Estimation of carbon sequestration and forest soil respiration using machine learning ‎‎models in Eastern Forests of Mazandaran Province

In this study, machine learning based models (linear regression, k-nearest neighbor, support vector regression, random forest) were evaluated to estimate carbon sequestration and soil respiration in the forests of eastern Mazandaran province. After identifying the sampling points, in each plot, the diameter and height of the trees were measured and the above-ground biomass of the trees was calculated using allometric models of Hyrcanian forest. Soil samples were taken from 0-20 cm depth and soil respiration was measured by CO2-port. Soil respiration was estimated using bulk density, moisture content, texture, soil temperature, total nitrogen, available phosphorus and potassium, organic carbon and above-ground trees biomass. Soil carbon sequestration was estimated using soil temperature and moisture and above-ground tree biomass. Random forest model (RMSE = 10.47 and R2 = 0.82) and support vector machine (RMSE = 0.77 and R2 = 0.90) had the highest performance in estimating carbon sequestration and soil respiration, respectively. Soil moisture had the highest relative importance in estimating carbon sequestration (random forest model) and soil respiration (support vector machine model). According to the obtained results, it is possible to estimate the amount of carbon sequestration and soil respiration in the forest with appropriate accuracy by having the above-ground biomass of trees and basic soil characteristics.