Classifying Complex Mountainous Forests with L-Band SAR and Landsat Data Integration: A Comparison among Different Machine Learning Methods in the Hyrcanian ForestReport as inadecuate




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1

Remote Sensing Group, Institute of Geology, TU Bergakademie Freiberg TUBAF, Bernhard-von-Cotta-Str. 2, D-09599 Freiberg, Germany

2

Remote Sensing Group, Helmholtz Institute Freiberg of Resource Technology, Halsbruecker Str. 34, D-09599 Freiberg, Germany





*

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Abstract Forest environment classification in mountain regions based on single-sensor remote sensing approaches is hindered by forest complexity and topographic effects. Temperate broadleaf forests in western Asia such as the Hyrcanian forest in northern Iran have already suffered from intense anthropogenic activities. In those regions, forests mainly extend in rough terrain and comprise different stand structures, which are difficult to discriminate. This paper explores the joint analysis of Landsat7-ETM+, L-band SAR and their derived parameters and the effect of terrain corrections to overcome the challenges of discriminating forest stand age classes in mountain regions. We also verified the performances of three machine learning methods which have recently shown promising results using multisource data; support vector machines SVM, neural networks NN, random forest RF and one traditional classifier i.e., maximum likelihood classification MLC as a benchmark. The non-topographically corrected ETM+ data failed to differentiate among different forest stand age classes average classification accuracy OA = 65%. This confirms the need to reduce relief effects prior data classification in mountain regions. SAR backscattering alone cannot properly differentiate among different forest stand age classes OA = 62%. However, textures and PolSAR features are very efficient for the separation of forest classes OA = 82%. The highest classification accuracy was achieved by the joint usage of SAR and ETM+ OA = 86%. However, this shows a slight improvement compared to the ETM+ classification OA = 84%. The machine learning classifiers proved t o be more robust and accurate compared to MLC. SVM and RF statistically produced better classification results than NN in the exploitation of the considered multi-source data. View Full-Text

Keywords: Landsat; ALOS-PALSAR; L-band; maximum likelihood classification; support vector machines; neural networks; random forest; topographic effects; Hyrcanian mountainous forest; Iran Landsat; ALOS-PALSAR; L-band; maximum likelihood classification; support vector machines; neural networks; random forest; topographic effects; Hyrcanian mountainous forest; Iran





Author: Sara Attarchi 1,* and Richard Gloaguen 1,2

Source: http://mdpi.com/



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