Abstract
Land use/ land cover (LULC) classification is a key research field in remote sensing. With recent developments of high-spatial-resolution sensors, Earth-observation technology offers a viable solution for land use/land cover identification and management in the rural part of the cities. There is a strong need to produce accurate, reliable, and up-to-date land use/land cover maps for sustainable monitoring and management. In this study, SPOT 7 imagery was used to test the potential of the data for land cover/land use mapping. Catalca is selected region located in the north west of the Istanbul in Turkey, which is mostly covered with agricultural fields and forest lands. The potentials of two classification algorithms maximum likelihood, and support vector machine, were tested, and accuracy assessment of the land cover maps was performed through error matrix and Kappa statistics. The results indicated that both of the selected classifiers were highly useful (over 83% accuracy) in the mapping of land use/cover in the study region. The support vector machine classification approach slightly outperformed the maximum likelihood classification in both overall accuracy and Kappa statistics.
Original language | English |
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Pages (from-to) | 143-146 |
Number of pages | 4 |
Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
Volume | 42 |
Issue number | 2W1 |
DOIs | |
Publication status | Published - 26 Oct 2016 |
Event | 3rd International GeoAdvances Workshop, GeoAdvances 2016: ISPRS Workshop on Multi-Dimensional and Multi-Scale Spatial Data Modeling - Istanbul, Turkey Duration: 16 Oct 2016 → 17 Oct 2016 |
Bibliographical note
Publisher Copyright:© 2018 International Society for Photogrammetry and Remote Sensing. All Rights Reserved.
Keywords
- Classification
- Istanbul
- Land cover / land use
- Maximum likelihood classification
- SPOT 7
- Support vector machine