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Improving Forest Above-Ground Biomass Estimation by Integrating Individual Machine Learning Models

文献类型： 外文期刊

作者： Luo, Mi 1 ; Anees, Shoaib Ahmad 3 ; Huang, Qiuyan 1 ; Qin, Xin 4 ; Qin, Zhihao 1 ; Fan, Jianlong 6 ; Han, Guangping 7 ; Zhang, Liguo 1 ; Shafri, Helmi Zulhaidi Mohd 8 ;

作者机构： 1.Nanning Normal Univ, Sch Geog & Planning, Key Lab Remote Sensing Subtrop Agr, Nanning 530001, Peoples R China

2.Nanning Normal Univ, Key Lab Environm Change & Resources Use Beibu Gulf, Nanning 530001, Peoples R China

3.Univ Agr, Dept Forestry, Dera Ismail Khan 29050, Pakistan

4.Guangxi Vocat Normal Univ, Nanning 530001, Peoples R China

5.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arid & Semiarid A, Beijing 100081, Peoples R China

6.Natl Satellite Meteorol Ctr, Beijing 100081, Peoples R China

7.Guangxi Zhuang Autonomous Reg Inst Nat Resources R, Nanning 530028, Peoples R China

8.Univ Putra Malaysia UPM, Fac Engn, Dept Civil Engn, Serdang 43400, Malaysia

关键词： above-ground biomass; ensemble model; CatBoost; machine learning

期刊名称：FORESTS （ 影响因子：2.4； 五年影响因子：2.7 ）

ISSN：

年卷期： 2024 年 15 卷 6 期

页码：

收录情况： SCI

摘要： The accurate estimation of forest above-ground biomass (AGB) is crucial for sustainable forest management and tracking the carbon cycle of forest ecosystem. Machine learning algorithms have been proven to have great potential in forest AGB estimation with remote sensing data. Though many studies have demonstrated that a single machine learning model can produce highly accurate estimations of forest AGB in many situations, efforts are still required to explore the possible improvement in forest AGB estimation for a specific scenario under study. This study aims to investigate the performance of novel ensemble machine learning methods for forest AGB estimation and analyzes whether these methods are affected by forest types, independent variables, and spatial autocorrelation. Four well-known machine learning models (CatBoost, LightGBM, random forest (RF), and XGBoost) were compared for forest AGB estimation in the study using eight scenarios devised on the basis of two study regions, two variable types, and two validation strategies. Subsequently, a hybrid model combining the strengths of these individual models was proposed for forest AGB estimation. The findings indicated that no individual model outperforms the others in all scenarios. The RF model demonstrates superior performance in scenarios 5, 6, and 7, while the CatBoost model shows the best performance in the remaining scenarios. Moreover, the proposed hybrid model consistently has the best performance in all scenarios in spite of some uncertainties. The ensemble strategy developed in this study for the hybrid model substantially improves estimation accuracy and exhibits greater stability, effectively addressing the challenge of model selection encountered in the forest AGB forecasting process.

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