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在地中海盆地在相反的温和和干旱条件下建模气候驱动的植被变化

Marco Bianchini¹, Mohamed Tarhouni², Matteo Francioni¹

¹Department of Agricultural, Food and Environmental Sciences Università Politecnica delle Marche Ancona Italy.

Ecology and evolution

|January 13, 2025

概括

气候变化正在改变地中海植被,温带地区看到草原的扩张,干旱地区偏好林地. 这些变化影响息地适宜性和生态系统动态,需要适应性规划.

科学领域:

生态与环境科学生态与环境科学
气候变化研究气候变化研究
遥感和地理信息系统 (GIS) 的应用

背景情况:

地中海盆地是一个气候变化热点,经历了显著的气候变化.
自发植被对环境变化敏感,因此它是气候影响的关键指标.
了解未来的植被分布对于保护和土地管理至关重要.

研究的目的:

调查气候变化对两个对比的地中海地区自发植被的影响.
预测不同气候变化情景下的未来植被分布 (SSP 245和SSP 585).
评估不同建模算法的有效性,以预测息地适宜性.

主要方法:

选择了两个案例研究区域:蒙蒂·西比利尼 (温带) 和西迪·马克卢夫 (干旱).
利用CMCC-ESM2气候模型进行未来气候预测和光谱指数 (NDVI,SAVI) 作为植被代理.
采用K-means分类,使用ESA WorldCover 2020数据和仅存在点进行验证,并使用MaxEnt,随机森林,XG Boost和神经网络构建了预测模型.

主要成果:

预计这两个地区的气温将上升,降水量将下降.
观察到稳定的森林/灌木,但在温带地区不断扩张的不连续草原;在SSP585下,在干旱地区扩张的森林和减少的裸土.
确定了灌木和草原上升的转变,显著的面积分布变化和息地适合性的改变.

结论:

气候变化正在推动显著的植被再分配,改变地中海的生态系统动态.
MaxEnt被证明是最可靠的算法,用于预测小型存在数据集的息地适合性.
调查结果强调,需要在生态规划中采取适应性策略,以应对未来的环境挑战.

关键词:

气候变化气候变化气候变化机器学习是机器学习.牧养系统的牧养系统.预测植被模型的预测植被模型.牧场牧场牧场牧场

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