Monitoring of Changes in Land Use/Land Cover in Syria from 2010 to 2018 Using Multitemporal Landsat Imagery and GIS

Abstract

Understanding the effects of socio-ecological shocks on land use/land cover (LULC) change is essential for developing land management strategies and for reducing adverse environmental pressures. Our study examines the impacts of the armed conflict in Syria, which began in mid-2011, and the related social and economic crisis on LULC between 2010 and 2018. We used remote sensing for change detection by applying a supervised maximum likelihood classification to Landsat images of the three target years 2010, 2014, and 2018. Based on the computed extent of our LULC classes and accuracy assessment, we calculated area-adjusted estimates and 95% confidence intervals. Our classification achieved an overall accuracy of 86.4%. Compared to 2010, we found an increase in spatial extent for bare areas (40,011 km2), forests (2576 km2), and urban and peri-urban areas (3560 km2), whereas rangelands (37,005 km2) and cultivated areas (9425 km2) decreased by 2018. It is not possible to determine whether the changes in LULC in Syria will be permanent or temporary. Natural conditions such as climate fluctuations had an impact on the uses of the natural environment and cultivated areas during the study period, especially in regions suffering from water stress. Although seasonal precipitation patterns and temperature affect LULC change, however, we could not identify a prevailing climate trend towards more drought-prone conditions. Our analysis focuses on (potential) direct and indirect implications of the Syrian conflict on LULC change, which most notably occurred between 2014 and 2018. Conflict-related main drivers were human activities and demographic changes, which are mainly attributable to large-scale population displacement, military operations, concomitant socio-economic status, and control of local resources. As the study provides quantitative and qualitative information on the dynamics of LULC changes in Syria, it may serve as a framework for further relevant conflict-related research and support planning, management practices, and sustainable development.

An Assessment of Forest Cover Change and Its Driving Forces in the Syrian Coastal Region during a Period of Conflict, 2010 to 2020

“…The Mediterranean climate is the prevailing climate in the region [51,52]. This climate is characterized by hot and dry summers, while winters are wet and relatively cold [48,51]. Rainfall is concentrated mainly in the winter season (between November and March) [48].…”

“…This rapid increase in population combined with rural-to-urban migration has resulted in extensive urbanization and strong economic pressures on the region [48,52]. Statistics indicate that the population density in the narrow coastal strip was nearly 20 times the national average and 6 times the average population density in other areas of the study area [51,52,55]. Forests in the Syrian coast region are Mediterranean forests, and their spread is often linked to climate and landforms [57,58].…”

“…To improve detection of changes in forest cover during the classification, Landsat images captured in the dry season were chosen because the increase in vegetative surface cover in the growing season may lead to spectral confusion, which creates a complication in distinguishing among the various land use categories on the classified images [7,15], especially since the study area contains several tree crops (olives and fruit tree plantations) that are planted on the terraced hillsides [51]. The selected Landsat images were also completely free of clouds to reduce the effect of atmospheric attenuation and water vapor content on land use classification [7,51]. The selection of Landsat images during this period of the year also took into account the forest fires that broke out at the beginning of the dry season in the forests of the Syrian coast, especially the frequent fires that occurred on a large scale in the late summer of 2020 [64].…”

“…Due to the ongoing conflict in Syria, it was not possible to conduct field observations in the study area to collect ground truth data relevant to ascertain the accuracy of the classification and the accuracy of the forest cover maps that were created for the study area. Therefore, the supplementary data was used as external inputs in the post-classification phase, which was sufficient to determine the characteristics of the natural environment and land use in the study area [51]. These data included high resolution Google-Earth archive data and topographic maps in scales ranging from 1: 25,000 to 1: 1,50,000.…”

“…Although Landsat images are geometrically and radiometrically corrected by USGS [61], changes in the spectral characteristics of surface features due to different atmospheric conditions in a humid climate can cause difficulties in applying the pixel-based classification [15,51,68,69]. Therefore, pre-processing of the Landsat images was performed, which included geometric correction, atmospheric correction and radiometric processing [70,71].…”