Selection of the Most Suitable Tree and Shrub Species for the Afforestation of Iranian Northern Zagros Forests Using AHP and TOPSIS Techniques (Case Study: Perdanan Piranshahr)

Extended Abstract Background: Because human and non-human factors are present in most part of the Iranian northern Zagros Forests, an extensive portion of the forests in these areas have been lost or are at risk of destruction, making forestry initiatives increasingly important for the environment. Even enriching the existing forests is a particular approach to dealing with the quantitative and qualitative degradation processes. Furthermore, afforestation is a strategy for recovering degraded areas and is recognized as a method of protecting soil and water, combating desertification, providing wood, and increasing carbon and nitrogen stores. The selection of suitable tree/shrub species based on expert opinion and decision-making model criteria is one of the most effective factors in the success of afforestation and reforestation projects. The current study aimed to enhance the Iranian northern Zagros Forests in the Perdanan area of Piranshahr and to restore its destroyed regions (empty spots) by finding the most appropriate forest tree/shrub species. Methods: In this research, all destroyed spots, clearings, and empty spots of tree and shrub species were first identified and extracted as destroyed spots using the satellite images of 2022 from ESRI and the Google Company. To prepare the homogeneous units of the investigated area in the Pardanan forest lands of Piranshahr (northwest of Iran, the starting point of the Zagros forests), the topographical layers of the area, including slope, direction, height above sea level, geological maps, land ecology, and the map of micro-ecosystems, were combined to prepare the map of the ecological unit (homogeneous units) of the studied area. Because of the existence of destroyed spots, gaps, and empty spots of tree plant species, three one-kg soil samples from a depth of 0-30 cm were prepared from each of the environmental units. The soil samples passed through a two-mm sieve and thensubjected to measurements of soil physicochemical properties, including pH, electrical conductivity, texture, moisture, lime, nitrogen, organic carbon, phosphorus, potassium, calcium, magnesium, bulk density, and particle density factors. The findings of the soil samples were then analyzed with SPSS software to categorize the region based on soil properties by generating a dendrogram of the retrieved parameters using the K-Means Cluster technique. The acquired parameters were split into three good, medium, and poor stands after being separa