| IRANIAN LAND COVER MAPPING USING ADVANCED REMOTE SENSING AND MACHINE LEARNING METHODS |
| Paper ID : 1052-SMPR |
| Authors: |
|
Meisam Amani *1, Arsalan Ghorbanian2, Sahel Mahdavi1, Ali Mohammadzdeh2 1Wood Environment & Infrastructure Solutions, St. John's, NL, Canada, A1B1H3 2Photogrammetry and Remote Sensing Department, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology |
| 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. |
| Keywords: |
| Google Earth Engine, Remote sensing, Land cover, Iran |
| Status : Conditional Accept (Poster) |
