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.

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.

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.

Based on thermal infrared and multispectral remote sensing data, this study analyzed the thermal spring-related structures interpreted from remote sensing images. Thermal springs crop out at the intersections of asterisk- and lambda-shaped structures, with asterisk-shaped structures exhibiting more favorable conditions. By delving into remote sensing characteristics related to thermal springs, this study presented remote sensing factors like surface temperature, hydroxyl anomaly, soil moisture, hydrographic net, and elevation. Using mathematical geostatistics and prediction methods based on geographical information system (GIS), including the weight of evidence, prospecting information content method, and feature factor method, this study analyzed the geological, remote sensing, and geophysical factors related to thermal springs for mathematical geostatistics and prediction. The comprehensive analysis reveals 57 favorable geothermal areas, including 8 in category A, 18 in category B, and 31 in category C. All the category-A favorable geothermal areas include known geothermal sites, and one category-B favorable area reveals a 51.6 ℃ thermal spring, suggesting reliable prediction results. The methodology of this study provides a new approach for geothermal resource prediction.

Exploring the remote sensing-based classification of coastal wetlands is significant for their conservation and planning. Hence, this study investigated the Yellow River Delta with the 8-view Landsat8 OIL images from March to October 2019 as the data source. It constructed seven classification schemes based on different features of the images on the Google Earth Engine (GEE) cloud platform. Then, it employed the random forest classifier to classify different feature sets, with the scheme exhibiting the best classification effects selected for mapping the wetland categories of the Yellow River Delta. Considering poor data quality in August and September due to cloud contamination, this study filled in the cloudy zones using the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) algorithm. The results show that: ① The predicted images generated from the ESTARFM manifested a high correlation with the real image bands, with R values above 0.73, suggesting that the reconstructed images could be used in this study; ② The random forest algorithm was used to classify the surface feature types in the study area. Through optimal feature selection, the classification results of Scheme 7 demonstrated an overall accuracy of 92.28%, higher than those of conventional schemes, with a Kappa coefficient of 0.91, aligning with the actual wetland conditions. The results of this study can assist in deeply understanding the spatial distributions of different wetlands in the area, and provide a scientific basis for the conservation and planning of the regional ecological environment.

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.

This study aims to explore the applicability of various spatio-temporal information fusion algorithms based on remote sensing data to wetland areas characterized by frequent land-water conversion and diverse surface features. With the Poyang Lake sample area as the study area, this study examined five typical spatio-temporal information fusion algorithms (STARFM, ESTARFM, FSDAF, Fit-FC, and STNLFFM). Considering the differences in surface features among different periods, Landsat and MODIS remote sensing data were selected to conduct image fusion experiments for normalized difference vegetation indices (NDVIs) during low- and normal-water periods. Moreover, the accuracy of these algorithms was evaluated in spatial and spectral dimensions. The results of this study are as follows: ① In the case of only one pair of coarse- and fine-resolution images as input, the FSDAF exhibited the optimal fusion prediction effect for the low-water period, with an overall error of 0.433 5, whereas the STNLFFM manifested the optimal fusion prediction effect for the normal-water period, with an overall error of 0.514 7; ② In the case of two pairs of coarse- and fine-resolution images of low- and normal-water periods as input, the ESTARFM demonstrated the optimal fusion prediction effect, with an overall error of 0.467 0; ③ The applicability of different algorithms to a wetland area is associated with the proportion of water bodies in the study area. The STNLFFM displayed the optimal fusion prediction effect for water bodies.

The original high-spatial-resolution remote sensing images of coastal zones of islands often exhibit a gray tone, color cast, and indistinguishable surface feature information. In response to the increasing demand for geographic information security of coastal zones of islands, this study aims to obtain timely clear remote sensing images with rich information, moderate contrast, and uniform brightness for island reefs. Hence, it proposed an intelligent color enhancement method by combining deep learning with improved histogram matching for high-spatial-resolution remote sensing images of coastal zones of islands. First, data resampling and adaptive chunking were performed to obtain thinned images. Then, the MBLLEN network was applied to enhance the thinned images with true color. Finally, an improved histogram matching method was employed for color mapping of original images, obtaining remote sensing images with consistent colors and rich details conforming to human vision. The color-matching effects of these obtained remote sensing images were evaluated using both subjective and objective methods. The results show that compared to other commonly used color-matching methods like Retinex, HE, and MASK, the method proposed in this study yielded more satisfactory results characterized by consistent colors and rich details conforming to human vision. Therefore, the proposed method can effectively improve the visual effects of high-spatial-resolution remote sensing images of coastal zones of islands, effectively retain the details of original surface features, and significantly enhance color-matching efficiency.

The continuous advancement of urbanization in China leads to frequently changing urban buildings. Hence, grasping the change information of urban buildings duly and accurately holds critical significance for urban management, investigation of unauthorized construction, and disaster assessment. This study proposed a multi-level building change detection method combined with the digital surface model (DSM) and digital orthophoto map (DOM) generated from unmanned aerial vehicle (UAV) images. The proposed method consists of four steps: ① The dense point cloud and DOM generated from UAV images were pre-processed to generate differential normalized DSM (dnDSM) and extract vegetation zones; ② Candidate change zones were extracted using multi-level height difference thresholds, with vegetation and smaller zones eliminated; ③ The connected component analysis was conducted for lower-level candidate change zones. For connected objects, their higher-level change detection results were used to eliminate false detection results in the lower level; ④ The quantitative relationship between positive and negative height difference values of change objects was statistically analyzed to determine the change types. As demonstrated by experimental results, the proposed method can retain the change information of low-rise buildings detected through the lower height difference thresholds while ensuring correct and complete change information of high-rise buildings.

Identifying granitic pegmatite-type lithium deposits based on remote sensing technology is a significant method for lithium ore prospecting. To enhance the information extraction accuracy of the deep learning-based semantic segmentation method for granitic pegmatites, this study improved the classic U-Net network. A batch normalization module was added to the convolutional layer of the encoder part, with the ReLU activation function replaced by the ReLU6 activation function. Simultaneously, a composite loss function was constructed to improve operational efficiency and reduce the precision loss in the training process. The domestic GF-2 images of a granitic pegmatite-type lithium deposit were employed to create a dataset for experiments. The results show that the improved U-Net model effectively identified the information on granitic pegmatites in the study area covered by GF-2 images. Compared to the original U-Net network, U-Net model based on VGG backbone network, U-Net model based on MobileNetV3 backbone network, and conventional random forest model, the improved U-Net model has its average intersection over union increased by 14.69, 0.95, 5.08, and 35.34 percentage points, respectively. Moreover, its F1-score increased by 18.38, 1.02, 5.7, and 54.59 percentage points, respectively. Hence, the improved U-Net model achieves the high-precision automatic extraction of ore-bearing granitic pegmatite information from remote sensing images in areas with low vegetation coverage.

Considering spectral distortion and insufficient texture details in the pansharpening of hyperspectral images, this study proposed an adaptive pansharpening algorithm for hyperspectral images based on the intersecting cortical model (ICM) for image segmentation. First, hyperspectral images were matched and fused with multispectral images with similar spatial resolution. Then, the matching and fusion results were fused with high-resolution panchromatic images, obtaining the fusion results possessing both the high spatial resolution of panchromatic images and the spectral resolution of hyperspectral images. Moreover, the grey wolf optimizer (GWO) was employed in sharpening fusion to adaptively optimize ICM parameters, generating the optimal irregular segmentation regions, thus providing more accurate and comprehensive details and spectral information for hyperspectral images. Finally, experiments were conducted on the proposed algorithm using two hyperspectral datasets from the ZY-1 02D satellite. The experimental results demonstrate that the proposed algorithm manifested the optimal performance in the evaluation indices of spatial details and spectral information, substantiating its effectiveness.

Tasseled cap transformation (TCT), one of the most common methods in image enhancement, has been extensively applied in remote sensing. However, high-resolution satellite sensors (like GF-6 WFV) usually lack short-wave infrared bands, leading to distorted wetness components in TCT coefficients obtained using the conventional Gram-Schmidt (G-S) orthogonalization method. Hence, this study selected 12 GF-6 WFV images covering different regions, temporal phases, and seasons, as well as six synchronous Landsat8 images for wetness component regression, determining the wetness component coefficient of the GF-6 WFV sensor. Furthermore, it employed the inversed G-S algorithm to deduce the brightness, greenness, and other components, deriving the TCT coefficient of the GF-6 WFV sensor. This study found that: ①Adjusting the derivation order of the wetness component in TCT (that is, the derivation of the wetness component comes before that of other components like brightness and greenness) allows more effective derivation of the TCT coefficient of the GF-6 WFV sensor, avoiding the distortion of the wetness component; ②The TCT components of the GF-6 WFV sensor exhibited stable characteristics, with surface features displaying a typical “tasseled cap” distribution in the feature plane composed by various TCT components; ③Despite the differences in band setting and spectral response, GF-6 WFV and Landsat8 OLI sensors manifested high consistency in corresponding TCT components, with a correlation coefficient of up to 0.8.

Yunnan Province has abundant species resources but fragile ecosystems, and the ecological vulnerability is closely related to vegetation cover. Hence, based on the normalized difference vegetation index (NDVI) from the MOD13Q1 dataset for 2000—2022, this study dynamically monitored the spatio-temporal variations of vegetation using the maximum value composite (MVC), Theil-Sen median trend analysis, and Mann-Kendall significance test. Moreover, this study delved into the response of vegetation to factors like topography, climate change, and land cover through correlation analysis. The results show that: ① From 2000 to 2022, the overall vegetation coverage of Yunnan Province was relatively high, with average annual NDVI values ranging from 0.74 to 0.90, showing a fluctuating upward trend. Of the whole area, 91.17% exhibited an increasing vegetation coverage trend, with the fastest growth rate seen in northeastern Yunnan; ② Regional differences were observed in vegetation cover, which was higher in southeastern and southwestern Yunnan compared to northwestern, central, and northeastern Yunnan; ③ The NDVI values of Yunnan Province were relatively stable below the altitude of 3 900 m, and decreased with increasing altitude in the case of over 3 900 m; ④ The NDVI values were the lowest with slopes below 3°, and with an increase in slope, they increased first and then decreased; ⑤ The planar slope aspect displayed the lowest NDVI values, and other slope aspects showed minimal impact on vegetation growth; ⑥ From 2000 to 2022, the vegetation cover in central, southeastern, and northeastern Yunnan was positively correlated with precipitation, suggesting that precipitation in these areas was favorable for vegetation growth. However, the vegetation cover in southwestern and northwestern Yunnan showed a negative correlation with precipitation. Additionally, the vegetation cover in the whole region and various areas was positively correlated with temperature, suggesting that temperature is beneficial to vegetation growth. The results of this study will provide a scientific basis for strengthening ecological environment construction and ecological management in Yunnan Province.

Constructing ecological security patterns in territorial space can constrain and guide urban spatial development for coordinated and sustainable development of cities and regional environments. Based on the remote sensing ecological index and the minimum cumulative resistance model, this study identified the ecological security pattern factors for Dongguan City, Guangdong Province, including ecological source area, ecological corridor, ecological pinch point, and ecological barrier. Moreover, this study proposed related optimization recommendations. The results of this study indicate that: ① Dongguan City exhibits a favorable ecological base, contiguous ecological spaces, and evenly distributed ecological corridors that can effectively connect ecological source areas; ② Enhanced waterfront space protection and road greening construction provide effective paths for the flow of ecological elements; ③ Agricultural production and transportation manifest the most significant effects on ecological networks, followed by industrial production and domestic life; ④ Ecological sources, 12-m-wide ecological corridors, and ecological pinch points can be classified as key ecological protection areas, while 200-m-wide ecological corridors and ecological barriers can be categorized into key ecological restoration areas. This study will provide a scientific basis for constructing the ecological security pattern in territorial space for Dongguan.

In the early morning of August 18, 2022, a flash flood occurred in Datong Hui and Tu Autonomous County, Xining City, Qinghai Province, resulting in 26 deaths and 5 missing. This flash flood is a typical event of multiple casualties caused by a creek disaster. The superimposed effect of rainfall directly led to this flash flood. The early continuous rainfall caused soil moisture content to reach or approach saturation. On the night of August 17, 2022, local short-time heavy rainfall smashing historical records, which could not infiltrate into the soil or be retained by vegetation, resulted in a flash flood. As revealed by the comprehensive analysis of remote sensing data, digital elevation model, field data, and media data, the flash flood area exhibited a large catchment area, a narrow river valley, a high relative height difference, a shallow river channel, and many obstacles. Consequently, the flash flood manifested high potential energy, a long movement distance, and locally severe backwater overflow, destroying some houses, farmland, and roads on both sides of the river channel. Against the backdrop of global changes, low-risk areas of flash floods, including the arid region of northwest China and the Qinghai-Tibet Plateau, display significantly increased precipitation and frequent local short-time heavy rainfall. Hence, creeks in these low-risk areas are exposed to increasing risks of flash floods and even catastrophic ones. Additionally, heavy rainfall might induce the recurrence of flash floods in disaster areas.

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 ecological protection and high-quality development of the Yellow River basin has become a national strategy. Hence, conducting dynamic monitoring research on the extent of water bodies in the beach area of the Yellow River estuary to avoid potential inundation risks from the evolution of water bodies holds critical significance. Based on the Landsat remote sensing image dataset for wet seasons in the long term, this study extracted the maximum water body extents in the beach area of the Yellow River estuary at 10 time points from 1976 to 2020 using the decision tree-based multi-index land surface water body extraction method. Moreover, this study calculated the historical inundation frequency of each zone through overlay analysis, further identifying the inundation risks of urban and rural settlements and mining land. The findings reveal an area of 463.7 km² inundated over five times at 10 time points. Among 631 urban and rural settlements and mining land in 2015, 413, 52, and 20 exhibited low, medium, and high inundation risks, respectively. Overall, it is necessary to specify the relocation requirements, scientifically select relocation sites, and improve the infrastructure targeting construction land like urban and rural settlements in the beach area of the Yellow River estuary.

Rapid and accurate estimation and spatial distribution mapping of soil organic carbon content in farmland facilitate the refined management of soil and the development of smart agriculture. This study investigated three typical farmland areas in the Huangshui River basin of Qinghai Province using 296 soil samples and corresponding field in situ spectra collected synchronously. The unmanned aerial vehicle (UAV) with a hyperspectral camera was employed for image acquisition, and the soil samples were tested for spectral acquisition and organic carbon content in the laboratory. The spectral reflectance was transformed into seven different forms, and the main characteristic bands were screened out through correlation analysis. Using multiple linear regression, partial least squares regression, and random forest, the experimental spectra, field in situ spectra, and UAV spectra were modeled, with the accuracy of the models compared. The UAV spectra were corrected using the direct spectral conversion method, and the optimal model of corrected UAV spectra was used for modeling. The model was substituted into the UAV hyperspectral images for the organic carbon content mapping. Finally, the farmland areas meeting the mapping accuracy requirements were analyzed and discussed. The results show that: ① The multiple linear regression after logarithmic transformation of UAV hyperspectra failed to estimate the organic carbon content, with a relative percent deviation (RPD) of 1.375. Except for it, the experimental spectra, field in situ spectra, and original spectra of UAV hyperspectra as well as all conversion methods could estimate the organic carbon content, with coefficients of determination (R²) ranging from 0.562 to 0.942, root mean square errors (RMSEs) ranging from 1.713 to 5.211. and RPDs between 1.445 and 4.182; ② Among all spectral transformation methods, multiple scatter correction and first-order differential transformation exhibited the highest correlation with the organic carbon content, presenting characteristic bands of 429~449 nm, 498~527 nm, 830~861 nm, and 869 nm; ③ As revealed by the modeling results, the random forest model manifested the highest accuracy, followed by the partial least squares model and the multiple linear regression model in turn. The corrected UAV spectra yielded improved modeling accuracy; ④ The inversion accuracy of the three farmland areas all met the mapping requirements, with R² values above 0.88. Farmland A exhibited the highest average organic carbon content of 28.88 g·kg^-1 and an overall uniform spatial distribution. Farmland B manifested average organic carbon content of 13.52 g·kg^-1 and a significantly varying spatial distribution. Farmland C displayed the lowest average organic carbon content of 8.54 g·kg^-1 and significant differentiation between high and low values. This study can be referenced for the application of UAV hyperspectral remote sensing technology to the field-scale estimation and digital mapping of soil organic carbon content.

Due to the intricate geographical and geological environment, the mountainous-hilly area of Changli in northern Hunan Province is challenged by numerous, widespread, scattered, and frequent landslide hazards, which constitute the most significant geologic hazard that causes casualties and economic losses. The multi-source remote sensing technology integrating InSAR, optical remote sensing, LiDAR, and GIS is currently a high-feasibility and high-precision landslide hazard identification and monitoring technology, meeting the requirements for macroscale and timeliness. This study identified and extracted landslide hazards in the Changli area based on InSAR deformation rate data, multispectral images, and DEM data. First, two decision tree classification methods were employed to classify the land use types based on multispectral images, facilitating the observation of land use types and their distributions in the Changli area. Then, five topographic factors, including elevation, slope, aspect, undulation, and curvature, were extracted from DEM data to evaluate the landslide risk in the Changli area. Then, five topographic factors, such as elevation, slope, aspect, undulation and curvature, are extracted from DEM data to evaluate the landslide risk in the study area. Furthermore, the time-series surface microdeformation of the Changli area was measured based on SBAS-InSAR technology. Finally, landslide hazards were extracted and delineated in the GIS by combining risk assessment results and deformation rates. Additionally, based on the classification and regression tree (CART) results and the river system distribution in the Changli area, risk inference was conducted on zones with deformation rates exceeding -0.01 m/a except the delineated landslide hazard sites. This study identified several small-scale landslide hazards with high concealment in vegetation-covered and bare zones, delineating their spatial distribution ranges, which covered an area of 0.126 km². The multi-source remote sensing technology proved effective, demonstrating certain practical application value.

Under the intertwined effects of natural processes, geological disasters, and human disturbances, the health risks of vegetation in biosphere reserves have increased. Accurately extracting and identifying vegetation health information from complex large scenes faces technical challenges. This study investigated the Changhai pilot zone of the Jiuzhai Valley biosphere reserve by leveraging the macro, objective, and quantitative advantages of remote sensing technology. It proposed a fine-scale remote sensing monitoring method integrated with feature extraction and random forest for large-scene vegetation health, achieving the information extraction and target identification of unhealthy trees in typical biosphere reserves. The results show that: ① The random forest classification method combined with spectral and texture features can accurately extract unhealthy trees scattered in forests from high-resolution remote sensing images; ② The red-green ratio index, normalized difference vegetation index, correlation between red-edge and red bands, and corrected soil-adjusted vegetation index constitute typical features for extracting vegetation health information from remote sensing images; ③ The Changhai pilot zone exhibits a generally fair vegetation health status, with unhealthy trees accounting for 0.23%, and geological disasters exert positive effects on the spatial distribution of unhealthy trees. This study provides primary scientific data for vegetation health diagnosis of the Jiuzhai Valley biosphere reserve while showing generalization value for the remote sensing monitoring of ecological security in other biosphere reserves of China.

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.

Changes in land cover types play a significant role in investigating the changes in regional ecological environments. This study aims to accurately determine the changes in land cover types in the Bayannur section of the Yellow River basin from 1989 to 2020. Based on Landsat data images, and combining visual interpretation with supervised random forest classification, this study interpreted and classified the land cover types of banners/counties within the Bayannur section at an average interval of 10 years from 1989 to 2020. The accuracy verification reveals an overall classification accuracy of above 85% and a Kappa coefficient of above 0.80. As demonstrated by the transfer change matrix of land cover types, the Bayannur section during the study period saw a decrease of 22.17% in sandy land, a reduction of 26.18% in grassland, an increase of 20.83% in cultivated land, and subtle variations in water surfaces. Different areas exhibited distinct changes in land cover types. Desert steppe areas were characterized by mutual transformation between sandy land and grassland. Cultivated and sandy land areas primarily exhibited a shift from sandy land to cultivated land, significantly represented by Dengkou County, where the sandy land decreased by 32.17% and the cultivated land increased by 57.48% in 2020 compared to 1989. Changes in land cover types of desert steppe areas were driven by both social and natural factors, whereas those of cultivated and sandy land areas were predominantly subjected to social factors. The results of this study will provide effective data reference and support for more rational planning and utilization of land space.

Vegetation can indicate the changes in ecological environments. Analyzing the spatio-temporal variations and influencing factors of vegetation phenology holds critical significance for exploring the carbon, water, and energy balance of terrestrial ecosystems. In this study, the MOD13Q1 EVI dataset was employed to extract the start of season (SOS), the growing season length (GSL), and the end of season (EOS) for vegetation in Beijing from 2001 to 2020 using the double logistic (D-L) function fitting method and the dynamic threshold method. The spatio-temporal variations of vegetation phenology in urban and rural areas of Beijing were analyzed by constructing an urban-rural gradient zone. The response of vegetation phenological parameters to climate factors like temperature, precipitation, sunshine, and wind speed, as well as urban heat island intensity and urbanization, was investigated through regression and trend analyses. The results show that from 2001 to 2020, the vegetation phenology of Beijing manifested a trend of earlier SOS, extended GSL, and delayed EOS. Compared to grassland, woodland and shrubs manifested earlier SOS and later EOS, suggesting that the phenology of woody plants started earlier and ended later. As revealed by the relationship between climate factors and phenology, temperature, precipitation, sunshine, and wind speed all displayed certain effects on vegetation phenology in Beijing, with SOS and EOS being the most sensitive to sunshine and wind speed, respectively. The vegetation phenology was characterized by a significant gradient change along the urban-suburban-rural direction. Compared to the rural area, the urban area showed SOS 12.2 d earlier and EOS 18.9 d later on average. The urban nighttime heat island intensity was significantly correlated with the SOS of vegetation in the urban-rural gradient zone (p<0.01). Moreover, the SOS, GSL, and EOS were significantly linearly correlated with population density, urban built-up area, and GDP per square kilometer of land (p<0.01). Therefore, urbanization played a significant role in advancing SOS, extending GSL, and delaying EOS of vegetation phenology in Beijing.

Surface temperature, a significant parameter of surface energy balance and land surface processes, is closely associated with the changes in surface parameters. With the Yanhe River basin in the Loess Plateau as a study area, this study derived the surface temperatures through inversion using the atmospheric correction method based on the Landsat OLI/TIRS images of 2015, 2018, and 2020. Moreover, by extracting the normalized difference build-up index (NDBI), normalized differential vegetation index (NDVI), and normalized difference moisture index (NDMI), this study analyzed the relationships of surface temperatures with surface parameters and land use types, as well as the spatio-temporal variations of surface temperatures. The results demonstrate that the correlation coefficients between the inverted and verified values of surface temperatures in 2015, 2018, and 2020 were 0.569, 0.675, and 0.632, respectively, all exceeding 0.5, suggesting certain accuracy and feasibility. Concerning the spatio-temporal variations of surface temperatures, the low-, sub-medium-, and sub-high-temperature zones exhibited decreased areas, whereas medium- and high-temperature zones manifested significantly and slightly increased areas, respectively, suggesting that the surface temperatures tended to increase towards medium and high temperatures. In terms of the relationship with land use types, the surface temperatures of underlying surface cover types increased in the order of water area, forest land, grassland, cultivated land, and construction land. The quantitative relationship reveals significant correlations between changes in surface temperatures and surface parameters of the Yanhe River basin. Specifically, the changes in surface temperatures were positively correlated with the NDBI but negatively correlated with the NDVI and the NDMI. From the perspective of geographical environment factors, surface temperatures decreased with increasing altitude. Different slopes exhibited distinct surface temperatures, which were the highest on flat slopes and lower on steeper slopes. Additionally, different slope aspects manifested significantly different surface temperatures, which were significantly higher on sunny slopes compared to shady slopes. The findings of this study will serve as a reference for exploring surface water thermal environments in complex areas.

Industrial parks are like the engine of urban economic development. Exploring their spatio-temporal distributions holds critical significance for ascertaining urban spatial structures and sustaining the development of industrial parks. To objectively characterize the spatio-temporal distributions of industrial parks, this study employed the data of buildings made of color steel plates as auxiliary data for investigating industrial parks in Xining City, Qinghai Province. Combined with some information and road network data of industrial parks in the main urban area of Xining City from 2005 to 2020, this study delved into the spatio-temporal distributions of industrial parks over a long period in Xining City using network kernel density analysis, standard deviational ellipse, and equal sector analysis. The results show that: ① From 2005 to 2020, industrial parks in Xining City continued to increase at a growth rate of 73%, with the fastest growth rate observed in Chengbei District; ② Highly clustered industrial parks developed from single to multiple zones. Concerning the changes in the density of buildings made of color steel plates, newly built industrial parks were mostly distributed on the urban edge, and the cluster areas exhibited north-south crossing banded distributions, aligning with the urban spatial structure. Additionally, all industrial parks showed a northwest-southeast distribution and a less significant clustering trend from 2005 to 2020; ③ The expansion of industrial parks manifested phased and zonal development, with a gradually decreased expansion intensity, suggesting the tendency of stable development. The results of this study will provide objective spatio-temporal data support and methodology for the urbanization development research or structural transformation of industrial parks in Xining.

With the economic and social advancement in China, engineering construction has become a primary cause of geologic hazards. The space-air-ground integrated remote sensing monitoring technology can achieve three-dimensional monitoring at different scales, thus providing rich monitoring methods for geological hazards in major engineering construction. Based on the technology, this study investigated the Yanqing competition area of the Beijing 2022 Olympic Winter Games. Considering the various types of geological hazards in the Yanqing competition area, this study conducted dynamic monitoring of geological hazards in the area by integrating high-resolution optical remote sensing, time-series interferometric synthetic aperture radar (InSAR), unmanned aerial vehicle (UAV) photogrammetry, light detection and ranging (LiDAR), and ground-based InSAR. The dynamic monitoring results reveal the sedimentary source variations in the debris flow gully as well as the deformation zones and time-series deformation characteristics of engineering slopes and ski tracks. This study summarized the integrated remote sensing monitoring methods for geological hazards in major engineering construction, proposing an application assumption of multi-means, multi-platform, multi-disaster, and whole-process hazard monitoring.

In recent years, radar remote sensing has been extensively applied to extract high-precision deformation information of landslide surfaces. The techniques used include phase-based interferometry and amplitude-based pixel offset tracking (POT). However, large complex landslides exhibit significantly different deformation magnitudes over the spatio-temporal evolution, complicating the comprehensive monitoring of landslide deformation via single radar remote sensing. Hence, by analyzing the deformation detection capability of radar remote sensing, this study proposed monitoring the whole process of a landslide combined with the phase and amplitude information of synthetic aperture radar (SAR) images. This study investigated the Baige landslide occurring in Jinsha River in 2018 based on Sentinel-1 data from 2014 to 2021 and ALOS-2 data from 2014 to 2018. Combined with time-series interferometric SAR (InSAR) analysis and POT, this study acquired the pre- and post-disaster time-series deformations of the landslide. The results are as follows. Pre-disaster, the trailing edge of the Baige landslide exhibited an average annual rate of 20 mm/a, with deformation of the main landslide area up to about 45 m from December 2014 to July 2018. Post-disaster, the landslide gradually expanded to the trailing edge, with an average annual deformation rate reaching 200 mm/a, threatening the safety of some civilian houses. Therefore, the combined method in this study can achieve the multi-deformation magnitude extraction of large complex landslides from spatio-temporal dimensions.

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.

Based on the Google Earth Engine (GEE) cloud platform and Landsat series data, this study classified the surface features of the Bamen Bay area using the support vector machine (SVM) classification method. Furthermore, the classification results were employed to monitor the inter-annual variations of mangrove forests in the area. The analysis reveals that mangrove forests and terrestrial trees exhibit extraordinarily similar reflectance spectral curves except for infrared bands. Hence, they were effectively distinguished using the infrared band feature index and topographic data, achieving an overall classification accuracy of 91%. The classification results show that mangrove forests in the study area manifested a trend of decrease followed by increase. Specifically, they decreased from 2009 to 2013, remained almost unchanged from 2014 to 2016, and increased slowly from 2017 to 2021. The increase in mangrove forests and the decrease in pits and ponds occurred following the wetland restoration policy that requires planting mangrove forests in South China and tamarix chinensis in North China, suggesting remarkable effects of the policy for returning ponds to forests. The transfer matrix analysis reveals a mutual transfer between mangrove forests and pits, ponds, suggesting that deforesting for ponds and returning ponds to forests constitute the primary factors influencing the variations in mangrove forests. The inter-annual variation monitoring results of mangrove forests enable detailed analysis of the evolutionary process of mangrove forests and accurate quantification of the transformation between mangrove forests and other land types. Therefore, the factors influencing mangrove forest evolution can be analyzed from the perspective of economy and policy for more effective preservation of mangrove forests.