Guaranteeing the ecological security of the Dongting Lake Basin is of paramount importance for national-scale programs, such as the Yangtze River Economic Belt and aquatic conservation projects. Within this framework, carbon storage and its determining drivers act as essential indicators of regional ecological stability. However, the historical trajectory of carbon pools and their response to future multi-scenario land-use transitions remain insufficiently understood. Therefore, this study aims to quantify the spatiotemporal evolution of carbon storage in the Dongting Lake Basin from 2000 to 2020 and project its future dynamics under diverse development pathways. This study, utilizing land use data from 2000 to 2020 and the carbon density database of the Dongting Lake Basin, assessed land use changes over two decades and determined the spatiotemporal distribution of carbon storage. Additionally, using 17 driving factors and various spatial policies, the study projected the land use and land cover changes (LUCC) for 2030 under four scenarios: natural development, ecological protection, economic development, and planned development. The spatiotemporal distribution of carbon storage and its response mechanisms were analyzed for each scenario. The results showed that carbon storage was directly impacted by LUCC, with an overall “decrease-increase-decrease” trend from 2000 to 2020, resulting in a net increase of 3.685 × 10⁶ t. By 2030, the changes in carbon storage under the natural development, ecological protection scenario, economic development, and planned development scenarios were projected to be −1.008 × 10⁷ t, 1.276 × 10⁷ t, 3.292 × 10⁸ t, and −1.200 × 10⁵ t, respectively. Notably, the ecological protection scenario showed a significant positive growth in carbon storage, primarily driven by an increase in forest and wetland areas. Additionally, the spatial distribution of carbon storage exhibited a pattern of “high in the west and low in the east”. These results imply that to achieve the “Dual Carbon Strategy”, future land use planning in the Dongting Lake Basin should prioritize ecological protection and planned development models, including strict control of construction land expansion, increasing ecological land area, and enhancing carbon storage.

This study used Jilin-1 satellite data and unmanned aerial vehicle (UAV)-collected visible-thermal infrared imagery to calculate twelve spectral indices and evaluate their effectiveness in distinguishing post-fire forest areas and identifying human-altered land-cover changes in Jinyun Mountain, Chongqing. The research goals included mapping wildfire impacts with M-statistic separability, measuring land-cover distinguishability through Jeffries–Matusita (JM) distance analysis, classifying land-cover types using the random forest (RF) algorithm, and verifying classification accuracy. Cumulative human disturbances—such as land clearing, replanting, and road construction—significantly blocked the natural recovery of burn scars, and during long-term human-assisted recovery periods over one year, the Red Green Blue Index (RGBI), Green Leaf Index (GLI), and Excess Green Index (EXG) showed high classification accuracy for six land-cover types: road, bare soil, deadwood, bamboo, broadleaf, and grass. Key accuracy measures showed producer accuracy (PA) > 0.8, user accuracy (UA) > 0.8, overall accuracy (OA) > 90%, and a kappa coefficient > 0.85. Validation results confirmed that visible-spectrum indices are good at distinguishing photosynthetic vegetation, thermal bands help identify artificial surfaces, and combined thermal-visible indices solve spectral confusion in deadwood recognition. Spectral indices provide high-precision quantitative evidence for monitoring post-fire land-cover changes, especially under human intervention, thus offering important data support for time-based modeling of post-fire forest recovery and improvement of ecological restoration plans.