Vegetation and its spatiotemporal variations play a crucial role in regional ecological security and sustainable development. Examining vegetation dynamics in natural reserves provides valuable insights for optimizing vegetation patterns and management strategies. This study utilizes Landsat remote sensing imagery to investigate changes in vegetation pattern and coverage in the Cangshan mountain of the Cangshan Erhai National Nature Reserve, as well as assesses the effectiveness of conservation efforts. The results indicate the following: (1) The primary vegetation types in the Cangshan mountain include warm-temperate coniferous forests, deciduous broad-leaved forests, bamboo forests, and alpine meadows, exhibiting distinct vertical zonation patterns. The vegetated area expanded by 1146 hectares during the study period. (2) The average fractional of vegetation coverage (FVC) in the Cangshan mountain demonstrated an upward trend (0.82 in 1987 to 0.93 in 2017), with the proportion of highly FVC areas increasing from 59.67% in 1987 to 97.89% in 2017. (3) The vegetation landscape fragmentation in Cangshan mountain and various functional areas shows an increasing trend, while connectivity decreases, and is accompanied by a more intricate shape of the vegetation landscape. While conservation and management efforts have yielded certain results in safeguarding the vegetation in the Cangshan mountain, the degree of vegetation landscape fragmentation has intensified due to climate change and human activities. Thus, it is imperative for management authorities to promptly adjust protective measures within the Cangshan mountain. This study contributes to our understanding of vegetation changes within the Cangshan mountain and provides essential baseline information for optimizing and enhancing vegetation conservation management strategies within the reserve.

Detailed knowledge of landscape dynamics is crucial for many applications, from resource management to ecosystem service assessments. However, identifying the spatial distribution of the landscape using optical remote sensing techniques is difficult in mountainous areas, primarily due to cloud cover and topographic relief. Our study uses stable classification samples from mountainous areas to investigate an integrated approach that addresses large volumes of cloud-cover data (with associated data gaps) and extracts landscape time series (LTS) with a high time–frequency resolution. We applied this approach to map LTS in a typical cloudy mountainous area (Erhai watershed in northwestern Yunnan, China) using dense Landsat stacks, and then we also used the classified results to investigate the spatial–temporal landscape changes in the study area at biennial intervals. The overall accuracy of the landscape classification ranged from 81.75% to 88.18%. The results showed highly dynamic processes in the landscape throughout the study period. Forest was the main land cover type, covering approximately 39.19% to 41.68% of the total study area. Alpine meadow showed fluctuating trends, with a net loss of 11.22% and an annual reduction rate of −0.4%. Shrub cover increased by 1.26%, and water bodies showed a small decrease in area, resulting in an overall net change of −0.03%. Built-up land and farmland areas continued to expand, and their annual growth rates were 1.52% and 1.06%, respectively. Bare land showed the highest loss, with a net change of 228.97 km². In the Erhai watershed, all the landscape classes changed or transitioned into other classes, and a substantial decrease in bare land occurred. The biennial LTS maps allow us to fully understand the spatially and temporally complex change processes occurring in landscape classes; these changes would not be observable at coarse temporal intervals (e.g., 5–10 years). Our study highlights the importance of increasing the temporal resolution in landscape change studies to support sustainable land resource management strategies and integrate landscape planning for environmental conservation.

The evolution of landscape patterns in plateau lake basins directly influences the sustainable provision of ecosystem services. Revealing and predicting the impacts of landscape changes on ecosystem service value (ESV) under different development scenarios are essential for maintaining regional ecological security, enhancing ESV, and formulating policies for ecological conservation and restoration. As a typical representative of China’s plateau lake basin, the Erhai Lake Basin faces multiple challenges arising from rapid urbanization, tourism commercialization, and agricultural modernization. It is therefore crucial to understand its potential future landscape dynamics and their effects on ecosystem services. Based on landscape data, natural environmental data, and socio-economic data, we applied GIS-based spatial analysis and the equivalent factor method to simulate and assess landscape pattern changes and corresponding variations in ESV in 2030, 2040, and 2050 under three distinct scenarios. Local spatial autocorrelation analysis was further employed to identify the spatial clustering patterns of ESV. There were three findings: (1) From 2030 to 2050, forest increased continuously under the natural evolution scenario (NES) and ecological protection scenario (EPS) but declined under the economic growth scenario (EGS). Farmland expanded under the NES and EGS, whereas it decreased under the EPS. Grassland declined across all three scenarios, while built-up area showed consistent expansion. (2) In all simulated years, the total ESV of the Erhai Lake Basin ranked as EPS > NES > EGS. Between 2030 and 2050, total ESV exhibited an increasing trend under the EPS but declined under the other two scenarios, with the sharpest reduction under the EGS. Forests and water body were the main contributors to total ESV, while farmland and grassland played a critical role in driving ESV dynamics—the scale and direction of their transformation directly determined the overall ESV trends. (3) Across the three scenarios, ESVs all exhibit significant spatial heterogeneity. Local Moran’s I analysis indicated a dominant cluster of high values (HH) or a cluster of low values (LL), with LL clusters mainly concentrated in the northern basin and the western side of Erhai Lake, and HH clusters primarily located within the lake area. This study, through multi-scenario simulations, elucidates the spatiotemporal dynamics of landscape and ESV changes, providing valuable insights for green transformation, landscape spatial allocation, ecological restoration, and sustainable development in the Erhai Lake Basin.