Jiang Fugen, Deng Muli, Tang Jie, Fu Liyong, Sun Hua
Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha, 410004, China.
Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha, 410004, Hunan, China.
Carbon Balance Manag. 2022 Sep 1;17(1):12. doi: 10.1186/s13021-022-00212-y.
Fast and accurate forest aboveground biomass (AGB) estimation and mapping is the basic work of forest management and ecosystem dynamic investigation, which is of great significance to evaluate forest quality, resource assessment, and carbon cycle and management. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), as one of the latest launched spaceborne light detection and ranging (LiDAR) sensors, can penetrate the forest canopy and has the potential to obtain accurate forest vertical structure parameters on a large scale. However, the along-track segments of canopy height provided by ICESat-2 cannot be used to obtain comprehensive AGB spatial distribution. To make up for the deficiency of spaceborne LiDAR, the Sentinel-2 images provided by google earth engine (GEE) were used as the medium to integrate with ICESat-2 for continuous AGB mapping in our study. Ensemble learning can summarize the advantages of estimation models and achieve better estimation results. A stacking algorithm consisting of four non-parametric base models which are the backpropagation (BP) neural network, k-nearest neighbor (kNN), support vector machine (SVM), and random forest (RF) was proposed for AGB modeling and estimating in Saihanba forest farm, northern China.
The results show that stacking achieved the best AGB estimation accuracy among the models, with an R of 0.71 and a root mean square error (RMSE) of 45.67 Mg/ha. The stacking resulted in the lowest estimation error with the decreases of RMSE by 22.6%, 27.7%, 23.4%, and 19.0% compared with those from the BP, kNN, SVM, and RF, respectively.
Compared with using Sentinel-2 alone, the estimation errors of all models have been significantly reduced after adding the LiDAR variables of ICESat-2 in AGB estimation. The research demonstrated that ICESat-2 has the potential to improve the accuracy of AGB estimation and provides a reference for dynamic forest resources management and monitoring.
快速、准确地估算和绘制森林地上生物量（AGB）是森林经营和生态系统动态调查的基础工作，对于评估森林质量、资源评估以及碳循环与管理具有重要意义。冰、云和陆地高程卫星-2（ICESat-2）作为最新发射的星载激光雷达传感器之一，能够穿透森林冠层，具有在大尺度上获取准确森林垂直结构参数的潜力。然而，ICESat-2提供的沿轨冠层高度片段无法用于获取AGB的全面空间分布。为弥补星载激光雷达的不足，在本研究中，我们使用谷歌地球引擎（GEE）提供的哨兵-2影像作为媒介，将其与ICESat-2相结合，以实现连续的AGB制图。集成学习可以总结估算模型的优点并取得更好的估算结果。我们提出了一种由反向传播（BP）神经网络、k近邻（kNN）、支持向量机（SVM）和随机森林（RF）这四个非参数基模型组成的堆叠算法，用于中国北方塞罕坝林场的AGB建模和估算。
结果表明，堆叠算法在各模型中实现了最佳的AGB估算精度，决定系数R为0.71，均方根误差（RMSE）为45.67 Mg/ha。与BP、kNN、SVM和RF相比，堆叠算法的估算误差最低，RMSE分别降低了22.6%、27.7%、23.4%和19.0%。
与单独使用哨兵-2相比，在AGB估算中加入ICESat-2的激光雷达变量后，所有模型的估算误差均显著降低。该研究表明ICESat-2具有提高AGB估算精度的潜力，为动态森林资源管理和监测提供了参考。
