چکیده:
To understand and manage the natural and human-made ecosystems and develop long-term planning, it is necessary to model Land Use Change (LUC) and predict future changes. Therefore, we used Landsat satellite imagery, Multilayer Perceptron neural network (MLP) and Markov Chain model (MCA) to monitor the regional changes over 30 years in the central arid regions of Iran. In the present research, the stratified maps derived from the object-oriented algorithm were used to detect and map the changes of land use classes from 1986 to 2016. Furthermore, the land use in 2030 was predicted using Land use Change Modeler (LCM). Slop, contour elevation lines, distance from river, road, afforestation, agricultural lands/gardens, barren lands, poor rangelands, residential lands, rocky land, and sand dunes were considered as factors influencing the changes in the ANN. The Cramer's V coefficient was employed to select appropriate parameters with the highest significant correlation. Our results showed that the sub-models performed well (75-85%). Besides, the highest and lowest accuracy of sub-models were related to the distance from barren lands and distance from residential areas (75. 23 and 85. 91% , respectively). The results of land use change monitoring from 2016 to 2030 revealed that land use such as forest, residential lands, gardens, and sand dunes would be increased by about 0. 11, 1. 53, 2. 36 and 0. 56% , respectively, by 2030 compared to 2016. On the other, the area of barren land and poor rangeland would be reduced by 2. 88 and 1. 68% , respectively. Our results can be used in land change evaluations, environmental studies, and integrated planning and management regarding appropriate and logical use of natural resources and reducing resource degradation.
خلاصه ماشینی:
In this model, remote sensing data were used to obtain land use changes, GIS was employed to prepare urban land use map, and input variables were utilized to enter ANN with input, hidden, and output layers.
Then Object-oriented supervised classification method was used to prepare and extract land use maps of satellite images and 7 land uses class (forestry, agricultural area and gardens, barren lands, poor rangeland, residential lands, rocky lands, and sand dunes) were extracted.
Providing the future LU/LC map based on the modeled changes obtained from the Markov chain analysis, transition potential maps, and limiting and stimulating variables First, the role and ability of each spatial variation were evaluated in predicting possible Lu/LC changes by calculating Cramer's V coefficient.
The variable maps used in this research: A: Slope (%); B: Elevation (m); C: Distance from river (m); D: Distance from road (m); E: Distance from Afforestation; F: Distance from Agricultural land and Garden; G: Distance from Barren land; H: Distance from Poor rangeland; I: Distance from Residential land; J: Distance from Rocky land; K: Distance from Sand dune There are several methods for modeling the transition potential, and previous studies have shown that ANN is the strongest method among them (Eastman, 2006).
Table 3 Results of the evaluation of the accuracy of the models created from different scenarios (View the image of this page) Figure 6 shows the land use transition potential map obtained from the MLP-ANN model.
Prediction map of land use of 2030 by the Markov chain model (View the image of this page) Fig. 9.