Abstract
The arid and semi-arid regions of Zagros forests in the Middle East are constantly exposed to wildfire due to ecological conditions, and support systems are inefficient in controlling wildfires due to managerial and social weaknesses. Remote sensing and assessment tools are suitable for rapid prevention and action to identify the severity and location of a wildfire. This study investigated the natural resource management of Zagros Forestry in terms of protecting wildfire and combating forest wildfires using the NASA fire spatial data and the wildfire severity in the Google Earth Engine (GEE) platform. The land-use of the study area is produced by applying the Random Forest (RF) classification method and data from the Sentinel 2 satellite imagery for 2019. To separate the types of cultivation and vegetation of the region, the method of extracting the average vegetation index of the seasons is extracted from GEE. To evaluate fire risk, eleven human and ecological factors and two assessment models are applied to classify the probability fire risk therein. Furthermore, the outcome of AUC confirmed the Logistic Regression (LR) model; the accuracy of the LR (AUC=0.875049) model is satisfactory and is suitable for fire risk mapping in Zagros Forestry. Six high-risk areas of the wildfire were identified by MOLA, which overlap with protected areas. Out of a total of 20469.17 Ha of wildfire, 10426.41 Ha belong to these protected areas. 3826 Ha of this area were in the forests of Amygdalus spp, Quercus brant ii, pistacia Atlantica, and Quercus Infectoria, and 6600.41 Ha of it were in rangelands. Accordingly, an executive order was developed for the decision support system that reduces the risk of wildfire and helps extinguish the wildfire.
Response of Vicia variabilis to Some Ecological Factors in the Zagros Forests of Iran
