Ethnic minority architecture is a vital carrier of the cultural heritage of ethnic minorities in China, and its quick and accurate extraction from remote sensing images is highly important for promoting the application of remote sensing information in urban management and architectural heritage protection. Taking Buyi architecture in China as an example, this paper proposes a minority architectural heritage identification method that combines low-altitude unmanned aerial vehicle (UAV) remote sensing technology and an improved deep learning algorithm. First, UAV images are used as the data source to provide high-resolution images for research on ethnic architecture recognition and to solve the problems associated with the high costs, time consumption, and destructiveness of traditional methods for ethnic architecture recognition. Second, to address the lack of edge pixel features in the sample images and reduce repeated labeling of the same sample, the ethnic architecture in entire remote sensing images is labeled on the Arcgis platform, and the sliding window method is used to cut the image data and the corresponding label file with a 10% overlap rate. Finally, an attention mechanism SE module is introduced to improve the DeepLabV3+ network model structure and achieve superior ethnic building recognition results. The experimental data fully show that the model’s accuracy reaches as high as 0.9831, with an excellent recall rate of 0.9743. Moreover, the F1 score is stable at a high level of 0.9787, which highlights the excellent performance of the model in terms of comprehensive evaluation indicators. Additionally, the intersection/union ratio (IoU) of the model is 0.9582, which further verifies its high precision in pixel-level recognition tasks. According to an in-depth comparative analysis, the innovative method proposed in this paper solves the problem of insufficient feature extraction of sample edge pixels and substantially reduces interference from complex environmental factors such as roads, building shadows, and vegetation with the recognition results for ethnic architecture. This breakthrough greatly improves the accuracy and robustness of the identification of architecture in low-altitude remote sensing images and provides strong technical support for the protection and intelligent analysis of architectural heritage.

This study analyzed the changes in landscape patterns and the ecological health status of karst plateau wetlands, providing valuable insights into their conservation. Using land cover data from 1996 to 2021, DEM, and Landsat series satellite imagery, this study employed landscape ecology methods and the pressure–state–response (PSR) model framework. A regional landscape grid was constructed, and 13 indicators were selected to establish an ecological health evaluation system for karst plateau wetlands. This allowed us to explore the spatiotemporal change characteristics of the landscape pattern and the ecological health of karst plateau wetlands. The results showed that over a 25-year period, farmland, grassland, and construction land areas have increased, whereas forested land areas have decreased. Water bodies remained relatively stable but showed a trend of transitioning into grassland. Unused land showed no significant change. Landscape analysis indicated that grasslands experience the highest rate of fragmentation, complex shapes, and greater heterogeneity, whereas water bodies have the lowest fragmentation, more regular shapes, and lower heterogeneity. Other landscape types exhibited moderate characteristics. Overall, the landscape of the study area exhibited high fragmentation, specific patch aggregation, moderate patch density, and low diversity. A comprehensive ecological health evaluation revealed that the wetland health value remained at an “unhealthy” level from 1996 to 2021. Although there was a brief improvement in 2010, effective long-term recovery was not achieved. Spatially, the proportion of “diseased” areas peaked in 2006, and most grid zones remained in an “unhealthy” state over the years, with none reaching the “healthy” standard. These findings highlight the severe challenges faced by the wetland ecosystem.

Urbanization always leads to increasing challenges to the groundwater resources in karst regions due to intensive land use, infrastructure development, and the rapid transmission potential of pollutants. This study proposed an improved groundwater vulnerability assessment (GVA) framework by modifying the widely used COP (Concentration of flow, Overlying layers, and Precipitation) model, through the integration of three additional indicators: urban underground rail transportation (UURT), land use and cover (LULC), and karst development (K). Guiyang, a typical urbanized karst city in southwest China, was selected as the case study. The improved COP model, namely the COPK model, showed stronger spatial differentiation and a higher Pearson correlation coefficient ($r$) with nitrate concentrations ($r$ = 0.4388) compared to the original COP model (R = 0.3689), which validates the effectiveness of the newly introduced indicators. However, both R values remained below 0.5, even after model modification, suggesting that intensive human activities play a role in influencing nitrate distribution. The pollution load index (PI) was developed based on seven types of pollution sources, and it was integrated with the COPK vulnerability index using a risk matrix approach, producing a groundwater risk map classified into five levels. Global Moran’s I analysis (0.9171 for COP model and 0.8739 for COPK model) confirmed strong and significant spatial clustering patterns for the two models. The inclusion of UURT and LULC improved the model’s sensitivity to urban-related pressures and enhanced its capacity to detect local risk zones. It is a scalable tool for groundwater risk assessment in urbanized karst areas and offers practical insights for land use planning and sustainable groundwater management.

Accurate identification of tea diseases and pests is a key challenge in smart agriculture. Current approaches to tea disease and pest identification suffer from a scarcity of high-quality annotated image data and poor generalization of existing models in real-world field environments. To address these issues, this paper first constructs and releases a dataset of images of tea diseases and pests captured in real-world field scenarios. The dataset uses leaf-level annotations and covers six common tea disease and pest categories in Guizhou Province, China. It contains 549 high-resolution images covering varying lighting conditions, backgrounds, and disease severity levels. Based on this dataset, we propose a convolutional neural network model named GatedFCA-YOLO, which integrates a small-object detection layer with an adaptive attention mechanism. Specifically, the small-object detection layer preserves high-resolution details, effectively improving recall of minute lesions. Meanwhile, the GatedFCA module is designed to fuse a spatial gating mechanism with FCAttention. It enables adaptive feature enhancement and significantly boosts the model’s recognition robustness under complex backgrounds. Experimental results on our dataset show that GatedFCA-YOLO achieves 78.9% mAP@0.5, which is 3% increased compared to the baseline model YOLO11n, thereby verifying the effectiveness of the proposed method.

Habitat degradation has become a critical bottleneck that restricts watershed ecological security and sustainable development. This study aims to reveal the spatiotemporal evolution of habitats in the Chishui River Basin, in order to provide scientific guidance for regional ecological conservation and restoration. Based on land use datasets from 1990 to 2023 in the basin, the InVEST-PLUS model is adopted to quantify habitat quality and conduct exploratory multiscenario simulations of future habitat conditions across the study area. The main results are as follows: (1) The average habitat quality index of the Chishui River Basin tends to decrease, with the area of high-grade habitat decreasing by 50.30%. The basin has undergone four distinct phases: high-intensity degradation, continuous but low-intensity structural change, large-scale ecological restoration, and active bidirectional adjustment. (2) Spatial pattern of habitat quality is strongly heterogeneous. Specifically, high-quality habitats are primarily distributed in the northern region of Chishui River Basin, while degraded habitats are concentrated in the central and southeastern parts of the basin, with an expanding trend. Habitat changes are characterized by leapfrog transfer and path dependence. Notably, low-grade habitats show an extremely high persistence rate of 97.16%. (3) Simulation outputs under diverse scenarios show that ecological protection serves as an effective strategy for improving habitat quality within the basin. Habitat quality of the Chishui River Basin has displayed a phased evolutionary pattern of initial degradation followed by recovery. Strengthening ecological protection will be the core approach to ameliorate watershed habitat quality and safeguard ecological security in the future. The study provides a scientific reference for watershed ecological conservation and restoration, territorial spatial optimization, and the construction of ecological barriers in the upper reaches of the Yangtze River.

Alternanthera philoxeroides is a highly invasive alien species in China that causes waterway blockages, agricultural yield loss, biodiversity decline, and ecosystem degradation. This study assessed the invasion risk and environmental drivers of A. philoxeroides in Guizhou Province, a karst mountainous region in Southwest China. Occurrence records were obtained from field surveys and the Chinese Virtual Herbarium. The genetic algorithm for rule-set production (GARP) model and the jackknife method were employed to identify 13 key environmental indicators for predicting invasion risk. The global invasion risk index (GIRI) was applied to quantify the overall invasion risk. Additionally, the Geodetector model was utilized to analyze the spatially differentiated effects of six environmental factors. The results showed that A. philoxeroides poses a high invasion risk in Guizhou Province, and the invasion risk in the Yangtze River Basin within Guizhou is higher than that in the Pearl River Basin. The environmental factors influencing invasion risk, in order of impact, were slope, elevation, land use, river density, rocky desertification, and soil pH. Moreover, interactions among these factors further amplify the invasion risk. These findings provide valuable insights for developing targeted management strategies for A. philoxeroides in karst mountainous regions and support biodiversity preservation and regional ecological sustainability.