文献类型：期刊文献

英文题名：Integrating spaceborne LiDAR and Sentinel-2 images to estimate forest aboveground biomass in Northern China

作者：Jiang, Fugen[1,2,3] Deng, Muli[1,2,3] Tang, Jie[1,2,3] Fu, Liyong[1,4] Sun, Hua[1,2,3]

第一作者：Jiang, Fugen

通信作者：Sun, H[1];Sun, H[2];Sun, H[3]

机构：[1]Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Peoples R China;[2]Key Lab Forestry Remote Sensing Based Big Data &, Changsha 410004, Hunan, Peoples R China;[3]Key Lab State Forestry Adm Forest Resources Manag, Changsha 410004, Hunan, Peoples R China;[4]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China

年份：2022

卷号：17

期号：1

外文期刊名：CARBON BALANCE AND MANAGEMENT

收录：;Scopus(收录号：2-s2.0-85137586511);WOS:【SCI-EXPANDED(收录号:WOS:000848886700001)】；

基金：This research is supported by the Natural Science Foundation of China (31971578) and the Hunan Provincial Natural Science Foundation of China (2022JJ30078). This research is also supported by the Scientific Research Fund of Changsha Science and Technology Bureau (kq2004095).

语种：英文

外文关键词：Aboveground biomass; Carbon cycle and management; Remote sensing; ICESat-2; Google earth engine; Machine learning

摘要：Background 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. Results The results show that stacking achieved the best AGB estimation accuracy among the models, with an R-2 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. Conclusion 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.