To address the accuracy reduction in the semantic segmentation of remote sensing images due to insufficient extraction of contextual dependencies and loss of spatial details, this study proposed a semantic segmentation method based on context- and class-aware feature fusion. With ResNet-50 as the backbone network for feature extraction, the proposed method incorporates the attention module during downsampling to enhance feature representation and contextual dependency extraction. It constructs a large receptive field block on skip connections to extract rich multiscale contextual information, thereby mitigating the impacts of scale variations between targets. Furthermore, it connects a scene feature association and fusion module in parallel behind the block to guide local feature fusion based on global features. Finally, it constructs a class prediction module and a class-aware feature fusion module in the decoder part to accurately fuse the low-level advanced semantic information with high-level detailed information. The proposed method was validated on the Potsdam and Vaihingen datasets and compared with six commonly used methods, including DeepLabv3+ and BuildFormer, to verify its effectiveness. Experimental results demonstrate that the proposed method outperformed other methods in terms of recall, F1-score, and accuracy. Particularly, it yielded intersection over union (IoU) values of 90.44% and 86.74% for building segmentation, achieving improvements of 1.55% and 2.41%, respectively, compared to suboptimal networks DeepLabv3+ and A2FPN.

To address the issues of missing small-scale surface features and incomplete continuous features in segmentation results, this study proposed a densely connected multiscale semantic segmentation network (DMS-Net) model for land cover segmentation. The model integrates a multiscale densely connected atrous spatial convolution pyramid pooling module and strip pooling to extract multiscale and spatially continuous features. A position paralleling Channel attention module (PPCA) is employed to assess feature weights for high-efficiency expression. A cascade low-level feature fusion (CLFF) module is applied to capture neglected low-level features, further complementing details. Experimental results demonstrate that the DMS-Net model achieved an overall accuracy (OA) of 89.97 % and a mean intersection over union (mIoU) of 75.59 % on an iteratively extended dataset, outperforming traditional machine learning methods and deep learning models like U-Net, PSPNet, and Deeplabv3+. The segmentation results of the DMS-Net model reveal structurally complete surface features with clear boundaries, underscoring its practical value in multiscale extraction and analysis of remote sensing information for land cover.

Change detection using synthetic aperture radar (SAR) images based on deep learning has been a significant research topic in the field of remote sensing. However, it is limited by unreliable training samples and highly time-consuming training. Hence, this study proposed a novel unsupervised change detection method using SAR images based on the broad learning system (BLS). First, a reliable pre-classification method is presented by incorporating neighborhood information into similarity operators, adaptive dual-threshold segmentation, superpixel correction, and visual saliency analysis. This pre-classification method generates a pre-classification map and corresponding training samples. Second, the BLS network is trained using the training samples to generate the BLS-based prediction map for change detection. Third, the pre-classification map and the BLS-based prediction map are fused through two-stage voting to generate the final change detection map. The experimental results of five real SAR image datasets show that the proposed method can produce more reliable training samples and achieve higher accuracy in change detection. Moreover, its efficiency is significantly higher than the change detection model using SAR images based on deep learning.

To efficiently monitor citrus greening (also called Huanglongbing in Chinese) at the large plot scale, this study investigated the healthy, yellowing, and Huanglongbing-affected citrus leaves sampled quarterly from the ground in Mengshan County in Guangxi Province. By performing polymerase chain reaction (PCR), chlorophyll content, and hyperspectral detections on these leaf samples, this study analyzed the variation patterns of citrus characteristics under different states, extracting the effective bands and image features for Huanglongbing monitoring. Furthermore, this study constructed a monitoring model for healthy citrus to reduce the objects to be discriminated and identify abnormal citrus growth plots. Finally, this study extracted the Huanglongbing-affected plots using a multi-classifier algorithm based on the effective features from multitemporal Sentinel-2 images. The results of this study indicate that the Huanglongbing-affected and yellowing leaf samples yielded highly similar chlorophyll contents. In March and December, the Huanglongbing-affected citrus exhibited higher chlorophyll content compared to the yellowing citrus. However, the case was the opposite in June and September. The hyperspectral curves suggest that December is a significant period for identifying Huanglongbing and yellowing. The wavelengths ranging from 530 nm to 650 nm and 740 nm to 1050 nm proved effective for diagnosing Huanglongbing and yellowing. The feature indices based on the Sentinel-2 image for December, including the normalized difference vegetation index (NDVI), land surface water index (LSWI), nitrogen reflectance index (NRI), green normalized difference vegetation index (GNDVI), and inverted red edge chlorophyll index (IRECI), could effectively distinguish between healthy and abnormal growth plots of citrus. The feature indices based on the Sentinel-2 images covering periods from October to December and January to February of the following year, including the NDVI, modified normalized difference water index (MNDWI), normalized difference water index (NDWI), GNDVI, inverted red-edge chlorophyll index (IRECI), modified chlorophyll absorption ratio index 2 (MCARI2), normalized difference index based on Landsat bands 4 and 5 (NDI45), and pigment specific simple ratio chlorophyll index (PSSRa), showed advantages in monitoring Huanglongbing. The identification accuracy of Huanglongbing-affected plots in Mengshan County was 86.6 %, with a missed detection rate of 7.8 % and an error rate of 10.4 %. In 2021, Mengshan County held 964 Huanglongbing-affected plots covering an area of 220.13 hm², with an incidence rate of 2.02 % for large-scale Huanglongbing, mainly concentrated in Xinxu, Wenxu, and Mengshan towns, and Xiayi Yao Township. The combination of satellite remote sensing and ground measurement enables large-scale monitoring of Huanglongbing-affected plots. The monitoring technology in this study provides novel insights for the large-scale monitoring, prevention, and control of Huanglongbing.

In cloudy and rainy areas, the humid and hot climate and cloud contamination during the rainy season often cause the loss of optical data. Hence, optical data alone fail to enable the accurate monitoring of abandoned land. This study proposed a method for monitoring abandoned land in cloudy and rainy areas based on multisource remote sensing data. By integrating optical and synthetic aperture Radar (SAR) remote sensing data, this study extracted the multitemporal optical and SAR-derived features of vegetation and assessed their importance using the GINI index. Employing the random forest classifier, this study mapped the spatial distribution of abandoned land in Jiexi County in 2021. The results show that the proposed method achieved a relatively high accuracy in identifying abandoned land in cloudy and rainy areas, yielding an overall accuracy of 87.0%. This value represents an improvement of 6.7 and 13.8 percentage points, respectively, compared to the results derived solely from optical and SAR remote sensing features. The analysis reveals that the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), polarization entropy, normalized difference water index (NDWI), and anti-entropy are crucial for identifying abandoned land. Additionally, key months for distinguishing abandoned from non-abandoned land include February, April, June, August, and December. This study establishes a monitoring model for abandoned land based on multisource features and multitemporal phases, providing technical support for monitoring abandoned land in cloudy and rainy areas.

Arable land in hilly and mountainous areas exhibits small, narrow, and complex structures with blurred boundaries, posing challenges in extracting arable land information quickly and accurately. Hence, this study proposed a model for extracting the information on sloping arable land based on improved DexiNed and DeepLabv3+ networks in a cascade connection. First, the backbone network of the DeepLabv3+ model uses MobileNetV2 to replace the original Xception model. A closely related low-level information extraction method preliminarily fuses the lower- and higher-level information as the input of the original low-level information. Second, the original atrous spatial pyramid pooling (ASPP) module of the DeepLabv3+ model is optimized through dilated convolution, with dilation rate values of 2, 4, 8, and 16. Third, cascaded edge detection technology enables the interconnection of arable land edges and semantic features. The proposed model was applied to extract information on arable land in the Lufeng Dinosaur Valley in Yunnan Province using the GF-2 image as the data source. The results show that the proposed model with an improved architecture and algorithm identified the arable land more accurately, with the extraction results closely matching the image with real arable land annotated. With reduced extraction missing and errors, the proposed model exhibits enhanced accuracy and stability overall.

Extracting information about bare land is crucial for territorial planning, environmental protection, and sustainable development. However, current information extraction methods for bare land struggle to balance the extraction efficiency and accuracy in large-scale and multitemporal applications. This study constructed normalized difference indices based on the analysis of the hue-saturation-value (HSV) features. By combining texture features and vegetation index, this study proposed a simple, efficient hierarchical fine-scale information extraction method for bare land. This proposed method was applied to the urban area of Qufu City, Shandong Province, China. First, with three GF-1 satellite images as the data source, the red, green, and blue bands from the images were converted to the HSV color space. Based on the differences in H, S, and V components between bare land and other land types, the normalized difference SH and SV indices were constructed for preliminary hierarchical information extraction of bare land. Second, texture features were introduced to low-rise building areas and bare land, where the differences in H, S, and V components are nonsignificant. Different texture features were comparatively analyzed for further information extraction of bare land. Third, the normalized difference vegetation indices were used to achieve the final information extraction of bare land, followed by post-processing of the results. The results of this study demonstrate that the constructed normalized difference indices, combined with homogeneous texture features, showed the optimal extraction performance, with an overall accuracy of above 93% and a Kappa coefficient of above 0.84, outperforming other classification methods. Therefore, the proposed method proves effective in extracting information about bare land, serving as a novel approach for bare land information extraction.

Precipitation products, including the Global Precipitation Measurement (GPM) mission, have been widely used in river basin studies due to their advantages like continuous distributions and broad spatial ranges. However, they are limited by insufficient accuracy and low spatial resolution. Based on the random forest (RF), this study integrated multisource influencing factors to generate two daily precipitation products with high spatial resolution: RF1 and RF2. The two daily precipitation products were input to the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) model to simulate daily runoff changes in the Xinjiang River basin. Finally, this study assessed the contributions of RF1 and RF2 to the improvement of GPM’s hydrologic applicability. The results show that both RF1 and RF2 improved the accuracy and distribution details of GPM data. RF2 exhibited a higher correlation and lower error, whereas RF1 manifested superior performance in detecting precipitation events. The RF1-simulated runoff curves resembled GPM-derived curves, showing significant improvements. RF2 corrected partial GPM’s overestimates and more accurately revealed the peak values of real flow curves in some periods. However, the uneven distribution of monitoring stations affected RF2’s prediction in complex terrain areas, limiting its simulation accuracy. Overall, both RF1 and RF2 can effectively reflect daily precipitation changes in the Xinjiang River basin, improving GPM’s hydrologic applicability to varying degrees.

In recent decades, the surface mass balance (SMB) and the calving of outlet glaciers have accelerated the mass loss of the Greenland Ice Sheet (GrIS), with SMB’s contribution continuing to increase. Therefore, determining SMB’s spatiotemporal distribution is critical for understanding the mass balance of the GrIS. Currently, the regional climate model and the remote sensing observation of outlet glacier flux gates serve as two primary calculation methods for the GrIS’s SMB. However, the former method results in large uncertainties in the SMB simulation. The latter method can only indirectly estimate the overall SMB value for the upper reaches of the flux gate, failing to reflect the spatial distribution of SMB. This study proposed a method for estimating the GrIS’s SMB based on remote sensing data and ice flux divergence, obtaining the relatively accurate spatial distribution of SMB. First, the interannual variation in the elevation of the GrIS was derived from ICESat-2 laser altimetry data. Second, based on MEaSUREs-derived glacier flow velocity data and BedMachine-derived ice thickness data, the ice flux divergence was calculated using the pixel-based finite difference method to estimate the GrIS’s elevation changes caused by glacier flow. The GrIS’s elevation changes caused by SMB were then obtained by subtracting the elevation changes caused by glacier flow from the ICESat-2 elevation data. Third, through the firn densification model, the elevation changes caused by SMB were converted into mass changes to reflect the interannual spatial distribution of the GrIS’s SMB. The proposed method was applied to estimate the spatial distribution of the GrIS’s SMB in 2019 and 2020, yielding relatively high accuracy (RMSE=0.519 m w.e.) in comparison with the measured SMB from the observation station, and outperforming the regional climate model (RMSE=0.565 m to 0.877 m w.e.), ultimately demonstrating its reliability.

Green tides have emerged as a significant marine ecological disaster worldwide, rendering the accurate detection and area estimation of green algae crucial. To accurately estimate the coverage area of green algae communities in the monitoring of green tides based on low-resolution satellite images, this study proposed a dictionary learning-based method for estimating the area of green algae using hyperspectral images. The proposed method involves deriving the endmember spectrum database that is closest to the unknown surface feature spectra via online robust dictionary learning, obtaining the abundance map of green algae through sparse coding, and calculating the coverage area of green algae. It was verified through the experiment using the spectral images acquired by the geostationary ocean color imager (GOCI) on June 25, 2016, and June 21, 2020. The experimental results reveal that the calculated coverage areas of green algae on the two days were highly close to the approximate measured results, with a minimum error of only 2.15 %, suggesting that the proposed method outperforms traditional index-based hard thresholding algorithms. Independent of the pure pixel hypothesis, the proposed method can effectively address the mixed pixel problem and enhance area estimation accuracy in the absence of a pre-estimated number of endmembers or prior spectral information, thereby achieving high-precision subpixel-level area estimation of green algae.

In recent years, global warming has caused significant glacier retreats in the Qinghai-Tibet Plateau, leading to the rapid expansion of glacial lakes and an increased threat of glacial lake outburst floods (GLOFs). The Yadong River basin, located in Shigatse, Tibet, hosts a large number of glacial lakes. In 1940, a GLOF from Qiongbixiama Co severely damaged Yadong County 40 km downstream, causing house collapse and infrastructure destruction. Therefore, assessing the hazards of potential GLOSs in the Yadong River basin is vital for protecting the safety of people’s lives and property. This study conducted a survey and assessment of potential GLOFs based on Gaofen satellite data and Landsat remote sensing images. It derived basic elements including glaciers, glacial lakes, and moraines through remote sensing image interpretation. The results show that the Yadong River basin develops 28 glaciers and 228 glacial lakes, covering total areas of 34.03 km² and 7.79 km², respectively. The past 30 years have seen slight retreats of glaciers and slight expansions of glacial lakes. Combined with the elements derived from remote sensing images, the analytic hierarchy process and fuzzy comprehensive assessment were employed to preliminarily assess the hazard of regional glacial lakes, delineating the distribution of potentially hazardous glacial lakes. The assessment results reveal 15 potentially hazardous glacial lakes in the Yadong River basin, including five highly hazardous, eight moderately hazardous, and two slightly hazardous glacial lakes. These lakes are primarily distributed in the northeastern and northwestern high mountain areas in the Yadong River basin.

Over the past few decades, excessive groundwater exploitation has led to a significant decrease in the groundwater level and serious land subsidence in Taiyuan City. In recent years, Taiyuan has vigorously implemented strict groundwater management measures and the project of water diversion into Shanxi from the Yellow River, substantially alleviating groundwater overexploitation and gradually recovering groundwater levels in the city. Therefore, it is necessary to scientifically assess the effect of groundwater level revovery on land subsidence. Based on 2003—2010 synthetic aperture radar (SAR) data from ENVISAT and 2017—2021 SAR data from Sentinel-1, this study extracted the land subsidence information of Taiyuan City of both periods using persistent scatterer interferometric SAR (PS-INSAR). Accordingly, this study compared and analyzed the temporal evolution of land subsidence during the two periods by combining the groundwater extraction volumes, water volumes diverted from the water diversion project, and data on groundwater levels. The results show that the land subsidence in Taiyuan City has been significantly mitigated, with the urban area having shifted from subsidence to uplift. In the Xiaodian area, which underwent the most serious land subsidence, the subsidence area expanded. Nevertheless, the overall land subsidence rate decreased, and the subsidence center has moved southward. The main cause for the slowdown of the land subsidence and even the land uplift in Taiyuan is the continuous groundwater level recovery attributed to the reduced groundwater exploitation and the water diversion project. The results of this study provide a scientific basis for fine-scale land subsidence prevention and groundwater management in Taiyuan City under conditions of groundwater level recovery.

The Ke’eryin rare metal ore concentration area in the western Sichuan Basin (also referred to as the Ke’eryin ore concentration area), located in the eastern segment of the Songpan-Ganzi metallogenic belt, boasts abundant granitic pegmatite-hosted rare metal resources like lithium, niobium, beryllium, and tantalum. It stands as one of the most concentrated areas for hard-rock rare metal deposits in China, following the granitic pegmatite-type rare metal ore concentration areas in the Koktokay area in Altay in Xinjiang, and the Jiajika and Jiulong areas in the western Sichuan Basin. The Ke’eryin ore concentration area is characterized by tectonic denudation and deeply cut high mountains, resulting in inconvenient transportation, dense vegetation, and steep terrains. Consequently, most parts of the area are inaccessible to humans, hindering the implementation of traditional geological surveys. Based on the features and interpretation keys of high-resolution remote sensing images for ore-bearing granitic pegmatite veins in known deposits within the Ke’eryin ore concentration area, this study performed laboratory interpretation and partial field verification of these veins, revealing the distribution patterns and characteristics of granitic pegmatite veins in the area. Rare metal deposits hosted by granitic pegmatite veins intruded into weak structures such as fault zones and surrounding rock fractures within a range of 0 km to 5 km on the margin of the Ke’eryin complex rock mass. The exposed granitic pegmatite veins and boulders were identified as the most direct indicators for locating rare metal deposits. A prospecting method based on remote sensing geology was developed for highly vegetation-covered areas in the Ke’eryin ore concentration area, effectively addressing the limitations of traditional geological prospecting methods. Using the developed method, this study determined three critical prospecting areas in the northern and northwestern portions of the Ke’eryin complex rock mass, establishing them as the targets for subsequent strategic prospecting breakthroughs.

To determine the origin of surface substrate for soil salinization and alkalization in the Songnen Plain, this study investigated Songyuan City based on Sentinel-2 multispectral images. Considering various commonly used indices like the soil salinity index (SSI), soil water index (SWI), and vegetation index (VI), this study constructed the optimal 3D spectral feature model to calculate the soil salinization-alkalization index (SSAI) for inversion of the soil salinization-alkalization status. Surface water and groundwater in areas subjected to soil salinization and alkalization were sampled to test their salt ion concentrations, followed by the analysis of salt ion sources according to the groundwater levels. The surface substrate was explored through planar grid layout and vertical stratified sampling. A total of 2 362 soil samples were collected in various layers within a depth of 5 m to test their pH and texture for the construction of a 3D surface substrate model. The results of this study reveal a positive linear correlation between the inversion result of remote sensing data and the topsoil salt content (coefficient of determination: R²=0.74). The study area was characterized by alkalization of sodium bicarbonate, with soil salt ions originating primarily from groundwater. The deep multilayer argillaceous soils acted as an aquiclude to prevent the downward infiltration, migration, and dilution of salt ions along with water. This surface substrate condition serves as the objective cause of soil salinization and alkalization in the study area.

Land use demands vary under different development objectives. Scientifically and rationally regulating changes in land use are crucial to efficient land resource utilization and achieving ecological, developmental, and economic coordination in the Pearl River Delta urban agglomeration. Based on the land use data of the urban agglomeration of 1990, 2000, 2010, and 2020 and using the FLUS-Markov model, this study predicted the quantity and spatial changes in land use in the Pearl River Delta urban agglomeration by 2035 under three scenarios: natural development, ecological protection, and development priority. Furthermore, this study determined the differences in land use change under the three scenarios. Additionally, a simulation analysis of the land use in 2035 was conducted to facilitate the optimized land and space allocation under varying developmental objectives. The results indicate significant changes in the use of construction land in the Pearl River Delta urban agglomeration. From 1990 to 2020, the area of construction land, including urban land and infrastructure land increased by 4 945.25 km², representing an increase of 2.8 times. The simulations and predictions under three land use scenarios reveal that the urban land area will trend upward by the end of 2034, with its expansion speed being restricted under the ecological protection scenario, while the ecological land, such as forest land, grassland, and water area, will maintain an increasing trend until 2035. From 1990 to 2020, the arable land area decreased by 3 759.5 km². Under the three land use scenarios, the trend of arable land reduction will continuously decrease until 2035, with the decreasing trends slowing down from 2020 to 2035. Especially, under the development scenario, the area of construction land will continue to increase, the decreasing trend of the arable land area will be somewhat curbed, while the area of grassland and forest land will undergo a more serious decrease. Although dominant factors affecting arable land protection in the Pearl River Delta urban agglomeration vary across different development stages, the main factor is infrastructure construction such as rail transit roads.

This study designed a one-stop platform for automatically extracting patches from multisource domestic satellite images based on a deep learning framework. The platform focuses primarily on critical techniques including semantic segmentation of ground objects, swarm intelligence algorithms for patch extraction, and deep feature interpretation. To address challenges in remote sensing image interpretation, such as significant color differences, vast data volumes of single images, diverse multi-channel image representations, and considerable differences in the sizes of remote sensing targets, the platform incorporates intelligent semantic segmentation and swarm intelligence algorithms for automatic patch extraction into the framework. It offers a range of customizable general and specialized models while supporting the self-training of models. With functions including large-scale data management, data annotation, model training, model testing, patch extraction, and application analysis, the platform has been successfully applied to the intelligent semantic segmentation and patch extraction of ground objects like buildings, vegetation, farmland, industrial zones, and water bodies in Taiyuan City, Shanxi Province based on multisource domestic satellite images.

Exploring the response of ecosystem services to the changes in production-living-ecological land use under different scenarios is critical for the sustainable development of territorial space and the improvement of regional systems. This study investigated the ecological economic belt along the Yellow River in Ningxia Hui Autonomous Region. Based on the PLUS-MarKov model, it simulated the changes in production-living-ecological land use in the ecological economic belt in 2030 under the natural development scenario (NDS), ecological protection scenario (EPS), and urban development scenario (UDS). Moreover, it explored the response characteristics of ecosystem service value (ESV) to the changes in production-living-ecological land use. The results show that the production land exhibits both increase and decrease in the Yinchuan and Weining plains. The ecological land is somewhat reduced due to internal structural adjustment and optimization, concentrated in Shapotou, Baijitan, the Yellow River riparian shelterbelt in the north-central Yinchuan Plain, and the loess hilly soil and water conservation area in the south. The land for living continues to increase, expanding from urban to surrounding areas. The NDS reveals a reduced rate of changes in the production-living-ecological land use, while the EPS indicates a reduced rate of shift in ecological land use. In contrast, the UDS shows a higher rate of increase in land for living and higher rates of decrease in ecological and production land. The ESV is improved under the three scenarios, specifically in decreased order of EPS (11.48×10⁸ yuan), UDS (2.74×10⁸ yuan), and NDS (1.89×10⁸ yuan). The ESV improvement areas extend primarily from the north-central Yinchuan Plain, Weining Plain, and Lingyan Platform to the peripheries. The ESV reduction areas show varying distributions under different scenarios. To achieve high-quality development, the ecological economic belt should prioritize ecological protection, prevent the disorderly spreading of land for living, and ensure the retention of production land.

This study aims to explore the background conditions of the natural resource elements and the dynamic distribution of ecological conditions in typical agricultural and pastoral intertwined zones. Focusing on the Zhangbei area, this study selected 15 assessment factors by comprehensively considering four types of resources: land, vegetation, groundwater, and minerals. Accordingly, this study conducted a comprehensive assessment of natural resource elements in the study area using the analytic hierarchy process (AHP). Meanwhile, the 2019—2022 average remote sensing ecological index (RSEI) values were calculated using the Google Earth Engine platform. Based on these results, the study area is divided into six ecological functional zones. The results indicate that from 2019 to 2022, the natural resource dominance and RSEI values of the study area showed a gradual decreasing trend from the low mountainous area encircling the southern part to the hilly area in the northwestern part. The study area exhibited moderate natural resource dominance, with RSEI values all exceeding 0.52. This suggests relatively favorable ecological conditions. In addition, the assessment results of natural resource dominance are closely related to RSEI, showing a positive correlation spatially. This verifies the importance of the background conditions of natural resource elements on ecological status. The ecological function zoning proposed in this study can serve as a scientific reference for the planning and development of Zhangbei areas.

Constructing a regional green infrastructure (GI) network can alleviate the contradiction between land use and ecological development in the process of rapid urbanization, playing a significant role in future urban planning. This study investigated Lincang City, a typical mountain city in Southwest China. Employing the patch-generating land use simulation (PLUS) model, this study predicted the land use and land cover (LULC) in Lincang City in 2030 under the ecological priority scenario. Furthermore, this study extracted information about the ecological source areas and corridors by integrating the morphological spatial pattern analysis (MSPA), minimum cumulative resistance (MCR) model, and circuit theory. Finally, this study constructed an optimized GI network for 2030 adapted to the sustainable expansion of Lincang City. The results show that from 2020 to 2030, the construction land area in Lincang City is projected to expand by about 23 %, while forest land and grassland will decrease by 0.2 % and 1.3 %, respectively. The water area is expected to increase by 46.9 % under reasonable management and protection. The core zone of GI landscape elements will represent 56.12% of the total area, while the edges will make up 21.3%. The spurs, bridging zones, islets, perforations, and circuits will constitute the rest 22.6%. Under the sustainable urban expansion scenario, the GI scale remains overall stable, with a relatively scattered distribution in built-up areas. The optimized GI network will involve 12 ecological source areas and 24 ecological corridors. The GI network of Lincang City in 2030 constructed based on the MSPA-PLUS model strengthens the understanding of the GI network for the sustainable development of a mountain city, adapting to future urban development. This study provides novel insights into the urban planning of mountain cities including Lincang and critical implications for GI protection and regional ecological security maintenance.

Investigating spring phenology is critical for understanding the growth and development cycles of vegetation and the response mechanisms to climate and environmental changes. It also provides significant insights for guiding agricultural production and protecting and restoring ecosystems. This study reconstructed the time series of MOD13Q1 data for Beijing City from 2000 to 2022. Based on dynamic thresholding, this study extracted the spring phenology of vegetation in Beijing City over the past 23 years. Furthermore, this study analyzed the spatiotemporal changes in spring phenology in Beijing City using the Mann-Kendall (M-K) trend test. Finally, this study examined the differential responses of spring phenology to climate change through partial correlation analysis. The results of this study indicate that the average spring phenology of vegetation in Beijing City occurred on the 117th day of a year (in late April), advancing at an average rate of approximately 1.14 days per year over the past 23 years. Different duo exhibited distinct hierarchical variations in spring phenology. Forests showed the earliest spring phenology starting from the 107th day, followed by shrubs (the 117th day) and grasslands (the 119th day), with the latest being farmland (the 130th day). The impacts of average annual temperature on spring phenology exhibited significant spatial variations. A positive correlation was observed in water-rich areas such as rivers and reservoirs, whereas a significant negative correlation occurred in eastern Fangshan District. On a monthly scale, temperatures in November, December, January, and February significantly influenced spring phenology. As winter temperatures rose, the spring phenology of vegetation tended to advance. This study explores the response mechanisms of spring phenology of vegetation in Beijing City to temperature and precipitation, providing valuable insights for vegetation management under climate change.

The ecological security pattern serves as an indicator of ecosystem health and sustainability, playing a crucial role in enhancing human well-being. This study identified ecological source areas in the Guanzhong Plain based on three ecosystem services, including water conservation, soil conservation, and habitat provision. Considering regional characteristics, this study selected soil erosion sensitivity index, normalized difference vegetation index (NDVI), and nighttime lighting as disturbance factors to correct the basic resistance surface and identify ecological corridors. The results indicate that the primary and secondary ecological source areas in the Guanzhong Plain cover 3 011.85 km² and 8 434.51 km², respectively, corresponding to 5.22% and 14.62% of the total area. These areas, characterized by mountainous terrain and high vegetation cover, are primarily distributed in the Qinling Mountains in the south, the hilly and gully regions in northern Baoji City, and the junctions of Xianyang, Tongchuan, and Weinan cities. The resistance surface correction for Guanzhong Plain reduced 61 ecological corridors (total length: 1 613.4 km), leading to significant changes in their distribution, and ultimately rationalizing corridor identification. Overall, this study provides a novel case for constructing regional ecological security patterns and data support for ecological conservation and urban planning in the Guanzhong Plain.

Alpine wetlands, a critical part of the natural ecosystem in the Qinghai-Tibet Plateau, serve as extremely significant water conservation and climate regulation areas in China. Accurately extracting land cover information of alpine wetlands is crucial for local ecological security monitoring and protection. This study performed object-oriented classification of the data from the Zoige wetland, including the Zhuhai-1 hyperspectral remote sensing image, Sentinel-2A remote sensing image, and Landsat-8 OLI image, integrated with spectral, textural, and topographic features. The results show that the overall data classification accuracy of the three images exceeded 85 %, with a Kappa coefficient above 68 %. The optimal classification result was observed in the Zhuhai-1 hyperspectral remote sensing image. The three images showed generally consistent data classification results, with marsh wetlands being the dominant land type. They indicated roughly the same distribution of riverine and lacustrine wetlands and slightly varying distributions of alpine grasslands, with minor area differences. Additionally, they displayed minimally different distributions of desertified land and almost the same hydrographic net distribution except for slightly different tributary distributions. This study fully explores the combinations of spectral features favorable for image classification, improving the identification accuracy of remote sensing images and providing technical support for the conservation of alpine wetlands.

To explore the surface albedo responses to forest fires in the Great Xing’an Range, China, this study investigated the forest fire occurring in the zone under the supervision of the Jinhe Forestry Bureau on May 5, 2003. The changes in the surface albedo after the forest fire were analyzed based on the global land surface satellite (GLASS)-derived surface albedo and leaf area index (LAI) data. The results indicate that the surface albedo in the burned zone decreased in the short term (1 a) but increased significantly at a rate of 0.001 2/a in the mid- to-long term (10 a). The increasing trend in the surface albedo was slightly influenced by contemporaneous climate changes and human activities but was closely associated with the vegetation restoration process after the forest fire. Moreover, the increase in the surface albedo in the burned zone was highly correlated with LAI increase (r=0.682, p<0.01). Additionally, the vegetation masking effect further enhanced the increasing trend in surface albedo in the burned zone during the snow-covered period. Overall, the results of this study deepen the understanding of spatiotemporal variations in the surface albedo, laying a foundation for thoroughly assessing the influence of forest fires on global climate changes.

The change in carbon stocks is recognized as an important indicator of the carbon pool function. The effective, accurate assessment of carbon stocks is of great significance for research on regional carbon cycle and carbon source/sink dynamics, climate change mitigation, and regional sustainable development. Based on multi-time series remote sensing images (Sentinel-1 and Sentinel-2) and the Google Earth Engine (GEE) cloud computing platform, this study matched the photon point data of ICESat-2-derived vegetation canopy for the inversion of mangrove forest heights. Then, the inversion of the biomass of mangrove forests was conducted using a large-scale tree height-biomass model. Consequently, the heights, above-ground biomass, and carbon stocks of mangrove forests in Hainan Island were obtained, and their distribution and variations were further analyzed. The results indicate that in 2016, 2019, and 2022, mangrove forests in Hainan Island exhibited average heights of 6.99 m, 7.26 m, and 7.71 m, respectively, with an increasing trend observed in the highlights across all regions in the three years. Their total above-ground biomass was 400 939.48 t, 411 928.64 t, and 458 759.02 t, respectively, with average densities of 110.23 t/hm², 114.61 t/hm², and 120.02 t/hm², respectively. The above-ground biomass of Dongzhai Port and the Bamenwan area, both located in the northeastern part of Hainan, accounted for about 80% of the total. The carbon stocks of mangrove forests exhibited rates of increase ranging from 1% to 4.45% over the three years, with the top two growth rates occurring in Dongzhai Port and the Bamenwan area, respectively (4.45% and 3.17%). The results of this study can provide foundational data and a methodological reference for assessing carbon stocks of large-scale mangrove forests and serve as important parameters for mangrove forest management and protection in Hainan Island, holding THE value of widespread applications.

To explore the spatiotemporal evolution of the bottomland in Dongting Lake since the middle stage of the Republic of China, this study examined the historical topographic maps and aerospace remote sensing data concerning the study area for over 10 time periods since the 1930s. Based on remote sensing image interpretation, statistical data analysis, and historical comparison, this study analyzed the temporal variations in the bottomland area of Dongting Lake in various periods to infer the corresponding spatial distributions of the bottomland. The results show that the spatial development of the bottomland in Dongting Lake was primarily characterized by the rapidly advancing delta at the mouth of the east branch of the Ouchi River and Piaowei Islet in East Dongting Lake, the alluvial deposits along the Caowei and Songzhu rivers in the north of South Dongting Lake, and the “Jiangnan Grassland” landscape formed by the bottom uplift of Qili and Muping lakes. The bottomland area in Dongting Lake expanded from 1 622.17 km² in 1 938 to 1 962.28 km² in 2018, coupled with the 980.96 km² of reclaimed high bottomland, suggesting a net increase of 1 321.07 km². In terms of spatial distribution, the bottomland area exhibited an undulating trend rather than a continuous increase. It manifested a significant expansion from 1938 to 1948 and 1958 to 1998 but a slow shrinkage from 1948 to 1958 and 1998 to 2018. Overall, the results of this study provide objective data for preserving lakeshore ecosystems and biodiversity and serving ecological restoration and environmental conservation in the Yangtze River basin.

Mangrove forests are recognized as one of the most biodiverse and productive marine ecosystems globally. This study investigated Beibu Gulf, Guangxi Province. Using Landsat, Sentinel, and PALSAR SAR images from 1985 to 2019 as data sources, as well as the Google Earth Engine (GEE) cloud platform, this study established a multisource dataset by integrating spectral bands, spectral indices, texture features, digital elevation models (DEMs), and backscatter coefficients. Furthermore, 14 classification schemes were developed, and a mangrove remote sensing recognition model was built using an object-based random forest (RF) algorithm. Accordingly, the long-time-series spatiotemporal dynamics of mangrove forests in Beibu Gulf were monitored. The monitoring results show that the object-based RF algorithm demonstrates a high ability to identify mangrove forests. Specifically, Scheme 3 combined with data from 2019 yielded the highest overall accuracy (96.3%) and a kappa coefficient of 0.956, which are 16.3% and 0.195 higher than those of Scheme 1 combined data from 1995, respectively. The classification schemes differed in the producer’s and user’s accuracy of different surface features in the Beibu Gulf. Specifically, these schemes yielded the highest user’s and producer’s accuracy of mangrove forests exceeding 94.6% and 92.0%, respectively. From 1985 to 2019, the area of mangrove forests in Beibu Gulf showed an increasing trend, with an annual changing rate of 6.63%, and the area expanded from inland to coastal areas. The results of this study provide a reference for the protection and sustainable management of mangrove forests while also verifying the feasibility of monitoring long-term spatiotemporal dynamics of mangrove forests based on the GEE platform.

This study investigated the core area of the Jiangsu Yancheng Wetland National Reserve of Rare Birds based on 12 remote sensing images from 1983 to 2021 as data sources. Specifically, this study explored the spatiotemporal trajectory of Spartina alterniflora expansion and its impact on landscape patterns by combining landscape ecology methods with geographical information system (GIS) technology. The results show that from 1983 to 2021, Spartina alterniflora expanded significantly, leading to a 12.365 fold area increase. It experienced initial expansion, accelerated growth, and stagnation stages, which would be succeeded by the control and elimination stage. Its distribution area manifested a significant linear relationship with time. Its spatiotemporal trajectory was characterized by the migration of the western, eastern, and central clusters. In 1983, 1988, 1992, and 1997, the western clusters migrated primarily toward the southeast. In 2000, 2002, 2006, and 2009, the eastern clusters migrated principally toward the northeast. Both the western and eastern clusters showed a seaward trend. In 2011, 2014, 2017, and 2021, the central clusters displayed a significant landward trend despite disorderly migration. Spartina alterniflora expansion resulted in a cumulative contribution rate of 43.352% to regional landscape structure changes. Its contribution was consistent with its expansion stages, showing a low-high-low pattern. The area of Spartina alterniflora was significantly correlated with the regional landscape pattern index. The landscape pattern of Spartina alterniflora was significantly correlated with the regional landscape pattern, with significant correlations between type-scale indices, including largest patch index (LPI), total edge (TE), edge density (ED), and fractal dimension index of area-weighted mean (FRAC_ AM), and the regional landscape pattern index at the significance level of 0.01. However, the area of Spartina alterniflora was significantly negatively correlated with habitat quality. Overall, the results of this study suggest that the expansion of Spartina alterniflora profoundly affects landscape patterns and functions, warranting control according to local conditions.

Riparian zones have been extensively used in non-point source pollution control projects worldwide, and remote sensing has gradually become a significant means of non-point source pollution research. However, combining remote sensing technology with riparian zones for efficient pollution interception effects is still a challenge. With the Xingyun Lake basin in Yunnan Province as the study area, this study established a soil and water assessment tool (SWAT) model by coupling with remote sensing. It created a riparian zone by changing the land use type for scenario simulation, investigating the different effects of various widths and vegetation types on pollutant reduction. The key findings are as follows: ①The created riparian zone exhibited better interception effects for nitrogen compared to phosphorus; ② Concerning different vegetation types in the riparian zone, forest land manifested significantly better pollution interception effects than grassland. Moreover, the pollutant reduction rate gradually increased with an increase in the width of the riparian zone; ③A riparian zone consisting of 30-m-wide forest land and 30-m-wide grassland can reduce total nitrogen production by 5.20% and total phosphorus production by 6.03% while intercepting 19.83% of organic nitrogen and 21.30% of organic phosphorus into the lake, demonstrating the optimal pollution interception effects.

Accurately identifying plant communities in coastal wetlands is critical for strengthening the ecological quality monitoring and enhancing the ecosystem functions of coastal wetlands. With the Yellow River Delta as the study area, this study constructed a feature vector set including phenological, optical, red-edge, and radar features based on Sentinel-1/2 image data using the Google Earth Engine (GEE) platform. It classified the wetland plant communities in the Yellow River Delta in 2021 using the random forest algorithm. Moreover, it explored the effects of phenological features in classification. The results reveal an overall classification accuracy of 97.91 % and a Kappa coefficient of 0.97. In 2021, the distribution areas of Phragmites australis, Suaeda glauca, Spartina alterniflora, and Tamarix chinensis were 49.91 km², 39.91 km², 79.36 km², and 20.86 km², respectively. The phenological features of typical plant communities in the Yellow River Delta wetlands were effectively extracted based on the normalized difference vegetation index (NDVI) time-series fitting curves. The highly distinguishable features included the maximum value date, base value, growth amplitude, beginning-of-season growth rate, and end-of-season decline rate. Compared to other feature variables, phenological features contributed more significantly to the overall classification accuracy, suggesting their prominent role in classification. The results of this study provide a methodological reference and scientific basis for the plant community monitoring and ecological conservation of coastal wetlands in the Yellow River Delta.

Based on multitemporal remote sensing (Sentinel-1/2) image data, the digital elevation model (DEM), and the Google Earth Engine (GEE) platform, this study achieved the large-scale dynamic monitoring of the ranges and interspecific distributions of mangrove forests in Hainan Island in 2016, 2019, and 2022. The effects of Sentinel-1 polarization and DEM-derived topographic features were considered in distinguishing mangrove species. Compared to the species identification using only Sentinel-2 image data, the identification method incorporating polarization or topographic features improved the classification accuracy by 3.34 and 3.35 percentage points, respectively. Moreover, incorporating both polarization and topographic features into the identification process was more effective for the interspecific classification of mangrove forests, raising the classification accuracy by 4.07 percentage points and enabling more accurate extraction of different species information. The monitoring results indicate that the areas of mangrove forests in Hainan Island in 2016, 2019, and 2022 were 3628.738 hm², 3634.129 hm², and 3881.212 hm², respectively, showing an overall increase at an average annual rate of 1.127% over six years. Regarding population dynamics, the dominant species included Ceriops tagal and Rhizophora stylosa in the northern mangrove forest in Dongzhai port, and Bruguiera sexangula in the southern portion. The northern estuary of Bamen Bay was dominated by Bruguiera sexangula, while the Wenjiao River mouth exhibited richer species. In the Xinying, and Huachang bays, and Maniao Port, the dominant mangrove species shifted from Ceriops tagal and Rhizophora stylosa to Kandelia obovata and Lumnitzera racemosa over six years, with Sonneratia apetala spreading at the bay mouths. In Xinying Port, the dominant mangrove species shifted from Rhizophora stylosa and Rhizophora stylosa to Lumnitzera racemose. The distribution of Kandelia obovata in Dongfang City expanded gradually, while the species composition in Sanya City remained almost stable, with the growth area occupied primarily by Ceriops tagal. Overall, the method used in this study enhances the identification accuracy of mangrove species, allowing a fine-scale analysis of the evolutionary process of mangrove species, thereby supporting the formulation of the mangrove forest protection policy.