Mapping growing stock volume and forest live biomass: a case study of the Polissya region of Ukraine. (27th September 2017)

• Record Type: Journal Article
• Authors: Bilous, Andrii; Myroniuk, Viktor; Holiaka, Dmytrii; Bilous, Svitlana; See, Linda; Schepaschenko, Dmitry
• Is Part Of: Environmental research letters. Volume 12:Number 10(2017:Oct.)
• Publication Date: 2017-09-27
• Subjects: data fusion -- k-NN imputation -- random forest -- model-based inference -- confidence interval; Environmental sciences -- Periodicals; Human ecology -- Research -- Periodicals; Environmental health -- Periodicals; 333.7
• DOI: 10.1088/1748-9326/aa8352
• Languages: English
• ISSNs: 1748-9326

Abstract

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.