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

Amani, M.; Ghorbanian, A.; Mahdavi, S.; Mohammadzadeh, A.

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.

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Amani, M. / Ghorbanian, A. / Mahdavi, S. / et al: IRANIAN LAND COVER MAPPING USING LANDSAT-8 IMAGERY AND RANDOM FOREST ALGORITHM. 2019. Copernicus Publications.

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