用遥感和气候数据集来估计料可用性的参数和非参数方法进行比较
Sajad Alimahmoodi Sarab1, Farajollah Tarnian2, Ebrahim Karimi Sangchini3
1Forests and Rangelands Research Department, Khuzestan Agricultural and Natural Resources Research and Education Center (AREEO), Ahvaz, Iran. sajadali9@yahoo.com.
准确的牧场料可用性估计对于管理至关重要. 这项研究发现,将MSAVI2指数与使用C&RT回归的潜在蒸发和Transou指数结合起来,为干旱环境提供了强大的方法.
科学领域:
- 牧场生态 牧场生态
- 遥感 遥感 遥感 遥感
- 环境建模环境建模
背景情况:
- 有效的牧场管理依赖于精确的料可用性评估.
- 炎热的半干旱地区对准确的料估计提出了独特的挑战.
- 整合遥感和气候数据可以改善植被监测.
研究的目的:
- 开发和验证一种可靠的方法来估计热带半干旱牧场的料供应.
- 为了确定最佳的植被和气候指数用于料质量预测.
- 为了对比不同机器学习算法的性能,用于此估计.
主要方法:
- 在58个样本场地使用切割和称重方法进行料质量的现场测量.
- 分析 Sentinel-2 遥感数据和气候指标 (例如,潜在的蒸发透气).
- 多重线性回归 (MLR),随机森林 (RF) 和分类和回归树 (C&RT) 模型的应用,具有k倍交叉验证.
主要成果:
- 相关性分析确定了MSAVI2,潜在的蒸发透气指数和Transou指数作为重要变量.
- 最初的模型表现中等 (C&RT的R2升至0.68).
- 整合植被和气候指数显著提高了模型准确性,C&RT实现了0.91.2的R2.
结论:
- 通过C&RT回归处理的MSAVI2,潜在蒸发和Transou指数的组合提供了一个非常准确的方法来估计料可用性.
- 这种方法在炎热,半干旱和干旱的牧场环境中特别有效.
- 该研究强调了综合遥感和气候数据的潜力,以实现可持续的牧场管理.
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