Forest is a valuable heritage and one of the important factors in the ecosystem of each area that in addition to using and exploiting them, they should be preserved. Zagros’ forests, especially in Lorestan province due to negligence have been destroyed throughout past years. The aim of this research is to investigation, detect and modeling Romeshkan’ s forests’ cover changes. To do this, first changes that were taken place between 1987-2017 were extracted by satellite Landsat images and using Fuzzy Object-Based classification method, then, were classified in 5 classes (Agriculture, Forest, Range, water and Residential). Finally, classification results show that there is a sharp decrease of forested areas (81. 17 km 2 ) and an increase of Range and Farmlands over past 30 years in the forest area. In a period of 1987-2002 forest cover of the study area had not faced major changes, but most of the rangelands turned to farmlands. While in the second period from 2002 onwards forest cover dramatically dipped and its area decreased from 122. 58 km 2 2 to 43. 42 km in 2017, which the rate of forest covers decrement was around 79. 16 km 2. Moreover, in order to predict forest cover changes in the future CA-Markov model was applied that indicates 10. 70% of current forest covers will be reduced in 2030, and the main changes will be occurred between forest classes to farmlands and rangelands classess by 6. 901 and 9. 172 km 2, respectively.

The land surface has experienced significant land use and land cover changes especially with regard to the human built environment. Land use changes is an important component in understanding the interactions between human activities and the environment, so analyzing these changes from the past to the future on the basis of a dynamic approach is necessary. In this research, land use maps for Malekan County in East Azerbaijan (Iran) for the years 1987, 2000 and 2014 were extracted, respectively, using Landsat TM, ETM+and OLI using RS and GIS techniques. Land use changes detected over the past 27 years were analyzed. The CAMarkov model was used to predict the land use pattern for 2024.Results show that agricultural land and human built areas increased but bare land decreased during the period 1987-2014. The simulated land use map for 2024 indicates an incremental trend in agricultural land (from 24.53% to 25.67%) and also in human built areas (from 2.69% to 3.75%) during 2014-2028. These results can play useful role to improve land use management strategies in the study area.

Background and Objective Land use/cover changes (LU/LC) are considered as one of the most important issues in natural resource management, sustainable development and the environmental changes on a local, national, regional and global scale. Changing uses into each other and changing permissible uses into impermissible uses such as changing agricultural lands into residential regions or changing rangelands into eroded and low-yielding dry farming lands are always considered as importand issues in natural resources. Detection of the patterns of the land use changes and prediction of the changes in the future to carry out suitable planning for optimal utilization of uses in natural resource management reveal the need for modeling spatial and temporal changes of LU/LC. This study aims to assess the efficiency of the integrated model of Markov chain automatic cell (CAMarkov model) in simulation and prediction of spatial and temporal changes of Land use/Land cover (LU/LC) in Gorgan-rud river basin by applying threedimensional Pentius-Melinus analysis in calibration of land use changes by using three assessment indices of Quantity Disagreement, Allocation Disagreement and Figure of Merit as new indices in the assessment of the accuracy of CA-Markov model...

The purpose of this study was to survey and model the land use change in Khorramabad city. For this purpose, using three Landsat satellite images of years 1986, 2001 and 2016, the cover and land cover classes have been extracted from Khorramabad city and around it. Overall classification accuracy for image corresponding to the year 1365, 93/89, 1380, and 34/91 of 1395, 62/95 percent is obtained. In order to model the land use changes in Khorramabad, the input layers of elevation, slope, shadow, distance from the road, distance from the built area, distance from the agricultural land, distance from the forestland and the distance from the mountainous terrain were used. In the following, neural network models and CAMARKOV are used to model and predict land use changes by the year 1404. The results show a high accuracy of 65% for modeling land use change in Khorramabad city. It should be noted that this precision is reasonable for predicting and modeling land-use changes that is dynamic, because in addition to the variables considered in this research and other studies, other factors such as municipal regulations, The land stock exchange, state-owned large-scale housing policies, and so on, will have an impact on land use change.

Predicting land use changes using satellite imagery is now a useful tool for helping planners in complex situations. The purpose of this study was to detect and predict land use changes during the 28-year period (1986-2014) by CAMarkov model in the Safarood-Ramsar watershed of Mazandaran province. In this research, land use and NDVI maps were prepared using Landsat TM (1986), ETM+ (2000) and OLI (2014) satellite images. The accuracy of the CA-Markov model was estimated using the Kappa index of 87%. In order to calibrate the CA-Markov model, the land use map was prepared in 2014, and the Kappa coefficient of the mapping from modeling and user base map (2014) was 82%. The results showed that during the period between 1986 and 2014, the area of forest lands decreased by 10. 26% and the total area of residential areas increased by 3. 27%. The land use map for the years 2021 and 2028 was predicted by the CA-Markov model. The results showed that during the period 2014-2028, forested lands and rangelands decreased by 4. 92% and 1. 7%, respectively. Residential areas will increase by 8. 04% and the agricultural land will change slightly, indicating the changes in land use to residential land.