Quantitatively revealing the response of carbon stocks to land use change (LUCC) and analyzing the vulnerability of ecosystem carbon stock (ECS) services are of great significance for maintaining the carbon cycle and ecological security. For this study, China’s Guizhou Province was the study area. Land use data in 2000, 2010, and 2020 were selected to explore the impacts of LUCC on carbon stocks in multiple scenarios by combining the PLUS and InVEST models and then analyzing the vulnerability of ECS services. The results show that forest land plays an important role in improving ECS services in karst plateau mountainous areas. In 2000–2020, forest land expansion offset the carbon stock reduced by the expansion of built-up land, greatly improving the regional ECS function. Following the natural trend (NT), the total carbon stock in Guizhou Province will decrease by 1.86 Tg; however, under ecological protection (EP) measures, the ECS service performs a positive function for LUCC. Focusing on socioeconomic development (ED) will increase the vulnerability of the regional ECS service. In the future, the forest land area size should be increased, and built-up land should be restricted to better improve the service function of ECS in karst plateau mountainous areas.

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.

Accurate crop classification is the basis of agricultural research, and remote sensing is the only effective measuring technique to classify crops over large areas. Optical remote sensing is effective in regions with good illumination; however, it usually fails to meet requirements for highly accurate crop classification in cloud-covered areas and rainy regions. Synthetic aperture radar (SAR) can achieve active data acquisition by transmitting signals; thus, it has strong resistance to cloud and rain interference. In this study, we designed an improved crop planting structure mapping framework for cloudy and rainy regions by combining optical data and SAR data, and we revealed the synchronous-response relationship of these two data types. First, we extracted geo-parcels from optical images with high spatial resolution. Second, we built a recurrent neural network (RNN)-based classifier suitable for remote sensing images on the geo-parcel scale. Third, we classified crops based on the two datasets and established the network. Fourth, we analyzed the synchronous response relationships of crops based on the results of the two classification schemes. This work is the basis for the application of remote sensing data for the fine mapping and growth monitoring of crop planting structures in cloudy and rainy areas in the future.

During the 13th Five-Year Plan period (2015–2020), the Poverty alleviation relocation (PAR), led by the Chinese government in the karst regions of southwest China, aimed to cope with poverty and ameliorate the ecological environment. Nevertheless, few research results have involved quantitative assessment of the ecological effectiveness of PAR. Moreover, few studies on the ecological effects of migration relocation have distinguished the effects of relocation on climatic factors and other ecological restoration projects concerning the ecological environment. It remains unclear to what extent PAR affects the regional ecological environment. In order to quantitatively assess the extent of PAR’s ecological restoration contribution, we adopted the Remote Sensing Ecological Index (RSEI) model, which integrates the four more intuitive and critical influencing factors of greenness, moisture, dryness, and heat. On the Google earth engine (GEE) platform, utilizing its powerful remote sensing data storage capacity and computational capability, we quantitatively assessed the spatial and temporal distribution characteristics of ecological environmental quality (EEQ). As revealed by our research findings, overall EEQ showed a fluctuating upward trend over the period 1996–2021 in the study area, exhibiting an improvement of 22.66%. Mann–Kendall mutation test curves showed the most significant improvement occurred after 2015, with an improvement of 8.06%. Based on the residual analysis model, in order to remove the influence of climatic factors and other anthropogenic activities, and to assess the driving effectiveness of PAR, PAR was remarkedly effective in ameliorating EEQ, causing the RSEI to improve by 0.0221–0.0422. The LISA correlation model further analyzed that 44.91% of regional PAR implementation exerted a remarkable influence on RSEI change, of which 54.59% belonged to positive correlation. Aside from that, we also found that not all areas involved in PAR experienced ameliorated RSEI. In the western region, where the human–land conflict is prominent and the ecology is more fragile, PAR exhibited a significant effect in ameliorating EEQ, but in the eastern region, where the EEQ foundation is better, PAR did not show significant effect, and, thus, the ecological restoration effect of PAR exhibited noticeable geographical suitability.