Mapping growing stock volume and forest live biomass: a case study of the Polissya region of Ukraine

Abstract

Forest inventory and biomass mapping are important tasks that require inputs from multiple data sources. In this paper we implement two methods for the Ukrainian region of Polissya: random forest (RF) for tree species prediction and k -nearest neighbors ( k -NN) for growing stock volume and biomass mapping. We examined the suitability of the five-band RapidEye satellite image to predict the distribution of six tree species. The accuracy of RF is quite high: ~99% for forest/non-forest mask and 89% for tree species prediction. Our results demonstrate that inclusion of elevation as a predictor variable in the RF model improved the performance of tree species classification. We evaluated different distance metrics for the k -NN method, including Euclidean or Mahalanobis distance, most similar neighbor (MSN), gradient nearest neighbor, and independent component analysis. The MSN with the four nearest neighbors ( k = 4) is the most precise (according to the root-mean-square deviation) for predicting forest attributes across the study area. The k -NN method allowed us to estimate growing stock volume with an accuracy of 3 m ^3 ha ^−1 and for live biomass of about 2 t ha ^−1 over the study area.

Keywords

• data fusion
• k-NN imputation
• random forest
• model-based inference
• confidence interval