Affiliation:
1. Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
2. Department of Remote Sensing & GIS, Faculty of Geography, University of Tehran, Tehran 14155-4665, Iran
Abstract
Multi-temporal land use/land cover (LULC) change analysis is essential for environmental planning and recourses management. Various global LULC datasets are available now. However, they do not show strong consistency on a regional scale and are mainly time limited. Therefore, high-quality multi-temporal LULC mapping with reasonable consistency on a regional scale is still demanding. In this study, using the Landsat 7, Landsat 8, and the NASA digital elevation model (DEM), LULC mapping of the Tigris and Euphrates rivers basin (TEB) was performed by random forest (RF) classifier in the Google Earth Engine platform during 2000–2022. The spectral bands, spectral indices, morphological, and textural features were applied in the developed procedure. The results indicated that the proposed approach had accurate performance (accuracy = 0.893 and an F score = 0.820) with a good consistency with previous studies. The feature importance evaluation was carried out using Gini index, and spectral indices were identified as the most important features in LULC mapping. Overall, severe LULC change has happened in the TEB during the last two decades. Our results revealed the expansion of water and built-up classes while trees class has experienced a decreasing trend. From a regional perspective, three main areas in the east and south-east of Iraq, north-west of Iraq, and east of Syria were identified where LULC change was intense. These areas are prone to land degradation and dust storms emission problems, and it is necessary to take steps to prevent severe LULC changes in them.
Subject
Nature and Landscape Conservation,Ecology,Global and Planetary Change
Reference63 articles.
1. Evaluation of classification algorithms for land use land cover mapping in the snow-fed Alaknanda River Basin of the Northwest Himalayan Region;Singh;Appl. Geomat.,2021
2. Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y.-A., and Rahman, A. (2020). Land-use land-cover classification by machine learning classifiers for satellite observations—A review. Remote Sens., 12.
3. Detection of land use/land cover and land surface temperature change in the Suha Watershed, North-Western highlands of Ethiopia;Yeneneh;Environ. Chall.,2022
4. A machine learning-based classification of LANDSAT images to map land use and land cover of India;Singh;Remote Sens. Appl. Soc. Environ.,2021
5. Militino, A.F., Ugarte, M., and Pérez-Goya, U. (2018). Handbook of Mathematical Geosciences, Springer.
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