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Estimation of Aboveground Carbon Stocks in Forests Based on LiDAR and Multispectral Images: A Case Study of Duraer Coniferous Forests

文献类型： 外文期刊

作者： Su, Rina 1 ; Du, Wala 3 ; Ying, Hong 1 ; Shan, Yu 1 ; Liu, Yang 1 ;

作者机构： 1.Inner Mongolia Normal Univ, Coll Geog Sci, Hohhot 010022, Peoples R China

2.Inner Mongolia Normal Univ, Inner Mongolia Key Lab Remote Sensing & Geog Infor, Hohhot 010022, Peoples R China

3.Chinese Acad Agr Sci, Grassland Res Inst, Hohhot 010022, Peoples R China

4.Arshan Forest & Grassland Disaster Prevent & Mitig, Arshan 137400, Peoples R China

关键词： LiDAR; multispectral images; aboveground carbon stocks; multiple regression model

期刊名称：FORESTS （ 影响因子：2.9； 五年影响因子：3.0 ）

ISSN：

年卷期： 2023 年 14 卷 5 期

页码：

收录情况： SCI

摘要： The correct estimation of forest aboveground carbon stocks (AGCs) allows for an accurate assessment of the carbon sequestration potential of forest ecosystems, which is important for in-depth studies of the regional ecological environment and global climate change. How to estimate forest AGCs quickly and accurately and realize dynamic monitoring has been a hot topic of research in the forestry field worldwide. LiDAR and remote sensing optical imagery can be used to monitor forest resources, enabling the simultaneous acquisition of forest structural properties and spectral information. A high-density LiDAR-based point cloud cannot only reveal stand-scale forest parameters but can also be used to extract single wood-scale forest parameters. However, there are multiple forest parameter estimation model problems, so it is especially important to choose appropriate variables and models to estimate forest AGCs. In this study, we used a Duraer coniferous forest as the study area and combined LiDAR, multispectral images, and measured data to establish multiple linear regression models and multiple power regression models to estimate forest AGCs. We selected the best model for accuracy evaluation and mapped the spatial distribution of AGC density. We found that (1) the highest accuracy of the multiple multiplicative power regression model was obtained for the estimated AGC (R-2 = 0.903, RMSE = 10.91 Pg) based on the LiDAR-estimated DBH; the predicted AGC values were in the range of 4.1-279.12 kg C. (2) The highest accuracy of the multiple multiplicative power regression model was obtained by combining the normalized vegetation index (NDVI) with the predicted AGC based on the DBH estimated by LiDAR (R-2 = 0.906, RMSE = 10.87 Pg); the predicted AGC values were in the range of 3.93-449.07 kg C. (3) The LiDAR-predicted AGC values and the combined LiDAR and optical image-predicted AGC values agreed with the field AGCs.

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