Modelling height to crown base using non-parametric methods for mixed forests in China
The height to crown base (HCB) of a tree is a vital characteristic that reflects the self-thinning ability of a tree, and it is used to determine the crown size. and predict the crown recession rate. This study simulated the HCB of Spruce fir broadleaved mixed forest in Northeast China using four no...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
|
| Series: | Ecological Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954124004990 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832595391251480576 |
|---|---|
| author | Zeyu Zhou Huiru Zhang Ram P. Sharma Xiaohong Zhang Linyan Feng Manyi Du Lianjin Zhang Huanying Feng Xuefan Hu Yang Yu |
| author_facet | Zeyu Zhou Huiru Zhang Ram P. Sharma Xiaohong Zhang Linyan Feng Manyi Du Lianjin Zhang Huanying Feng Xuefan Hu Yang Yu |
| author_sort | Zeyu Zhou |
| collection | DOAJ |
| description | The height to crown base (HCB) of a tree is a vital characteristic that reflects the self-thinning ability of a tree, and it is used to determine the crown size. and predict the crown recession rate. This study simulated the HCB of Spruce fir broadleaved mixed forest in Northeast China using four non-parametric model approaches: generalized additive model, Cubist, boosted regression tree (BRT), and multiple adaptive regression spline. Because of the different genetic characteristics and growth patterns of different tree species, species-specific tree groups were formed, and the HCB of each species-specific group was simulated by the different models. Relative importance and partial dependence analyses were performed to identify the primary HCB predictors (including tree, stand, stand spatial structure, density and competition factors) and their relationships with the HCB of the four tree species groups. The relative importance was higher for individual tree variables (77.54 %, 31.02 %, 31.12 %, and 73.69 % for coniferous, spruce-fir, hard broadleaved, and soft broadleaved groups, respectively) and stand variables (5.00 %, 20.34 %, 11.03 %, and 8.71 % for coniferous, spruce-fir, hard broadleaved, and soft broadleaved groups, respectively) compared with stand spatial structure variables (4.57 %, 12.14 %, 21.91 %, and 5.89 % for coniferous, spruce-fir, hard broadleaved, and soft broadleaved groups, respectively), density indexes variables (2.17 %, 1.28 %, 4.05 %, and 2.87 % for coniferous, spruce-fir, hard broadleaved, and soft broadleaved groups, respectively), and tree species variables (10.79 %, 35.20 %, 31.90 %, and 8.84 % for coniferous, spruce-fir, hard broadleaved, and soft broadleaved groups, respectively). BRT and Cubist were the best approaches for modelling the four species-group specific HCBs. Although spatial structure variables had minor relative importance, further in-depth investigations are warranted. |
| format | Article |
| id | doaj-art-47f39fce1ebf4ff4bcdfb5cd704e76a3 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-47f39fce1ebf4ff4bcdfb5cd704e76a32025-01-19T06:24:39ZengElsevierEcological Informatics1574-95412025-03-0185102957Modelling height to crown base using non-parametric methods for mixed forests in ChinaZeyu Zhou0Huiru Zhang1Ram P. Sharma2Xiaohong Zhang3Linyan Feng4Manyi Du5Lianjin Zhang6Huanying Feng7Xuefan Hu8Yang Yu9Experimental Center of Forestry in North China, Chinese Academy of Forestry, Beijing 102300, ChinaExperimental Center of Forestry in North China, Chinese Academy of Forestry, Beijing 102300, China; State Key Laboratory of Efficient Production of Forest Resources, Key Laboratory of Forest Management and Growth Modelling, National Forestry and Grassland Administration, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China; Corresponding author at: Experimental Center of Forestry in North China, Chinese Academy of Forestry, Beijing 102300, China.Institute of Forestry, Tribhuvan University, Kathmandu 44600, NepalInternational Cooperation Center of National Forestry and Grassland Administration, Beijing 100714, ChinaState Key Laboratory of Efficient Production of Forest Resources, Key Laboratory of Forest Management and Growth Modelling, National Forestry and Grassland Administration, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, ChinaExperimental Center of Forestry in North China, Chinese Academy of Forestry, Beijing 102300, ChinaExperimental Center of Forestry in North China, Chinese Academy of Forestry, Beijing 102300, ChinaExperimental Center of Forestry in North China, Chinese Academy of Forestry, Beijing 102300, ChinaBeijing Key Laboratory of Greening Plants Breeding, Beijing Academy of Forestry and Landscape Architecture, Beijing 100102, ChinaExperimental Center of Forestry in North China, Chinese Academy of Forestry, Beijing 102300, ChinaThe height to crown base (HCB) of a tree is a vital characteristic that reflects the self-thinning ability of a tree, and it is used to determine the crown size. and predict the crown recession rate. This study simulated the HCB of Spruce fir broadleaved mixed forest in Northeast China using four non-parametric model approaches: generalized additive model, Cubist, boosted regression tree (BRT), and multiple adaptive regression spline. Because of the different genetic characteristics and growth patterns of different tree species, species-specific tree groups were formed, and the HCB of each species-specific group was simulated by the different models. Relative importance and partial dependence analyses were performed to identify the primary HCB predictors (including tree, stand, stand spatial structure, density and competition factors) and their relationships with the HCB of the four tree species groups. The relative importance was higher for individual tree variables (77.54 %, 31.02 %, 31.12 %, and 73.69 % for coniferous, spruce-fir, hard broadleaved, and soft broadleaved groups, respectively) and stand variables (5.00 %, 20.34 %, 11.03 %, and 8.71 % for coniferous, spruce-fir, hard broadleaved, and soft broadleaved groups, respectively) compared with stand spatial structure variables (4.57 %, 12.14 %, 21.91 %, and 5.89 % for coniferous, spruce-fir, hard broadleaved, and soft broadleaved groups, respectively), density indexes variables (2.17 %, 1.28 %, 4.05 %, and 2.87 % for coniferous, spruce-fir, hard broadleaved, and soft broadleaved groups, respectively), and tree species variables (10.79 %, 35.20 %, 31.90 %, and 8.84 % for coniferous, spruce-fir, hard broadleaved, and soft broadleaved groups, respectively). BRT and Cubist were the best approaches for modelling the four species-group specific HCBs. Although spatial structure variables had minor relative importance, further in-depth investigations are warranted.http://www.sciencedirect.com/science/article/pii/S1574954124004990Competition indexGini coefficientMachine learningGeneralized additive modelTree species-specific groupStand spatial structure |
| spellingShingle | Zeyu Zhou Huiru Zhang Ram P. Sharma Xiaohong Zhang Linyan Feng Manyi Du Lianjin Zhang Huanying Feng Xuefan Hu Yang Yu Modelling height to crown base using non-parametric methods for mixed forests in China Ecological Informatics Competition index Gini coefficient Machine learning Generalized additive model Tree species-specific group Stand spatial structure |
| title | Modelling height to crown base using non-parametric methods for mixed forests in China |
| title_full | Modelling height to crown base using non-parametric methods for mixed forests in China |
| title_fullStr | Modelling height to crown base using non-parametric methods for mixed forests in China |
| title_full_unstemmed | Modelling height to crown base using non-parametric methods for mixed forests in China |
| title_short | Modelling height to crown base using non-parametric methods for mixed forests in China |
| title_sort | modelling height to crown base using non parametric methods for mixed forests in china |
| topic | Competition index Gini coefficient Machine learning Generalized additive model Tree species-specific group Stand spatial structure |
| url | http://www.sciencedirect.com/science/article/pii/S1574954124004990 |
| work_keys_str_mv | AT zeyuzhou modellingheighttocrownbaseusingnonparametricmethodsformixedforestsinchina AT huiruzhang modellingheighttocrownbaseusingnonparametricmethodsformixedforestsinchina AT rampsharma modellingheighttocrownbaseusingnonparametricmethodsformixedforestsinchina AT xiaohongzhang modellingheighttocrownbaseusingnonparametricmethodsformixedforestsinchina AT linyanfeng modellingheighttocrownbaseusingnonparametricmethodsformixedforestsinchina AT manyidu modellingheighttocrownbaseusingnonparametricmethodsformixedforestsinchina AT lianjinzhang modellingheighttocrownbaseusingnonparametricmethodsformixedforestsinchina AT huanyingfeng modellingheighttocrownbaseusingnonparametricmethodsformixedforestsinchina AT xuefanhu modellingheighttocrownbaseusingnonparametricmethodsformixedforestsinchina AT yangyu modellingheighttocrownbaseusingnonparametricmethodsformixedforestsinchina |