Jisheng Xia

Terrestrial vegetation productivity is vital for carbon sequestration and food provision. In Yunnan Province, complex terrain and diverse climates cause abrupt and nonlinear vegetation changes that linear trend analyses fail to capture, while existing attribution methods rarely distinguish causal drivers from spurious correlations or quantify thresholds with uncertainty. Using MODIS NDVI data (2002–2022), we developed an integrated framework combining multi-model trajectory diagnosis, land-use spatial analysis, GCCM causal screening, XGBoost-SHAP attribution, and GAM threshold refinement. Positive changes dominated 76.50% of the area; positive abrupt changes peaked in 2011–2014, negative in 2008–2009. GCCM excluded NL_change, retaining 12 factors. The positive model achieved F1 = 0.521 and AUC = 0.689, with climate factors accounting for 62.8% of mean absolute SHAP importance. The negative model achieved AUC = 0.858 (random CV) and 0.821 (spatial block CV), with human activity accounting for 17.9%—nearly double its positive share. Carbon_change ranked 10th for positive but 3rd for negative changes, consistent with an asymmetric association pattern. GAM-smoothed SHAP curves identified model-response thresholds with 95% confidence intervals: PRE_trend showed a stable zero-crossing near −8 mm/yr and a peak at 0–5 mm/yr (R² = 0.72); DEM showed a monotonic negative relationship with no stable zero-crossing (R² = 0.83); TEM_trend exhibited a stable zero-crossing near 0.02 °C/yr (R² = 0.57). Spatial block cross-validation confirmed acceptable ranking ability and stable SHAP rankings. This framework links change identification, model-based attribution, and model-response threshold identification, offering a transferable paradigm for complex terrain regions.

Rural communities shrink when residents leave, farmland and housing are underused, and local economic and service functions weaken. This study examines where this process occurred in 545 townships in the Yunnan section of the Jinsha River Basin and which local conditions were most closely associated with it in 2000, 2010, and 2020. We combined population, land, and economic indicators with spatial analysis and an ensemble machine-learning model designed to explain how each factor contributed to prediction. Rural shrinkage was widespread in 2000, eased substantially by 2010, and became spatially polarized again by 2020. Spatial clustering weakened and then strengthened across the three years. Coordination among population, land, and economic subsystems improved overall but remained uneven. Public-service accessibility was most prominent in the 2000 model, whereas vegetation conditions and distance to the provincial capital were more prominent in 2020. The results identify priority locations for monitoring, clarify how environmental, accessibility, and service conditions combine in different places, and provide a transferable framework for diagnosing rural shrinkage while separating robust spatial evidence from policy options that still require local testing.

Regional economic development serves as a crucial indicator of societal vitality and the efficiency of resource allocation. Nighttime light (NL) remote sensing data is a reliable reflection of regional economic activities, making it essential to analyze its spatiotemporal variations and influencing factors for economic growth. This study employs space–time cubes, incorporating hotspot and outlier analysis, to explore the dynamics of NL in the Dianchi Lake basin between 2000 and 2022, focusing on shifts in centroids, temporal patterns, and spatial clustering. Various machine learning models were tested, with the most effective model utilizing the SHAP algorithm to uncover the nonlinear relationships between explanatory variables and NL. The findings reveal that economic hotspots are predominantly concentrated around Dianchi Lake, exhibiting high–high spatial clustering, whereas cold spots are mainly distributed in the northern and southern regions and are characterized by low–low clustering. In addition, human activity indicators (GDP, road density, and population) and climatic factors (temperature and precipitation) are positively associated with economic development, while topographic factors (DEM and slope) show negative associations.

Identifying and locating sewer defects is crucial for minimizing the risk of sewer-related accidents. Currently, spatial localization of sewer defects in closed circuit television (CCTV) inspection data is primarily performed through manual visual inspections by professional technicians, which is time-consuming and costly. Consequently, there is an urgent need to develop automated solutions. This study proposes an innovative method for automatic defect location, which involves defect tracking based on rules, calculating the distance of the defect relative to the pipe endpoint, and converting this distance into physical world coordinates. Following experimental validation, the multiple object tracking accuracy (MOTA) metric of defect tracking within the proposed method ranged from -1.46 to 0.89 across ten test videos. The proposed method can be easily integrated into practical engineering applications, thereby alleviating the workload of professional technicians in obtaining the geographic locations of sewer defects in CCTV inspection data and reducing both time and labor costs associated with this process.

Canopy gaps are vital microhabitats for forest carbon cycling and species regeneration, whose accurate extraction is crucial for ecological modeling and smart forestry. However, traditional monitoring methods have notable limitations: ground-based measurements are inefficient; remote-sensing interpretation is susceptible to terrain and spectral interference; and traditional algorithms exhibit an insufficient feature representation capability. Aiming at overcoming the bottleneck issues of canopy gap identification in mountainous forest regions, we constructed a multi-task deep learning model (ES-Net) integrating an edge–semantic collaborative perception mechanism. First, a refined sample library containing multi-scale interference features was constructed, which included 2808 annotated UAV images. Based on this, a dual-branch feature interaction architecture was designed. A cross-layer attention mechanism was embedded in the semantic segmentation module (SSM) to enhance the discriminative ability for heterogeneous features. Meanwhile, an edge detection module (EDM) was built to strengthen geometric constraints. Results from selected areas in Yunnan Province (China) demonstrate that ES-Net outperforms U-Net, boosting the Intersection over Union (IoU) by 0.86% (95.41% vs. 94.55%), improving the edge coverage rate by 3.14% (85.32% vs. 82.18%), and reducing the Hausdorff Distance by 38.6% (28.26 pixels vs. 46.02 pixels). Ablation studies further verify that the synergy between SSM and EDM yields a 13.0% IoU gain over the baseline, highlighting the effectiveness of joint semantic–edge optimization. This study provides a terrain-adaptive intelligent interpretation method for forest disturbance monitoring and holds significant practical value for advancing smart forestry construction and ecosystem sustainable management.

The Jinsha River Basin in Yunnan serves as a crucial ecological barrier in southwestern China. Objective ecological assessment and identification of key driving factors are essential for the region’s sustainable development. The Remote Sensing Ecological Index (RSEI) has been widely applied in ecological assessments. In recent years, interpretable machine learning (IML) has introduced novel approaches for understanding complex ecological driving mechanisms. This study employed Google Earth Engine (GEE) to calculate three vegetation indices—NDVI, SAVI, and kNDVI—for the study area from 2000 to 2022, along with their corresponding RSEI models (NDVI-RSEI, SAVI-RSEI, and kNDVI-RSEI). Additionally, it analyzed the spatiotemporal variations of these RSEI models and their relationship with vegetation indices. Furthermore, an IML model (XGBoost-SHAP) was employed to interpret the driving factors of RSEI. The results indicate that (1) the RSEI levels in the study area from 2000 to 2022 were primarily moderate; (2) compared to NDVI-RSEI, SAVI-RSEI is more susceptible to soil factors, while kNDVI-RSEI exhibits a lower saturation tendency; and (3) potential evapotranspiration, land cover, and elevation are key drivers of RSEI variations, primarily affecting the ecological environment in the western, southeastern, and northeastern parts of the study area. The XGBoost-SHAP approach provides valuable insights for promoting regional sustainable development.

Scanning forests with LiDAR is an efficient method for conducting forest resource surveys, including estimating tree diameter at breast height (DBH), canopy height, and segmenting individual trees. This study uses three-dimensional (3D) forest test data and point cloud data simulated by the Helios++ V1.3.0 software, and proposes a voxelized trunk extraction algorithm to determine the trunk location and the vertical structure of single tree trunks in forest areas. Firstly, the voxel-based shape recognition algorithm is used to extract the trunk structure of tree point clouds, then the random sample consensus (RANSAC) algorithm is used to solve the vertical structure connectivity problem of tree trunks generated by the above method, and the Alpha Shapes algorithm is selected among various point cloud surface reconstruction algorithms to reconstruct the surface of tree point clouds. Then, building on the tree surface model, a light projection scene is introduced to locate the tree trunk coordinates at different heights. Finally, the convex hull of the trunk bottom is solved by the Graham scanning method. Accuracy assessments show that the proposed single-tree extraction algorithm and the forest vertical structure recognition algorithm, when applied within the light projection scene, effectively delineate the regions where the vertical structure distribution of single tree trunks is inconsistent.

Wild edible mushrooms are a characteristic product in Yunnan, but no quantitative evaluation system yet exists for them. This study puts forward a sustainable development potential index of characteristic agricultural products (SDPI) based on various methods. It also performs a correlation analysis of multi-source points of interest (POI) and online shopping data related to wild edible mushrooms in Yunnan from a quantitative point of view, to understand the economics of wild edible mushrooms and to explore the sustainable development potential of such mushrooms in Yunnan from the perspectives of the tourism and sales markets. The results show that Dêqên Tibetan and the central region dominated by Kunming dominate both the tourism and sales markets and have a high SDPI. In contrast, the current situation and development prospects of the wild edible mushroom market in cities such as Lincang and Nujiang Lisu are poor. Yunnan Province has a large wild edible mushroom market and a promising development prospect. This paper provides comprehensive reference information for the development of Yunnan wild edible mushroom production economics.