Land use/land cover (LULC) data serve as a critical information source for understanding the complex interactions between human activities and global environmental change. The subtropical karst region, characterized by fragmented terrain, spectral confusion, topographic shadowing, and frequent cloud cover, represents one of the most challenging natural scenes for remote sensing classification. This study reviews the evolution of multi-source data acquisition (optical, SAR, LiDAR, UAV) and preprocessing strategies tailored for subtropical regions. It evaluates the applicability and limitations of various methodological frameworks, ranging from traditional approaches and GEOBIA to machine learning and deep learning. The importance of uncertainty modeling and robust accuracy assessment systems is emphasized. The study identifies four major bottlenecks: scarcity of high-quality samples, lack of scale awareness, poor model generalization, and insufficient integration of geoscientific knowledge. It suggests that future breakthroughs lie in developing remote sensing intelligent models that are driven by few samples, integrate multi-modal data, and possess strong geoscientific interpretability. The findings provide a theoretical reference for LULC information extraction and ecological monitoring in heterogeneous geomorphic regions.

Unmanned Aerial Vehicle Remote Sensing (UAV-RS) has emerged as a transformative technology in high-resolution Earth observation, with widespread applications in precision agriculture, ecological monitoring, and disaster response. However, a systematic understanding of its scientific evolution and structural bottlenecks remains lacking. This study collected 4985 peer-reviewed articles from the Web of Science Core Collection and conducted a comprehensive scientometric analysis using CiteSpace v.6.2.R4, Origin 2022, and Excel. We examined publication trends, country/institutional collaboration networks, keyword co-occurrence clusters, and emerging research fronts. Results reveal an exponential growth in UAV-RS research since 2015, dominated by application-driven studies. Hotspots include vegetation indices, structure from motion modeling, and deep learning integration. However, foundational challenges—such as platform endurance, sensor coordination, and data standardization—remain underexplored. The global collaboration network exhibits a “strong hubs, weak bridges” pattern, limiting transnational knowledge integration. This review highlights the imbalance between surface-level innovation and deep technological maturity and calls for a paradigm shift from fragmented application responses to integrated systems development. Our findings provide strategic insights for researchers, policymakers, and funding agencies to guide the next stage of UAV-RS evolution.

Karst desertification is a major ecological and environmental issue that threatens regional ecological security and sustainable development; its dynamic monitoring is of great significance for evaluating the effectiveness of ecological restoration and promoting regional sustainable development. Based on the Web of Science database, this paper offers a bibliometric-informed narrative review of the evolution of remote sensing monitoring and information extraction technologies for karst desertification from 1987 to 2025. It focuses on analyzing research progress in methods such as multi-source remote sensing data fusion, deep learning models, and integrated GIS analysis, with regard to improving the accuracy of information extraction and the ability to identify spatiotemporal dynamics of karst desertification. This paper also compares the advantages and limitations of different technologies in terms of high-resolution identification and long-term dynamic monitoring. Building on this foundation and drawing on relevant domestic and international research findings, this study examines the development trends and major challenges facing karst desertification monitoring technologies. It further outlines the direction for establishing a long-term, standardized dynamic monitoring system, with the aim of providing a scientific basis for ecological governance and sustainable development in the karst regions of Southwest China.

Conducting landscape ecological risk (LER) assessments and identifying their driving mechanisms is a key approach to balancing regional development with ecological conservation and enhancing ecological security. Taking the Beipanjiang River Basin (Guizhou section)—a typical karst mountainous watershed—as the study subject, this research analyzed the spatiotemporal evolution of LER in the basin using landscape pattern indices and spatial autocorrelation analysis based on five sets of land-use data from 1985 to 2024. The optimal geographical detector (OPGD) was employed to identify the driving factors. The results are as follows: (1) From 1985 to 2024, the average LER index in the study area decreased by 37.07%. The proportion of the area classified as the lowest-risk zone increased from 18.76% to 38.83%, while the proportions of the medium-risk and higher-risk zones decreased to 14.97% and 3.55%, respectively, indicating an optimization of the ecological security pattern. (2) The center of gravity of the lowest-risk zones shifted northwestward, while the medium-risk and higher-risk zones contracted northeastward. The global Moran’s I ranged from 0.477 to 0.574. High–high clustering zones contracted in the north, and regional stress capacity decreased. Low–low clusters expanded in the south, forming a continuously consolidated ecological barrier zone. (3) The interactions between landscape fragmentation and factors such as elevation, precipitation, and NDVI all exhibited a two-factor enhancement effect, with the risk pattern being primarily driven by the synergy of multiple factors. This study provides a scientific basis for adjusting the land-use structure of the watershed, mitigating ecological risks, and promoting high-quality sustainable development.