文献类型：期刊文献

英文题名：Uncertainty Analysis of Remote Sensing Estimation of Chinese Fir (Cunninghamia lanceolata) Aboveground Biomass in Southern China

作者：Hu, Yaopeng[1] Fu, Liyong[1] Qiu, Bo[2] Xie, Dongbo[1] Wu, Zheyuan[1] Lei, Yuancai[1] Ye, Jinsheng[3] Wang, Qiulai[3]

第一作者：Hu, Yaopeng

通信作者：Fu, LY[1]

机构：[1]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Southwest Forestry Univ, Coll Forestry, Kunming 650233, Peoples R China;[3]Guangdong Forestry Survey & Planning Inst, Guangzhou 510520, Peoples R China

年份：2025

卷号：16

期号：2

外文期刊名：FORESTS

收录：;EI(收录号：20250917949097);Scopus(收录号：2-s2.0-85218465257);WOS:【SCI-EXPANDED(收录号:WOS:001430805100001)】；

基金：This study is supported by the Fundamental Research Funds for the Central Nonprofit Research Institution of CAF under Grant CAFYBB2022ZB002.

语种：英文

外文关键词：estimation models; machine learning; plot scale; uncertainty analysis; Chinese fir

摘要：Forest aboveground biomass (AGB) is not only the basis for forest carbon stock research, but also an important parameter for assessing the forest carbon cycle and ecological functions of forests. However, there are various uncertainties in the estimation process, limiting the accuracy of AGB estimation. Therefore, we extracted the spectral features, vegetation indices and texture factors from remote sensing images based on the field data and Landsat 8 OLI remote sensing images in Southern China to quantify the uncertainties. Then, we established three AGB estimation models, including K Nearest Neighbor Regression (KNN), Gradient Boosted Regression Tree (GBRT) and Random Forest (RF). Uncertainties at the plot scale and models were measured by using error equations to analyze the influences of uncertainties at different scales on AGB estimation. Results were as follows: (1) The R2 of the per-tree biomass model for Cunninghamia lanceolata was 0.970, while the uncertainty of the residual and parameters for per-tree biomass model was 4.62% and 4.81%, respectively; and the uncertainty transferred to the plot scale was 3.23%. (2) The estimation methods had the most significant effects on the remote sensing models. RF was more accurate than other two methods, and had the highest accuracy (R2 = 0.867, RMSE = 19.325 t/ha) and lowest uncertainty (5.93%), which outperformed both the KNN and GBRT models (KNN: R2 = 0.368, RMSE = 42.314 t/ha, uncertainty = 14.88%; GBRT: R2 = 0.636, RMSE = 32.056 t/ha, uncertainty = 6.3%). Compared to KNN and GBRT, the R2 of RF was enhanced by 0.499 and 0.231, while the uncertainty was decreased by 8.95% and 0.37%, respectively. The uncertainty associated with the scale of remote sensing models remains the primary source of uncertainty when compared to the plot scale. On the remote sensing scale, RF is the model with the best estimation effect. This study examines the impact of both plot-scale and remote sensing model-scale methodologies on the estimation of AGB for Cunninghamia lanceolata. The findings aim to offer valuable insights and considerations for enhancing the accuracy of AGB estimations.