m.h. mokhtari

Desertification is one of the most serious ecological environmental problems in the arid regions. Quantitative assessment of the desertification process is important for the prevention and control of desertification. In this research, the IMDPA model was used to evaluate the quantitative and qualitative desertification situation in the northwest of Yazd. Three criteria of soil, vegetation, and wind erosion were considered in this model. Several indicators were defined for each criterion with a weight of 0 (low) to 4 (very severe). The geometric mean of all three criteria was used to prepare a map of sensitive areas to desertification in ArcGIS. The results indicated that more than 92% of the research area was in the extreme class of desertification, and only the dunes work unit was in a very intense class. Finally, the whole of the research area with a final score of 3.04 was placed in the extreme class of desertification intensity. Also, the soil criterion with the highest weight score of 3.26 has had the greatest impact on the desertification of the northwest region of Yazd. Therefore, it is necessary to implement remedial and revitalization operations in this region according to the expansion of the phenomenon of desertification and the high influence of the soil criteria. The results of the research showed the intensity of desertification, the potential, and the sensitivity of the region to the phenomenon of desertification can be referred to as a departure from the natural functioning of the system.

Soil temperature is a key factor that controls physical, chemical and biological properties of soil and its processes. Since soil temperature is measured at synoptic stations and data availability, especially in arid lands, is limited, capability of satellite images to estimate soil temperature at different depths evaluated in the Yazd-Ardakan basin, as the study area. Daily soil temperature at 5, 10, 20, 30, 50 and 100 cm depth measured at synoptic stations of Yazd, Meybod, and Mehriz for the periods of 2014 to 2016, and Landsat 8 satellite images of were used as the main data in this research. Then, using split-window surface temperature, Land Surface Temperature (LST) maps were estimated. Temperature trend from soil surface to a depth of 100 cm were examined seasonally. Using simple linear regression and artificial neural network techniques, the relationship between temperature of surface soil and soil temperatures at different depths were predicted. Results showed that the artificial neural networks had greater accuracy than the linear regression method in all seasons. The lowest accuracy of this method is related to the soil temperature at 5 cm depth. Artificial neural networks can be used for predicting of soil temperature till depth of 100 cm, using land surface temperature obtained by Landsat 8 images. To validate the results, soil temperatures at depth of 30 cm for 16 selected points in the study area were compared with estimated soil temperature using Landsat images and artificial neural network. Absolute error of measurements show that the maximum error was observed to depth of 30 cm (3.7 ℃). Therefore, using the measured soil surface temperature by applying the split-windows and artificial neural network can be used to predict soil temperature.

Detecting, predicting and quantifying the trends of landscape pattern change in the forests of Gharasu watershed area are necessary so as to assess the crises or prevent them. To this aim, the land use maps belonging to the years 1987, 2002 and 2018 were classified through the maximum likelihood method, and the forest area changes were estimated. Then, through the Geomod model and the forest change probability map derived from the multi-criteria evaluation method, a forestland map was generated for the year 2041. Moreover, the quantitative characteristics and the spatial distribution of the forested area were evaluated using ten landscape metrics. The results revealed that 2632 hectares had been deforested over the last 31 years; also, it is predicted that 2084.7 more hectares of the forests will be reduced until 2041. The analysis of the landscape metrics also showed that the forest landscape had become more limited and fragmented, as well as becoming less regular and integrated. Through the landscape analysis approach, six of the ten metrics used in this study proved to have a regular trend of change. They include class area, number of patches, patch density, patch area mean, limiting circle and pore size. Thus, it can be concluded that Geomod is a quite successful model in predicting the forest areas for the year 2041.