Estimation of aboveground tree carbon stock using SPOT-HRG data (a case study: Darabkola forests)

Forests are among the most important carbon sinks on earth. However, their complex structure and vast areas preclude accurate estimation of forest carbon stocks. Data sets from forest monitoring using advanced satellite imagery are now used in international policy agreements. Data sets enable tracking of emissions of CO₂ into the atmosphere caused by deforestation and other types of land-use changes. The aim of this study is to determine the capability of SPOT-HRG Satellite data to estimate aboveground carbon stock in a district of Darabkola research and training forest, Iran. Preprocessing to eliminate or reduce geometric error and atmospheric error were performed on the images. Using cluster sampling, 165 sample plots were taken. Of 165 plots, 81 were in natural habitats, and 84 were in forest plantations. Following the collection of ground data, biomass and carbon stocks were quantified for the sample plots on a per hectare basis. Nonparametric regression models such as support vector regression were used for modeling purposes with different kernels including linear, sigmoid, polynomial, and radial basis function. The results showed that a third-degree polynomial was the best model for the entire studied areas having an root mean square error, bias and accuracy, respectively, of 38.41, 5.31, and 62.2; 42.77, 16.58, and 57.3% for the best polynomial for natural forest; and 44.71, 2.31, and 64.3% for afforestation. Overall, these results indicate that SPOT-HRG satellite data and support vector machines are useful for estimating aboveground carbon stock.