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J. Imaging 2016, 2(1), 1; doi:10.3390/jimaging2010001

Non-Parametric Retrieval of Aboveground Biomass in Siberian Boreal Forests with ALOS PALSAR Interferometric Coherence and Backscatter Intensity

1
Department of Earth Observation, Friedrich-Schiller-University Jena, Loebdergraben 32, Jena D-07743, Germany
2
Centre for Landscape and Climate Research, University of Leicester, Bennett Building, University Road, Leicester LE1 7RH, UK
3
National Centre for Earth Observation, University of Leicester, Bennett Building, University Road, Leicester LE1 7RH, UK
4
Gamaya AG, Bâtiment C, EPFL Innovation Park, Lausanne 1015, Switzerland
*
Author to whom correspondence should be addressed.
Academic Editors: Francisco Rovira-Más and Gonzalo Pajares Martinsanz
Received: 30 October 2015 / Revised: 2 December 2015 / Accepted: 15 December 2015 / Published: 25 December 2015
(This article belongs to the Special Issue Image Processing in Agriculture and Forestry)
View Full-Text   |   Download PDF [15965 KB, uploaded 25 December 2015]   |  

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−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. View Full-Text
Keywords: SAR; MaxEnt; random forests; estimation error; forest; biomass; carbon SAR; MaxEnt; random forests; estimation error; forest; biomass; carbon
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Stelmaszczuk-Górska, M.A.; Rodriguez-Veiga, P.; Ackermann, N.; Thiel, C.; Balzter, H.; Schmullius, C. Non-Parametric Retrieval of Aboveground Biomass in Siberian Boreal Forests with ALOS PALSAR Interferometric Coherence and Backscatter Intensity. J. Imaging 2016, 2, 1.

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