Abdalhossein Boali; Hamidreza Asgri; Ali Mohammadian Behbahani; Abdolrassoul Salmanmahiny; Babak Naimi
Introduction: Desertification involves processes that are both the product of natural factors and the mismanagement of human beings. Adafic parameters and processes affecting soil condition, climate, groundwater, vegetation and management are the most important factors affecting the phenomenon of desertification ...
Introduction: Desertification involves processes that are both the product of natural factors and the mismanagement of human beings. Adafic parameters and processes affecting soil condition, climate, groundwater, vegetation and management are the most important factors affecting the phenomenon of desertification in many arid and semi-arid regions. These parameters are investigated using different models in different regions.Therefore, this study was conducted to evaluate the desertification intensity based on groundwater and soil criteria in the west of Golestan province.Material and methods: To determine the work unit map, topographic, geological, aerial photo interpretation, panchromatic band and multispectral Landsat satellite images and field visits were used. In this study, the Madalus method was used to prepare a map and evaluate the desertification situation. The geostatistical methods used in this research include kriging method, local estimator method (GPI), radial function method (RBF) and distance distance method (IDW). in Madalus model, 6 criteria and 20 indicators were used to evaluate the intensity of desertification. The scoring of each of the indicators was determined at the unit level in the region. The map of each criterion is also obtained through the geometric mean relationship between the indicators. To evaluate the accuracy of the model results, the desertification class in each of the work units according to the field evidence, observational and visual evaluation was performed and then the statistical comparison of the model desertification class with an expert opinion was performed. SPSS software and Mann-Whitney non-parametric test were used to validate the model results.Results and discussion: In this study, according to the histogram of the data, the normality of the parameters was investigated. The mean square squared error (RMSE) was used to determine the most appropriate interpolation method. The weighted average score of desertification intensity of area 135 was obtained, which indicates the middle class. In terms of zoning of desertification intensity, the region was classified into three classes: low and insignificant with a frequency of 27 and medium with a frequency of 60 and severe with a frequency of 13. Among the criteria of desertification, the criterion of management and policy with an average weight of 148 points is the dominant and effective criterion of desertification, followed by the criterion of vegetation (145), the criterion of soil (141), the criterion of erosion (138), the criterion of climate. (122) and groundwater criteria (121) were in the next rank of effective desertification criteria in the region. Also, the most important indicators of desertification are the indicators of drought resistance, conservation operations and soil salinity, respectively. These indicators have increased the trend of desertification in the work units of abandoned lands, saline and wetland lands and saline lands located in the northeastern parts of the region.Based on the spatial distribution of classes with low and insignificant desertification intensity in the southern and eastern part of the region, the middle class in the western, central and northern parts and finally the severe desertification class are located in the northeastern parts of the region.Conclusion: According to the obtained results, which indicate high evapotranspiration of the region, expansion of land salinity, unprincipled road construction and incomplete drainage.it seems that the management of desertification in the west of Golestan province, as one of the agricultural hubs, should be in the managerial priority of the officials and experts of the executive departments.Accordingly, it is proposed in order to control the process of desertification and achieve sustainable development in the region .treatment of industrial and domestic effluents for reuse for various purposes, Use of modern irrigation systems for agricultural lands, Placing low-yield crops in terms of water consumption in the region's crop rotation and As well as the necessary training to justify farmers to use pesticides and chemical fertilizers in the area.
Ali Mohamadian Behbahani; Zahedeh Heidarizadi
Volume 16, Issue 4 , January 2019, , Pages 153-166
Principal land use management requires accurate and timely information in the form of maps. Considering the widespread and unsustainable changes in land use, including the destruction of natural resources in recent years, it is essential to study the changes in land covers over the time ...
Principal land use management requires accurate and timely information in the form of maps. Considering the widespread and unsustainable changes in land use, including the destruction of natural resources in recent years, it is essential to study the changes in land covers over the time using satellite imagery. Because the conservation of natural resources requires continuous monitoring of an area, land-use change models are now used to identify and predict land-change trends and land degradation. One of the most widely used models in predicting land use change is the automated Markov chain model. The purpose of this study is to monitor land use changes in Abu Ghovair Plain in the past years and predict their status in the next 13 years.
Material and methods:
In this study, in order to detect the changes in the study, TM, ETM+, and OLI images of Landsat satellite were used in the years 1990, 2003 and 2016, respectively. After applying geometric and atmospheric corrections to images, the land use map was created for each year. Then, to predict the changes in 2029 using the Markov chain in the Idrisi Selva software, the mapping of the years 1990 and 2003 were selected as the input to the model. Then, 13 years of forecasting changes were considered until 2016 to get the matrix of the likelihood of user changes. Then, data from the Markov chain method and the map of 2016 were used as input data for the CA-Markov cell method.
Results and discussion:
The results of monitoring satellite images from 1969 to 2016 indicated a gradual increase in sandy areas by 62 km2 and its movement towards poor rangelands and shrubs. The agricultural lands were increased so that at the end of the period their size has increased by 67.68 km2. Residential land has also been expanded over the years, and the size of the shrubland has been reduced. After tracking the changes, the 2016 map was simulated by the model. Evaluating the accordance between the simulated map and the actual map with the Kappa index confirmed the accuracy of the model. Then, the 2029 map was prepared to predict the changes over the coming years. The discovery of changes in 2029 indicated that if the current trend continues, the area of the sand zones will increase to the extent of covering 15% of the area. In this period, the most changes will occur in the middle part of the southeast to the south of the area. The size of the shrubland will decrease by 13 km. The changes in agricultural lands continue to grow and will encompass 10% of the whole region in 2029.
Comparison between the simulated map of 2016 generated by the model and actual map with Kappa index showed that Auto-Markov model is a suitable model for predicting land use change and can be used to accurately assess the future status of land use and vegetation.