Ecology of Iranian Forest

     In this research, the status of forests in Ilam city with the aim of preparing the spatial distribution map of oak forests degradation using remote sensing and geographic information system was investigated. In order to achieve the extent and location of degradation in the studied area, the forest area map for 2001 using Landsat 7 ETM⁺ satellite images and forest area map for 2013 using OLI Satellite Landsat 8 images, Field visits and a method of determining educational samples were prepared. To investigate the factors affecting on degradation, first, the spatial variables including distance from residential areas, distance from roads, slope, aspect and altitude were extracted from topographic maps of 1: 50000. Then the degradation location data of the region forests with the physiographic and human spatial variables was entered the model. For modeling and estimating the spatial distribution of degradation in the study area forests, logistic regression and step-by-step method and forward step type were used. According to the results obtained from the statistical model, the development of human-made areas, population growth, reducing the distance of roads from the forest areas, the middle altitudes and reducing the slope percentage that triggered agroforestry activities and increase of agricultural land area had the highest impact on the degradation of the Ilam city forests, respectively. In fact, of 73349 hectares of forests in Ilam city, 5311 hectares (7.2%) have been reduced, of which 2125 hectares (2.9%) are related to the phenomenon of oak drying and 3186 hectares (4.3%). ) It is related to the construction of settlements, road construction, etc. Based on this, a spatial distribution map of the destruction of forests in Ilam city was prepared.

Extended Abstract
Introduction and Objective: The occurrence of fires is one of the important factors that determine the different characteristics of many terrestrial ecosystems. For a long time, fires have severely affected forest areas, and sometimes their negative effects remain for several years after the occurrence of the fire, so that the state of vegetation does not return to its previous state. The aim of this study is to model the restoration of vegetation in Zagros forests (Ilam province) following fire.
Material and Methods: We used various climatic and environmental data as independent variables (vegetation at the time of fire (NDVI+1), burn severity index, temperature and precipitation anomaly, average temperature, annual precipitation, slope, aspect, and elevation) and NDVI +5 as dependent variable for the modeling (using random forest, decision tree and gradient boosting) the vegetation recovery following fire. Landsat satellite images were used to prepare indices indicating vegetation density status and burn severity, and after preprocessing the images, these indices were prepared by spectral ratio. Climatic variables (precipitation, average temperature, minimum temperature and maximum temperature) were also estimated according to the regression relationships between these variables and the elevation in the study area. Finally, three machine learning algorithms, including decision tree, random forest, and gradient boosting, were used for modeling, and also the accuracy of these models were evaluated.
Results: The results showed that among the various variables investigated, the annual precipitation, average annual temperature, normalized vegetation difference index (NDVI) and burn intensity index at the time of fire were the most important factors affecting the vegetation restoration post fire in these forests. The precipitation and temperature were the most important factors affecting the restoration among the mentioned factors. Also, the results showed that among the different models, the gradient boosting algorithm with R² = 0.66 models vegetation restoration better than other models. In this model, the climatic factors were the most important in the vegetation recovery.
Conclusion: According the relationships between the NDVI and other studied factors and the results of the modeling; it is possible to explain the effective role of climate factors in the vegetation restoration in the study area.