遥感数据对预测地中海本土物种分布的贡献
Ahmed R Mahmoud1, Emad A Farahat2, Loutfy M Hassan2
1Botany and Microbiology Department, Faculty of Science, Helwan University, P.O. Box: 11795, Helwan, Egypt. ahmedrabeey@science.helwan.edu.eg.
Scientific reports
|April 11, 2025
概括
将遥感数据与环境变量相结合,可显著改善物种分布模型 (SDM),用于预测全球变化下的生物多样性影响. 组合模型显示,在地中海物种保护方面,其精度更高.
科学领域:
- 生态生态学 生态生态学
- 生物多样性研究的研究.
- 遥感应用 遥感应用
背景情况:
- 全球变化改变了物种分布,需要准确的生物多样性影响评估.
- 物种分布模型 (SDM) 是评估这些影响的关键工具.
- 遥感数据越来越多地提高了SDM性能.
研究的目的:
- 评估从遥感数据中获得的光谱指数在物种分布建模中的贡献.
- 为了比较模型的预测准确度,仅使用光谱指数,仅使用环境变量或两者的组合.
- 评估三种主要的地中海本土物种的分布:西米莱亚hirsuta,阴道,和Limoniastrum monopetalum.
主要方法:
- 使用MaxEnt软件进行物种分布建模.
- 开发了三种模型类型:仅光谱指数 (仅RS),仅环境变量 (仅EN) 和组合 (CM).
- 采用杰克刀测试来确定重要的预测变量.
主要成果:
- 组合模型 (CM) 与只有RS和只有EN的模型相比,表现出更高的性能和准确性.
- 环境因素 (例如距离海岸线,温度) 和光谱指数 (例如NDWI,LST) 都是重要的预测因素.
- 这些模型成功地预测了三种地中海目标物种的分布.
结论:
- 整合各种数据源,包括遥感光谱指数和环境变量,大大提高了SDM的准确性.
- 这种综合方法为在异质景观中对物种分布模式提供了更全面的了解.
- 这些发现对于制定全球变化下的生物多样性的有效保护和管理策略至关重要.
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