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.

Details

Title
Mapping growing stock volume and forest live biomass: a case study of the Polissya region of Ukraine
Author
Bilous, Andrii 1 ; Myroniuk, Viktor 1   VIAFID ORCID Logo  ; Holiaka, Dmytrii 1 ; Bilous, Svitlana 1 ; See, Linda 2   VIAFID ORCID Logo  ; Schepaschenko, Dmitry 3   VIAFID ORCID Logo 

 National University of Life and Environmental Sciences of Ukraine, Heroyiv Oborony 15, 03041, Kyiv, Ukraine 
 International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361 Laxenburg, Austria 
 International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361 Laxenburg, Austria; Author to whom any correspondence should be addressed. 
Publication year
2017
Publication date
Oct 2017
Publisher
IOP Publishing
e-ISSN
17489326
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2549180759
Copyright
© 2017. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.