Land Cover Change Investigation in the Southern Syrian Coastal Basins During the Past 30-Years Using Landsat Remote Sensing Data

Abstract

Land cover change and deforestation are important global ecosystem hazards. As for Syria, the current conflict and the subsequent absence of the forest preservation are main reasons for land cover change. This study aims to investigate the temporal and spatial aspects and trends of the land cover alterations in the southern Syrian coastal basins. In this study, land cover maps were made from surface reflectance images of Landsat-5(TM), Landsat-7(ETM+) and Landsat-8(OLI) during May (period of maximum vegetation cover) in 1987, 2002 and 2017. The images were classified into four different thematic classes using the maximum likelihood supervised classification method. The classification results were validated using 160 validation points in 2017, where overall accuracy was 83.75%. Spatial analysis was applied to investigate the land cover change during the period of 30 years for each basin and the whole study area. The results show 262.40 km2 reduction of forest and natural vegetation area during (1987-2017) period, and 72.5% of this reduction occurred during (2002-2017) period due to over-cutting of forest trees as a source of heating by local people, especially during the conflict period. This reduction was particularly high in the Alabrash and Hseen basins with 76.13 and 79.49 km2 respectively, and was accompanied by major increase of agriculture lands area which is attributed to dam construction in these basins which allowed people to cultivate rural lands for subsistence or to enhance their economic situation. The results of this study must draw the relevant authorities’ attention to preserve the remaining forest area.

Assessing the Spatio-Temporal Pattern of Land Use and Land Cover Changes in Osun Drainage Basin, Nigeria

“…Forest decreased from a total land area of 5223.61km 2 (52.62%) in 1984 to 2154.87 km 2 (21.71%) in 2015. This is similar to the study of Hammad et al (2018) in the southern Syria coastal basin where forest area decreased from about 64% in 1987 to about 38% in 2017. The forest area receded at an annual average rate of 1.84% during the period under investigation.…”

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

“…The surface elevation varies between 0 to 1550 m above sea level, with an average of 775 m ( Figure 2). Depending on the values of the elevation and the slope (Figure 2), the region can be divided into three basic geomorphological units, namely, the coastal strip or the coastal area that is characterized by its narrow plains (elevation, 0-121 m, slope, 0-2%), plateaus (elevation, 121-589 m, slope, 2.1-10%), and mountains (elevation, 590-1550 m, slope, 10.1-29%) [46][47][48]. The disturbances caused by the tectonic movements that extended from the early Pleistocene to the Upper Holocene, which were represented by the rise and fall of the sea level notably during the Tyrrhenian period, in addition, many geomorphological processes which were mainly controlled by climatic factors, had a clear impact on the formation and development of the bedrock in the region [48,49].…”

“…The region economy is mainly based on traditional agriculture, where fertile plains are cultivated with citrus fruits, olives, and field crops such as wheat. Vegetable cultivation and greenhouse cultivation are also widespread in the southern region (Akkar plain) [47,48,52]. The coast region witnessed a remarkable increase in population between 2011 and 2020 as a result of the large-scale displacement of civilian population from the internal areas to the Lattakia and Tartous governorates, which remained relatively untouched by the fighting during the conflict (Figure 3) [56].…”

“…For this purpose, Landsat-5 thematic mapper (TM) and Landsat-8 Operational Land Imager (OLI) images for the three target years 2010, 2015, and 2020 were obtained from the official website of the US Geological Survey (USGS) [61]. Landsat images, which have a spatial resolution of 30 m, have spectral properties suitable for detecting changes in LULC [22,27,47,62,63]. Details of the Landsat images used in this study are shown in Table 2.…”

“…This classification method is known for its strong theoretical basis and its ability to accommodate changing spectral signatures of different land uses [27]. This classification algorithm also achieved the best results for the purposes of classifying land uses in sub-humid and humid areas as comparable environments using similar data [3,13,15,20,47,72]. Training samples for the classifier were obtained using visual interpretation from high resolution Google Earth data and Normalized Difference Vegetation Index (NDVI) images to compare with the color combination chosen for the Landsat image bands.…”