Topography and soil factors are known to play crucial roles in the species composition of plant communities in subtropical evergreen-deciduous broadleaved mixed forests. In this study, we used a systematic quantitative approach to classify plant community types in the subtropical forests of Hubei Province (central China), and then quantified the relative contribution of drivers responsible for variation in species composition and diversity. We classified the subtropical forests in the study area into 12 community types. Of these, species diversity indices of three communities were significantly higher than those of others. In each community type, species richness, abundance, basal area and importance values of evergreen and deciduous species were different. In most community types, deciduous species richness was higher than that of evergreen species. Linear regression analysis showed that the dominant factors that affect species composition in each community type are elevation, slope, aspect, soil nitrogen content, and soil phosphorus content. Furthermore, structural equation modeling analysis showed that the majority of variance in species composition of plant communities can be explained by elevation, aspect, soil water content, litterfall, total nitrogen, and total phosphorus. Thus, the major factors that affect evergreen and deciduous species distribution across the 12 community types in subtropical evergreen-deciduous broadleaved mixed forests include elevation, slope and aspect, soil total nitrogen content, soil total phosphorus content, soil available nitrogen content and soil available phosphorus content.

Plant diversity is an important parameter in maintaining forest ecosystem services, functions and stability. Timely and accurate monitoring and evaluation of large-area wall-to-wall maps on plant diversity and its spatial heterogeneity are crucial for the conservation and management of forest resources. However, traditional botanical field surveys designed to estimate plant diversity are usually limited in their spatiotemporal resolutions. Using Sentinel-1 (S-1) and Sentinel-2 (S-2) data at high spatiotemporal scales, combined with and referenced to botanical field surveys, may be the best choice to provide accurate plant diversity distribution information over a large area. In this paper, we predicted and mapped plant diversity in a subtropical forest using 24 months of freely and openly available S-1 and S-2 images (10 m × 10 m) data over a large study area (15,290 km²). A total of 448 quadrats (10 m × 10 m) of forestry field surveys were captured in a subtropical evergreen-deciduous broad-leaved mixed forest to validate a machine learning algorithm. The objective was to link the fine Sentinel spectral and radar data to several ground-truthing plant diversity indices in the forests. The results showed that: (1) The Simpson and Shannon-Wiener diversity indices were the best predicted indices using random forest regression, with ȓ² of around 0.65; (2) The use of S-1 radar data can enhance the accuracy of the predicted heterogeneity indices in the forests by approximately 0.2; (3) As for the mapping of Simpson and Shannon-Wiener, the overall accuracy was 67.4% and 64.2% respectively, while the texture diversity’s overall accuracy was merely 56.8%; (4) From the evaluation and prediction map information, the Simpson, Shannon-Wiener and texture diversity values (and its confidence interval values) indicate spatial heterogeneity in pixel level. The large-area forest plant diversity indices maps add spatially explicit information to the ground-truthing data. Based on the results, we conclude that using the time-series of S-1 and S-2 radar and spectral characteristics, when coupled with limited ground-truthing data, can provide reasonable assessments of plant spatial heterogeneity and diversity across wide areas. It could also help promote forest ecosystem and resource conservation activities in the forestry sector.

Accurate extraction of river network from the Digital Elevation Model (DEM) is a significant content in the application of a distributed hydrological model. However, the study of river network extraction based on DEM has some limitations, such as location offset, inaccurate parallel channel and short circuit of meandering channels. In this study, we proposed a new enhancement method for NASADEM V001 in the Danjiangkou Reservoir area. We used Surface Water Occurrence (SWO) and Sentinel-2 data to describe vertical limit differences between morphological units to complement actual flow path information from NASADEM data by a stream burning method. The differences between the extracted river network and the actual river network were evaluated in three different geographical regions. Compared with the actual river centerline, the location error of the river network extraction was significantly reduced. The average offset distances between river network extraction and the actual river network were 68.38, 36.99, and 21.59 m in the three test areas. Compared with NASADEM V001, the average offset distances in the three test areas were reduced by 7.26, 40.29, and 42.35%, respectively. To better estimate accuracy, we also calculated and compared the accuracy of the river network based on MERIT Hrdro and HydroSHEDS. The experimental results demonstrated that the method can effectively improve the accuracy of river network extraction and meet the needs of hydrological simulation.

Medium spatial resolution remote sensing images are widely used for cropland mapping. In areas where cropland is fragmented, however, the limitation of the spatial resolution may lead to inaccurate or even impossible mapping of small croplands. Super-resolution mapping is an effective method to address this issue by transforming coarse-resolution fraction images, derived from spectral unmixing, into fine-resolution land cover maps. In practical applications, a crucial obstacle of this approach is the difficulty in collecting training samples for spectral unmixing and super-resolution mapping. To address this problem, this paper proposed a novel super-resolution cropland mapping approach by simulating spectral and spatial training samples. Specially, a mixture spectral simulation method was used to generate training samples for the regression unmixing model to estimate cropland fraction images. A multi-level feature fusion U-NET model was proposed for super-resolution cropland mapping and was trained with simulated training samples considering fraction errors. The proposed method was tested in the Jianghan Plain, China, by generating 2.5m cropland maps from the 10m Sentinel-2 images. The results show that the proposed method can accurately extract more small and linear land cover features, preserve the spatial structure of the boundaries and achieve higher accuracy than other cropland mapping methods. This method overcomes the dependency on actual samples collection in traditional methods, better utilizes spectral and spatial features in remote sensing data, and reduces the impact of spectral unmixing errors on the final fine-resolution cropland maps.

Context Forest ecosystems are increasingly exposed to climatic and anthropogenic disturbances under accelerating global change, making their resilience crucial for sustaining ecological functions. While resilience has become a key concept in forest management, its associations under combined pressures of climate stressors and fragmentation remain insufficiently understood, especially in metropolitan forested landscapes. Objectives We quantified and mapped forest resilience in the Core area of the Wuhan Metropolitan Region (CWMR), a rapidly urbanizing region in subtropical China, from 2004 to 2023, and investigated the associations between climatic factors, landscape fragmentation, and spatial patterns of forest resilience. Methods Forest resilience was quantified using the lag-1 temporal autocorrelation (TAC) of normalized difference vegetation index (NDVI) time series, based on critical slowing down theory. Key climatic factors associated with forest resilience were first identified using eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP). Structural equation modeling (SEM) was then used to examine the direct and indirect associations among landscape fragmentation, climatic factors, and forest resilience. Results We found that 60.54% of focal forests experienced a decline in resilience over the past two decades, with an average annual reduction of 0.0046. XGBoost and SHAP analyses identified potential evapotranspiration and land surface temperature as important climatic predictors associated with forest resilience, with variability in potential evapotranspiration showing a stronger association than its long-term mean. Structural equation modeling revealed that patch shape complexity, indicative of stronger edge effects, exhibited the strongest associations with forest resilience through both direct pathways and indirect pathways linked to climatic conditions, highlighting their potential role in amplifying atmospheric drought stress. Conclusions Our findings reveal a critical role of forest landscape fragmentation and climate interactions in regulating vegetation resilience. These results highlight the urgent need for adaptive forest management, such as optimizing patch edges and regulating forest microclimates, to support the persistence of metropolitan forest under rising warm-drought climate conditions.