Mapping forest aboveground carbon stock of combined stratified sampling and RFRK model with mean annual temperature and precipitation

被引：0

作者：

Min Peng ^{[1
]}

Mingrui Xu ^{[2
]}

Jialong Zhang ^{[1
]}

Bo Qiu ^{[1
]}

Chenkai Teng ^{[1
]}

Chaoqing Chen ^{[2
]}

机构：

[1] Southwest Forestry University,The 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 So

[2] Southwest Forestry University,Forest Resource

[3] Panzhihua City Natural Resources Survey and Statistics Center,College of Soil and Water Conservation

来源：

Scientific Reports | / 15卷 / 1期

关键词：

Random forest residual kriging; Forest aboveground carbon stock; Mapping; Stratified sampling;

D O I：

10.1038/s41598-025-02338-8

中图分类号：

学科分类号：

摘要：

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.

引用