Wang Haijun, Kong Xiangdong, Phewnil Onanong, Luo Ji, Li Pengju, Chen Xiyong, Xie Tianhui
Research Center of Agricultural Economics, School of Economics, Sichuan University of Science and Engineering, Zigong, Sichuan, China.
Faculty of Environment, Kasetsart University, Bangkok, Thailand.
PeerJ. 2024 Nov 27;12:e18586. doi: 10.7717/peerj.18586. eCollection 2024.
The alpine wetlands in western Sichuan are distributed along the eastern section of the Qinghai-Tibet Plateau (QTP), where the ecological environment is fragile and highly sensitive to global climate change. These wetlands are already experiencing severe ecological and environmental issues, such as drought, retrogressive succession, and desertification. However, due to the limitations of computational models, previous studies have been unable to adequately understand the spatiotemporal change trends of these alpine wetlands.
We employed a large sample and composite supervised classification algorithms to classify alpine wetlands and generate wetland maps, based on the Google Earth Engine cloud computing platform. The thematic maps were then grid-sampled for predictive modeling of future wetland changes. Four species distribution models (SDMs), BIOCLIM, DOMAIN, MAXENT, and GARP were innovatively introduced. Using the WorldClim dataset as environmental variables, we predicted the future distribution of wetlands in western Sichuan under multiple climate scenarios.
The Kappa coefficients for Landsat 8 and Sentinel 2 were 0.89 and 0.91, respectively. Among the four SDMs, MAXENT achieved a higher accuracy (α = 91.6%) for the actual wetland compared to the thematic overlay analysis. The area under the curve (AUC) of the MAXENT model simulations for wetland spatial distribution were all greater than 0.80. This suggests that incorporating the SDM model into land change simulations has high generalizability and significant advantages on a large scale. Furthermore, simulation results reveal that between 2021 and 2100 years, with increasing emission concentrations, highly suitable areas for wetland development exhibit significant spatial differentiation. In particular, wetland areas in high-altitude regions are expected to increase, while low-altitude regions will markedly shrink. The changes in the future spatial distribution of wetlands show a high level of consistency with historical climate changes, with warming being the main driving force behind the spatiotemporal changes in alpine wetlands in western Sichuan, especially evident in the central high-altitude and northern low-altitude areas.
川西高山湿地分布于青藏高原东部，生态环境脆弱，对全球气候变化高度敏感。这些湿地已面临干旱、逆行演替和荒漠化等严重生态环境问题。然而，由于计算模型的局限性，以往研究未能充分了解这些高山湿地的时空变化趋势。
基于谷歌地球引擎云计算平台，采用大样本和复合监督分类算法对高山湿地进行分类并生成湿地地图。然后对专题地图进行网格采样，以预测未来湿地变化。创新性地引入了四种物种分布模型（SDM），即BIOCLIM、DOMAIN、MAXENT和GARP。以WorldClim数据集作为环境变量，预测了川西在多种气候情景下湿地的未来分布。
Landsat 8和哨兵2的Kappa系数分别为0.89和0.91。在四种SDM中，与专题叠加分析相比，MAXENT对实际湿地的预测精度更高（α = 91.6%）。MAXENT模型模拟湿地空间分布的曲线下面积（AUC）均大于0.80。这表明将SDM模型纳入土地变化模拟具有很高的通用性和大规模显著优势。此外，模拟结果显示，在2021年至2100年期间，随着排放浓度增加，湿地发育高度适宜区呈现出显著的空间分异。特别是，高海拔地区的湿地面积预计将增加，而低海拔地区将明显缩小。湿地未来空间分布的变化与历史气候变化高度一致，变暖是川西高山湿地时空变化的主要驱动力，在中部高海拔和北部低海拔地区尤为明显。
