Tree density has been underestimated in the mountainous regions of Northeast China
Previous attempts to quantify tree density have often underestimated the numbers of trees in mountainous regions with complex terrain. We surveyed trees with a diameter at breast height (DBH) of ≥10 cm across 1,926 plots. By utilizing recursive feature elimination (RFE), we identified six key variab...
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| Format: | Article |
| 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/S1470160X25005850 |
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| author | Yunkun Song Wenqiang Xie Fang Wu Xuefeng Cui Xiaodong Yan Shuaifeng Song Jun Ren Hui Bai Yu Zhang Wei Pang Yueying Xiao Wang Zhan |
| author_facet | Yunkun Song Wenqiang Xie Fang Wu Xuefeng Cui Xiaodong Yan Shuaifeng Song Jun Ren Hui Bai Yu Zhang Wei Pang Yueying Xiao Wang Zhan |
| author_sort | Yunkun Song |
| collection | DOAJ |
| description | Previous attempts to quantify tree density have often underestimated the numbers of trees in mountainous regions with complex terrain. We surveyed trees with a diameter at breast height (DBH) of ≥10 cm across 1,926 plots. By utilizing recursive feature elimination (RFE), we identified six key variables for our meta-learner in the stacking process, including the soil silt content, soil clay content, elevation, Normalized Difference Vegetation Index (NDVI), precipitation in the wettest month, and precipitation in the coldest quarter, all of which were found to influence tree density. We developed a stacking ensemble learning algorithm, which ultimately generated a tree density map with a spatial resolution of 30 m for the mountainous regions of Northeast China. The estimated tree count is approximately 27.497 billion. Compared to global tree density datasets, our approach increased R2 to 0.454, while root mean square error (RMSE) and bias improved by 47.90 % and 74.52 %, respectively. This approach can increase the accuracy of local tree density simulations, which is crucial for the precise modeling of the forest carbon sequestration potential, the development of targeted forest conservation strategies, and the implementation of effective carbon management practices. |
| format | Article |
| id | doaj-art-cecb0bc9e084459cac6b4488b4bd4572 |
| institution | OA Journals |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-cecb0bc9e084459cac6b4488b4bd45722025-08-20T02:07:27ZengElsevierEcological Indicators1470-160X2025-07-0117611365510.1016/j.ecolind.2025.113655Tree density has been underestimated in the mountainous regions of Northeast ChinaYunkun Song0Wenqiang Xie1Fang Wu2Xuefeng Cui3Xiaodong Yan4Shuaifeng Song5Jun Ren6Hui Bai7Yu Zhang8Wei Pang9Yueying Xiao10Wang Zhan11State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaSchool of Systems Science, Beijing Normal University, Beijing 100875, ChinaSchool of Systems Science, Beijing Normal University, Beijing 100875, China; Corresponding authors.State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; Corresponding authors.College of Urban and Environmental Sciences, Peking University, Beijing 100871, ChinaJilin Provincial Academy of Forestry Sciences, Changchun 130013, ChinaJilin Forest Industry Baishishan Forestry Co.,Ltd., Jiaohe 132500, ChinaJilin Forest Industry Baishishan Forestry Co.,Ltd., Jiaohe 132500, ChinaJilin Forest Industry Baishishan Forestry Co.,Ltd., Jiaohe 132500, ChinaJilin Forest Industry Baishishan Forestry Co.,Ltd., Jiaohe 132500, ChinaJilin Forest Industry Baishishan Forestry Co.,Ltd., Jiaohe 132500, ChinaPrevious attempts to quantify tree density have often underestimated the numbers of trees in mountainous regions with complex terrain. We surveyed trees with a diameter at breast height (DBH) of ≥10 cm across 1,926 plots. By utilizing recursive feature elimination (RFE), we identified six key variables for our meta-learner in the stacking process, including the soil silt content, soil clay content, elevation, Normalized Difference Vegetation Index (NDVI), precipitation in the wettest month, and precipitation in the coldest quarter, all of which were found to influence tree density. We developed a stacking ensemble learning algorithm, which ultimately generated a tree density map with a spatial resolution of 30 m for the mountainous regions of Northeast China. The estimated tree count is approximately 27.497 billion. Compared to global tree density datasets, our approach increased R2 to 0.454, while root mean square error (RMSE) and bias improved by 47.90 % and 74.52 %, respectively. This approach can increase the accuracy of local tree density simulations, which is crucial for the precise modeling of the forest carbon sequestration potential, the development of targeted forest conservation strategies, and the implementation of effective carbon management practices.http://www.sciencedirect.com/science/article/pii/S1470160X25005850Mountainous regionsForestTree densityStackingEnsemble algorithm |
| spellingShingle | Yunkun Song Wenqiang Xie Fang Wu Xuefeng Cui Xiaodong Yan Shuaifeng Song Jun Ren Hui Bai Yu Zhang Wei Pang Yueying Xiao Wang Zhan Tree density has been underestimated in the mountainous regions of Northeast China Ecological Indicators Mountainous regions Forest Tree density Stacking Ensemble algorithm |
| title | Tree density has been underestimated in the mountainous regions of Northeast China |
| title_full | Tree density has been underestimated in the mountainous regions of Northeast China |
| title_fullStr | Tree density has been underestimated in the mountainous regions of Northeast China |
| title_full_unstemmed | Tree density has been underestimated in the mountainous regions of Northeast China |
| title_short | Tree density has been underestimated in the mountainous regions of Northeast China |
| title_sort | tree density has been underestimated in the mountainous regions of northeast china |
| topic | Mountainous regions Forest Tree density Stacking Ensemble algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X25005850 |
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