Research on Estimating and Evaluating Subtropical Forest Carbon Stocks by Combining Multi-Payload High-Resolution Satellite Data
Forest carbon stock is an important indicator reflecting the structure of forest ecosystems and forest quality, and an important parameter for evaluating the carbon sequestration capacity and carbon balance of forests. It is of great significance to study forest carbon stock in the context of curren...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
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| Series: | Forests |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4907/14/12/2388 |
| _version_ | 1797381058646245376 |
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| author | Yisha Du Donghua Chen Hu Li Congfang Liu Saisai Liu Naiming Zhang Jingwei Fan Deting Jiang |
| author_facet | Yisha Du Donghua Chen Hu Li Congfang Liu Saisai Liu Naiming Zhang Jingwei Fan Deting Jiang |
| author_sort | Yisha Du |
| collection | DOAJ |
| description | Forest carbon stock is an important indicator reflecting the structure of forest ecosystems and forest quality, and an important parameter for evaluating the carbon sequestration capacity and carbon balance of forests. It is of great significance to study forest carbon stock in the context of current global climate change. To explore the application ability of multi-loaded, high-resolution satellite data in the estimation of subtropical forest carbon stock, this paper takes Huangfu Mountain National Forest Park in Chuzhou City as the study area, extracts remote sensing features such as spectral features, texture features, backscattering coefficient, and other remote sensing features based on multi-loaded, high-resolution satellite data, and carries out correlation analyses with the carbon stock of different species of trees and different age groups of forests. Regression models for different tree species were established for different data sources, and the optimal modeling factors for multi-species were determined. Then, three algorithms, namely, multiple stepwise regression, random forest, and gradient-enhanced decision tree, were used to estimate carbon stocks of multi-species, and the predictive ability of different estimation models on carbon stocks was analyzed using the coefficient of determination (R<sup>2</sup>) and the root mean square error (RMSE) as indexes. The following conclusions were drawn: for the feature factors, the texture features of the GF-2 image, the new red edge index of the GF-6 image, the radar intensity coefficient sigma, and radar brightness coefficient beta of the GF-3 image have the best correlation with the carbon stock; for the algorithms, the random forest and gradient-boosting decision tree have the better effect of fitting and predicting the carbon stock of multi-tree species, among which gradient-boosting decision tree has the best effect, with an R<sup>2</sup> of 0.902 and an RMSE of 10.261 t/ha. In summary, the combination of GF-2, GF-3, and GF-6 satellite data and gradient-boosting decision tree obtains the most accurate estimation results when estimating forest carbon stocks of complex tree species; multi-load, high-resolution satellite data can be used in the inversion of subtropical forest parameters to estimate the carbon stocks of subtropical forests. The multi-loaded, high-resolution satellite data have great potential for application in the field of subtropical forest parameter inversion. |
| first_indexed | 2024-03-08T20:45:48Z |
| format | Article |
| id | doaj.art-852140d0ee3a438c8d944c83e6a2fd28 |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-08T20:45:48Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-852140d0ee3a438c8d944c83e6a2fd282023-12-22T14:09:36ZengMDPI AGForests1999-49072023-12-011412238810.3390/f14122388Research on Estimating and Evaluating Subtropical Forest Carbon Stocks by Combining Multi-Payload High-Resolution Satellite DataYisha Du0Donghua Chen1Hu Li2Congfang Liu3Saisai Liu4Naiming Zhang5Jingwei Fan6Deting Jiang7College of Geography and Tourism, Anhui Normal University, Wuhu 241002, ChinaCollege of Geography and Tourism, Anhui Normal University, Wuhu 241002, ChinaCollege of Geography and Tourism, Anhui Normal University, Wuhu 241002, ChinaCollege of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, ChinaCollege of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, ChinaCollege of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, ChinaCollege of Geography and Tourism, Xinjiang Normal University, Urumqi 830054, ChinaCollege of Remote Sensing and Surveying Engineering, Nanjing University of Information Science & Technology, Nanjing 211500, ChinaForest carbon stock is an important indicator reflecting the structure of forest ecosystems and forest quality, and an important parameter for evaluating the carbon sequestration capacity and carbon balance of forests. It is of great significance to study forest carbon stock in the context of current global climate change. To explore the application ability of multi-loaded, high-resolution satellite data in the estimation of subtropical forest carbon stock, this paper takes Huangfu Mountain National Forest Park in Chuzhou City as the study area, extracts remote sensing features such as spectral features, texture features, backscattering coefficient, and other remote sensing features based on multi-loaded, high-resolution satellite data, and carries out correlation analyses with the carbon stock of different species of trees and different age groups of forests. Regression models for different tree species were established for different data sources, and the optimal modeling factors for multi-species were determined. Then, three algorithms, namely, multiple stepwise regression, random forest, and gradient-enhanced decision tree, were used to estimate carbon stocks of multi-species, and the predictive ability of different estimation models on carbon stocks was analyzed using the coefficient of determination (R<sup>2</sup>) and the root mean square error (RMSE) as indexes. The following conclusions were drawn: for the feature factors, the texture features of the GF-2 image, the new red edge index of the GF-6 image, the radar intensity coefficient sigma, and radar brightness coefficient beta of the GF-3 image have the best correlation with the carbon stock; for the algorithms, the random forest and gradient-boosting decision tree have the better effect of fitting and predicting the carbon stock of multi-tree species, among which gradient-boosting decision tree has the best effect, with an R<sup>2</sup> of 0.902 and an RMSE of 10.261 t/ha. In summary, the combination of GF-2, GF-3, and GF-6 satellite data and gradient-boosting decision tree obtains the most accurate estimation results when estimating forest carbon stocks of complex tree species; multi-load, high-resolution satellite data can be used in the inversion of subtropical forest parameters to estimate the carbon stocks of subtropical forests. The multi-loaded, high-resolution satellite data have great potential for application in the field of subtropical forest parameter inversion.https://www.mdpi.com/1999-4907/14/12/2388carbon storageGF satellitesrandom foreststhe gradient promotion decision tree |
| spellingShingle | Yisha Du Donghua Chen Hu Li Congfang Liu Saisai Liu Naiming Zhang Jingwei Fan Deting Jiang Research on Estimating and Evaluating Subtropical Forest Carbon Stocks by Combining Multi-Payload High-Resolution Satellite Data Forests carbon storage GF satellites random forests the gradient promotion decision tree |
| title | Research on Estimating and Evaluating Subtropical Forest Carbon Stocks by Combining Multi-Payload High-Resolution Satellite Data |
| title_full | Research on Estimating and Evaluating Subtropical Forest Carbon Stocks by Combining Multi-Payload High-Resolution Satellite Data |
| title_fullStr | Research on Estimating and Evaluating Subtropical Forest Carbon Stocks by Combining Multi-Payload High-Resolution Satellite Data |
| title_full_unstemmed | Research on Estimating and Evaluating Subtropical Forest Carbon Stocks by Combining Multi-Payload High-Resolution Satellite Data |
| title_short | Research on Estimating and Evaluating Subtropical Forest Carbon Stocks by Combining Multi-Payload High-Resolution Satellite Data |
| title_sort | research on estimating and evaluating subtropical forest carbon stocks by combining multi payload high resolution satellite data |
| topic | carbon storage GF satellites random forests the gradient promotion decision tree |
| url | https://www.mdpi.com/1999-4907/14/12/2388 |
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