Assessment of the soil protection function of forest ecosystems using GIS-based Multi-Criteria Decision Analysis: A case study in Adıyaman, Turkey

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Bozali N.

Global Ecology and Conservation, vol.24, 2020 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 24
  • Publication Date: 2020
  • Doi Number: 10.1016/j.gecco.2020.e01271
  • Title of Journal : Global Ecology and Conservation
  • Keywords: Soil protection, Erosion risk map, GIS, AHP, EROSION RISK, AREAS, COVER, MODEL


© 2020 The AuthorsForest ecosystems provide many ecosystem services including soil erosion prevention. Forest areas prone to soil erosion risk should be carefully determined and the appropriate management interventions should be designed to ensure the soil protection service of the forest ecosystems. In Turkey, the soil protection function of forests is determined by considering mainly the topographical condition (i.e., slope) of forest landscape. In this study, GIS-based Multi-Criteria Decision Analysis (MCDA) was developed and used to determine forest areas for soil protection function based on erosion risk factors including bedrock, crown closure, ground slope and rainfall. The priorities of the risk factors were determined using Analytical Hierarchy Process (AHP) technique and the spatial data layer of each factor was used to generate the map of soil protection function for a case study area located in the city of Adıyaman, Turkey. The results indicated that the most effective factor on erosion risk was slope, followed by bedrock type. It was found that 36.25% of the study area was under low erosion risk, while 21.47% was classified as high and very high risk. On the other hand, the areas subject to soil protection function was found to be 12.05% of the area when using the classical method which was based on solo ground slope factor. Obviously, the difference (9.42%) comes from the combined use of various other erosion risk factors such as crown closure, bedrock and ground slope. The methodology presented provides decision makers with a practical and an effective prediction approach of soil erosion to develop and take necessary action for minimizing soil loss in forest ecosystems.