Mapping the Forest Canopy Height in Northern China by Synergizing ICESat-2 with Sentinel-2 Using a Stacking Algorithm
Mapping the Forest Canopy Height in Northern China by Synergizing ICESat-2 with Sentinel-2 Using a Stacking Algorithm
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DOI：
10.3390/rs13081535
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发表时间：
2021-04-01
期刊：
影响因子：
5
通讯作者：
Sun, Hua
中科院分区：
文献类型：
--
作者：
Jiang, Fugen;Zhao, Feng;Sun, Hua
森林冠层高度在森林质量评价和资源管理中起着至关重要的作用。准确、快速地估计和绘制区域森林冠层高度对于了解植被生长过程和生态系统的内部结构至关重要。本文采用多元线性回归(MLR)、支持向量机(SVM)、k近邻(KNN)和随机森林(RF)相结合的叠加算法，利用ICESat-2(冰、云和陆地高程卫星-2)的数据，对基于云的计算平台Google Earth Engine(GEE)获取的Sentinel-2图像进行协同处理，获得了最佳的森林冠层高度预测结果。中国，为实现对河北省承德市塞罕坝机械林人工林林分冠层高度的连续测绘而开展的研究。结果表明，叠加法对森林冠层高度的预测精度最高，R-2为0.77，均方根误差为1.96m，与最大似然法、支持向量机、KNN和RF相比，其均方根误差分别降低了25.2%、24.9%、22.8%和18.7%。由于Sentinel-2图像和ICESat-2数据是公开可用的，这为未来准确绘制全球森林冠层高度的连续分布打开了大门。
The forest canopy height (FCH) plays a critical role in forest quality evaluation and resource management. The accurate and rapid estimation and mapping of the regional forest canopy height is crucial for understanding vegetation growth processes and the internal structure of the ecosystem. A stacking algorithm consisting of multiple linear regression (MLR), support vector machine (SVM), k-nearest neighbor (kNN), and random forest (RF) was used in this paper and demonstrated optimal performance in predicting the forest canopy height by synergizing Sentinel-2 images acquired from the cloud-based computation platform Google Earth Engine (GEE) with data from ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2). This research was conducted to achieve continuous mapping of the canopy height of plantations in Saihanba Mechanical Forest Plantation, which is located in Chengde City, northern Hebei province, China. The results show that stacking achieved the best prediction accuracy for the forest canopy height, with an R-2 of 0.77 and a root mean square error (RMSE) of 1.96 m. Compared with MLR, SVM, kNN, and RF, the RMSE obtained by stacking was reduced by 25.2%, 24.9%, 22.8%, and 18.7%, respectively. Since Sentinel-2 images and ICESat-2 data are publicly available, this opens the door for the accurate mapping of the continuous distribution of the forest canopy height globally in the future.