Comparing the potential of tree-based and area-based forest height metrics for aboveground biomass estimation in complex forest landscapes
Forest height metrics are vital for predicting aboveground biomass (AGB), a key indicator of carbon dynamics and sustainable forest management. This study explores the comparative performance of tree-based and area-based forest height metrics in AGB estimation using 755 field plots across Beijing, i...
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| Language: | English |
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Elsevier
2025-07-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X25005400 |
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| author | Weiyan Liu Yuling Chen Haitao Yang Guangcai Xu Shiyu Yan Lingjun Li Ling Chen Qinghua Guo |
| author_facet | Weiyan Liu Yuling Chen Haitao Yang Guangcai Xu Shiyu Yan Lingjun Li Ling Chen Qinghua Guo |
| author_sort | Weiyan Liu |
| collection | DOAJ |
| description | Forest height metrics are vital for predicting aboveground biomass (AGB), a key indicator of carbon dynamics and sustainable forest management. This study explores the comparative performance of tree-based and area-based forest height metrics in AGB estimation using 755 field plots across Beijing, incorporating field-measured tree diameter at breast height and LiDAR-derived tree heights. Two modeling approaches—parametric mixed-effects models (MEM) and non-parametric machine learning (ML) algorithms—were applied to evaluate predictive accuracy. The results indicate the following: (1) Tree-based metrics that align more closely with forestry definitions demonstrate higher predictive accuracy than area-based metrics, particularly Lorey’s mean height and top height. (2) Among machine learning models, CatBoost, which incorporates Lorey’s mean height, achieve the highest accuracy (R2 = 0.688, relative RMSE = 41.85 %, MAE = 18.15 Mg/ha). (3) While area-based metrics are widely used in large-scale assessments due to their scalability, our results underscore the superior precision of tree-based metrics in AGB estimations, showing an 11.0 % to 23.1 % improvement of R2 over the area-based metrics. (4) Regional variations across Beijing further highlight the need to tailor metric selection to specific landscape and modeling objectives. These findings provide critical insights for optimizing AGB estimation methods, supporting carbon stock assessments, forest monitoring, and management strategies. |
| format | Article |
| id | doaj-art-d02e6f05647048af98256cc03631cd8f |
| institution | DOAJ |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-d02e6f05647048af98256cc03631cd8f2025-08-20T03:21:51ZengElsevierEcological Indicators1470-160X2025-07-0117611361010.1016/j.ecolind.2025.113610Comparing the potential of tree-based and area-based forest height metrics for aboveground biomass estimation in complex forest landscapesWeiyan Liu0Yuling Chen1Haitao Yang2Guangcai Xu3Shiyu Yan4Lingjun Li5Ling Chen6Qinghua Guo7State Forestry and Grassland Administration Key Laboratory of Forest Resources & Environmental Management, Beijing Forestry University, Beijing 100083, ChinaInstitute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, ChinaInstitute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, ChinaBeijing Green Valley Technology Co., Ltd., Haidian District, Beijing 100091, ChinaBeijing Green Valley Technology Co., Ltd., Haidian District, Beijing 100091, ChinaBeijing Municipal Ecological and Environmental Monitoring Center, Beijing 100048, ChinaState Forestry and Grassland Administration Key Laboratory of Forest Resources & Environmental Management, Beijing Forestry University, Beijing 100083, China; Corresponding authors.Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China; Institute of Ecology, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China; Corresponding authors.Forest height metrics are vital for predicting aboveground biomass (AGB), a key indicator of carbon dynamics and sustainable forest management. This study explores the comparative performance of tree-based and area-based forest height metrics in AGB estimation using 755 field plots across Beijing, incorporating field-measured tree diameter at breast height and LiDAR-derived tree heights. Two modeling approaches—parametric mixed-effects models (MEM) and non-parametric machine learning (ML) algorithms—were applied to evaluate predictive accuracy. The results indicate the following: (1) Tree-based metrics that align more closely with forestry definitions demonstrate higher predictive accuracy than area-based metrics, particularly Lorey’s mean height and top height. (2) Among machine learning models, CatBoost, which incorporates Lorey’s mean height, achieve the highest accuracy (R2 = 0.688, relative RMSE = 41.85 %, MAE = 18.15 Mg/ha). (3) While area-based metrics are widely used in large-scale assessments due to their scalability, our results underscore the superior precision of tree-based metrics in AGB estimations, showing an 11.0 % to 23.1 % improvement of R2 over the area-based metrics. (4) Regional variations across Beijing further highlight the need to tailor metric selection to specific landscape and modeling objectives. These findings provide critical insights for optimizing AGB estimation methods, supporting carbon stock assessments, forest monitoring, and management strategies.http://www.sciencedirect.com/science/article/pii/S1470160X25005400Forest aboveground biomassForest height metricsLiDARMachine learningMixed-effects models |
| spellingShingle | Weiyan Liu Yuling Chen Haitao Yang Guangcai Xu Shiyu Yan Lingjun Li Ling Chen Qinghua Guo Comparing the potential of tree-based and area-based forest height metrics for aboveground biomass estimation in complex forest landscapes Ecological Indicators Forest aboveground biomass Forest height metrics LiDAR Machine learning Mixed-effects models |
| title | Comparing the potential of tree-based and area-based forest height metrics for aboveground biomass estimation in complex forest landscapes |
| title_full | Comparing the potential of tree-based and area-based forest height metrics for aboveground biomass estimation in complex forest landscapes |
| title_fullStr | Comparing the potential of tree-based and area-based forest height metrics for aboveground biomass estimation in complex forest landscapes |
| title_full_unstemmed | Comparing the potential of tree-based and area-based forest height metrics for aboveground biomass estimation in complex forest landscapes |
| title_short | Comparing the potential of tree-based and area-based forest height metrics for aboveground biomass estimation in complex forest landscapes |
| title_sort | comparing the potential of tree based and area based forest height metrics for aboveground biomass estimation in complex forest landscapes |
| topic | Forest aboveground biomass Forest height metrics LiDAR Machine learning Mixed-effects models |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X25005400 |
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