farhad zolfaghari

Investigation and monitoring of desertification in arid and semi-arid regions is a major concern for societies and governments due to its increasing rate. It is essential to identify areas at risk of desertification to manage and control this phenomenon in the shortest possible time and at minimum cost. The objective of this study is to create a map of desertification intensity in the MoradAbad plain of Saravan using the Albedo-NDVI model, which is based on remote sensing. Two Albedo and NDVI indicators were extracted from Landsat 8 satellite images in Erdas Imaging software after necessary corrections. A linear regression was formed between the two indicators by selecting 200 pixels corresponding to each indicator. Based on the slope coefficient of the line obtained from linear regression, the equation for determining the intensity of desertification was obtained. A map of the intensity of desertification was prepared based on Jenks’ natural refractive index. To evaluate the accuracy of the model, a clutter matrix was formed between 100 corresponding points. The results of linear regression between NDVI and Albedo indices showed that these two indices have a high negative correlation with each other (R = -0.85). The results of the desertification severity classification based on this model showed that 35% of the area is in the very severe class and only 5% of the area is without degradation. The model’s accuracy value was obtained with a kappa coefficient equal to 0.58, indicating good accuracy of the model.

Nowadays, climate change and global warming caused by increasing concentrations of greenhouse gases, especially carbon dioxide, is one of the major challenges facing sustainable development. Carbon accumulation in plant biomass and soils is the simplest and economically way to reduce levels of this atmospheric gas. No research has been done on the assessment of the potential of soil carbon deposition and accumulation in the Capparis decidua and Prosopis cineraria species.

The objective of this study was to evaluate the soil carbon accumulation in Capparis decidua and Prosopis cineraria plant species in Keshtegan of Saravan, Iran. Therefore, in order to determine the amount of carbon stored in the soil, soil sampling was done by random-systematic method. One hundered-meter transects were randomly selected in the study areas and sampling points were dug at similar intervals along the transect for sampling.Soil samples were taken from depth of 0 to 30 centimeters under the canopy of Capparis decidua and Prosopis cineraria and bare soil as control (20 samples from each area). Soil organic carbon, soil bulk density, pH, salinity and content of clay, silt and sand were recorded.

The analysis of the data showed that there is a significant difference between the investigated treatments in terms of the amount of clay, organic carbon and carbon accumulation. The average comparison results showed that there is a significant difference between the soil covered by plant species and the soil of the control area. The amount of soil carbon accumulated in the area covered by Capparis decidua (1.32 tons per hectare) was significantly higher than that in area covered by Prosopis cineraria (0.75 tons per hectare) and the control area (0.25 tons per hectare). It shows the positive effect of two plant species on the amount of soil carbon accumulation. The average amount of organic carbon in the area with the Capparis decidua, Prosopis cineraria and the control area was  0.75, 0.31 and 0.1 tons per hectare, respectively.Soil organic matter and sand percentage under the canopy of both plant species were higher than the control. In terms of other characteristics, no significant difference was observed in the three regions. According to the results, it can be stated that the presence of plant canopy can increases the amount of carbon accumulation in the soil and led to global warming mitigation.

In this research, two categories were used to investigate the effect of the change of clarion surface water of Khash plain. The data on 2010 to 2020 obtained from the Liz Hydrometer Station on the Syanjah River Khash Plain, on the other hand, the base rainfall data was obtained from the Khash Station. Based on the measured regression model, the base period of the relationship between Dubai and precipitation at the Khash basin level was modeled. With the implementation of the SDSM statistical microscale model, the simulated rainfall values were obtained in the statistical period of 2020-2040, under two lines of release 4.5 and 8.5, which was used as an input to the regression model for remodeling in the climate change period. The results showed that in the period of climate change 2040-2020, despite the fact that the rainfall of the Khash basin has increased in the cold months of the year, the simulated runoff of the period 2040-2020, in both RCP4.5 release trajectories and RCP8.5, has had a decreasing trend. The results indicated that the climate change period 2020-2040, in the RCP4.5 release, had reduced about 0.02 cubic meters per second to the base period, and in the RCP8.5 release track, this was a decrease of 0.016 cubic meters per second.

Vegetation is one of the most important key components in arid regions for reducing of the effects of erosion and determining the severity of desertification. Decrease in vegetation leads to increase in surface albedo. Accessing and preparing desertification intensity map at the fastest possible time and at the lowest cost is one of the concerns of governments. In the present study, in order to identify the best vegetation index for preparing the desertification intensity map, MSIL-1C data of Sentinel 2 satellite in the arid region of Sistan has been used. For this purpose, the relationship between surface albedo and each of the different vegetation indices of the NDVI, RVI, DVI, PVI, SAVI and TSAVI were conducted. After determining the linear regression equation between the albedo and each of the mentioned indices, the relevant desertification intensity equation was calculated and the desertification intensity map of the studied area at 5 classes was prepared based on albedo and each of the mentioned indices. The results showed the strongest relationship in the study area was between albedo and NDVI, with a correlation coefficient of 0.63, and the lowest correlation of 0.37 was between the albedo and PVI indices. Based on the present study among the indices studied, the NDVI is the best for the preparation of maps of desertification intensity in the arid region of Sistan. Based on this index, 20.3% of the region was classified as severe and 32.9% of the region grouped into the moderate desertification class.

Different vegetation covers have different albedo levels. On the other hand, surface albedo is one of the most important components of surface radiation balance, which can be used to identify severely degraded and desertified regions. Vegetation can be considered as one of the most important key components in arid regions to reduce the effects of erosion and desertification due to the effects of vegetation for land surface stability. Expansion of desertification and also changes in vegetation cover, could be change the surface Albedo. The purpose of this study is to determine the desertification intensity based on spectral indices, Albedo, Topsoil Grain Size Index (TGSI) and Normalized Difference Vegetation Index (NDVI) using remote sensing technology. Identification the damaged areas with the lowest cost in the shortest time, using Sentinel-2 images with a spatial resolution of 10 meters is one of the objectives of this study. Also, this study will introduce the best indicator for monitoring desertification intensity in arid regions for the first time in the Sistan and Baluchestan region based on spectral indices using Sentinel-2 images.

 The following steps were performed to evaluate the intensity of desertification and identify the appropriate indicator in order to mapping the desertification intensity: 1) Selection the images and perform image preprocessing operations using SNAP software; 2) Calculation of TGSI, NDVI and Albedo indices; 3) Investigation the correlation between indices using SPSS®24 software. 4) Preparation of desertification intensity map of the region and obtaining the equation of desertification intensity using ArcGIS®10.3 software. In the first step of this research, Sentinel-2A satellite data related to MSIL-1C sensor was selected on August 20, 2020. The images were selected in such a way that the growing season of the plants is not annual and temporary, and also the day was selected when there is no cloud cover. The required images were downloaded and used from the URL address: http://scihub.copernicus.eu/.

 The results of linear regression between NDVI and Albedo indices showed that, these two indices had negative correlation, and the correlation coefficient in Souran and Zabol was 0.76 and 0.63, respectively. The results showed that with increasing NDVI, decreased of the albedo index occurred. Also, the results of linear regression model showed strong and positive relationship between TGSI and Albedo indices, as the correlation coefficient of Souran and Zabol was 0.78 and 0.81, respectively. The results showed that the TGSI and the albedo simultaneously decreased or increased. Desertification intensity in the study areas was determined based on the equation I= a × Index ± Albedo and also by using Natural Breaks (Jenks) method in ArcGIS software, desertification intensity of study areas classified to 5 degrees, 1. Without desertification, 2. Low desertification, 3. Moderate desertification, 4. Severe desertification, and 5. Extremely desertification. In this study Albedo, NDVI and TGSI indices were extracted based on Sentinel-2 satellite data. The results of linear regression between NDVI and Albedo showed that there is strong negative relationship between these indices that was consistent with the results of similar studies. The high and negative correlation, means that any increase in the vegetation cover will lead to decrease the Albedo. On the other hands the areas with high Albedo, indicate degradation of vegetation cover and bare soil. In the regions with sever desertification intensity, the value of surface Albedo was high and the vegetation cover was low. Classification of desertification intensity in Sistan region based on Albedo-NDVI model showed that 27.73% of the area were in the class of without desertification intensity, 18.03% in the low class, 32.92% in the moderate class, 20.3% were in the severe class and only 1.02% of the area were in the very severe desertification intensity class. Also, the classification of desertification intensity in Souran based on Albedo-NDVI model showed 4.82% of the area without desertification, 8.44% in low class, 50.97% in moderate class, 34.48% in severe class and 1.3% of the area were in very severe desertification class. The highest percentage of desertification intensity of the area were in the moderate class. The results of linear regression between TGSI and Albedo indices also showed that there is a positive and strong relationship between these indices. The results showed that the relationship between TGSI and Albedo indices was stronger than the relationship between NDVI and Albedo indices and in both regions the correlation coefficient was higher. One of the main reasons for this is the dispersion of vegetation cover in arid areas. The relationship between TGSI and Albedo better shows the spatial characteristics of vegetation-free areas as well as areas with very low vegetation cover to determine the intensity of desertification. The TGSI index reflects the coarse particle size of the topsoil, which has a positive relationship with the fine sand content of the topsoil. Whatever the larger particle size of the topsoil, will have the greater desertification intensity. In the areas where the content of fine sand in the topsoil is high, the high range of TGSI index will be seen.

 In this study, using Sentinel-2 multispectral images and remote sensing technique, we extracted the intensity of desertification in different arid regions of the Sistan and Baluchestan province, for the first time in Iran. Based on the spectral reflection that occurred from the ground and the spatial resolution of 10 meters, we studied the intensity of desertification in two areas. Based on the results of this research, we suggest to use the combination of Albedo-NDVI and Albedo-TGSI models in order to monitoring the desertification intensity in arid regions of Iran. The results of this study showed that areas without desertification and low intensity of desertification are better identified based on Albedo-TGSI model.

Nowadays, the destructive factors of the environment and natural resources are so widespread that the alarm of destruction of these natural wonders is sounded all over the world. Due to the importance of this topic, the study was conducted to estimate the willingness to pay of local communities to conserve and restore desert areas of Gosht plain in Saravan in order to attract people's participation to finance projects for the preservation and rehabilitation of desert areas. The sampling process was conducted using Cochran's formula with 303 samples by simple random sampling method. Conditional valuation method and Logit model were used to estimate the people’s willingness to pay. The results showed that gender, education, participation in rural government institutions, employment of trusted executives, the history of participation in educational-developmental classes, being native, income and directly being beneficiaries from the environment of the region has a positive and significant effect on the willingness of people to pay for Gosht plain protection. The variable of age and suggested amount had a negative and significant effect on willingness to pay, but the size of household had no significant effect on willingness to pay. Moreover, the variables of existence of a management organization for participation, acquaintance with the environment, having environmental literacy, and government financial support of the projects had a positive and significant effect on the willingness to pay for the Gosht plain protection. The average of peoples’ willingness to pay monthly in 1398, was obtained 113890 Rials per each person.

After desertification mapping for proper management is very important to recognize the similar work units. Because, the two geomorphological units or two work units that have the same desertification intensity classes, do not necessarily require the same management. In this study, to identify the similar work units that need the same management, at first the intensity of desertification was evaluated according to 4 important criteria in the region (climate, soil, vegetation, and wind erosion) based on the IMDPA model and then classified and identified the similar work units using Cluster analysis method. The results of desertification intensity based on the IMDPA model showed that there are two classes of extreme and medium with the areas about 59.32 and 40.68 percent respectively. This is while the results of cluster analysis showed that all geomorphological units are classified in six different clusters. According to the results, two work units with the same desertification intensity are not necessarily in the same cluster, and work units with the same intensity are in different clusters. Therefore it can be said using the clustering method with desertification models to identify similar units for planning and implementation of management programs will have well efficiency.

Any Changes in a dry land surface ecosystem will be affected by the climate near the ground or microclimate in the vertical plane. In recent decades' wetland drying cause to reduced vegetation significantly. Assessing Zabol synoptic station statistics shown an increased temperature of this place. It seems that there has a direct relationship between the changes in land surface vegetation and increases the ambient temperature. The situation ground roll on microclimate has been investigated to illustrate this relationship. In this study we compare and assessment temperatures at depth of 5cm and surface and height about 150 cm and heat fluxes and energy in three microsites with different vegetation cover. The distance between the experiment microsites is about 20 km, and the elevation difference is less than 10 meters. Microsite A with the total vegetation average about 65%, B microsites 20% and microsites C with 100% bare soil. It evaluated the equation ρc_p z_a (dT_air)/dt to investigate the role and effects of vegetation on the ground surface. Data analyzes showed temperatures in the period of study at the C microsites were higher than other microsites. It seems the lack of vegetation in microsites C has a major role in the higher air temperature. In micro site C At 00:30 Am (local time) the air temperature was 3.2ºC higher than microsite A and B. The results showed there is a direct relationship between the vegetation cover percentage and air temperature because of different soil heat fluxes and surface temperature.

In this research, desertification intensity of saravan region was evaluated using IMDPA‌ the newest method of Assessment of Desertification Potential in arid and semi arid regions of Iran. For this purpose 4 criteria including climate, vegetation cover, soil and wind erosion were selected. Each criterion was assessed based on it's indices which result in qualitative mapping of each criterion based on geometric average of the indices. Then, sensitive map of region was extracted using geometric average of all criteria. Thematic databases were integrated and elaborated in a GIS. By laminate of thematic databases layers and using this formula DM=(SI×WEI×VI×CLI)1/4 to ‌Calculate ‌the geometric mean Criteria, the desertification intensity map was obtained with analysis IMDPA Model. The result showed that 45.24% of study area was found to be in medium and about 54.39% in high class of desertification And 0.37% of the region including residential aren’t in any classification. Analysis of desertification criteria in Saravan region showed, climate criteria is a major problem with a geometric average of 3.1 which shows high class while soil with a geometric average of 2.35 has lowest effect in desertification. Also vegetation covers and wind erosion criteria with a geometric average of 2.62 and 2.87 show high class desertification.

This research was aimed to investigate the effects of crescent pond structure on soil and vegetation properties in the Saravan rangelands. For this purpose, two treatments including crescent pond and control treatment were determined. For each treatment, the density, canopy cover percentage, and litter were estimated by random-systematic sampling using five transects of 100 m and 50 plots. Two soil samples were taken along each transect from 0-30 cm depth and soil physico-chemical properties were measured. Data were analyzed statistically. According to the obtained results, significant differences were found for all vegetation parameters between the crescent structure and control treatment, indicating the positive effect of crescent structure on vegetation parameters. The results of soil properties indicated reduced soil salinity; however, the organic matter, potassium, and pH showed no difference between the two regions. According to the results, due to the climatic conditions of the study area, longer time is needed to achieve better results.

The main objective of this research is an investigation and preparing of wind erosion intensity map. IRIFR model used in this study which has been developed by Iranian researchers for Iran’s climatic conditions. At first, the working unit map was prepared Then nine effective factors in wind erosion such as Geological Formation, Landform and Topography, Wind and Climatic Conditions, Soil Surface, Plant Cover, Wind Erosion, Soil Moisture, Deposits of Wind and Management Process are determined for each working unit. Each factor scored according to IRIFR model and for each one a raster GIS layer was created. According to the results, active sand dunes and Saline Lands showed the highest value with the rate of annual sedimentation more than 3879.4 ton/km2 ,while medium sandy plain with low density showed the least value with an annual rate of sedimentation of 708.73 ton/km2. Also in whole area, 35.74 percent equivalent 46491.36 hectares are classified in high class and 63.73 percent equivalent 82902.43 hectares are classified in medium class of wind erosion.

Today satellite imagery is the latest instrument for detection of changes in land cover and water surfaces. In this paper to detect water and canebrake changes in Hamoon international wetland Landsat multispectral scanner (MSS) data from 1976 and ETM+ data from 2005 were processed and analyzed. Ground truth was obtained from Google Earth. After qualitative controls and geometric correction of points, images processed and different bands merged. The results showed that Hamoon area was 397541.27 ha in 1976 and its green canebrake cover at that time was 122836.24 ha. Also in 2005 after re-inundation, these areas decreased to 327673.69 ha for Hamoon and 17295.01ha for green canebrake cover. These results showed the decrease in water area by 17.57 % and green canebrake cover by 85.92 in 30 year time interval. Decrease in green canebrake cover has adverse effect on the climate of Hamoon basin and human life due to increasing of dust emissions through removal the lake sediments.