Land Use/Land Cover Change Analysis Using Multi-Temporal Remote Sensing Data: A Case Study of Tigris and Euphrates Rivers Basin

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.

Automated summary

Multi-temporal land use/land cover (LULC) change analysis is crucial for environmental planning and resources management. However, existing global LULC datasets lack consistency on a regional scale and have limited time coverage. This study developed a high-quality multi-temporal LULC mapping approach using Landsat imagery and a random forest classifier. The results showed accurate performance and identified the most important features for LULC mapping. Severe LULC changes were observed in the Tigris and Euphrates rivers basin, particularly in certain areas where land degradation and dust storms emission are pressing issues.