Land Cover Classification using Random Forest Technique and DEM Auxiliary Data

Abstract

This article has been written based on findings and results of author’s master thesis. Nowadays, every month/year changes occur on the surface of the Earth from a small build ups till large-scale natural disasters that destroyed entire settlements. Observations and analysis using satellite data can help to improve and prevent any natural or anthropogenic disaster, plan and calculate beneficial usage of the land cover. Remote Sensing and Spatial Analysis tools are one of the approaches for them. This study tries to sustain one of the advantageous approaches for extracting information from semi-automatic mapping. Besides, article provides semi-automatic methodology of extracting information from multispectral image for large areas. For that, the Sentinel-2a (S2) optical data with 10 m resolution for Land Cover Classification System (LCCS) classification and as auxiliary data to extract the urban and industrial areas DEM and build up indices were used. Object-based image classification with random forest classification techniques using LCCS classes was done with an accuracy of 88%. Moreover Salzkammergut was chosen as a study area. The Salzkammergut is a historical and recreational area part of Austria, consisting of 52 districts from three federal states and with no official administrative boundaries.