مشخصات پژوهش

صفحه نخست /Prediction of spatial land ...
عنوان Prediction of spatial land use changes based on LCM in a GIS environment for Desert Wetlands – A case study: Meighan Wetland, Iran
نوع پژوهش مقاله چاپ‌شده
کلیدواژه‌ها LCM Prediction Meighan wetland Land use changes Artificial Neural Network
چکیده Meighan Wetland and its surroundings have undergone many changes in recent years. This study aims to monitor and predict land use changes using the land change model (LCM) module in the Meighan wetland. Land Sat images of years 2000, 2007 and 2015 were used to produce digital land use maps. The images were classified into seven classes including; Salt lake, Agriculture, Rangeland, Manmade, Wastewater, Wetland and Mine areas. The LCM module in Idrisi GIS software was used to analyze the land use changes for predicting the land uses status in 2015, based on artificial neural network (ANN) and Markov Chain analysis. The ANN was trained with various influencing factors including; distance from road, distance from manmade areas, distance from land changed edge, distance from stream, elevation and slope. The results indicated that 1663.88 ha of the rangeland cover and 715.68 ha of the salt lake cover have been degraded during the 2000–2015 period. Furthermore, the wetland, mine, wastewater and manmade degradation are increased to 724 ha, 335 ha, 37 ha and 270 ha in comparison to initial land conditions. Also, the result shows that rangeland and salt lake areas will decrease in the year 2030 compare to 2015, while, wetland, mine and manmade land changes may increase.
پژوهشگران امیر انصاری (نفر اول)