使用ICESat-2数据进行森林树冠高度的连续绘制,并使用谷歌地球引擎支持的多时间多源遥感数据加权内核集成
Jalal Mansouri1, Mohsen Jafari2, Alireza Taheri Dehkordi3
1Department of Civil and Environmental Engineering, School of Engineering, Shiraz University, Shiraz, Iran.
概括
这项研究引入了一种新的加权内核方法,从卫星数据创建连续的森林树冠高度地图,提高伊朗北部的准确性.
科学领域:
- 林业林业 林业 林业 林业
- 遥感 遥感 遥感 遥感
- 地理空间分析是什么
背景情况:
- 森林树冠高度 (FCH) 对于理解森林结构至关重要.
- 太空LiDAR可以提供准确但不连续的FCH数据.
- 现有的方法很难将基于点的LiDAR与连续遥感数据相结合.
研究的目的:
- 为伊朗北部生成连续的森林树冠高度 (FCH) 地图.
- 开发一种新的权重内核 (WK) 方法,将ICESat-2 ATL08数据与多源遥感 (RS) 数据集集成在一起.
- 提高FCH估计的空间连续性和准确性.
主要方法:
- 集成ICESat-2 ATL08 FCH数据与使用谷歌地球引擎的Sentinel-1/2,ALOS-2 PALSAR-2和FABDEM数据集.
- 开发并应用了一种新的加权内核 (WK) 方法,用于重新采样RS数据以匹配ATL08段.
- 使用机器学习模型,包括随机森林,用于FCH映射.
主要成果:
- WK方法显著改善了ATL08段内的RS数据的表示,增强了大约6%的特征相关性.
- 随机森林模型实现了高的确定系数 (R2 = 0.71) 和较少的错误 (RMSE = 4.92 m,MAPE = 29.95%).
- 结合春季和秋季的Sentinel-1/2数据,与夏季数据集相比,R2增加了6%,RMSE减少了0.5m.
结论:
- 建议的WK方法有效地整合了基于点的LiDAR和连续RS数据,以准确地绘制FCH地图.
- 该方法成功生成了连续的FCH地图,解决了先前研究的局限性.
- 这项研究提高了FCH估计的准确性,为森林管理和研究提供了有价值的数据.
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