Land Use/Land Cover Change Detection in the Baer and Al-Bassit Region, Latakia, Syria, 1972–2018

(Ola Ali Merhej , Mahmoud Ali, Ali Thabeet, and Younes Idriss)

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Abstract

Monitoring land use/ land cover (LULC) changes is important for assessing the dynamics between land cover types and understanding the anthropogenic impact on these changes. Remote sensing techniques also represent important tools to achieve this goal. This paper aimed at mapping and analysing LULC changes in the Baer and Al-Bassit region of the Latakia Governorate in Syria. For this goal, 15 multi-temporal Landsat images from the period of 1972–2018 were used, and each image was classified using maximum likelihood algorithm-supervised classification into four categories of land use: forests, agricultural land, water and urban areas. Accuracy assessment of all images was performed; the average value of the overall accuracy of the classification was 89%, and the average value of the Kappa index was 0.85. The area of each land use category was calculated in each LULC map, and each category’s trends over the study period were analysed using linear regression analysis. The forest category was the only group that decreased (by 21.8% between 1972 and 2018), compared to an increase in all other categories over the same period (0.6%, 4.3% and 16.8% for water, urban areas and agricultural land, respectively). This indicates a conversion of forests into agricultural land and urban areas. The results of this study can be used as an efficient tool to manage and improve the Baer and Al-Bassit forests in terms of physiographical and human characteristics; they could also facilitate the creation of a database for LULC changes in this region.

KEYWORDS
Landsat, linear regression, LULC dynamic, remote sensing, satellite Images, supervised classification

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