森林火灾后脉冲恢复:在长期陆地卫星时间序列分析中,通用添加模型 (GAM) 的优势
Nima Arij1, Shirin Malihi2, Abbas Kiani3
1Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Mirdamad St., Tehran 19967-15433, Iran.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
野火的恢复因地区而异;澳大利亚的森林恢复得很快,而美国的森林却没有. 一般化添加模型 (GAM) 准确地捕捉复杂的植被恢复模式,优于监测生态系统弹性的简单模型.
科学领域:
- 生态生态学 生态生态学
- 遥感 遥感 遥感 遥感
- 林业林业 林业 林业 林业
背景情况:
- 全球野火正在增加,这给评估火灾后植被恢复和生态系统弹性带来了挑战.
- 长期卫星数据分析对于了解干扰后的植被动态至关重要.
- 现有的模型可能无法充分捕捉火灾后恢复模式的复杂性.
研究的目的:
- 用Landsat时间序列分析和比较美国和澳大利亚的火灾易发生态系统中火灾后的植被恢复.
- 评估不同统计模型 (线性,物流,LOESS,GAM) 在估计植被恢复方面的性能.
- 确定影响植被恢复的关键因素,并建立一种可转移的监测森林弹性方法.
主要方法:
- 利用长期的Landsat卫星图像通过增强植被指数 (EVI) 和Otsu值来提取植被面积.
- 模拟植被恢复到火灾前的水平,使用线性,物流,本地估计的分散图平滑 (LOESS) 和通用添加模型 (GAM).
- 使用像Akaike信息标准 (AIC) 和交叉验证的根平均平方误差 (RMSE_cv) 等指标比较模型性能.
主要成果:
- 澳大利亚森林迅速恢复到火灾前的水平,与美国森林不同.
- 在澳大利亚,温度是影响恢复的主要气候因素 (斯皮尔曼 ρ = 0.513,p < 10−8),而在加利福尼亚,没有气候变量显著影响恢复.
- 一般化添加模型 (GAM) 始终优于其他模型,准确地捕捉非线性和异质的恢复模式 (例如,美国AIC:142.89,澳大利亚AIC:46.70).
结论:
- 火灾后的植被恢复是非线性和生态系统特定的,需要先进的建模技术.
- 像线性回归和逻辑回归这样的简单模型不足以准确评估复杂的恢复动态.
- 一般化添加模型 (GAM) 提供了一种强大且可转移的方法,用于在越来越多的野火制度面前使用遥感数据监测森林弹性.
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