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模拟伊朗不同生物气候地区的NPP和NDVI时间序列
Fahimeh Sayedzadeh1, Saied Soltani2, Reza Modarres1
1Department of Natural Resources, Isfahan University of Technology, Isfahan, 84156- 83111, Iran.
Environmental monitoring and assessment
|November 1, 2024
概括
本研究以净初级生产 (NPP) 和规范差异植被指数 (NDVI) 的时间序列分析为模型,对伊朗的植被变化进行模型化. 萨里马模型准确地预测了植被动态,有助于生态系统保护工作.
科学领域:
- 生态生态学 生态生态学
- 遥感 遥感 遥感 遥感
- 时间序列分析时间序列分析
背景情况:
- 植被动态是重要的生态系统组成部分,表现出季节性变化.
- 对植被覆盖的准确建模对于了解生态系统健康和生物多样性至关重要.
- 伊朗多样化的生物气候地区为植被监测带来了独特的挑战.
研究的目的:
- 调查和建模伊朗生物气候地区净初级生产 (NPP) 和规范差异植被指数 (NDVI) 的时间变化.
- 为核电站和NDVI数据开发和评估季节性自回归集成移动平均 (SARIMA) 时间序列模型.
- 为植物健康提供短期预测,以支持管理和保护战略.
主要方法:
- 使用的中等分辨率成像光谱辐射仪 (MODIS) 产品 (NPP的MOD17A2,NDVI的MOD13Q1).
- 开发了SARIMA时间序列模型来分析NPP和NDVI的季节性和趋势.
- 使用自相关函数 (ACF) 来识别季节性模式,特别是在12个月的滞后.
主要成果:
- 在NPP和NDVI中都观察到强烈的季节性,NPP显示出更明显的季节性模式.
- 萨里马模型在捕捉植被动态方面表现出有效性,在不同地区和变量之间具有不同的性能指标.
- 模型评估表明,基于RMSE,R2,MRE和CE标准的NPP时间序列的性能优越,而NDVI模型在ME标准方面表现出色.
结论:
- 时间序列模型,特别是SARIMA,可以有效地参数化植被覆盖的动态.
- 开发的模型为NPP和NDVI提供了有价值的短期 (24个月) 预测.
- 这些预测对于旨在减少植被退化和保护生态系统和生物多样性的规划和管理具有重要作用.
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