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Title: Non-parametric retrieval of aboveground biomass in Siberian boreal forests with ALOS PALSAR interferometric coherence and backscatter intensity
Authors: Stelmaszczuk-Górska, M. A.
Rodriguez-Veiga, P.
Ackermann, N.
Thiel, C.
Balzter, Heiko
Schmullius, C.
First Published: 25-Dec-2015
Publisher: MDPI
Citation: Journal of Imaging, 2015, 2 (1), pp. 1-24
Abstract: The main objective of this paper is to investigate the effectiveness of two recently popular non-parametric models for aboveground biomass (AGB) retrieval from Synthetic Aperture Radar (SAR) L-band backscatter intensity and coherence images. An area in Siberian boreal forests was selected for this study. The results demonstrated that relatively high estimation accuracy can be obtained at a spatial resolution of 50 m using the MaxEnt and the Random Forests machine learning algorithms. Overall, the AGB estimation errors were similar for both tested models (approximately 35 t∙ha[Subscript: −1]). The retrieval accuracy slightly increased, by approximately 1%, when the filtered backscatter intensity was used. Random Forests underestimated the AGB values, whereas MaxEnt overestimated the AGB values.
DOI Link: 10.3390/jimaging2010001
ISSN: 2313-433X
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (
Appears in Collections:Published Articles, Dept. of Geography

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