Carbon sequestration and storage are among the most important ecosystem services provided by tropical forests. Improving the accuracy of the carbon mapping of tropical forests has always been a challenge, particularly in countries and regions with limited resources, with limited funding to provide high-resolution and high-quality remote sensing data. This study aimed to examine the use of land-cover and elevation-based methods of aboveground carbon mapping in a tropical forest composed of shrubs and trees. We tested a geostatistical method with an ordinary kriging interpolation using three stratification types: no stratification, stratification based on elevation, and stratification based on land-cover type, and compared it with a simple mapping technique, i.e., a lookup table based on a combination of land cover and elevation. A regression modelling with land cover and elevation as predictors was also tested in this study. The best performance was shown by geostatistical interpolation without stratification and geostatistical interpolation based on land cover, with a coefficient of variation (CV) of the root mean square error (RMSE) of 0.44, better than the performance of lookup table techniques (with a CV of the RMSE of more than 0.48). The regression modeling provided a significant model, but with a coefficient of determination (R2
) of only 0.29, and a CV of the RMSE of 0.49. The use of other variables should thus be further investigated. We discuss improving aboveground carbon mapping in the study area and the implications of our results for forest management.
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