Urban Land Use and Land Cover Change Analysis Using Random Forest Classification of Landsat Time Series
University of Isfahan · University of Tehran · +2 more institutions
Abstract
Efficient implementation of remote sensing image classification can facilitate the extraction of spatiotemporal information for land use and land cover (LULC) classification. Mapping LULC change can pave the way to investigate the impacts of different socioeconomic and environmental factors on the Earth’s surface. This study presents an algorithm that uses Landsat time-series data to analyze LULC change. We applied the Random Forest (RF) classifier, a robust classification method, in the Google Earth Engine (GEE) using imagery from Landsat 5, 7, and 8 as inputs for the 1985 to 2019 period. We also explored the performance of the pan-sharpening algorithm on Landsat bands besides the impact of different image…
Citation impact
- FWCI
- 29.66
- Percentile
- 100%
- References
- 77
Authors
4Topics & keywords
- Land cover
- Remote sensing
- Series (stratigraphy)
- Land use
- Environmental science
- Physical geography
- Geography
- Geology