Mapping forest aboveground carbon stock of combined stratified sampling and RFRK model with mean annual temperature and precipitation
Abstract Accurately estimating forest aboveground carbon stock (ACS) is essential for achieving carbon neutrality. At present, most non-parametric models still have errors in estimating carbon stock in regions. Given the autocorrelation inherent in spatial interpolation, combining non-parametric mod...
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Nature Portfolio
2025-05-01
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| author | Min Peng Mingrui Xu Jialong Zhang Bo Qiu Chenkai Teng Chaoqing Chen |
| author_facet | Min Peng Mingrui Xu Jialong Zhang Bo Qiu Chenkai Teng Chaoqing Chen |
| author_sort | Min Peng |
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| description | Abstract Accurately estimating forest aboveground carbon stock (ACS) is essential for achieving carbon neutrality. At present, most non-parametric models still have errors in estimating carbon stock in regions. Given the autocorrelation inherent in spatial interpolation, combining non-parametric models with spatial interpolation offers significant potential. In this study, we combined the random forest (RF) with the ordinary kriging and co-kriging of the mean annual temperature, precipitation, slope, and elevation to establish the random forest residual kriging (RFRK) model. Meanwhile, we also developed the multiple linear regression residual kriging (MLRRK) model and the random forest residual kriging (RFRK) model. Finally, we selected the optimal model for the estimation and mapping of the ACS. The results indicate that: (1) the model achieves an R2 of 0.871, P of 90.4%, and RMSE of 3.948 t/hm2; (2) the RFCK model with mean annual precipitation (RFCKpre) outperforms the one with mean annual temperature (RFCKtem), while the RFOK model exhibits the lowest accuracy; (3) the RFCKpre exponential model has the highest accuracy, with the highest R2 of 0.63 and RI (0.23), the lowest RMSE of 9.3 and SSR (41,612). These findings suggest that the RFRKpre model has improved the accuracy of estimating the ACS of regional forests. |
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| language | English |
| publishDate | 2025-05-01 |
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| spelling | doaj-art-ba8e501511404658aa5c1d378cd82a2d2025-08-20T02:33:31ZengNature PortfolioScientific Reports2045-23222025-05-0115111610.1038/s41598-025-02338-8Mapping forest aboveground carbon stock of combined stratified sampling and RFRK model with mean annual temperature and precipitationMin Peng0Mingrui Xu1Jialong Zhang2Bo Qiu3Chenkai Teng4Chaoqing Chen5The Key Laboratory of Forest Resources Conservation and Utilization in the Southwest Mountains of China Ministry of Education; Key Laboratory of National Forestry and Grassland Administration On Biodiversity Conservation in Southwest China; Yunnan Provincial Key Laboratory for Conservation and Utilization of In-Forest Resource, Southwest Forestry UniversityCollege of Soil and Water Conservation, Southwest Forestry UniversityThe Key Laboratory of Forest Resources Conservation and Utilization in the Southwest Mountains of China Ministry of Education; Key Laboratory of National Forestry and Grassland Administration On Biodiversity Conservation in Southwest China; Yunnan Provincial Key Laboratory for Conservation and Utilization of In-Forest Resource, Southwest Forestry UniversityThe Key Laboratory of Forest Resources Conservation and Utilization in the Southwest Mountains of China Ministry of Education; Key Laboratory of National Forestry and Grassland Administration On Biodiversity Conservation in Southwest China; Yunnan Provincial Key Laboratory for Conservation and Utilization of In-Forest Resource, Southwest Forestry UniversityThe Key Laboratory of Forest Resources Conservation and Utilization in the Southwest Mountains of China Ministry of Education; Key Laboratory of National Forestry and Grassland Administration On Biodiversity Conservation in Southwest China; Yunnan Provincial Key Laboratory for Conservation and Utilization of In-Forest Resource, Southwest Forestry UniversityCollege of Soil and Water Conservation, Southwest Forestry UniversityAbstract Accurately estimating forest aboveground carbon stock (ACS) is essential for achieving carbon neutrality. At present, most non-parametric models still have errors in estimating carbon stock in regions. Given the autocorrelation inherent in spatial interpolation, combining non-parametric models with spatial interpolation offers significant potential. In this study, we combined the random forest (RF) with the ordinary kriging and co-kriging of the mean annual temperature, precipitation, slope, and elevation to establish the random forest residual kriging (RFRK) model. Meanwhile, we also developed the multiple linear regression residual kriging (MLRRK) model and the random forest residual kriging (RFRK) model. Finally, we selected the optimal model for the estimation and mapping of the ACS. The results indicate that: (1) the model achieves an R2 of 0.871, P of 90.4%, and RMSE of 3.948 t/hm2; (2) the RFCK model with mean annual precipitation (RFCKpre) outperforms the one with mean annual temperature (RFCKtem), while the RFOK model exhibits the lowest accuracy; (3) the RFCKpre exponential model has the highest accuracy, with the highest R2 of 0.63 and RI (0.23), the lowest RMSE of 9.3 and SSR (41,612). These findings suggest that the RFRKpre model has improved the accuracy of estimating the ACS of regional forests.https://doi.org/10.1038/s41598-025-02338-8Random forest residual krigingForest aboveground carbon stockMappingStratified sampling |
| spellingShingle | Min Peng Mingrui Xu Jialong Zhang Bo Qiu Chenkai Teng Chaoqing Chen Mapping forest aboveground carbon stock of combined stratified sampling and RFRK model with mean annual temperature and precipitation Scientific Reports Random forest residual kriging Forest aboveground carbon stock Mapping Stratified sampling |
| title | Mapping forest aboveground carbon stock of combined stratified sampling and RFRK model with mean annual temperature and precipitation |
| title_full | Mapping forest aboveground carbon stock of combined stratified sampling and RFRK model with mean annual temperature and precipitation |
| title_fullStr | Mapping forest aboveground carbon stock of combined stratified sampling and RFRK model with mean annual temperature and precipitation |
| title_full_unstemmed | Mapping forest aboveground carbon stock of combined stratified sampling and RFRK model with mean annual temperature and precipitation |
| title_short | Mapping forest aboveground carbon stock of combined stratified sampling and RFRK model with mean annual temperature and precipitation |
| title_sort | mapping forest aboveground carbon stock of combined stratified sampling and rfrk model with mean annual temperature and precipitation |
| topic | Random forest residual kriging Forest aboveground carbon stock Mapping Stratified sampling |
| url | https://doi.org/10.1038/s41598-025-02338-8 |
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