Laboratory of Geodynamic and Geomatic, Department of Geology, Faculty of Sciences, 24010 Chouaïb Doukkali, Morocco.
Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia.
Sensors (Basel). 2018 Jul 11;18(7):2230. doi: 10.3390/s18072230.
Oases can play a significant role in the sustainable economic development of arid and Saharan regions. The aim of this study was to map the desertification-sensitive areas in the Middle Draa Valley (MDV), which is in the southeast of Morocco. A total of 13 indices that affect desertification processes were identified and analyzed using a geographic information system. The Mediterranean desertification and land use approach; which has been widely used in the Mediterranean regions due to its simplicity; flexibility and rapid implementation strategy; was applied. All the indices were grouped into four main quality indices; i.e., soil quality; climate quality; vegetation quality and management quality indices. Each quality index was constructed by the combination of several sub-indicators. In turn; the geometric mean of the four quality index maps was used to construct a map of desertification-sensitive areas; which were classified into four classes (i.e., low; moderate; high and very high sensitivity). Results indicated that only 16.63% of the sites in the study were classified as least sensitive to desertification; and 50.34% were classified as highly and very highly sensitive areas. Findings also showed that climate and human pressure factors are the most important indicators affecting desertification sensitivity in the MDV. The framework used in this research provides suitable results and can be easily implemented in similar oasis arid areas.
绿洲在干旱和撒哈拉地区的可持续经济发展中可以发挥重要作用。本研究的目的是绘制摩洛哥东南部中德拉河谷（MDV）的荒漠化敏感区图。使用地理信息系统分析了 13 个影响荒漠化过程的指数。地中海荒漠化和土地利用方法由于其简单性、灵活性和快速实施策略，已在地中海地区得到广泛应用。所有指数都分为四个主要质量指数，即土壤质量、气候质量、植被质量和管理质量指数。每个质量指数都是由几个子指标组合而成的。反过来，使用这四个质量指数图的几何平均值来构建一个荒漠化敏感区图，该图分为四个类别（即低、中、高和极高敏感性）。结果表明，研究中只有 16.63%的地点被归类为对荒漠化最不敏感，而 50.34%的地点被归类为高度和极高敏感地区。研究结果还表明，气候和人为压力因素是影响 MDV 荒漠化敏感性的最重要指标。本研究中使用的框架提供了合适的结果，并可以在类似的绿洲干旱地区轻松实施。
