As forests are an indispensable component of terrestrial ecosystems, accurate and effective estimation of forest carbon stocks is an important basis for effectively responding to climate change and achieving the goal of carbon neutrality.

Taking the arboreal forests in Changsha City, Hunan Province, as the research object, the arboreal forests in Changsha City were categorized according to the dominant species and species groups into fir group, masson pine group, camphor group, foreign pine group, oak group and maple. Landsat-8 remote sensing data were used to extract band information, texture characteristics, vegetation index and topographic factors to obtain 80 modeling factors. Combined with the data from the Third National Land Survey of Changsha City, Multiple linear regression (MLR), Support vector regression (SVR) and Random forest (RF) algorithms were used to construct forest carbon stock inversion models for different dominant tree species and species groups.

The coefficients of determination of the RF model (R²=0.933 9-0.967 9) were significantly higher than those of the MLR model (R²=0.011 8-0.584 5) and the SVR model (R²=0.229 7-0.904 1), and the R_MSE and M_AE were significantly lower. Comparing the inversion results, the spatial distribution values of forest carbon stock without inversion of dominant tree species and species group classification ranged from 13.68-40.98 t·hm^-2, while the spatial distribution values of forest carbon stock after inversion by dominant tree species and species group classification ranged from 6.03-57.98 t·hm^-2 and forest carbon stock in Changsha City in 2020 was 4.946 8 Tg.

The forest carbon stock inversion model constructed by the RF algorithm after the classification of dominant tree species and species groups eliminated the problems of over-fitting and underestimation of the peak value when estimating under unclassified conditions, and provided a reference for the remote sensing inversion of forest carbon stock on a large scale.