جستجوی مقالات مرتبط با کلیدواژه « Gharesou » در نشریات گروه « محیط زیست »

Human activities are one of the factors that have a significant impact on the land use change patterns, and these changes are in most cases inconsistent with the environment. Therefore, land-use prediction models for sustainable land-use planning are a requirement. In this research, using the Cellular Automata Markov Chain model, modeling of land use change for the Gharehsou basin has been improved to predict the future. Promotion of the Markov chain model has been done by entering the population growth rate and per capita requirements of each land use in the model. Validation of the upgraded model by comparing the map of the predicted land use change for 2014 and the results of the upgraded model have been reviewed. The results of the comparison of these two maps showed that the results of the upgraded model were much more similar to the current situation since the population growth rate and, consequently, per capita needed for urban development are the most important driving force to disrupt the ratio of changes in a timely trend. Therefore, considering this factor in the Markov chain model more flexibility and great results closer to reality, which is practical in the Gharehsou basin. Finally, the upgraded CA-Markov chain model was used to predict the 2030 basin.

 Human activities change land cover at unprecedented speeds and large spatial scales. The rapid changes in ecosystems in recent decades have been more along all the human history and have led to unprecedented changes in ecosystems and environmental performance at local, regional and global scales. This research aims to investigate the process of landscape quantitative changes in Gharesou and Mereg watersheds.
Investigation

For monitoring changes, Landsat satellite imagery of 1984 (TM), 2000 (ETM+) and 2016 (OLI-TIRS) were used. The classification was performed using Artificial Neural Network (ANN) model. Land cover was classified into seven categories. Metrics PLAND, NP (Number of Patch), PD (Patch Density), and ED (Edge Density) at the Level of Class, NP, LPI (Largest Patch Index), CONTAG (Contagion), and SHDI (Shannon's diversity index) were calculated at the level of Landscape for the study area.

The results indicate that the highest percentage of the land cover belongs to agricultural lands and then medium rangelands. The numbers of patches of agricultural land in the period of 1984-2004 has increased due to the conversion of rangelands to agricultural lands and then between years 2000-2016 have decreased due to the conversion to medium ranges with a lesser rate. The number of forest patches in the first period has decreased because of conversion to medium rangelands and increased during the second period.

The study of the landscape metrics indicates the increase in continuity, the reduction of fragmentation and diversity in the first time period, and in contrast, in the second time period, continuity has decreased and fragmentation and diversity have increased. Given the fact that the process of landscape changes varies over two different periods, it can generally be noted that the fragmentation and diversity of the landscape have declined and the spatial continuity has increased.