فصلنامه سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی
سال چهاردهم شماره 1 (بهار 1402)

 Recently, the use of GIS in urban planning has been developed with the rapid expansion of cities and the dramatic increase in the amount of information that must be processed for urban management. This study investigates the best landfill site for Zahedan city using the performance of decision support tools, Network analysis process (ANP) and Weighted linear combination (WLC) for weighting criteria, and map standardization methods based on Boolean and Fuzzy logic in the form of multi-criteria decision-making. Indeed, based on the variables' impacts in locating the waste landfill in Zahedan city, using multi-criteria decision-making methods (MCDM) to weigh and prioritize and evaluate the effective factors are considered to identify the optimal location regarding the ecological potential of the region. The proposed model indicates the priorities of creating different types of decision-making during the evaluation analysis of the development capabilities of the study area.

Developing a multi-criteria evaluation method in a GIS environment to determine and estimate the capability of desirable landfills in Zahedan city is considered. Thus, by preparing a questionnaire by the Delphi method, 18 sub-criteria in two groups of criteria: 1. ecological criteria (Slope, altitude, soil, erosion, fault, precipitation, wind, direction, surface water, groundwater, vegetation, land use, and geology); 2. Socio-economic criteria (Distance from city, village, mine, airport, and road) is determined, and regarding expert's perceptions and using Network Analysis Process (ANP) in super decision software, weights of each criterion were calculated; and in the next step, the layers of criteria were evaluated in a database based on ArcGIS and stored as benchmark maps; and finally using the WLC method were considered to combine all layers to extract the map of a suitable landfill site in Zahedan city.

 By fuzzifying 18 layers (criteria) with fuzzy logic and also applying constraints with Boolean logic, 18-layer maps are prepared and by merging layers with one of the common methods of weight linear composition in Multi-criteria decision, the final landfill location map has been explored. In terms of the spatial distribution of suitable landfills in Zahedan city, according to 5 categories of classification, it was found that the highest level of the region is categorized as the low capable class (99.76%) and suitable areas for landfilling in total is around 0.231, also no part of the Zahedan city has a very high or high capability for using as the landfill location, while around the city of Nusratabad, areas with very high and high capability are observed. It was also found that mainly lands with both low and medium capability, are located in the suburban areas of Zahedan and Nusratabad, with 22 units for the city of Zahedan and 35 units for the city of Nusratabad.

 Reviewing the research literature shows the strengths of using a multi-criteria decision-making method to locate landfills, enabling the use of a robust set of interactive tools to regulate compensability between criteria, which allows a quick assessment of the relationship between the criteria. Other strengths of this method include the ability to integrate homogeneous data sets such as qualitative and quantitative criteria using specialized knowledge, the flexibility to select specific criteria for different study areas or various issues, to implement one or more decision-making groups, the flexibility to change the level of criterion importance and different choices for acceptable levels of decision-making risks. By comparing the final outputs related to other areas, it can be concluded that the final results are close and the method is suitable for landfill locations everywhere. Therefore, it is suggested that for other areas, the evaluation of land capability should be examined with the proposed method in this research. However, since the location of landfills by different criteria and the influence of public opinion depends on scientific analysis, we assume that this method has significant potential to support the decision-making complexities of real-world applications.

 According to Article 45 of the Constitution of the Islamic Republic of Iran and Article 2 of the Law on Fair Water Distribution, rivers are a national asset. They are in possession of the Islamic State. Therefore, the government is obliged to study and determine the bed and river boundaries, and if it recognizes the aristocracy in the bed and their area for disturbing water or electricity issues, to evacuate or tin and suppress them. Today, due to the increase in the economic value of land and the demand for land construction in lands along rivers and waterways, unfortunately, the process of using the riverbed has increased, which is a threat to access to safe water and its protection for future generations. Occupying rivers is associated with reduced land use and land use change. This disrupts the natural flow of the river, resulting in flooding and social, economic, and environmental damage. It is not possible to manage water resources, especially flood management, without knowing and analyzing the flow of rivers, flood zoning, and determining their bed boundaries and boundaries. At present, a land survey is being conducted to determine the extent of the floods and to determine the extent of the riverbed. This method is very time-consuming and expensive to perform. In this regard, using satellite imagery and aerial photographs instead of terrestrial mapping can be helpful in speeding up studies and reducing costs. Much research has been done in our beloved country of Iran and the world on the use of satellite images in various fields. In particular, several studies have used satellite imagery to study the changes in land use in watersheds and to study the morphological changes of the river. As noted, research has been conducted on the use of satellite imagery in hydrological studies and watersheds, but for the first time in this study, it is possible to use satellite imagery to map the river and extract its cross-sectional areas for flooding and riverbed delimitation. Has been studied. In recent years, the bed of the Ardak River above the Eradak Dam has been extensively occupied and altered. This has led to an increase in the number of floods and a decrease in the quantity and quality of water in the Ardak Dam, which supplies part of Mashhad's drinking water. For flood management and quantitative and qualitative protection of the Ardak dam, flood zoning and determination of the Ardak riverbed is necessary. At present, the ground mapping must be done first. Land surveying to map the river and extract its cross sections requires a lot of time and money. Therefore, the aim of this study was to investigate the possibility of using satellite images with a resolution of 28.28 m instead of terrestrial mapping to increase the speed of work and reduce the cost of studies of water projects and projects and river engineering.

ASTER satellite imagery and HEC-GeoHMS software were used to draw the catchment area and extract its physical parameters. The existing riverbed map and its margin were prepared and added to the land use map of the basin as a new layer. The HEC-HMS hydrological model was used to simulate precipitation and runoff. First, a metering and validation model was used for five rainfall and runoff events. The precipitation histogram for different return periods was then introduced to the HEC-HMS model based on the basin concentration time. The execution and flood model were simulated with different return periods. The river's geometric information was extracted in transverse sections from both terrestrial mapping and satellite imagery using the HEC-GeoRAS appendix. Information from river flow modeling in HEC-RAS software was transferred to the GIS environment through HEC-GeoRAS extension and in the mentioned environment, flood zoning and riverbed boundary determination were determined by two methods of using land mapping information and using satellite images.

 The results indicate that the flooding area and the determination of the riverbed can be done by using satellite images with a resolution of 28 × 28 m. In this case, the statistical indicators of the mean relative error and regression correlation coefficient were 13.2 and 92%, respectively. If cross-sectional crossings are taken at several points along the river route and replaced by cross-sections obtained by satellite imagery, the accuracy of flood zoning and riverbed delimitation will be enhanced by the use of satellite imagery. If at a distance of 150 meters and at a distance of 8 km, 47 cross-sections are located and grounded and replaced in the HEC-RAS model by cross-sections obtained from satellite images, the error of using the satellite imagery method for flood zoning and riverbed delimitation Will be reduced to 8.1%.

It is possible to use satellite images with a quality of 28 × 28 m to determine the river bed limit. This method is associated with the average relative error and regression correlation coefficient of 13.2% and 92%, respectively, which can be reduced by 8% with ground cutting.

 The protection of natural resources, especially land, and their use, has long been considered. It can be said that because land use has undergone many changes over time, these changes have direct and many effects on the ecosystem and the environment and consequently have various consequences, including these consequences that can be used to change land use. The area was affected by the rapid expansion of urbanization and its effects on land-use patterns in the surrounding environment and, finally, land fragmentation in these areas. Accordingly, in many cases, converting land use from its natural state to artificial land use has irreversible consequences. To reduce the consequences, this can be adapted to the land use structure. Land appropriateness refers to matching the capacities of a plot of land and its land use, and the disproportionate allocation of land use and disregard for its changes has many consequences such as socio-economic segregation, environmental depletion, and loss of resources. Decisions in land and resource management should always be guided in a way that does not conflict with the interests of society and the natural environment. In this regard, one of the effective ways to control and minimize the damage and consequences of land use is to adapt its structure so that, based on the characteristics of land resources and their capabilities, the land can be spatially distributed and arranged more rationally. This study aims to identify and zone the appropriateness of land use structure with the existing capabilities in Hamedan and to evaluate the efficiency of the multilayer perceptron neural network method in the field of land use structure optimization in this city.

 In this study, to adapt the land use structure in the city of Hamedan, based on the research background and according to the effective criteria in the field of land use structure, various indicators were selected, including 12 land use indicators, slope, average temperature, and average rainfall. Average humidity, average wind speed, geology, soil type, distance from the river, distance from wells, distance from main roads, and vegetation type. Then, using the field visit, the points with user suitability were registered as educational points. After preparing the layers of the mentioned indicators, these layers were standardized in the software environment of the GIS system. In the next step, the multilayer perceptron neural network uses the after-release algorithm by importing layers affecting the optimization of the land use structure as input and using the middle layer of distance. From appropriate points in terms of land use structure, this network was implemented with the structure of 1-10-12 to adapt the land use structure in Hamedan. From 35% of the total image pixels, the distance from the agricultural proportions as training points falls into three categories the first part (70%) for network training, the second part (15%) for stopping calculations when the error is increasing, and the third part (15%) was used for network verification. Finally, the final land suitability map was drawn. The resulting layer had a value between 0 and 1 which was divided into five land suitability classes. In the present study, after identifying the factors affecting the land use structure and adapting its structure, and preparing each of them, the mentioned layers were standardized. Then, using the field visit, the points with appropriate use were recorded as educational points. Thus, the land use structure was adjusted by the multilayer perceptron neural network model with 58 replications. The results of the neural network validation and the resulting output layer indicate the high accuracy of the network in fitting the land use structure so that the square root mean values of error (RMSE), and absolute error (MAE). Correlation coefficient (R2) in the implementation process of the network is equal to 0.19, 0.21, and 0.89, respectively, indicating the network's high accuracy in implementing the optimizing process. Completely inappropriately divided, and the results showed that most of the areas covered some somewhat suitable and perfectly suitable lands with 32.62 and 28.13% of the total area, respectively.

 In the present study, after identifying the factors affecting the land use structure and adapting its structure, and preparing each of them, the mentioned layers were standardized. Then, using the field visit, the points with appropriate use were recorded as educational points. Thus, the land use structure was adjusted by the model of a multilayer perceptron neural network with 58 replications. The results of the neural network validation and the resulting output layer indicate the high accuracy of the network in fitting the land use structure so that the square root mean values of error (RMSE), and absolute error (MAE). The correlation coefficient (R2) in the implementation process of the network is equal to 0.19, 0.21, and 0.89, respectively, which indicates the network's high accuracy in the implementation of the optimizing process. Completely inappropriately divided, and the results showed that most of the areas covered some somewhat suitable and perfectly suitable lands with 32.62 and 28.13% of the total area, respectively.

 The results of optimizing land use structure in Hamedan show that most of the area is not suitable for agricultural activities in terms of effective factors. In this area, most urban land uses completely barren and uncultivable lands, lands. There are mountainous, rocky, and low-quality pastures, mainly in the western and southwestern areas of Hamedan. Also, in this area, lands that have been relatively suitable in terms of a proportion are quite suitable in terms of 12 factors in the best conditions for agricultural and horticultural activities and are the best place for developing agricultural activities. Thus, to change the land use conditions towards a more appropriate trend while paying attention to integrated urban-rural planning for Hamedan and its surrounding settlements. It is recommended to pay attention to land use planning rural-urban plans and projects because the rapid expansion of Hamedan and its suburban spaces has created numerous challenges in terms of land suitability. In such a way that about 23.1% of the lands are ready to be transformed into unsuitable and completely unsuitable conditions. In addition, 32.62% of land use is subject to change to semi-suitable conditions. Based on what has been said, controlling, supervising, and directing the constructions and preventing the over-horizontal expansion of the city of Hamedan and its surrounding spaces by urban and rural stakeholders (local management) is proposed to rangeland and agricultural lands. The findings of the study also indicated that the highest area of land in this area is related to somewhat suitable and perfectly suitable land and the lowest area belongs to unsuitable and somewhat unsuitable land. Therefore, it can be said that the city of Hamedan is currently in a semi-suitable situation in terms of land suitability, which can have a more favorable trend in the future with proper planning and policies.

The morphometric parameters of the catchment are very suitable indicators for the analysis of geomorphological processes. Erosion studies and sediment production are among the most important research carried out by geoscientists, especially geomorphologists, to implement soil and water conservation programs, reduce erosion, change the hydraulic flow of rivers, and prevent the reduction of reservoir dam lake capacity. To measure the geometric (geometric) characteristics of a river, the term morphometry or river shaping is used. In fact, morphometrics is the quantitative analysis of the geomorphic features of landforms in an area. Morphometric analysis is one of the effective methods for prioritizing sub-basins that can indicate the status of the drainage network of the basin. Investigation of morphometric features of the Piveh Gene watershed is based on morphometric and geomorphometric indices. Considering the importance of studying morphometric characteristics in watershed studies and examining the degree of erosion in this study, the aim is to analyze the morphometric features with the type of landform and predict the amount of erosion through landforms.

 In the present study, for morphometric analysis, ArcGIS software, a digital elevation model (DEM) with an accuracy of 20 meters, prepared from 1:50,000 digital topographic maps of the National Mapping Organization and Aster satellite images were used. Has been. To extract the number of waterways, ArcView software, a digital terrestrial model (DEM), has been used. For the slope parameter and the slope direction and height of the study area, we used a topographic map and a digital elevation model of the earth. In order to prepare the drainage density parameter, the existing elevation waterways were extracted from the digital elevation model using the module (Spectral indices) in Archydro and the digital elevation model of the Aster satellite. A threshold of 25-50 cells was selected for drainage network extraction and the drainage network was plotted. In the last step, waterways were classified by astral method and morphometric parameters were extracted. To separate the landforms of the region, a digital model of height with a resolution of 20 meters was used and then the type of landforms were identified based on TPI or topographic position index and according to equation TPIi = Z0 – Σ n-1 Zn/n (Z0 Model point height under evaluation, Zn The height of the grid, n The total number of surrounding points considered in the evaluation) comparing the height of each cell in a digital model TPI, Height is adjacent to the average height of the cells. Finally, the average height decreases from the height value in the center.

 Morphometric parameters studied in this paper include the number of streams (Nu), the rank of streams (U), the length of streams (L), bifurcation coefficient (Rb), roughness coefficient (Bb), drainage density (Dd), frequency of streams  (F), shape factor (Rf), roundness coefficient (Rc) and rectangle coefficient are equivalent (Re). The results showed that according to the number of waterways (184 waterways), the existence of first, second, and third-degree waterways, the length of waterways, the high ratio of waterway lengths to the area of the basin, and the high unevenness coefficient of the erodible area And requires optimal planning and management. Also, landform studies in the study area showed that with the help of morphometric features, they determined the susceptibility of landforms to erosion in the area. So that after preparing the landforms using the topographic position index (TPI) and considering erosion-sensitive areas through morphometric features, erosion-sensitive landforms in the study area were identified. By comparing the landform map and the erosion zoning map of the study area, it was found that Class 2 landforms (U-shaped valley) and Class 4 landforms (high drains) have the highest erosion. The results showed that with increasing the drainage density, the amount of erosion increases.

After mapping the landforms using the topographic position index (TPI) and considering erosion-sensitive areas through morphometric features, erosion-sensitive landforms in the study area were identified. So that the increase in the number of waterways and their length in the watershed indicates an increase in erosion. Then, the topographic position index (TPI), which distinguishes between hollow and bulge, was considered as one of the geomorphometric indicators. The lower and upper limits of the index (TPI) for the study area were calculated as -39.21 and 33.51, respectively. Areas with negative TPI indicate low topography (concavities and pits) while areas with positive TPI indicate high topography (convex or ridges). The presence of dimples and holes (in areas with low TPI) increases the latency of surface currents in the area and causes water infiltration, which in turn can have a significant impact on the storage of precipitation and surface runoff. Have. The results of studies of morphometric parameters indicate that the erodibility conditions of the region are more favorable and the situation is critical. Analysis of classified data showed that the area and length of the canal are effective in erosion. By comparing the landforms map and the waterways map of the study area, it was found that the 4th floor landforms (U-shaped valleys) and the 3rd-floor landforms (high drainages) have the highest erodibility. Also, with an increasing degree of unevenness, the amount of erosion in the area increases, which in landforms located at high altitudes, such as ridges (Class 8 and 10 landforms), the highest amount and, consequently, the highest sensitivity of these landforms are determined. Class 3 locations have the highest drainage density. Due to its natural features, morphometric and physiographic features, the study area is round, which makes the time of short concentration and literal peak larger and more prone to flooding. By examining other morphological components, we came to the conclusion that the study area is prone to erosion.

 Land degradation is one of the destructive phenomena that threaten the stability and security of ecosystems, especially in arid areas. Land degradation can lead to reduced soil fertility and productivity, population migration and displacement, food insecurity, and ecosystem destruction. Despite widespread efforts to combat land degradation, this problem has not only not diminished in recent decades but has gradually intensified. Therefore, monitoring land degradation and revealing its characteristics is essential for land management and recovery, and this monitoring in arid areas facilitates proper management and control of this phenomenon. Monitoring of land degradation in these areas is possible using remote sensing data so that this science will be widely used to monitor land degradation in areas. Considering the importance of land degradation and the need for land monitoring, this study was performed to understandthe degradation situation in Isfahan city properly. Also, this study tries to create appropriate and timely management for the spread of degradation using modeling of environmental indicators obtained from satellite data in the period 2011-2021.

In this study, Landsat satellite imagery, TM, and OLI sensors were used to study the trend of land-use change. In addition, the data from field visits were also used as ancillary information. Satellite images were processed and analyzed in ENVI software environment. The supervised maximum classification method was used to prepare a map of land-use changes. Then, all land uses in the study area were divided into agricultural lands, rangelands, barren and saline lands, and urban and man-made areas. Finally, the obtained layers were transferred to ArcGIS software to calculate the land use area and prepare a suitable output map. After investigating land-use changes, SI soil salinity indices and Albedo climatic index, NDVI, and the LSM vegetation index were designed using the maximum likelihood method. SI soil salinity index is one of the main indicators of land degradation assessment. This index extracted from satellite images can assess soil salinity in arid and semi-arid regions, calculated using Equation SI=√(ρ_Blue×ρ_Red ) (ρBlue and ρRed, are the red and blue bands on the TM and OLI sensors, respectively). The surface albedo index obtained from remote sensing data is a physical parameter that expresses the sun's surface reflection characteristics and short wavelengths. This physical parameter is affected by vegetation, soil moisture, and other surface conditions. Therefore, by studying the changes in Albedo, it is possible to look at the changes in the ground surface and the result of land degradation. Equation AIbedo = 0.356 ρ_Blue + 0.130ρ_Red +0.373ρ_NIR+0.085ρ_SWIR1+0.072ρ_SWIR2-0.018 (The ρ band corresponds to the Landsat TM and OLI sensor images) was used to calculate the surface albedo in TM and OLI sensors in this study. The NDVI index, which is obtained from Landsat satellite images, TM and OLI sensors, was used to study the vegetation in this study. This index is most sensitive to changes in vegetation and is less susceptible to the effects of climate and soil, except in cases where vegetation is low. Another important parameter for land degradation is soil moisture content, which was studied using changes in the LSM index. Finally, the primary component analysis (PCA) method between Albedo, SI, NDVI, and LSM indices was used to estimate land degradation (LD) in 2011, 2016, and 2021. First, the desired indicators were normalized, and then the amount of land degradation for each year was estimated. So that large amounts of land degradation indicate the maximum land degradation.

 The trend of land-use changes in Isfahan city in four uses of agricultural lands, rangelands, barren and saline lands, and urban and man-made areas in the period of 2011-2021 showed that between 2011-2016, agricultural lands and rangelands have decreased by 5.7 and 5.06, respectively. In contrast, barren and saline lands and urban and man-made areas increased by 10.45% and 1.51%, respectively. On the other hand, from 2016 to 2021, agricultural lands and rangelands have decreased by 0.75 and 1.25 percent, respectively, and barren and salty lands, urban and man-made areas have increased by 1.51 and 0.5 percent, respectively. Also, from 2011 to 2021, agricultural lands and rangelands decreased by 6.45 and 6.32 percent, respectively, and land use of barren and salty lands, urban and man-made areas increased by 11.96 and 0.8 percent, respectively. The study of the trend of land use changes showed that in this period of 10 years, the trend of destruction of agricultural lands and rangelands was decreasing, and barren and saline land and urban and man-made areas were increasing. The changes in desertification classes showed that the medium, high, and very high desertification classes have increased. The area of desert lands rose from 3428, 2817, and 1340 in 2011 to 4079, 4276, and 4302 Km2 in 1400, respectively. Low and very low classes have changed from 2826 and 5295 in 2011 to 574 and 2475 Km2 in 2021. These changes indicate an increase in desertification in Isfahan, which is due to land-use changes, especially the conversion of rangelands into agricultural lands and frequent droughts and drying of the Zayanderud River, which abandoned agricultural lands and turned them into barren and salty lands. On the other hand, with the dryness of the air, frequent droughts, and drying of the Zayanderud River, the soil moisture has decreased, which has caused salinization of the soil and increased unusable quality lands of this city. Also downstream of the Zayanderud River is Gavkhoni Wetland, one of the most important wetlands in Iran. Due to the reduction of incoming water, the surrounding beds have become barren and saline lands, which indicates the increasing desertification of this wetland.

 It can be concluded that by using the indicators estimated from remote sensing images, it is possible to monitor the destruction and desertification process with reasonable accuracy and put the necessary measures to deal with this destructive phenomenon on the agenda. In this study, the process of land degradation in Isfahan city was estimated over time, based on which the necessary programs and policies can be applied to deal with this phenomenon.

land degradation and desertification in arid areas are the most important environmental challenges in the world. This process due to the lack of precipitation and the occurrence of drought, while the unreasonable exploitation of natural and agricultural areas with increasing demand to provide human food needs, affects various environmental and socio-economic dimensions. So, the continuation of this condition during recent years with the destruction of vegetation and soil, wind and water erosion, soil salinity, soil compaction, and declining groundwater aquifers have significant consequences for the production of agricultural products and biodiversity in an arid region. Since the pattern and dimensions of vegetation changes are the most important factors in detecting land degradation, monitoring the vegetation changes is the best approach to analyzing land degrading and desertification trends in an arid region. Therefore, according to the capabilities of remote sensing data due to the wide coverage and multi-timed,  the use of satellite imagery to monitor vegetation changes by using vegetation index is one of the best methods that developed in recent years. Moreover, concurrent access to high spatial and temporal resolution imageries is one of the important factors that affect the monitoring of vegetation changes. To achieve this goal, It needs to incorporate different satellites with high spatial (e.g., Landsat satellite) and temporal (e.g., MODIS satellite) images. The purpose of this study is the monitoring vegetation changes using daily Landsat simulated images at 30 m Spatial Resolution in three years of wet, normal, and drought in the Nimroze area.

 The study area is located in the north of the Sistan and Baluchistan provinces. Low precipitation (50 mm), high temperature (48 oC), high evaporation (5 m), and 120-day winds are among the specific climatic conditions that characterize this region. In this study, at first, the hydrological drought status of the Hirmand River was investigated. Using the Hydrostats package in R software, the amount of threshold of flood by running the related codes (by running codes such: daily.cv, ann.cv, high. spell, and low. spell) during the statistical period of study (29 years) was calculated. To determine wet, normal, and drought years calculated the length of periods that flood is higher (high. spell. lengths) and lower (low. spell. lengths) than the threshold. To increase the accuracy of monitoring vegetation changes, it needs to incorporate different satellites with high spatial (e.g., Landsat) and temporal (e.g., MODIS) images. To achieve this purpose, in this study, the Enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was evaluated with actual satellite data (OLI, ETM+, TM image). For this purpose at first, pre-processing (geometric, radiometric, and atmospheric correction) was performed on satellite images, and by using the ESTRFM model, simulated daily Landsat images at 30 m spatial resolution for wet, normal, and drought years. In-field operations from different plant communities by GPS were sampled. Comparing filed data with the Normalized difference vegetation index (NDVI) and the soil-adjusted vegetation index (SAVI), the vegetation index that had the highest correlation with field data was selected. To investigate vegetation changes, using the vegetation index (the vegetation index with high correlation), the map of vegetation for each year was prepared (wet, normal, and drought years). After the classification maps of vegetation, by comparison, approach (cross tab), the map of vegetation changes was extracted.

 The results of analyzing wet and dry periods showed that, flood volume in dry years compare to normal and wet years decreased 31 and 82 percentages, respectively. To incorporation MODIS and Landsat (OLI, ETM+, TM) Images, using enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), finding indicate that this model improves the accuracy of predicted fine-resolution reflectance and preserves spatial details for heterogeneous landscapes too. So that the mean coefficient of determination (R2) of blue, green, red and near-infrared estimation bands with actual satellite images data is 0.91, 0.89, 0.92 and 0.91 respectively. Also the average Root-Mean-Square Error (RMSE) in four bands obtained 0.01, 0.027, 0.028 and 0.031 successively. Comparing the obtained field data with the Normalized difference vegetation index (NDVI) and the soil adjusted vegetation index (SAVI), indicate that SAVI index has the highest correlation (R2= 87) with vegetation of study region. By calculate the regression model (using SAVI and field data) and classify the vegetation maps of wet, normal and drought years, 6 class obtained (class1=0-10%, class2=20-10%, class3=20-30%, class4=40-50%, class5=60-80% and class6=>80%). The results of investigation vegetation changes indicate that during the drought period 70% of study area has less than 10% vegetation (equal to 138176.3 hectares) and during normal and wet years by increasing vegetation, this area decreased by 30 and 48% respectively (equal to 66269.98 and 50559.7 hectares, respectively). According to the results during the study period, the most vegetation changes is relate to conversion of class 1 to class 2 (equivalent to 48.5%). moreover 18 and 27% of vegetation changes relate to class 1 and 2 to class 4 and 5 respectively (equal to 16284.26 and 11471.88 hectares, respectively). Also the finding indicates that the most vegetation changes occurrence in wetland-forest (28%), forest-rangeland (21%) and poor rangeland (19%) land uses respectively. Field study also showed that, the most important plant species that grows in this land-use such as the results of analyzing wet and dry periods showed that flood volume in dry years compare to normal and wet years decreased by 31 and 82 percent, respectively. To incorporation MODIS and Landsat (OLI, ETM+, TM) Images, using enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the finding indicates that this model improves the accuracy of predicted fine-resolution reflectance and preserves spatial details for heterogeneous landscapes too. So that the mean coefficient of determination (R2) of blue, green, red, and near-infrared estimation bands with actual satellite images data is 0.91, 0.89, 0.92, and 0.91 respectively. Also, the average Root-Mean-Square Error (RMSE) in four bands obtained 0.01, 0.027, 0.028, and 0.031 successively. Comparing the obtained field data with the Normalized difference vegetation index (NDVI) and the soil-adjusted vegetation index (SAVI), indicate that the SAVI index has the highest correlation (R2=87) with the vegetation of the study region. By calculating the regression model (using SAVI and field data) and classifying the vegetation maps of wet, normal, and drought years, 6 classes obtained (class1=0-10%, class2=20-10%, class3=20-30%, class 4=40-50%, class5=60-80% and class6=>80%). The results of the investigation of vegetation changes indicate that during the drought period, 70% of the study area has less than 10% vegetation (equal to 138176.3 hectares) and during normal and wet years by increasing vegetation, this area decreased by 30 and 48% respectively (equal to 66269.98 and 50559.7 hectares, respectively). According to the results during the study period, most vegetation changes are related to the conversion of class 1 to class 2 (equivalent to 48.5%). moreover, 18 and 27% of vegetation changes relate to class 1 and 2 to class 4 and 5 respectively (equal to 16284.26 and 11471.88 hectares, respectively). Also, the finding indicates that the most vegetation changes occur in wetland-forest (28%), forest-rangeland (21%), and poor rangeland (19%) land use respectively. The field study also showed that the most important plant species that grow in this land use such as Aeluropus littoralis, Chenopodiace sp, Tamarix aphylla, Haloxylon aphylum are adaptive to climatic regime in study area.

 In this research for the first time in the Nimroz region of Sistan Vegetation changes were studied using Landsat simulated images during periods of low water, normal, and high water years. Due to low rainfall and harsh climate in the study area, floods in the Helmand River are the only source of water supply required in the study area. The results of analyzing wet and dry periods showed that flood volume in dry years compared to normal and wet years has decreased by 31 and 82, respectively. According to the reduction of flood volume during a drought year, 70% of the study area has poor vegetation and during normal and wet years, providing plants with water needs and increasing vegetation, this area had decreased by 30% and 48%, respectively. According to the results of this study, change in hydrological conditions of the Hirmand River has a significant role in vegetation changes in the study area by using simulated images with high spatial and temporal resolution can improve the accuracy of monitoring vegetation changes to control and management the desertification in Sistan area.