove forests showed the most intense conversion with spartina alterniflora areas and mudflats, with 152 hm² of spartina alterniflora areas and mudflats being converted to mangrove forests and 122 hm² of mangrove forests being converted to spartina alterniflora areas over the 20 years; ③ During 2000—2020, the mangrove landscape in the study area showed decreased fragmentation, increased patch aggregation, continuously expanded landscape dominance, and landward migration of the mangrove forest centroid; ④ Among the factors affecting the area of mangrove forests in the nature reserve, the control of invasive vegetation and moderate aquaculture can increase the area of mangrove forests, while climate changes and invasive vegetation had adverse effects on the growth of mangrove forests. The results of this study will provide a method reference and data basis for the conservation and management of mangrove wetlands in Shankou, Guangxi.

The integration of multi-source remote sensing images and multi-feature parameters is effective in the accurate identification of target ground objects. However, excess feature parameters can cause data redundancy, reducing classification accuracy. Focusing on a sugarcane planting area with Karst landforms, this study extracted the spectral, index, texture, topographic, and polarization features of the ground objects in the study area from Sentinel-1/2 images and SRTM digital elevation data. The index features involved the red edge index calculated based on the red-edge band, which was scarce in data derived from remote sensing sensors, and the texture features included the Radar image textures. In the experiment, six schemes were designed to explore the effects of different image features and the random forest-based optimal feature association on sugarcane information extraction. The results show that for the classification of ground objects in the study area using spectral features combined with other feature types, the importance of the feature types ranked in descending order of spectral features, index features, texture features, topographic features, and polarization features. Among the six schemes, the scheme based on the random forest algorithm, integrating different feature variables, yielded the optimal information extraction effect for sugarcane, with both user and producer accuracy higher than 97%, overall accuracy of 95.49%, and a Kappa coefficient of 0.94.

Myrica rubra is a specialty crop in Zhejiang Province. Its cultivation area in Zhejiang ranks first in China. This study aims to comprehensively investigate and analyze the suitability of Myrica rubra planting in Zhejiang and better serve the Myrica rubra planting by scientifically using modern meteorological observation data. Based on the distributed simulation of climate factors, this study introduced the influencing factors related to soil and terrain and determined the weights of these factors through the analytic hierarchy process (AHP). Then, in combination with the suitability grade indices of various influencing factors, this study divided Zhejiang into regions suitable, fairly suitable, and unsuitable for Myrica rubra planting. The results are as follows: Regions with a suitable climate occupy most of Zhejiang, indicating superior climate resources; Zhejiang Province enjoys excellent soil conditions and roughly varies between regions fairly suitable and suitable for Myrica rubra planting regarding soil conditions; The terrain varies greatly and is a key factor in the suitability of precise Myrica rubra planting. The regions with suitable terrains have altitudes of 250~450 m and slopes of 5°~25°; Except for northern Zhejiang and the boundary between Shaoxing and Ningbo cities, Zhejiang is suitable or fairly suitable for Myrica rubra planting. This study achieved the spatial simulation of meteorological factors, thus providing data support for the development and improvement of the Myrica rubra planting layout in Zhejiang and being of great practical significance for improving the yield and quality of Myrica rubra.

Lakes constitute a crucial part of terrestrial ecosystems. Changes in the water areas of lakes significantly influence environments and human production activities. Poyang Lake, the largest freshwater lake in China, has experienced many floods and droughts in recent years, thus necessitating its dynamic monitoring. With 175-phase Landsat images of Poyang Lake from 2000 to 2021 as the data source, this study comparatively analyzed four water body extraction methods: the normalized difference water index (NDWI), the modified normalized difference water index (MNDWI), the automated water extraction index (AWEI), and the spectrum photometric method (SPM), determining the optimal water body extraction index for Poyang Lake. Moreover, based on the 175-phase area data, this study delved into the inter-annual area variation trend from 2000 to 2021 as well as the intra-annual seasonal variations. Furthermore, it established the area - water level model by combining 50 sets of water level data from 2009 to 2013 and 2017 to 2018. The results show that: ① The AWEI model, outperforming the other three models in the extraction accuracy, was employed for the water body extraction of Poyang Lake; ② The area of Poyang Lake exhibited significant seasonal variations, large inter-annual fluctuations in the wet season, and relatively gentle inter-annual fluctuations in the dry season; ③ The area - water level piecewise linear model of the Tangyin gauging station proved optimal, which can predict the water coverage area based on real-time water level observations in Poyang Lake, compensating for the limitation of visible spectral remote sensing methods in monitoring the lake water coverage during cloudy and rainy weather.

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.

Zayu County, located in the southeastern portion of the Qinghai-Tibet Plateau, is characterized by a large area, significantly varying topographic and climatic characteristics, and frequent landslides. The hazard identification and early warning of landslides are critical to disaster prevention and mitigation in the county. Based on the data acquired from January 2020 to November 2022, including 162 scenes of Sentinel-1A Radar remote sensing images taken on ascending and descending passes and high-resolution optical remote sensing images, this study conducted hazard identification, cataloging, mapping, analysis, and assessment of active landslides in Zayu County using the integrated remote sensing (IRS) technique on the Google Earth platform. A total of 237 active landslide hazards were identified primarily along the Gongrigabuqu River (the western tributary of the Zayu River), Zayu River, both sides of the Nujiang River, and the eastern Zayu River to the western Nujiang River. As revealed by the statistical analysis of the interpretation results combined with quantitative factors such as topography (elevation, slope, lithology) and natural environment (rainfall, temperature), the landslides in Zuobu and Azha villages pose high disaster risks, necessitating further mitigation measures. With relatively accurate results, this study can serve as a reference for disaster prevention and mitigation in Zayu County.