Abstract:
Biomass or carbon stock is one indicator of the sustainability of forest stands. Stable and proportional quantity of biomass indicates forest sustainability is in good condition. In order to maintain forest sustainability, related parties are required to always be active in forest monitoring, one of which is the condition of standing biomass. This study aims to examine a number of spatial interpolation methods to estimate the biomass distribution of tropical rainforest stands in Mandiangin Hill, South Kalimantan. Spatial interpolation aims to overcome the limitations of sample data in the field in large forest areas. Several spatial interpolation methods are implemented in this study, namely IDW, GPI, RBF, LPI, and Kriging. A total of 50 sample plots were set up in the field to measure forest stand biomass. Even when the semivariogram was analyzed, only 40 of the sample points could be included in the analysis. Where 30 points are used as training samples for spatial interpolation input and 10 points are used as testing samples to validate the interpolation results. The validation of the spatial interpolation results was carried out using MAPE and RMSE. The research results show that IDW with a power value of 2 is the most optimal spatial interpolation method for estimating forest stand biomass. Besides having relatively small MAPE and RMSE, IDW is also more practical than other spatial interpolation methods. Other methods that can be used as alternatives to IDW for forest stand biomass are RBF with Completely Regularized Spline kernel function and Empirical Bayesian Kriging with Linear kernel function. Furthermore, to obtain more accurate spatial interpolation results, the sample points must be made more numerous and spread more evenly within the region to be estimated.
