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

Bilous, A., Myroniuk, V., Holiaka, D., Bilous, S., See, L. ORCID: https://orcid.org/0000-0002-2665-7065, & Schepaschenko, D. ORCID: https://orcid.org/0000-0002-7814-4990 (2017). Mapping growing stock volume and forest live biomass: a case study of the Polissya region of Ukraine. Environmental Research Letters 12 (10) e105001. 10.1088/1748-9326/aa8352.

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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 m3 ha−1 and for live biomass of about 2 t ha−1 over the study area.

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