| Abstrak/Abstract |
Indonesia is one of the countries with the largest area of tropical peatlands in the world. These wide
peatlands have a vital role in the carbon cycle and carbon storage in huge quantities, thus strict
conservation in the area is necessary. One effort to carry out conservation is to understand the spatial
distribution of carbon stock in peatlands. This study aims to map the spatial distribution of carbon stock
based on peat thickness modeling using machine learning algorithms, i.e., Random Forest (RF),
Quantile Regression Forests (QRF), and Cubist. A case study was conducted in a part of Lake Sentarum
National Park, Indonesia where human interference is still limited. Digital elevation model (DEM) and
synthetic aperture radar (SAR) data were included as the input variables. The results showed that RF
performed the best among other models to estimate peat thickness (mean = 1.877 m) with an RMSE
(root mean square error) of 0.483 m and an R2 of 0.786. DEMs are the most important parameters in
our analysis compared to SAR data. Based on the best model, we estimated the total volume of
5,112,687 m3 in the study area, produced at 12.5 m resolution, which was then converted to a total of
carbon stock at 0.337 ± 0.106 Mt carbon. |
