Hamed Eskandari Damaneh; Gholamreza Zehtabian; Hassan Khosravi; Hosein Azarnivand; Aliakbar Barati
Volume 28, Issue 3 , October 2021, , Pages 520-536
Abstract
In the present study, the existing land uses in the Minab plain were simulated using the CA-Markov combined method. For this purpose, land use maps for the years 2000, 2010 and 2020 were generated using Landsat satellite images using the maximum probability classification method and after evaluating ...
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In the present study, the existing land uses in the Minab plain were simulated using the CA-Markov combined method. For this purpose, land use maps for the years 2000, 2010 and 2020 were generated using Landsat satellite images using the maximum probability classification method and after evaluating the model, the land use map for 2030 and 2040 was predicted using the combined CA-Markov method. Analysis of land use change patterns in Minab plain showed that during the statistical period 2000-2020 in the level of land uses in this area has changed significantly so that during this 20-year period the area of agricultural land use, urban and man-made areas, saline lands and rangelands and barren lands respectively from 38.91, 25.99, 20.09 and 15 % in 2000 to 40.75, 40.02, 12.44 and 6.80 percent in 2020. Evaluation of the model using kappa index above 90% indicates the high accuracy of the model for predicting land uses. Prediction of changes in 2030 and 2040 show that the use of agricultural lands and urban areas and man-made are increasing at a rate of 0.05 and 0.39 %, respectively, which are advancing from the east of the plain to the west; Meanwhile, the uses of saline areas, rangelands and barren lands are decreasing at a rate of 0.44%, which is more evident in the west and northwest of this plain. Finally, one of the most important executive strategies of planners and officials to prevent land use change and ultimately land degradation in this area, can be to improve the cultivation pattern, new irrigation methods, nourish the bed of this plain and maintain and restore native vegetation.
Ali Khenamani; Hasan Fathizad; Mohamad Ali Hakimzadeh
Volume 25, Issue 4 , February 2019, , Pages 723-734
Abstract
Over the past decades, due to increased population and consequent increase in the need for food, we have seen extensive changes in land use, and in particular, the increase of agricultural lands. The aim of this study was to evaluate the changes in land use in the Bartash plain ...
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Over the past decades, due to increased population and consequent increase in the need for food, we have seen extensive changes in land use, and in particular, the increase of agricultural lands. The aim of this study was to evaluate the changes in land use in the Bartash plain in Dehloran city of Ilam province during 26 years from 1988 to 2014 using the object-oriented approach. To accomplish this research, the necessary corrections were made after the acquisition of Landsat TM (1988), ETM + (2001) and Landsat 8 (2014) satellite images, and then, using the object-oriented method, the land use map was prepared for the three time periods. The results of the evaluation of the accuracy of the produced maps show that the highest accuracy and Kappa coefficient with the values of 90 and 95% correspond to the image of 2001, and the lowest them with the value of 80 and 90% was related to the image of 1988. Total accuracy and Kappa coefficient in the image of 2014 with 90% and 92%, respectively show a good accuracy. The results of land use change trend showed that the land use of the fair rangeland had the most changes with a decrease of more than 21 thousand hectares. Agricultural lands are in the next place, showing an increase of over 15,000 hectares (twofold) that could be due to the increase in population and the availability of adequate water resources in this area. The land use of poor rangelands also shows an increasing trend of 1.5 fold, indicating the degradation of fair rangelands. The saline lands initially show an increasing trend but then show a decreasing trend due to converting to agricultural lands. The overall accuracy (900-90) and kappa coefficient (95-90) indicate the high accuracy of this method in determining the land use.
Mohammad Ali Zare Chahooki; Narges Naseri Hesar; Mohammad Jafary
Volume 25, Issue 2 , August 2018, , Pages 298-309
Abstract
The study was performed with the aim of modeling the distribution habitats of Eshtehard rangelands using Maximum Entropy Method and determining the factors affecting each habitat. Vegetation and environmental data including soil characteristics and topography were collected. ...
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The study was performed with the aim of modeling the distribution habitats of Eshtehard rangelands using Maximum Entropy Method and determining the factors affecting each habitat. Vegetation and environmental data including soil characteristics and topography were collected. The initial map was prepared based on slope, elevation and direction maps and satellite images. At each site, three transects with a length of 750 m were established, two transects along the most important environmental gradients and one transect perpendicular to them. A number of 45 plots along each transect was placed at a distance of 50 meters. The size of plot sampling was determined to be two square meters according to the type and distribution of plant species with minimal area method. Soil profiles were dug at the beginning and end of each transect. Sampling was done from the depths of 0-20 cm and 20-80 cm. The list of species and the percentage of vegetation in each plot were determined. For each sampling unit, the latitude and longitude data, slope, direction, and elevation were also determined. Then the desired characteristics were measured in the laboratory. GIS and Geostatistics methods were used to map the environmental variables. The species distribution models were produced using the species presence data and Maximum Entropy Method (Maxent). The Kappa coefficient index and the area under the curve (AUC) were used to evaluate the accuracy of the distribution maps. The agreements of actual and predicted maps for Pteropyrum olivieri was well (K=0/7) and it was acceptable for Halocnemum strobilaceum, Salsola richteri-Artemisia sieberi, Artemisia sieberi, Artemisia sieberi–Stipa barbata (K=0/66, 0/64, 0/57, 0/66).
Leila Khalasi Ahvazi; Mohammad ali zare chahouki
Volume 23, Issue 2 , September 2016, , Pages 287-275
Abstract
Artificial Neural Network (ANN) is new information processing structures that uses special methods for biological neural networks. The main purpose of this study was to modeling of Seidlitzia rosmarinus distribution in northeast rangelands of Semnan by ANN model. For this purpose, vegetation sampling ...
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Artificial Neural Network (ANN) is new information processing structures that uses special methods for biological neural networks. The main purpose of this study was to modeling of Seidlitzia rosmarinus distribution in northeast rangelands of Semnan by ANN model. For this purpose, vegetation sampling was carried out in each vegetation type along three transects of 750 m, on which 15 plots were established with an interval of 50 m. Soil samples were taken from two depths of 0-20 cm and 20-80 cm in starting and ending points of each transect. To provide the prediction map of plant species distribution, different layers of environmental factors used in the model are required. The geostatistics method was applied for mapping soil properties based on the prediction model obtained from ANN method for S. rosmarinus. The back-propagation neural network with three-layer- perceptron network was designed to generate the ANN model and seven neurons in the input layer, ten neurons in the hidden layer, and one neuron in the output layer were used. The accuracy of the prediction map was tested with actual vegetation maps and the Kappa coefficient was calculated to be 72%, indicating a very good agreement. Results showed that this species is distributed in rangelands with a pH of 8.1-8.3, an EC 0.22-0.26 dS/m, in a silty-sandy textured soil, and an altitude of 1600-1750 meters. it is highly correlated with lime and pH in two depths.