Estimation of the aboveground carbon stocks based on tree species identification in Saihanba plantation forest
The expansion of plantation forests has been identified as a significant potential strategy for natural initiatives aimed at mitigating the ongoing global climate change. Consequently, the rapid and accurate estimation of aboveground carbon stocks in plantation forests is a crucial prerequisite for...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-04-01
|
| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X25003000 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850182958353219584 |
|---|---|
| author | Ao Zhang Xiaohong Wang Xin Gu Xiangyao Xu Xintong Gao Linlin Jiao |
| author_facet | Ao Zhang Xiaohong Wang Xin Gu Xiangyao Xu Xintong Gao Linlin Jiao |
| author_sort | Ao Zhang |
| collection | DOAJ |
| description | The expansion of plantation forests has been identified as a significant potential strategy for natural initiatives aimed at mitigating the ongoing global climate change. Consequently, the rapid and accurate estimation of aboveground carbon stocks in plantation forests is a crucial prerequisite for regional carbon balance assessment and the formulation of global climate change response strategies. In this study, based on the multi-temporal Sentinel-2 remote sensing image data, five classification schemes were devised to identify the dominant tree species of all forest patches or sub-compartments in the Saihanba Mechanical Forestry using the Random Forest algorithm. Then, the sub-compartments were classified by dominant tree species, and the aboveground carbon stocks of major five types with more than 500 sub-compartments were estimated through a spatial gridded method. The results were shown that: 1) The identification effect of Scheme IV, as ascertained by screening three types of effective feature vectors based on the random forest algorithm, was the most effective, with an overall accuracy (OA) and kappa coefficient of 89.7% and 0.863, respectively. Moreover, the identification effect of Larch (Larix principis-rupprechtii Mayr) was the most optimal in all schemes, with the highest PA of 92.9%. The most obvious enhancement in the identification accuracy of Spruce (Picea asperata Mast.) was evident, with a notable increase in PA from 38.0% to 55.7%, signifying a substantial 17.7% rise; 2) The dominant tree species in Saihanba exhibited distinct spatial patterns. White Birch (Betula platyphylla Sukaczev) was primarily distributed in the Yinhe Forest in the east, Mongolica (Quercus mongolica Fisch) was predominantly concentrated in the Dahuanqi Forest in the south, Pinus (Pinus sylvestris var. mongholica Litv.) was mainly distributed in the west, the Sandaohekou Forest and the Qiancengban Forest, and Larch were found throughout the entire region. 3) In conjunction with the findings of tree species identification via the gridding estimation approach, it became evident that the aggregate aboveground carbon stocks of each dominant tree species within the Saihanba Mechanical Forestry region amounts to 3313.28 thousand tons. Furthermore, the discrepancy between this estimation and the direct measurement outcomes of the forest management inventory (FMI) was minimal, exhibiting a relative error of only −5.2%, estimation precision of 94.8%. This study would provide a critical methodological advancement for regional/local-scale carbon stocks monitoring in plantation ecosystems, with both scientific and practical implications for global climate governance. |
| format | Article |
| id | doaj-art-9e9a3cc2c24f436186a6edfb93b60fe6 |
| institution | OA Journals |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-9e9a3cc2c24f436186a6edfb93b60fe62025-08-20T02:17:29ZengElsevierEcological Indicators1470-160X2025-04-0117311337010.1016/j.ecolind.2025.113370Estimation of the aboveground carbon stocks based on tree species identification in Saihanba plantation forestAo Zhang0Xiaohong Wang1Xin Gu2Xiangyao Xu3Xintong Gao4Linlin Jiao5College of Mining Engineering, North China University of Science and Technology, Tangshan 063210 Hebei, ChinaCollege of Mining Engineering, North China University of Science and Technology, Tangshan 063210 Hebei, China; Tangshan Key Laboratory of Remote Sensing of Resources and Environment, Tangshan 063210 Hebei, China; Hebei Industrial Technology Institute of Mine Ecological Remediation, Tangshan 063210 Hebei, ChinaCollege of Mining Engineering, North China University of Science and Technology, Tangshan 063210 Hebei, ChinaCollege of Mining Engineering, North China University of Science and Technology, Tangshan 063210 Hebei, ChinaCollege of Mining Engineering, North China University of Science and Technology, Tangshan 063210 Hebei, ChinaCollege of Mining Engineering, North China University of Science and Technology, Tangshan 063210 Hebei, China; Tangshan Key Laboratory of Remote Sensing of Resources and Environment, Tangshan 063210 Hebei, China; Hebei Industrial Technology Institute of Mine Ecological Remediation, Tangshan 063210 Hebei, China; Corresponding author at: College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, Hebei, China.The expansion of plantation forests has been identified as a significant potential strategy for natural initiatives aimed at mitigating the ongoing global climate change. Consequently, the rapid and accurate estimation of aboveground carbon stocks in plantation forests is a crucial prerequisite for regional carbon balance assessment and the formulation of global climate change response strategies. In this study, based on the multi-temporal Sentinel-2 remote sensing image data, five classification schemes were devised to identify the dominant tree species of all forest patches or sub-compartments in the Saihanba Mechanical Forestry using the Random Forest algorithm. Then, the sub-compartments were classified by dominant tree species, and the aboveground carbon stocks of major five types with more than 500 sub-compartments were estimated through a spatial gridded method. The results were shown that: 1) The identification effect of Scheme IV, as ascertained by screening three types of effective feature vectors based on the random forest algorithm, was the most effective, with an overall accuracy (OA) and kappa coefficient of 89.7% and 0.863, respectively. Moreover, the identification effect of Larch (Larix principis-rupprechtii Mayr) was the most optimal in all schemes, with the highest PA of 92.9%. The most obvious enhancement in the identification accuracy of Spruce (Picea asperata Mast.) was evident, with a notable increase in PA from 38.0% to 55.7%, signifying a substantial 17.7% rise; 2) The dominant tree species in Saihanba exhibited distinct spatial patterns. White Birch (Betula platyphylla Sukaczev) was primarily distributed in the Yinhe Forest in the east, Mongolica (Quercus mongolica Fisch) was predominantly concentrated in the Dahuanqi Forest in the south, Pinus (Pinus sylvestris var. mongholica Litv.) was mainly distributed in the west, the Sandaohekou Forest and the Qiancengban Forest, and Larch were found throughout the entire region. 3) In conjunction with the findings of tree species identification via the gridding estimation approach, it became evident that the aggregate aboveground carbon stocks of each dominant tree species within the Saihanba Mechanical Forestry region amounts to 3313.28 thousand tons. Furthermore, the discrepancy between this estimation and the direct measurement outcomes of the forest management inventory (FMI) was minimal, exhibiting a relative error of only −5.2%, estimation precision of 94.8%. This study would provide a critical methodological advancement for regional/local-scale carbon stocks monitoring in plantation ecosystems, with both scientific and practical implications for global climate governance.http://www.sciencedirect.com/science/article/pii/S1470160X25003000Tree species identificationForest aboveground carbon stocksRandom forestGridding methodSaihanba |
| spellingShingle | Ao Zhang Xiaohong Wang Xin Gu Xiangyao Xu Xintong Gao Linlin Jiao Estimation of the aboveground carbon stocks based on tree species identification in Saihanba plantation forest Ecological Indicators Tree species identification Forest aboveground carbon stocks Random forest Gridding method Saihanba |
| title | Estimation of the aboveground carbon stocks based on tree species identification in Saihanba plantation forest |
| title_full | Estimation of the aboveground carbon stocks based on tree species identification in Saihanba plantation forest |
| title_fullStr | Estimation of the aboveground carbon stocks based on tree species identification in Saihanba plantation forest |
| title_full_unstemmed | Estimation of the aboveground carbon stocks based on tree species identification in Saihanba plantation forest |
| title_short | Estimation of the aboveground carbon stocks based on tree species identification in Saihanba plantation forest |
| title_sort | estimation of the aboveground carbon stocks based on tree species identification in saihanba plantation forest |
| topic | Tree species identification Forest aboveground carbon stocks Random forest Gridding method Saihanba |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X25003000 |
| work_keys_str_mv | AT aozhang estimationoftheabovegroundcarbonstocksbasedontreespeciesidentificationinsaihanbaplantationforest AT xiaohongwang estimationoftheabovegroundcarbonstocksbasedontreespeciesidentificationinsaihanbaplantationforest AT xingu estimationoftheabovegroundcarbonstocksbasedontreespeciesidentificationinsaihanbaplantationforest AT xiangyaoxu estimationoftheabovegroundcarbonstocksbasedontreespeciesidentificationinsaihanbaplantationforest AT xintonggao estimationoftheabovegroundcarbonstocksbasedontreespeciesidentificationinsaihanbaplantationforest AT linlinjiao estimationoftheabovegroundcarbonstocksbasedontreespeciesidentificationinsaihanbaplantationforest |