Improving Plot-Level Model of Forest Biomass: A Combined Approach Using Machine Learning with Spatial Statistics

Forests

Lei Gao¹  Xiaohua Wei²  Qi Chen³  Xiaoman Zheng⁴  Shudi Zuo⁴  Shaoqing Dai⁴  Yin Ren⁴  Chengdong Xu⁵ 

[1] CSIRO, Waite Campus, Urrbrae, Adelaide, SA 5064, Australia;Department of Earth, Environmental and Geographic Sciences, University of British Columbia, Kelowna, BC V1V 1V7, Canada;Department of Geography and Environment, University of Hawai’i at Mānoa, Honolulu, HI 96822, USA;Key Laboratory of Urban Environment and Health, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Metabolism of Xiamen, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China;University of Chinese Academy of Sciences, Beijing 100049, China;

关键词: forest aboveground biomass;    plot-level model;    machine learning;    spatial statistical model;    model combination;   

DOI  :  10.3390/f12121663

来源: DOAJ

【 摘 要 】

Estimating the aboveground biomass (AGB) at the plot level plays a major role in connecting accurate single-tree AGB measurements to relatively difficult regional AGB estimates. However, AGB estimates at the plot level suffer from many uncertainties. The goal of this study is to determine whether combining machine learning with spatial statistics reduces the uncertainty of plot-level AGB estimates. To illustrate this issue, this study evaluates and compares the performance of different models for estimating plot-level forest AGB. These models include three different machine learning models [support vector machine (SVM), random forest (RF), and a radial basis function artificial neural network (RBF-ANN)], one spatial statistic model (P-BSHADE), and three combinations thereof (SVM & P-BSHADE, RF & P-BSHADE, and RBF-ANN & P-BSHADE). The results show that the root mean square error, mean absolute error, and mean relative error of all combined models are substantially smaller than those of any individual model, with the RF & P-BSHADE combined method generating the smallest values. These results indicate that a combined approach using machine learning with spatial statistics, especially the RF & P-BSHADE model, improves the accuracy of plot-level AGB models. These research results contribute to the development of accurate large-forested-landscape AGB maps.

【 授权许可】

Unknown