Volume XLII-4/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 77–81, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-77-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 77–81, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-77-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  18 Oct 2019

18 Oct 2019

IRANIAN LAND COVER MAPPING USING LANDSAT-8 IMAGERY AND RANDOM FOREST ALGORITHM

M. Amani1, A. Ghorbanian2, S. Mahdavi1, and A. Mohammadzadeh2 M. Amani et al.
  • 1Wood Environment & Infrastructure Solutions, St. John’s, NL, Canada
  • 2Dept. of Photogrammetry and Remote Sensing, Geodesy and Geomatics Engineering Faculty, K. N. Toosi University of Technology, Tehran, Iran

Keywords: Google Earth Engine, Remote Sensing, Land Cover, Iran, Landsat-8

Abstract. Land cover classification is important for various environmental assessments. The opportunity of imaging the Earth’s surface makes remote sensing techniques efficient approaches for land cover classification. The only country-wide land cover map of Iran was produced by the Iranian Space Agency (ISA) using low spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and a basic classification method. Thus, it is necessary to produce a more accurate map using advanced remote sensing and machine learning techniques. In this study, multi-temporal Landsat-8 data (1,321 images) were inserted into a Random Forest (RF) algorithm to classify the land cover of the entire country into 13 categories. To this end, all steps, including pre-processing, classification, and accuracy assessment were implemented in the Google Earth Engine (GEE) platform. The overall classification accuracy and Kappa Coefficient obtained from the Iran-wide map were 74% and 0.71, respectively, indicating the high potential of the proposed method for large-scale land cover mapping.