دکتر محمد زارع

Plant ecosystem functions include four categories provisioning, regulating, supporting, and cultural services. So far, various methods have been proposed for evaluating or monetary valuation of ecosystem services. However, the void of a technique that can simultaneously assess all ecosystem services was felt, and thus a new ecosystem service evaluation model was presented. This model is a composite index from the group of indices-numerical models. This model evaluated all the services provided by the ecosystem, which affect human well-being, as the main criterion, and the four ecosystem services as secondary criteria. The sub-divisions of each of the sub-criteria are evaluated as indicators of the model (Three provisioning services: food, medicine, wood, or fibers; Six regulating services: carbon sequestration, runoff and flood control, microclimate regulation, pollination, biological control, and soil quality; Two supporting services: biodiversity conservation, and nutrient cycling; Three cultural services: aesthetics, entertainment and tourism, educational, and research aspects). To score the indicators, proxy indicators were designed and used as a Rapid Assessment Checklist (RAC) to score each index. The score of each index was obtained from the arithmetic mean of the related proxy indices, the score of each sub-criterion was obtained from the geometric mean of associated indicators, and the final score of the main criteria was also obtained from the geometric mean of all sub-criteria. The range of the model’s scores was designed from 0 to 4, in four degrees. First, scoring tables of 43 proxy indicators for all 158 studied species were created. Then field visits and scoring of proxy indicators were done. Finally, the data was obtained by statistical analysis; and the score of fourteen ecosystem services was calculated for each of the 158 studied species. By comparing the scores of the Ecosystem Service Evaluation Model (ESEM), the best species are ranked in terms of fourteen ecosystem services.

The growth of urbanization and the expansion of cities have caused significant changes in the surrounding land cover. Changes in land surface characteristics as a result of rapid urbanization can affect climatic conditions in seasonal and long-term periods on a local, regional and global scales. Land surface change (LST) change is directly related to land cover changes, so that land cover change causes changes in the land surface temperature. Yazd plain as the study area, has witnessed many changes in land cover during the past two decades due to immigrants, so studying its temperature changes is very important. The aim of this study was to investigate the changes in land surface temperature of the Yazd plain over an 18-year period and compare it with land cover changes in order to determine the effect of these land cover changes on land surface temperature. The distinction between the current study and previous studies is the effect of land cover change on land surface temperature in different months of the year.

In this study, Landsat 5 and 8 satellite images were used to investigate the effect of land cover changes on land surface temperature in 2001 and 2019. Land surface temperature map were estimated for Landsat 5 satellite images using single band method, and for Landsat 8 images using split-window algorithm. The land cover maps for the studied years were prepared using the supervised classification method of maximum likelihood. Changes in land cover made from 2001 to 2019 and its effect on land surface temperature were examined.

In the land cover map of the study area, six land cover types of mountainous rangelands, plain rangelands, agriculture, sand dunes, barren lands and residential areas were identified. For better separation of land cover, images related to the peak of vegetation period were used in the classification processes. After determining the degree of class segregation, the supervised classification was performed by the maximum likelihood method, and then land cover changes were detected in the area. In order to investigate the changes made in the study area, the area and changes of each land cover were also extracted. Urban areas with an increase of 12.1% area had the highest growth, and agricultural lands with 28% reduction had the highest reduction in area. At the same time, the study period with increasing migration from villages to urban areas, the phenomenon of urbanization and industrialization of the study area can be the reason for this issue. Also, the 24.5% increase in the area of sand dunes is due to recent droughts and the placing of the study area in the sedimentation site of the wind erosion process. Although little vegetation in sand dunes has stabilized them, in recent years with the loss of this cover, the area of wind erosion process has increased. Land cover changes map was extracted from the area in 18-year period. The Land surface temperature maps for four months of the year were prepared using the single channel method in 2001, and the split window algorithm in 2019. For better comparison, the relevant Land surface temperature indexes per month was classified into 10 categories at 2 °C intervals and the areas of the classes were compared with each other. In order to investigate the effect of land cover change on land surface temperature, the mean, minimum and maximum land surface temperatures in each land cover on the mentioned dates were examined. The results show that the land surface temperature in each area of the earth is influenced by surface factors and its characteristics and the temperature obtained in different land cover is different. Land surface temperatures in bare lands and sand dunes that have been converted into other land covers have dropped significantly, so that, in July and September it dropped about 2°C. Most of the temperature increase is related to the conversion of agricultural lands to bare lands, sand dunes and mountainous rangelands. Contrary to the existing hypotheses that the greatest increase in temperature is related to changing agricultural to residential land cover, in this area located in arid regions of central Iran, the conversion of agricultural lands to bare lands and sand dunes has led to higher temperatures. The change in land surface temperatures in the changed coverage in February was higher than in other months and less in September.

The results of this study showed that during 2001-2019, the area of residential lands and sand dunes increased and the area of agricultural lands, bare lands, and rangelands decreased. The results of land surface temperature assessment in changed land cover showed that the land surface temperature in bare lands and sand dunes that have been converted to other coatings has decreased significantly. Conversion of agricultural lands to bare lands and sand dunes has led to higher temperatures than other land cover type and physical development has created numerous environmental problems.

The increasing exploitation of groundwater reserves and consequently the drop in the water level and reduction of the reserves have seriously caught the attention of officials and planners to the integrated management of groundwater and surface water resources. The evaluation and management of water resources are considered as one of the key factors in comprehensive development. Boein Aquifer in Isfahan Province located in the Gavkhooni Basin is studied in this study. The Standard Precipitation Index (SPI), Reclamation Drought Index (RDI) and Groundwater Standard Index (GRI) in the time scales of three, six, 12, 24 and 48 months and Cultivated Land Index (CLI) in annual time scale are calculated. Results showed that the highest correlation between the SPI and GRI is in 18 and 24 months' time scale, and there is a delay of 1.5 to 2 years between these two droughts. Contrary to the results of some references, the correlation between meteorological drought index and GRI does not always increase with increasing the time scale. A synchronic study of the SPI, GRI and CLI shows a large difference between the SRI and the other two indicators in 2004-2008 periods that may be related to the poor management of the region. However, more close values obtained for these three indices in the recent years mainly due to the better management of water consumption by relevant organizations. To determine homogeneous drought regions, different combinations of temporal and spatial scales were used by creating 39 different scenarios and running 390,000 simulations. None of the heterogeneity measures were met in 90% of cases. This indicated that the linear moment technique is not a suitable method for determining homogeneous arid regions of groundwater probably due to the non-random data. Therefore, the linear moment technique will be more useful if the data is random.

Temperature is one of the most important physical parameters that control the transfer and exchange of energy between different layers of the earth and the atmosphere. LST estimation methods based on satellite images require surface and atmospheric parameters such as surface emissivity, average air temperature, atmospheric transfer coefficient, and water vapor as input. Uncertainty in these parameters causes errors in the retrieval of land surface temperature. This study aimed to compare the accuracy of different methods for estimating atmospheric water vapor in estimating land surface temperature using Landsat 8 images. In this study, atmospheric water vapor was estimated using FLAASH atmospheric correction methods, MODIS sensor images, and SWCVR method. Then, the impact of atmospheric water vapor on land surface temperature accuracy was investigated using the split window and single-channel methods. Validation of Land surface temperature images was performed using cross-validation and ground measurement methods. Therefore, 20 Landsat 8 images related to 2018 and 2019 were used to estimate atmospheric water vapor by the FLAASH atmospheric correction and SWCVR methods, and land surface temperature estimation. MODIS radiance images were used to estimate atmospheric water vapor and the land surface temperature product of this sensor was used for cross-validation. The surface temperature was measured using a thermometer in places with homogeneous cover, for ground-based validation. Results showed that among water vapor estimation methods, the SWCVR method is more suitable for estimating land surface temperature and the split-window method based on the SWCVR method shows the lowest RMSE and MADE at 3.47 and 3.18. Results of RMSE image classification of split-window algorithm based on the SWCVR showed that 1.67% of the area has an error of more than 4 °C and 98% of the study area has less than 4 °C error.

Specifics regarding land cover and land use is an essential element of the planning process, as it can undoubtedly lead towards the debate around the present plans and patterns and the necessity to modify land use included in a regional plan. In this research, land use maps were prepared using Landsat TM (2000), (2008) and OLI (2016) satellite imaged. Land cover mapping was conducted after pre-processing and processing satellite images, creation of training samples and assessing maps accurate was done by coefficient kappa and overall accuracy. Supervised classification technique with maximum likelihood method were used to show the land use map. In this research, we use the 2000 and 2008 land cover maps to predict the 2016 land cover map and then use the 2008 and 2016 land cover maps to predict the 2024 land cover map.According to the results, with passing time the area of built-up area and mountainous increased with the passage of time while the dense poor rangeland, rich rangeland and agriculture area decreased during the period 2000-2016. The results of predicting changes in the time interval 2016-2024, showed that 55/0 of agriculture, 82% of rich rangeland, 80% of poor rangeland, 51% of built-up, and 0.97 of mountainous will remain unchanged

The number of stormy days is determined by various factors such as wind speed, rainfall, soil moisture and so on. The study of this index in the country can be considered in various plans. The purpose of this research is mapping of the number of dusty stormy days in Iran and selecting the best model based on the climatic data of 150 meteorological stations for the period of 25 years (1986-2010).

Dust stormy days’ data of the studied stations were analyzed using variogram curves to represents their spatial correlation. Gaussian variogram (R2=0.96) shows the highest correlation between the data. Then, map of the number of dust stormy days in Iran were prepared using different geostatistical and mathematical methods. For this purpose, several mathematical interpolation methods including Inverse Distance Method (IDW), Global Polynomial Interpolation (GPI), Radial Basis Function (RBF), Local Polynomial Interpolation (LPI), and geostatistical method of Kriging were used. To select the best interpolation method among several geostatistical and mathematical methods, statistical indicators of Root Mean Square (RMS) and correlation coefficient between observed and predicted data were used.

Results show that the highest correlation between predicted and observed data (R2 = 0.74) was found in kriging indicator method. The southeast and southwest of the country have the highest number of dust storm days.

High number of dust stormy days in the southeast is resulting from drying of Hammon lakes and blowing of 120-day winds in Sistan plain, and entering of dust from Arabic countries form the direction of southwest. North part of the country has the lowest number of dust storm days.

Specific climatic and geographical conditions of Yazd province have always exposed this region to a severe storm, and air pollution. Low visibility is the primary impact of air pollution due to atmospheric phenomena. Increasing dust density, along with a severe decrease in visibility, has harsh effects on the health of living things, socio-economic sections, and transportation systems. Therefore, identifying hazardous areas considering low visibility during dust events is important to manage and conserve the health of area residents. This study aims to evaluate the trend of changes in horizontal visibility of areas affected by dust storms in Yazd province, Iran. For this purpose, the quantitative model of horizontal visibility based on the relationship between Aerosol Optical Depth (AOD) of MODIS sensor derived from Deep Blue algorithm and horizontal visibility data derived from meteorological stations in the study area was introduced the horizontal visibility of dust events from 2015 to 2017 was mapped using geostatistics Kriging method. Results showed a significant relationship between aerosol optical depth and horizontal visibility data at the 99% level. The AOD value ranges from 0.1 in areas with no dust to 1.9 in areas with severe dust at six studied events, and the visibilities coincide with AOD values from 64 m in dust centers to 19951 m beyond these centers were estimated. Therefore, AOD image measures with more than 0.8 have horizontal visibility less than 1000 m. Determining of horizontal visibility extent without spatial limitation and identification of areas affected dust events are advantages of the quantitative model of horizontal visibility and its mapping. According to land cover/use map in Yazd province, mineral and constructing sites, high-density dirt roads, sand dunes, poor rangelands are the most effective factors in increasing of dust density and low horizontal visibility in pathways of dust storms of this province.

Iranshahr County with an area of about 30,000 km2 is one of the warm and arid regions of Iran. This region has faced a drought crisis in recent years that has caused irreparable damage to vegetation. Considering the necessity of awareness of the response of plant species to drought to preserve and restore the ecosystem, in this study, the effect of drought on the species of Prosopisesp, Salvadorapersica and Calligonumcomosum were investigated. In the present study, the drought trend and its periods were determined using the SPI index, and also the changes in the density of three plant species within plots of 50 × 50 m during two periods of wet and drought were investigated. Results showed that parameters of drought and species had a significant effect on plant density in all three species. In general, shrub species were more vulnerable than tree species and species with less root depth were deaden earlier in drought conditions than species with deeper roots.

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.

Introduction :

Petrological and geological studies are of great importance in natural resources management. The fractal technique is used as an instrument to achieve accurate results in a shorter time. Geological maps are very useful in natural resources management, industry, especially refineries, and mine exploration. Due to the large scale of available geological maps, small scale geological maps should be provided in details. The fractal dimension, as a measure of surface roughness over a variety of scales, can be used to model the dissipation of erosive products due to climatic elements and fluvial transport. Nowadays, by using new fractal technique and terrestrial survey, more accurate results on geological units can be obtained in a shorter time. This research aims to compare the performance of two techniques of quantitative parameters non-dimensionalization in geomorphology such as drainage network density index, and fractal dimension, to separate geological units in the Taft watershed, Yazd province.

 Materials and Methods:

 Taft watershed, as the study area, located in the Yazd province, that is situated between 53° 43' 38'' to 54° 14' 54'' E. longitude and 31° 33' 22'' to 31° 49' 06'' N. latitude. There is high diversity of geological and lithological units, including gd (Granodiorite), K^(t-1) (Taft lime), and K^S (conglomerate and sandstone) in this watershed. Three geological units selected in the study area. In each geological unit, , 10 plots of 2 km×2 km (samples), and 10 plots of 2 km×2 km (tests) were selected respectively inside and outside of the study area for analysis. To identify and distinguish three studied geological units, drainage network was drawn in each geological units through geological map of the Iranian Geological Survey and satellite images of the Google Earth and field observation. Afterwards, using Fractalyse and ArcGIS softwares, their fractal dimension and density were calculated. Output results of the Fractalyse software is some numbers that one of them indicates the fractal dimension of those lines. Fractal dimension number is between one and two. The area and network length of each plot were calculated by ArcGIS 10.2 software. Then, the drainage network density of each plot was calculated by equation 1. Drainage Network Density= Drainage Network Length (km)/ Plot area (km2) (1) Efficiency of the two dimensionless indices of drainage network density and fractal in separating geological units were compared by two

A) Validation: In each geological unit, the calculated numbers of the samples and tests should be averaged individually. Then, equation 2 is used to calculate the validation of each geological units. Validity= sample/test (2) Sample: Average fractal dimension of drain networks for sample plots. Test: The mean fractal dimension of drainage networks for test plots. Or Sample: Average density of drainage networks for sample plots. Test: The average density of the drainage networks for test plots. B) Comparing the sample and test by using QQ diagram, the line equation, the coefficient of determination and the angel of deviation: In drainage network fractal dimension, QQ diagram is plotted between samples and tests' fractal dimension. Line equation, coefficient of determination and angel of deviation were calculated. Moreover, the QQ graphs were plotted and calculations carried out on the drainage network density.

 Results and discussion:

Results of the first comparing method (validation) in both techniques are very good and similar. In second comparing method (i.e. QQ graph, angle of deviation, and coefficient of determination), coefficient of determination in the drainage network density in K^S, gd and K^(t-1)geological units were 0.99, 0.93 and 0.94, respectively, which are more than drainage network fractal dimension. The standard deviation in drainage network fractal dimension in K^S and gd and K^(t-1)geological units are +17.05, - 1.48, and +8.37 respectively, these values in the drainage network density are much lower (i.e. better). The angle of deviation in drainage network density of K^S, gd and K^(t-1) geological units are + 0.21, -2.4, and +0.22, respectively. According to the results, the drainage network density index is better than the drainage network fractal dimension in identifying and separating of the studied geology and lithological units of K^S, gd and K^(t-1) in the region. 

Conclusion:

 The results of the accuracy assessment of both techniques are very good and similar to each other. Therefore, in this comparison, both techniques of drainage network density index and drainage network fractal dimension have high efficiency in identifying and separating of the geological studies unit. However, The drainage network density technique is the best technique of quantitative parameters non-dimensionalization in geomorphological studies in identifying and separating of geological units in the Taft watershed, Yazd. 

Drought assessment and monitoring using traditional methods rely on rainfall data, which are limited in arid lands and often is very difficult to obtain near real time and costly. In contrast, remote sensing technology is a method for monitoring of large-scale drought.In this research, drought condition was analyzed using drought indices such as TVDI and NDVI from MODIS sensor data for the Yazd-Ardakan plain, Iran. First, relationship between the drought indices with climatic elements were detected. Coefficient of correlation between TVDI and SPI_6 and SPI_12 were 0.68 and 0.71, respectively. Correlation between NDVI and SPI_6 and SPI_12 were 0.49 and 0.51, respectively. Point correlation between TVDI and SPI_6 in 2004 (as a normal year), 2007 (dry) and 2012 (wet year), were 0.64, 0.78 and 0.67 and for the SPI_12 in the above-mentioned years were 0.65, 0.79 and 0.69, respectively. In other word, efficiency of the TVDI in 2007 is better than the other two years. Correlation of NDVI and SPI_6 in 2004, 2007 and 2012, were 0.41, 0.50 and 0.56, respectively. The correlation between NDVI and SPI_12 in 2004, 2007 and 2012, were 0.52, 0.57 and 0.59, respectively. TVDI which takes into account thermal and reflective bands, and soil moisture, is more accurate than the NDVI, which considers only amount of vegetation of the study area. Results showed that the relationship between vegetation and temperature is negative, while, the relationship between vegetation and precipitation is positive. Using of TDVI can compensate defects of the NDVI and used for identifying and monitoring drought.

In recent years, frequency and intensity of dust storms have been increased because of human destructive activities and caused significant loss in different aspects of hygienic and health, environmental and socio-economic sections. Therefore, detection and trace of dust storms in shortest time is the first effective step in preparation and implementation of strategic and operational plans in regions where are affected by dust storms. Using of remote sensing techniques in running of detector algorithms of dust storm plumes via satellite images are efficient methods to detect dust storm events, especially in large areas presented by researchers who study in this field. In this research has been tried to compare the most important algorithms to detect dust plumes and introduce the most suitable algorithm for the Yazd province suffered dust storm events and their loss.  To detect dust storms, the events with wind speed of more than 10 m/s and horizontal visibility of less than 1000 m were identified using meteorological synoptic stations data in the study area during 2010-2015. In the following, six common algorithms for detection of dust plume including Ackerman, Miller, Roskovensky and Liou, Thermal Infrared dust index (TDI), Thermal Infrared Integrated Dust Index (TIIDI) and Normalized Difference Dust Index (NDDI) were examined via satellite data of MODIS sensor in four dust storm events. In order to evaluate the accuracy assessment of dust maps, relation between the produced maps and horizontal visibility records of the meteorological stations located in the study area, as well as, Aerosol Optical Depth (AOD) of dust based on deep blue algorithms were analyzed.   Results showed that Roskovensky and Liou, Thermal Infrared dust index (TDI) and Thermal Infrared Integrated Dust Index (TIIDI) had better accuracy and precision to detect dust storm events based on aerosol optical depth (AOD) maps and horizontal visibility records of the study area. According to the results, TDI and TIIDI algorithms had better performance to detect dust plume in 03/02/2011 event. Correlation of TDI and TIIDI maps with AOD were 0.65 and 0.49, respectively, which were significant at 1% level, while, Roskovenskey and Liou algorithm presented more suitable result to detect dust storm in 10/02/2015 event. Correlation of Roskovensky and Liou map with AOD and horizontal visibility records were 0.68 and 0.76, respectively, which were significantly at 1% and 5% levels, respectively. In 13/04/205 and 20/07/2015 events, TIIDI and TDI algorithms showed better performance to detect dust plumes. Correlation of TIIDI with AOD was 0.81 which was significant at 5% level. Also, the correlation between TDI and horizontal visibility records was 0.71 which was significant at 1% level.Discussion and Some dust storm events are analyzed by remote sensing techniques via methods such as indices and algorithms related to dust detection, interpretation of false color composite and brightness temperature difference of features and phenomena in satellite images. These methods are very efficient to detect dust especially, the dust plumes which their visual detection are difficult because of low density. However, the results of indices of dust detection are different, because of various chemical composite of dust particles and resulted in different spectral and thermal properties in different regions. According to the results, none of the algorithms could detect dust plumes in all events. They were just able to detect dust plumes in one or two events. However, the algorithms that used thermal bands or combination of thermal and reflective bands in their equations had been more effective to detect dust storm. To have better dust storm detection, using threshold ranges according spectral and thermal properties are required in each region and even in each event. Using quantities data of aerosol optical depth of dust as well as horizontal visibility records of meteorological stations are suitable tools to adjust to the real situation and analyze accuracy assessment of dust maps made by different algorithms.

Dust event is one of the common and destructive phenomenon in arid and desert regions. This phenomenon has negative impacts on human life and environment. Dust storms, in addition to soil loss, can cause and aggravate health problems, food production reduction, economical damages into the industrial, agricultural and communication sections. Therefore, accurate investigative of this phenomenon is necessary. The aim of this research was regional analysis of dust storm index (DSI) in 44 meteorological stations of Iran. At first stage, the dust storm index for each station was calculated using hourly dust data. Next, monthly averages of dust storm index (DSI) were used for regional analysis using linear moments approach. Based on regional analysis, the study area is divided to six homogeneous dust storm index regions. Pearson Type III (PE3) and Generalized Logistic (GLO) distribution models were the best regional distribution models for 1, 4, 5, 6 homogeneous regions, and 2, 3 homogeneous regions, respectively. Estimation of the dust storm index and its regional analysis can be used in many environmental studies, decision making and management processes in relation to combating desertification and dust storms.

One of the methods to determine the water quality is applying water quality index. This comprises several parameters with different effects on water quality. Therefore, one important problem is to calculate the exact value for each of the parameters. The purpose of the present study is to compare the effects of equal weights and weighted parameters on the results of water quality index in Fasa plain employing Entropy theory. To this end 9 Chemical parameters from 55 groundwater samples were investigated and ranked using the water quality index. Then, the weight of the parameters was calculated with the weights that obtained by the Shannon Entropy method. Thereafter, the significant difference was investigated between two groups. In order to determine the efficiency of each of these two methods, the Spearman's correlation was employed between the rating of wells quality in two methods and the rating of wells quality which was calculated by Sholer diagram. At the end, the clustering and qualitative zoning of wells were plotted. The results showed that there was a significant difference between the results of the water quality index whose weights obtained from the equal weights method and Shannon Entropy method in 0.01 level. Also, the high correlation (0.926) between ratings of wells quality by Sholer diagram method and ratings of wells quality was obtained by Shannon Entropy method indicates the high significance of this method in calculating water quality index.

Surface albedo is one of the key controlling geophysical parameters in surface energy budget research and environmental and climate studies. As an efficient tool for monitoring earth surfaces, remote sensing is widely used for estimating albedo over recent decades and various algorithms of satellite data have been developed to estimate albedo. Up to now, albedo has been estimated using different types of sensors. This study presents procedures for computing of albedo using OLI sensor Landsat 8 data in different geomorphology facies, arable soil and asphalt in the Yazd- Ardakan Plain. In this way, required data from each nine images metadata of Landsat 8 were extracted for summer and autumn, 2017 and then albedo was calculated using meteorological data. Results showed that there is a difference between values of albedo in different geomorphology facies. Surface albedo average of clay land, pavement, sand dune, bare land, sand sheet, arable land and asphalt are 0.30, 0.22, 0.24, 0.26, 0.24, 0.23 and 0.24, respectively. The highest calculated albedo is related to clay land, while the lowest is observed in desert pavement facies.

Desertification relates to the both the process and end state of drylands degradation. Salinization and alkalinization are two indicators of soil degradation in arid and semi-arid regions. The main objectives of this research is monitoring of soil salinity using high spectral and spatial resolution of remote sensing to assess desertification in the Marvast plain، Yazd province. Two images of Terra satellite، ASTER synchronous to 2003 and 2010 are used. After preprocessing and analyzing of the images، relationship between parameters of soil salinity (i. e. SAR and EC) and spectral reflections were determined and، both two satellite images were classified using maximum likelihood method. Then، the surface area of each class and the amount of its changes were calculated. Results showed that during the period of 7 years (2003-2010)، area of non-saline lands has decreased while، the area of saline land has increased، which leads to the salinization of agricultural lands، reduction of its yield and also extent of desertification in this region. Accuracy of EC map classification for 2003 and 2010 images are 87. 5% and، 82. 5%، respectively. Kappa coefficients for both images are 0. 83 and 0. 76. Accuracy of SAR map classification for 2003 and 2010images are 87. 5% and 87. 5%، respectively. Kappa coefficients for these two images are 0. 81 and 0. 77، respectively. Generally، it can be conclude that using of remote sensing data، especially ASTER images has high efficiency for change detection analysis in soil salinity and natural resources management.

Land cover is defined as a physical and biological cover of the earth’s surface that contains water, vegetation and bare soil. Limiting factors in arid areas such as water shortage and soil salinity are important factors to study the occurred changes in land cover. Due to the large expanse of land, change detection by the traditional methods is not sufficient and efficient; therefore, using of new methods such as remote sensing technology is necessary and vital. The overall aim of this study was to assess changes in land cover in arid lands of Marvast plain, located in south of Yazd. In this study, ASTER (Terra) data for summer month of July for years of 2003 and 2010 as well as ETM+ data (Landsat) for July 2002 and 2010 was used. In the first step, after pre-processing operations such as geometric correction, land cover maps of the study area were classified into 5 classes (agricultural land, rangeland, bare land, desert and wetland) using Maximum Likelihood method in both satellite types. Then, taking ground truth data, the classified maps accuracy were assessed by calculating the Kappa coefficient and overall accuracy. The results of ETM+ classification show that the areas of agricultural land and wetland were increased and decreased, respectively in time span of 2002-2010. The same changes were also observed in change classification maps of ASTER for the period of 2003-2010. In this time period, the area of wetland is decreased and changed into desert area. In addition, rangeland area has reduced and changed into agricultural land. The results show that produced maps of both sensors have accuracy of above 85% which reflects high performance of both images in land cover change analysis. In spite of the fact that ASTER data is a little more accurate, results of using Landsat data which has more accessibility and lower cost, for change detection analysis is also acceptable and valuable.

Sand dunes movement is one of the critical processes of desertification. Mulching is one of the methods of sand movement control. Oil mulches have been used in Iran. Because of high cost and negative environmental impacts of oil mulches، changes in mulch type sand mulching methods is vital. Therefore، in this research different combinations of clay and lime were used as stabilizer. Sandy soil from the Yazd-Ardakan plain is used as bed treatment and clay particles (taken from Meybod area) were used as mulch in this research. The treatments were prepared using different ratios of the above mentioned materials. One liter of water was added to the each mulch combination and was sprinkled on the plot of 100 cm (length) × 30 cm (width) × 4 cm (height) sand. A completely randomized design is used as research plan with three repeating. Physical parameters، such as thickness، compressive strength، impact resistance and abrasion resistance which are created by mulches، and wind erodibility of the treatment were measured. The measured data were analyzed using SPSS software. Results show that the measured compressive strength، impact resistance and abrasion resistance of the clay–lime mulch is increase in the ratio of 200 gr clay and 10 gr lime in one liter of water.