University of KashanDesert Ecosystem Engineering2538-633671920220827Site Suitability Analysis of Wind Power plants using Fuzzy Hierarchical Analysis (Case Study: Sistan Plain)Site Suitability Analysis of Wind Power plants using Fuzzy Hierarchical Analysis (Case Study: Sistan Plain)11611264110.22052/deej.2018.7.19.1FASanazTanakianHosseinPiri SahragardMeysamAmiriJournal Article20220827Introduction: Energy as a contributor to human well-being plays an important role in the sustainable development of human societies. The growing demand for energy, higher standards of living, global warming, and decreasing fossil fuel resources have focused the global attention on renewable energies (Kaya and Kahraman, 2010). Owing to the rapid development of wind energy extraction technologies, low cost of this type of energy, and easy installation of wind turbines, this kind of energy is considered to be a viable alternative to current energy systems (Yang et al., 2016; Zaim et al., 2014; Tsoutsos et al., 2016). The present study aimed to identify the factors with more weight and more suitable sites for wind power plants in Sistan by considering climatic criteria (wind speed and direction), geographical criteria (elevation and slope), socioeconomic criteria (distance from residential areas, distance from routes, and proximity to electrical grids) and environmental criteria (distance from protected areas and waterways) through fuzzy hierarchical analysis and GIS.<br /> <br />Materials and Methods: The present study applied available 20-year weather statistics including wind speed, wind direction, temperature, and pressure in the Sistan region (Zabol, Zahak, Hirmand, and Hamoun) and the regions around the Sistan plain (Zahedan, Nehbandan, Birjand, Qaen, Shahdad, and Bam) from 1996 to 2016. Initially, expert opinion was used to extract climate measures (including wind velocity and direction), geographical factors (elevation and slope), socio-economic criteria (distance from residential areas, distance from communication pathways and vicinity to energy transport networks), and environmental scales (distance from protected areas and waterways) as significant and effective factors, which were later compared in pairs. Criteria and subcriteria weights were then obtained using fuzzy hierarchical analysis with Fuzzy AHP SolVer software. Maps needed for locating areas for the construction of wind power plants in the study area were prepared using GIS according to the sub-criteria. In the next step, each of the maps was classified using GIS. Based on the paired comparisons, each class was then scored according to the expert opinion, and the weight of each class was thus obtained using Fuzzy AHP SolVer. Finally, a map of suitable sites for the construction of wind power plants was developed, and the final map was provided in the form of a raster map with a precision of 500 square meters.<br /> <br />Results<br />Determining the weight of criteria: According to the study results, the climatic criterion is of greatest importance among the criteria considered for locating wind power plants, and the environmental criteria was identified as the least important. The most important criteria were found to be wind speed with a weight of 0.57 (among the climate criterion), elevation with a weight of 0.66 (among the geographical criteria), distance from route, and proximity to electrical grids with a weight of 0.42 (among the socioeconomic criteria), and distance from protected areas with a weight of 0.66 (among the environmental criteria).<br />Final map for the location of wind power plants: The resulting map was classified into four classes (excellent, good, moderate, and poor) according to relative average change in each parameter. The results of the present study showed that an area of 5941 km2 of the total area of the study area (16208 km2) (36.6%) includes restricted areas, and the authorized areas for the construction of wind power plants (in the excellent class) are part of the Sistan plain (Nimruz and Hamoon cities) with an area of 7130 km2 (44%). In terms of land suitability, 611 km2 (3.7%) of the area was classified in the moderate class and 462.5 km2 (15.1%) in the good class. In addition, the regions of Bandan and Sefidabeh around the Sistan plain with an area of 62.5 km2 (0.38%) are among the areas less suitable for construction according to the criteria.<br /> <br />Discussion & conclusion: Wind speed is one of the most important climatic criteria considered for the construction of wind power plants. The higher the wind speed, the greater the power generated by the wind turbines. On the other hand, the more a wind blows in one direction, the higher the wind is scored as it more effectively rotates the turbine blades. Consistent with the findings of the present study, wind speed was reported to be the main climatic criterion for the construction of wind power plants (Azizi et al., 2014). among the geographical criteria, elevation was found to be the most important one for the construction of wind power plants in the studied area. The possibility for building facilities and agricultural activities decreases with higher elevation as a limiting factor (Sabokbar et al., 2010). Therefore, areas with higher elevations must be avoided when locating a suitable site as higher elevations increase the investment cost (Bennui, 2007).<br />Considering general results of this study, we can conclude that 44 percent of the regions highly suitable for establishment of wind power plants, contributing to 7130 kilometers in area. Resulting regions with high potential for wind power plant establishment, mainly areas in the central plaint of Sistan (Nimruz and Hamoon cities) were selected from regions consistent with the inclusion criteria of this study. According to the study results, the combined use of fuzzy hierarchical analysis and GIS as a decision-making support system can be an effective strategy to identify more potential areas to create the conditions for the regional sustainable development, reduce the costs, and speed up the implementation of development projects and plans aimed at new energies. The study results emphasized the necessity of more serious and effective efforts for proper management and effective solutions to fully exploit the potential of the Sistan region. It is also imperative that part of the investments is spent annually on sustainable development plans for new energies, in particular wind energy in Sistan. The production of electricity as a sustainable energy can play a significant role in economic, social, and cultural development in the region.Introduction: Energy as a contributor to human well-being plays an important role in the sustainable development of human societies. The growing demand for energy, higher standards of living, global warming, and decreasing fossil fuel resources have focused the global attention on renewable energies (Kaya and Kahraman, 2010). Owing to the rapid development of wind energy extraction technologies, low cost of this type of energy, and easy installation of wind turbines, this kind of energy is considered to be a viable alternative to current energy systems (Yang et al., 2016; Zaim et al., 2014; Tsoutsos et al., 2016). The present study aimed to identify the factors with more weight and more suitable sites for wind power plants in Sistan by considering climatic criteria (wind speed and direction), geographical criteria (elevation and slope), socioeconomic criteria (distance from residential areas, distance from routes, and proximity to electrical grids) and environmental criteria (distance from protected areas and waterways) through fuzzy hierarchical analysis and GIS.<br /> <br />Materials and Methods: The present study applied available 20-year weather statistics including wind speed, wind direction, temperature, and pressure in the Sistan region (Zabol, Zahak, Hirmand, and Hamoun) and the regions around the Sistan plain (Zahedan, Nehbandan, Birjand, Qaen, Shahdad, and Bam) from 1996 to 2016. Initially, expert opinion was used to extract climate measures (including wind velocity and direction), geographical factors (elevation and slope), socio-economic criteria (distance from residential areas, distance from communication pathways and vicinity to energy transport networks), and environmental scales (distance from protected areas and waterways) as significant and effective factors, which were later compared in pairs. Criteria and subcriteria weights were then obtained using fuzzy hierarchical analysis with Fuzzy AHP SolVer software. Maps needed for locating areas for the construction of wind power plants in the study area were prepared using GIS according to the sub-criteria. In the next step, each of the maps was classified using GIS. Based on the paired comparisons, each class was then scored according to the expert opinion, and the weight of each class was thus obtained using Fuzzy AHP SolVer. Finally, a map of suitable sites for the construction of wind power plants was developed, and the final map was provided in the form of a raster map with a precision of 500 square meters.<br /> <br />Results<br />Determining the weight of criteria: According to the study results, the climatic criterion is of greatest importance among the criteria considered for locating wind power plants, and the environmental criteria was identified as the least important. The most important criteria were found to be wind speed with a weight of 0.57 (among the climate criterion), elevation with a weight of 0.66 (among the geographical criteria), distance from route, and proximity to electrical grids with a weight of 0.42 (among the socioeconomic criteria), and distance from protected areas with a weight of 0.66 (among the environmental criteria).<br />Final map for the location of wind power plants: The resulting map was classified into four classes (excellent, good, moderate, and poor) according to relative average change in each parameter. The results of the present study showed that an area of 5941 km2 of the total area of the study area (16208 km2) (36.6%) includes restricted areas, and the authorized areas for the construction of wind power plants (in the excellent class) are part of the Sistan plain (Nimruz and Hamoon cities) with an area of 7130 km2 (44%). In terms of land suitability, 611 km2 (3.7%) of the area was classified in the moderate class and 462.5 km2 (15.1%) in the good class. In addition, the regions of Bandan and Sefidabeh around the Sistan plain with an area of 62.5 km2 (0.38%) are among the areas less suitable for construction according to the criteria.<br /> <br />Discussion & conclusion: Wind speed is one of the most important climatic criteria considered for the construction of wind power plants. The higher the wind speed, the greater the power generated by the wind turbines. On the other hand, the more a wind blows in one direction, the higher the wind is scored as it more effectively rotates the turbine blades. Consistent with the findings of the present study, wind speed was reported to be the main climatic criterion for the construction of wind power plants (Azizi et al., 2014). among the geographical criteria, elevation was found to be the most important one for the construction of wind power plants in the studied area. The possibility for building facilities and agricultural activities decreases with higher elevation as a limiting factor (Sabokbar et al., 2010). Therefore, areas with higher elevations must be avoided when locating a suitable site as higher elevations increase the investment cost (Bennui, 2007).<br />Considering general results of this study, we can conclude that 44 percent of the regions highly suitable for establishment of wind power plants, contributing to 7130 kilometers in area. Resulting regions with high potential for wind power plant establishment, mainly areas in the central plaint of Sistan (Nimruz and Hamoon cities) were selected from regions consistent with the inclusion criteria of this study. According to the study results, the combined use of fuzzy hierarchical analysis and GIS as a decision-making support system can be an effective strategy to identify more potential areas to create the conditions for the regional sustainable development, reduce the costs, and speed up the implementation of development projects and plans aimed at new energies. The study results emphasized the necessity of more serious and effective efforts for proper management and effective solutions to fully exploit the potential of the Sistan region. It is also imperative that part of the investments is spent annually on sustainable development plans for new energies, in particular wind energy in Sistan. The production of electricity as a sustainable energy can play a significant role in economic, social, and cultural development in the region.https://deej.kashanu.ac.ir/article_112641_8ac80ec96538975294f0a848e1c0fc93.pdfUniversity of KashanDesert Ecosystem Engineering2538-633671920220827Edaphically investigation of the Somaq habitat and its relationship with biological diversity (Case study: Bideskan habitat, Ferdows, Southeastern Khorasan, Iran)Edaphically investigation of the Somaq habitat and its relationship with biological diversity (Case study: Bideskan habitat, Ferdows, Southeastern Khorasan, Iran)173211264210.22052/deej.2018.7.19.11FASholehGhollasimodHadiMemarianJournal Article20220827Introduction: The natural ecosystems of Iran are the most important origins of speciation in the world and protecting this diversity is of great importance. Sumac species in Bideskan habitat is considered as one of the most important rangeland sub-products that in addition to regional economic prosperity, it provides sustainable employment for villagers and is considered as an important species for soil conservation. Therefore, by studying the environmental conditions and the needs of the Sumac species, it would be possible to judge about its geographic distribution, density and activity in different environments.<br /> <br />Material and methods: The habitat of Bideskan with an area of 3685 hectares is considered as one of the sub basins of the Lut Desert great basin in Iran. During May 2016, through the field visit, vegetation information and environmental factors were monitored. The area was divided into two parts of the natural Sumac beds and the control area, however with the same geological formation. In each section, 15 plots (total of 30 plots and soil samples) were taken by a random-systematic approach. A size of 10 × 10 m for the sampled plot was considered according to the type of plants and their distribution in the area. Within each plot, information related to the plants, including number and type, were recorded. During sampling, it was determined that the rootage direction of the Rhus coriaria L. followed the longitudinal growth in the soil and moved in the direction of the gradient, as a result, soil samples were taken from a depth of 0-30 cm. In the next step, taken soil samples were dried and in order to prepare for soil tests, they were passed through a 2 mm sieve. Subsequently, the parameters soil texture, saturation moisture content, pH, EC, OM, lime, Na, K, Ca, Mg, cation exchange capacity (CEC), and total nitrogen (N) were determined in laboratory. To determine the diversity and species richness, the number of species was counted in each plot within the two habitats of Sumac and control. Shannon-Weiner Species Diversity Index, Simpson and Fisher Alpha were calculated based on the frequency of plant species using the EstimateS win 9.1. The independent t-test was used in Statistical Package for Social Sciences (SPSS) software to compare the measured variables in two areas of Sumac habitat and control.<br /> <br />Results and discussion: In total 24 species belong to 14 families were identified in area. The most dominted familiy was Asteraceae having 8 species of total number of species, followed by Apiaceae, Geraniaceae, Poaceae with 2 species each. Hemicryptophytes were the most important biological form with 14 species (54.16%), the Trophites with 7 species (33.33%), Geophytes with 2 species (8.33%) and Phanerophytes with 1 species (4.16%) were measured. Asteraceae species had a better adaptation to climatic conditions than other families. This could be due to a better compatibility of this family with climatic harsh conditions, which gives them a high potential of distribution. The presence of 58.33% Hemicryptophyte can be attributed to the dry-cold climate and mountainous topographic conditions. Furthermore, the abundance of Therophyte (33%) revealed low rainfall, recent droughts, unfavorable conditions of the enclosure and grazing and, consequently, the degradation caused by the impact of the pressures from these factors, are among the reasons that affect annual plants.According to the Shannon-Weiner index, the Sumac habitat with the index of 1.942 is more diverse than the control area (with an index of 1.667). The higher the evenness index, the distribution of species within the plot or a range is more uniform; therefore, the Sumac habitat with uniformity equal to 0.717 has more uniform distribution compared to the control area. According to the Shannon-Weiner and Simpson diversity indices, the Sumac habitat is more diverse, although its number of species (15 species) is less than the number of species in the control area (17 species). The student's t-test of independent samples on soil data showed that the soil fctors EC, ECe, Ca, OM, TNV, pH, K and silt content had significant differences in two regions. Moreover, four factors of EC, K, OM and TNV in the Sumac habitat showed a significant increase compared to the control area. The falling of shoot organs on the soil surface could be the main reason for the increase of K and OM under the stratum of the Sumac plant. The increase in the content of litters causes the increase of soil porosity, the decrease of bulk density and thus the soil gets better permeability conditions. The results confirm the significant role of Rhus coriaria in soil conservation planning. Therefore, it is necessary to encourage local farmers to preserve this species.<br />Conclusion: Awareness of the relationship between soil characteristics and the distribution of plant species is vital for the sustainable use of rangelands. Therefore, this study was aimed to determine the effect of soil characteristics in the distribution of vegetation cover, especially Rhus coriaria L. in Bideskan habitat. The sampling results indicated that the most important vegetative form of the region, is Hemicryptophytes, 58.35% of the species population of the area, followed by Throphytes with 33% . The student's t-test of independent samples on soil data showed that the soil properties EC, ECe, Ca, OM, TNV, pH, K and silt content had significant differences in two regions. Moreover, four factors of EC, K, OM and TNV in the Sumac habitat showed a significant increase compared to the control area. According to the results of this research, it can be stated that it is possible to establish Sumac (Rhus coriaria L.) as a species compatible with arid areas (calcareous soils with higher EC) to control erosion and sedimentation and improve revival of green spaces in mountainous regions of arid and semi arid areas, especially in Khorasan province and others areas with similar ecological conditions.<br /> Introduction: The natural ecosystems of Iran are the most important origins of speciation in the world and protecting this diversity is of great importance. Sumac species in Bideskan habitat is considered as one of the most important rangeland sub-products that in addition to regional economic prosperity, it provides sustainable employment for villagers and is considered as an important species for soil conservation. Therefore, by studying the environmental conditions and the needs of the Sumac species, it would be possible to judge about its geographic distribution, density and activity in different environments.<br /> <br />Material and methods: The habitat of Bideskan with an area of 3685 hectares is considered as one of the sub basins of the Lut Desert great basin in Iran. During May 2016, through the field visit, vegetation information and environmental factors were monitored. The area was divided into two parts of the natural Sumac beds and the control area, however with the same geological formation. In each section, 15 plots (total of 30 plots and soil samples) were taken by a random-systematic approach. A size of 10 × 10 m for the sampled plot was considered according to the type of plants and their distribution in the area. Within each plot, information related to the plants, including number and type, were recorded. During sampling, it was determined that the rootage direction of the Rhus coriaria L. followed the longitudinal growth in the soil and moved in the direction of the gradient, as a result, soil samples were taken from a depth of 0-30 cm. In the next step, taken soil samples were dried and in order to prepare for soil tests, they were passed through a 2 mm sieve. Subsequently, the parameters soil texture, saturation moisture content, pH, EC, OM, lime, Na, K, Ca, Mg, cation exchange capacity (CEC), and total nitrogen (N) were determined in laboratory. To determine the diversity and species richness, the number of species was counted in each plot within the two habitats of Sumac and control. Shannon-Weiner Species Diversity Index, Simpson and Fisher Alpha were calculated based on the frequency of plant species using the EstimateS win 9.1. The independent t-test was used in Statistical Package for Social Sciences (SPSS) software to compare the measured variables in two areas of Sumac habitat and control.<br /> <br />Results and discussion: In total 24 species belong to 14 families were identified in area. The most dominted familiy was Asteraceae having 8 species of total number of species, followed by Apiaceae, Geraniaceae, Poaceae with 2 species each. Hemicryptophytes were the most important biological form with 14 species (54.16%), the Trophites with 7 species (33.33%), Geophytes with 2 species (8.33%) and Phanerophytes with 1 species (4.16%) were measured. Asteraceae species had a better adaptation to climatic conditions than other families. This could be due to a better compatibility of this family with climatic harsh conditions, which gives them a high potential of distribution. The presence of 58.33% Hemicryptophyte can be attributed to the dry-cold climate and mountainous topographic conditions. Furthermore, the abundance of Therophyte (33%) revealed low rainfall, recent droughts, unfavorable conditions of the enclosure and grazing and, consequently, the degradation caused by the impact of the pressures from these factors, are among the reasons that affect annual plants.According to the Shannon-Weiner index, the Sumac habitat with the index of 1.942 is more diverse than the control area (with an index of 1.667). The higher the evenness index, the distribution of species within the plot or a range is more uniform; therefore, the Sumac habitat with uniformity equal to 0.717 has more uniform distribution compared to the control area. According to the Shannon-Weiner and Simpson diversity indices, the Sumac habitat is more diverse, although its number of species (15 species) is less than the number of species in the control area (17 species). The student's t-test of independent samples on soil data showed that the soil fctors EC, ECe, Ca, OM, TNV, pH, K and silt content had significant differences in two regions. Moreover, four factors of EC, K, OM and TNV in the Sumac habitat showed a significant increase compared to the control area. The falling of shoot organs on the soil surface could be the main reason for the increase of K and OM under the stratum of the Sumac plant. The increase in the content of litters causes the increase of soil porosity, the decrease of bulk density and thus the soil gets better permeability conditions. The results confirm the significant role of Rhus coriaria in soil conservation planning. Therefore, it is necessary to encourage local farmers to preserve this species.<br />Conclusion: Awareness of the relationship between soil characteristics and the distribution of plant species is vital for the sustainable use of rangelands. Therefore, this study was aimed to determine the effect of soil characteristics in the distribution of vegetation cover, especially Rhus coriaria L. in Bideskan habitat. The sampling results indicated that the most important vegetative form of the region, is Hemicryptophytes, 58.35% of the species population of the area, followed by Throphytes with 33% . The student's t-test of independent samples on soil data showed that the soil properties EC, ECe, Ca, OM, TNV, pH, K and silt content had significant differences in two regions. Moreover, four factors of EC, K, OM and TNV in the Sumac habitat showed a significant increase compared to the control area. According to the results of this research, it can be stated that it is possible to establish Sumac (Rhus coriaria L.) as a species compatible with arid areas (calcareous soils with higher EC) to control erosion and sedimentation and improve revival of green spaces in mountainous regions of arid and semi arid areas, especially in Khorasan province and others areas with similar ecological conditions.<br /> https://deej.kashanu.ac.ir/article_112642_118002cf4e2a7b9bba83ecf6c619fefc.pdfUniversity of KashanDesert Ecosystem Engineering2538-633671920220827Application of Landsat satellite images and artificial neural network algorithm in study of land use changes in Ilam dam basinApplication of Landsat satellite images and artificial neural network algorithm in study of land use changes in Ilam dam basin334611264310.22052/deej.2018.7.19.25FAAdelSoltani0000-0002-4452-3669MiladSoltani0000-0002-7458-4796Journal Article20220827Introduction: Population growth has increased the pressure on natural environment, and unsustainable exploitation and the land use changes have damaged ecosystems. Consequently the need for food and water has led humans to devote more land to cultivate and use it under his control. Indeed, remote sensing satellites are the most common source of data for identifying, quantifying and mapping for patterns of land use change. Therefore, detecting land use changes using remote sensing data in the GIS environment can provide a good understanding of how land use changes are made, and present appropriate solutions in management. In the meantime, Landsat satellite imagery has the potential to detect land-use changes, land cover and modeling due to the proper location resolution and long-term archiving of images. There are several methods for discovering variations that in the meantime, the past classification comparison method is highly common. This method was first used by Gordon (1980).<br />Materials and methods: So far, many studies have been conducted on land use monitoring using different data, methods and algorithms. The aim of this study is discover the land use change trend during the two periods before and after the dam construction. So, in this study, in order to identify the dam construction effects in changing the dam basin, Ilam dam which is one of the largest dams in Ilam province, has been investigated. First by using Landsat satellite imagery and Artificial Neural Network classification algorithm, the land use map of the basin was prepared in the years of 1989, 2000, and 2017. Satellite data can play an effective role in providing land cover mapping, because of its specific features including wide coverage, repeatability, multi-spectrum, diversity and land cover, and continuous upgrading. Landsat satellite imagery has the high potential to identify land cover, land-use changes and modeling due to the strong archive and high temporal resolution. In this study, for providing land use change for different years have used of TM sensor images on 14/5/1989 and 28/5/2000, as well as OLI sensor on the 11/5/2017. In order to discover land use changes due to the construction of the dam, using the past classification comparison method, land use changes were determined during two periods. 7 species land use including lake, forest, pasture, dry land farming, garden, residential and barren lands were used for classification by surveying the study area. Gathering data about the changes required the use of techniques and tools that can scan large areas at a cost effective and short-term. Change detection is one of the major applications of remote sensing. Accordingly, various digital methods have been developed for change detecting in land covers. The main factors for the successful implementation of change detecting are selecting the appropriate dates for image acquisition and the use of accurate detection methods. In the changes detection to eliminate the effects of external sources such as the angle of the sun and the seasonal and geological differences, the spectral reflection of the data should be similar. The data used should be at a similar time interval (in terms of season and month), and on the other hand, be in the appropriate seasons. Because of the difference in weather conditions in two different times, the difference between the calibrations of the sensors, the humidity and exposure conditions can affect the numbers and the digital image of two different times. For this purpose, the images used in this study were selected in late of May and early of June. As Mather (2005) states, atmospheric corrections in remote sensing surveys is necessary; especially in cases where the goal is to determine variations in different periods. Because the effects of the atmosphere reduce the contrast between objects and reduce the contrast of the image, and it actually causes the problem of extraction of information. For this reason COST method was used to reduce atmospheric effects.<br /> Result: The results of this study showed that the forests have decreased by about 10354.79 ha during a 28-year period; In other words, during this period, about 49% of the forest area in the studied area was lost, which represents an annual degradation of about 369.82 hectares, equivalent to a degradation rate of about 1.75%.<br />Discussion and Conclusion: The results showed that the accuracy of land use maps of different years is more than 85%, which indicates the reliability of these maps. Also, according to the results during the two mentioned periods, as well as the general period of 28 years, the area of forest land and barren lands has decreased and the level of lake use, rangeland, residential, dry farming and garden has increased. The results of this study are consistent with Rahmani et al. (2013), Arokhi and Niazi (2013) and Saghafian et al. (2007). According to the FAO statistics for the years 1990 to 2000, the annual degradation level of forest land has been estimated at 0.2% per annum worldwide. The most important reason for decreasing the area of forests in the studied area is the development of the phenomenon of oak decline. This phenomenon, which has strongly affected the forests of the west of the country during the last decade, is considered one of the most important environmental problems in the country and has destroyed thousands of hectares of western forests in the country. One of the provinces that is heavily exposed is Ilam province (Karami et al., 2018).Introduction: Population growth has increased the pressure on natural environment, and unsustainable exploitation and the land use changes have damaged ecosystems. Consequently the need for food and water has led humans to devote more land to cultivate and use it under his control. Indeed, remote sensing satellites are the most common source of data for identifying, quantifying and mapping for patterns of land use change. Therefore, detecting land use changes using remote sensing data in the GIS environment can provide a good understanding of how land use changes are made, and present appropriate solutions in management. In the meantime, Landsat satellite imagery has the potential to detect land-use changes, land cover and modeling due to the proper location resolution and long-term archiving of images. There are several methods for discovering variations that in the meantime, the past classification comparison method is highly common. This method was first used by Gordon (1980).<br />Materials and methods: So far, many studies have been conducted on land use monitoring using different data, methods and algorithms. The aim of this study is discover the land use change trend during the two periods before and after the dam construction. So, in this study, in order to identify the dam construction effects in changing the dam basin, Ilam dam which is one of the largest dams in Ilam province, has been investigated. First by using Landsat satellite imagery and Artificial Neural Network classification algorithm, the land use map of the basin was prepared in the years of 1989, 2000, and 2017. Satellite data can play an effective role in providing land cover mapping, because of its specific features including wide coverage, repeatability, multi-spectrum, diversity and land cover, and continuous upgrading. Landsat satellite imagery has the high potential to identify land cover, land-use changes and modeling due to the strong archive and high temporal resolution. In this study, for providing land use change for different years have used of TM sensor images on 14/5/1989 and 28/5/2000, as well as OLI sensor on the 11/5/2017. In order to discover land use changes due to the construction of the dam, using the past classification comparison method, land use changes were determined during two periods. 7 species land use including lake, forest, pasture, dry land farming, garden, residential and barren lands were used for classification by surveying the study area. Gathering data about the changes required the use of techniques and tools that can scan large areas at a cost effective and short-term. Change detection is one of the major applications of remote sensing. Accordingly, various digital methods have been developed for change detecting in land covers. The main factors for the successful implementation of change detecting are selecting the appropriate dates for image acquisition and the use of accurate detection methods. In the changes detection to eliminate the effects of external sources such as the angle of the sun and the seasonal and geological differences, the spectral reflection of the data should be similar. The data used should be at a similar time interval (in terms of season and month), and on the other hand, be in the appropriate seasons. Because of the difference in weather conditions in two different times, the difference between the calibrations of the sensors, the humidity and exposure conditions can affect the numbers and the digital image of two different times. For this purpose, the images used in this study were selected in late of May and early of June. As Mather (2005) states, atmospheric corrections in remote sensing surveys is necessary; especially in cases where the goal is to determine variations in different periods. Because the effects of the atmosphere reduce the contrast between objects and reduce the contrast of the image, and it actually causes the problem of extraction of information. For this reason COST method was used to reduce atmospheric effects.<br /> Result: The results of this study showed that the forests have decreased by about 10354.79 ha during a 28-year period; In other words, during this period, about 49% of the forest area in the studied area was lost, which represents an annual degradation of about 369.82 hectares, equivalent to a degradation rate of about 1.75%.<br />Discussion and Conclusion: The results showed that the accuracy of land use maps of different years is more than 85%, which indicates the reliability of these maps. Also, according to the results during the two mentioned periods, as well as the general period of 28 years, the area of forest land and barren lands has decreased and the level of lake use, rangeland, residential, dry farming and garden has increased. The results of this study are consistent with Rahmani et al. (2013), Arokhi and Niazi (2013) and Saghafian et al. (2007). According to the FAO statistics for the years 1990 to 2000, the annual degradation level of forest land has been estimated at 0.2% per annum worldwide. The most important reason for decreasing the area of forests in the studied area is the development of the phenomenon of oak decline. This phenomenon, which has strongly affected the forests of the west of the country during the last decade, is considered one of the most important environmental problems in the country and has destroyed thousands of hectares of western forests in the country. One of the provinces that is heavily exposed is Ilam province (Karami et al., 2018).https://deej.kashanu.ac.ir/article_112643_b6a7e5ed1aefd7143b4e39167ea9ec99.pdfUniversity of KashanDesert Ecosystem Engineering2538-633671920220827The Effect of Low-level Clouds Type on Runoff Simulation Accuracy Using SWAT modelThe Effect of Low-level Clouds Type on Runoff Simulation Accuracy Using SWAT model476211264410.22052/deej.2018.7.19.35FAHamidNouriAlirezaIldoromiSabahSalehiJournal Article20220827Introduction: Patterns of spatial and temporal rainfall impact on runoff and outlet hydrograph (Cordery, 1993; James, 1994). Results of different studies have clarified that simulation by using diverse rainfall data could increase the reliance of results. These were much more sensible in which areas encounter with data scarcity (Mello et al., 2008; Bekiaris et al., 2008). Rainfall properties in sensitive analyses are influenced by atmospheric synoptic condition, topography, local condition, and type, speed, and direction of clouds in the sky of basin. Not many studies have done regarding evaluation of clouds and their roles in hydrological model accuracy. The evaluation of cloud type effects directly and indirectly, spatial and temporal precipitation pattern changes, temperature’s features, moisture, radiation, and snow ablation influence on runoff patterns. This is an important step towards hydrological model accuracy recognition. In this study, different precipitations due to low-level cloud types effects on accuracy of water and soil model (SWAT) are evaluated in Garin dam catchment of Nahavand, Hamedan province.<br />Materials and methods: Necessary data are including topography map, land use map, soil map, daily discharge, precipitation amounts, relative moisture, wind speed, solar radiation, and synoptic code of low-level clouds (from 2000 to 2010). SWAT model for seven types of clouds was performed and the results were analyzed. Surface runoff volume and maximum runoff were estimated using daily precipitation and SCS curve number. Runoff proportion event during interval time of basin based on whole daily precipitation was calculated using statistical method. Time interval of basin was estimated by Manning formula which includes channel and hillside. Sensitive analyses were done by using SWAT_CUP software and SUFI_2 algorithms. In this study, sensitive analyses in two steps were done. The first step was carried out before model calibration and when it was necessity for recognition of features parameters and their effects on water production. The second step was done after model calibration for sensitive recognition of each parameter on simulation accuracy. Based on that, at the first step the sensitive analyses of input parameters on surface runoff were done. To determine the amount of input parameters, the range of them was defined and four different numeral distances were chosen and simulation was done by using them. Relative sensitivity index (Sr) shows the outlet function changes proportion for input parameters changes (Feyereisen et al. 2007) Sensitive analyses of 22 parameters and their effects in four numeral distances were evaluated and the model was performed manually more than 100 times. Calibration and validation was done from 2002 to 2007 and 2008 to 2010, respectively. Important parameters for calibration are including curve number, channel discharge, coefficient of surface runoff lag, ground water lag coefficient, soil surface evaporation compensation factor, and Manning coefficient of flooded plain. Nash – Sutcliffe Index, R index, P factor, and R factor were used for calibration and validation. After calibration and validation of model, type of low-level clouds from synoptic codes (Cumulus, Stratocumulus, Cumulonimbus, Stratus, and …) during precipitation event in each rainy day were lined next to simulated and observed data and RMSE index was estimated for each group.<br />Results: Results of model calibration indicated that Nash – Sutcliffe Index, R2 index, P factor, and R factor were 0/80, 0/81, 0/67, and 0/83, respectively. These factors were 0/74, 0/89, 0/53, and 0/52 in model validation period, individually. RMSE index of observed and simulated discharge data was estimated for different clouds. Findings showed that, at the presence of Stratocumulus, RMSE is the least (0/24) which means the highest accuracy of SWAT was seen when this cloud led to precipitation in the area. Accuracy simulation of rain-producing Stratus or Fractostratus clouds was acceptable with 0/38 RMSE. The weakest simulation of rainfall- runoff in SWAT model was seen in rain-producing Cumulonimbus clouds when they have a Serious shaped top. RMSE index of these precipitations in different clouds are nearly triple (0/74) this index in Stratocumulus (0/24).<br />Discussion and Conclusion: Garin dam has been built as one of the important structures to control flow for agriculture land irrigation and drinking water in Nahavand county. Hydrological condition analyses of the basin by choosing the best rainfall-runoff simulation model and accurately performance of this model brings about awareness of managers, farmers, experts, and residents of region. In this study, empirical- semi-distributed model (SWAT) was used in ArcGIS for runoff simulation in Garin dam catchment. According to cloud type effect on energy transformation and rainfall condition change, after model calibration and validation, RMSE and NS was estimated for different precipitation due to of clouds types. The results showed that the precipitation generated of convective clouds had higher RMSE error (about twice or three times) rather than non-convective clouds for rainfall-runoff simulation by this model. It is due to the intensity and time difference of convective rainfall than non-convective. Also, convective clouds were formed by local and vertical development but non-convective cloud were generated by wide and horizontal development. These clouds conditions probably cause accuracy of the precipitation recorded by meteorology stations were increased in the study area. Therefore, it is suggested that type of recorded or predicted clouds during rainfall events, their effects on rainfall and temperature feature, and their accuracy on runoff simulation should be considered for rainfall-runoff simulation of this basin Evaluation of cloud features including their cover and thickness, their effects on input parameters, and accuracy of rainfall-runoff simulation in different basins and climates are recommended. The results confirmed the other researchers’ findings about sensitive analyses (Akhavan et al., 2008; Amiri, 2005; Najafi, 2001; Refsgaard, 2012; Steenbergen and Willems, 2012). The results also are consistent with investigation of low-level clouds effects (considering the cloud cover and convection) on hydrological models (Nouri et al. 2012).<br /> Introduction: Patterns of spatial and temporal rainfall impact on runoff and outlet hydrograph (Cordery, 1993; James, 1994). Results of different studies have clarified that simulation by using diverse rainfall data could increase the reliance of results. These were much more sensible in which areas encounter with data scarcity (Mello et al., 2008; Bekiaris et al., 2008). Rainfall properties in sensitive analyses are influenced by atmospheric synoptic condition, topography, local condition, and type, speed, and direction of clouds in the sky of basin. Not many studies have done regarding evaluation of clouds and their roles in hydrological model accuracy. The evaluation of cloud type effects directly and indirectly, spatial and temporal precipitation pattern changes, temperature’s features, moisture, radiation, and snow ablation influence on runoff patterns. This is an important step towards hydrological model accuracy recognition. In this study, different precipitations due to low-level cloud types effects on accuracy of water and soil model (SWAT) are evaluated in Garin dam catchment of Nahavand, Hamedan province.<br />Materials and methods: Necessary data are including topography map, land use map, soil map, daily discharge, precipitation amounts, relative moisture, wind speed, solar radiation, and synoptic code of low-level clouds (from 2000 to 2010). SWAT model for seven types of clouds was performed and the results were analyzed. Surface runoff volume and maximum runoff were estimated using daily precipitation and SCS curve number. Runoff proportion event during interval time of basin based on whole daily precipitation was calculated using statistical method. Time interval of basin was estimated by Manning formula which includes channel and hillside. Sensitive analyses were done by using SWAT_CUP software and SUFI_2 algorithms. In this study, sensitive analyses in two steps were done. The first step was carried out before model calibration and when it was necessity for recognition of features parameters and their effects on water production. The second step was done after model calibration for sensitive recognition of each parameter on simulation accuracy. Based on that, at the first step the sensitive analyses of input parameters on surface runoff were done. To determine the amount of input parameters, the range of them was defined and four different numeral distances were chosen and simulation was done by using them. Relative sensitivity index (Sr) shows the outlet function changes proportion for input parameters changes (Feyereisen et al. 2007) Sensitive analyses of 22 parameters and their effects in four numeral distances were evaluated and the model was performed manually more than 100 times. Calibration and validation was done from 2002 to 2007 and 2008 to 2010, respectively. Important parameters for calibration are including curve number, channel discharge, coefficient of surface runoff lag, ground water lag coefficient, soil surface evaporation compensation factor, and Manning coefficient of flooded plain. Nash – Sutcliffe Index, R index, P factor, and R factor were used for calibration and validation. After calibration and validation of model, type of low-level clouds from synoptic codes (Cumulus, Stratocumulus, Cumulonimbus, Stratus, and …) during precipitation event in each rainy day were lined next to simulated and observed data and RMSE index was estimated for each group.<br />Results: Results of model calibration indicated that Nash – Sutcliffe Index, R2 index, P factor, and R factor were 0/80, 0/81, 0/67, and 0/83, respectively. These factors were 0/74, 0/89, 0/53, and 0/52 in model validation period, individually. RMSE index of observed and simulated discharge data was estimated for different clouds. Findings showed that, at the presence of Stratocumulus, RMSE is the least (0/24) which means the highest accuracy of SWAT was seen when this cloud led to precipitation in the area. Accuracy simulation of rain-producing Stratus or Fractostratus clouds was acceptable with 0/38 RMSE. The weakest simulation of rainfall- runoff in SWAT model was seen in rain-producing Cumulonimbus clouds when they have a Serious shaped top. RMSE index of these precipitations in different clouds are nearly triple (0/74) this index in Stratocumulus (0/24).<br />Discussion and Conclusion: Garin dam has been built as one of the important structures to control flow for agriculture land irrigation and drinking water in Nahavand county. Hydrological condition analyses of the basin by choosing the best rainfall-runoff simulation model and accurately performance of this model brings about awareness of managers, farmers, experts, and residents of region. In this study, empirical- semi-distributed model (SWAT) was used in ArcGIS for runoff simulation in Garin dam catchment. According to cloud type effect on energy transformation and rainfall condition change, after model calibration and validation, RMSE and NS was estimated for different precipitation due to of clouds types. The results showed that the precipitation generated of convective clouds had higher RMSE error (about twice or three times) rather than non-convective clouds for rainfall-runoff simulation by this model. It is due to the intensity and time difference of convective rainfall than non-convective. Also, convective clouds were formed by local and vertical development but non-convective cloud were generated by wide and horizontal development. These clouds conditions probably cause accuracy of the precipitation recorded by meteorology stations were increased in the study area. Therefore, it is suggested that type of recorded or predicted clouds during rainfall events, their effects on rainfall and temperature feature, and their accuracy on runoff simulation should be considered for rainfall-runoff simulation of this basin Evaluation of cloud features including their cover and thickness, their effects on input parameters, and accuracy of rainfall-runoff simulation in different basins and climates are recommended. The results confirmed the other researchers’ findings about sensitive analyses (Akhavan et al., 2008; Amiri, 2005; Najafi, 2001; Refsgaard, 2012; Steenbergen and Willems, 2012). The results also are consistent with investigation of low-level clouds effects (considering the cloud cover and convection) on hydrological models (Nouri et al. 2012).<br /> https://deej.kashanu.ac.ir/article_112644_e25a413c82e56b2721872b4595eca82c.pdfUniversity of KashanDesert Ecosystem Engineering2538-633671920220827Aran va Bidgol assessment of desertification area based on the criteria of vegetationAran va Bidgol assessment of desertification area based on the criteria of vegetation638011264510.22052/deej.2018.7.19.45FAAbasaliValiSayed HojjatMousaviMohsenZarepourJournal Article20220827Introduction: Currently, desertification is a catch-up of many countries in the world, including developing countries. This problem is seen not only in dry and semi-arid areas, but also in parts of the semi-humid areas. Desertification involves processes that are both natural causes and human inferiority. Land degradation is referred to as desertification due to one or a combination of processes, such as wind erosion, water erosion, degradation of vegetation, degradation of water resources and soil salinity which exacerbates their environmental or human factors. In this regard, human factors play a key role in the emergence of the phenomenon of desertification, because they act as stimulants in addition to their direct role in harming the environment and provide stimulation and enhancement of environmental factors. The phenomenon of desertification has high severity in areas with high desertification potential. Therefore, coping with this phenomenon, especially in the mentioned areas, is very useful in protecting the ecosystem. In this regard, it is possible to reduce the severity of this phenomenon by preventing its development and advancement by providing appropriate managerial approaches and methods.<br />Effectiveness of implementation of desertification plans on the area under the project management can be investigated from five dimensions or aspects of water quality, water erosion, wind erosion, salinisation of water and soil resources. Because it is based on the foundation of validated and documented researches carried out in this regard. The type of effects that desertification has on the environment, or in other words, the kind of feedback that is expected from the implementation of a natural resource plan in the desert area and desertification control, is necessarily available in one of the five dimensions.<br />Sustainability measures in the territories affected by desertification plans include vegetation cover, erosion, wind erosion, water drainage and water resources. In this regard, plants play an important role in terms of the ecological structure of each area, soil conservation, moisture storage and increased permeability of descendants. The structure and composition of each plant community are largely controlled and influenced by environmental factors. In fact, these factors cause the establishment of different plant species in different habitats or prevent vegetation from settling in a place. In this research, an effective evaluation of the combat to desertification plans implemented in Aran and Bidgol City has been done based on vegetation criteria. Therefore, the objectives of this research are to identify combat to desertification plans implemented in Aran and Bidgol region based on the identification of plans, evaluation of combat to desertification plans based on the quantitative and qualitative index of vegetation, and ultimately determining the degree of stability of the ecosystems and the fate of the planting hands.<br />Materials and Methods: Aran and Bidgol City with an area of 6051 km2 located in northern Isfahan Province. The city is bounded to the north by the Salt Lake and the provinces of Semnan and Qom, from the west to Kashan, from the south to Natanz and from the east to Ardestan. The total area of the city covers about 1900 km2 of the desert and sand dunes. In order to carry out this research, firstly, the plans for desert restoration in Aran and Bidgol were identified based on their identity. For this purpose, descriptive identification and desertification studies were received by referring to the Natural Resources Department of Isfahan Province, which included the combat to desertification plans implemented in Ab Shirin areas, Nasr Abad, Siazgeh, Aran and Bidgol Road, Abu Zaid Abad, Fakhre and Rijen. Then, with reference to executive areas, four projects such as Ab Shirin, Aran Road, Fakhre and Rijen were used to harvest a series of vegetation parameters such as plant height, density, canopy percentage and length of collision, to evaluate the combat to Desertification plans and desert restoration projects. The field sampling method is a linear transect that has similar results to the Quadrat method. The length of the trench was selected according to the type of vegetation in the area between 10 and 100 m. Field observations also include canopy components, plant height and crop density. In order to measure the canopy cover percentage in the large diameter range, the species encountered with the transect were determined and measured using tape measurements. Indicators used to evaluate the combat to desertification plans include canopy cover, species diversity and rangeland quality. In this regard, the percentage of canopy is defined according to four classes and, depending on the percentage of canopy in each region, the necessary scoring is required. The number of plant species per unit area is also scaled according to the species in the design. Also, the quality of rangeland based on the current prevalence of perennial plants in terms of rangeland value and prevention of wind erosion in vegetation composition is compared with the pre-implementation conditions. Sustainability of the combat to desertification plans is also based on the final score of the ecosystem from the perspective of various indicators. One of the main criteria for determining the effectiveness of desalination plans is vegetation index, which itself is influenced by various indicators and according to the scores that can be taken, the severity of the desertification and the rate of success of the implemented projects can be measured. According to the information obtained in the field surveys in different designs, as well as the scores given on canopy indexes, variety and quality of the rangelands, it is possible to evaluate effectively the implementation of the combat to desertification projects in four study areas.<br />Findings and Results: The Ab Shirin area with a mean height of 176.88 cm is the highest plant species and the Aran Road with an average height of 143.4 cm is the shortest plant species. The maximum range of altitude variations is related to the Ab Shirin area, and the other three regions show small and close variations. The Rijen area with the average density of 5626 species per hectare is the densest area of the project, and the Aran Road with an average density of 828.8 species per hectare has the lowest density in the project areas. Also, the maximum range of density changes is related to Ab Shirin area. The results of the correlation between the measured density and NDVI with a coefficient of determination of 0.779 and a sig. of 0.048 at the level of 95% are significant. In the Ab Shirin area, due to the implementation of the combat to desertification plans, the area of incremental changes is equal to 4775.14 hectares. This increase in the density of cover is observed in some areas of the region, in which their designs have been implemented. In addition, the area of the fallen floor in this region is equal to 8591.86 hectares due to the recent drought in the region, as well as the land use change and smuggling of wood for the coal to produce coal in this area, and the area of change of the class without change in this region is 32094.6 hectares. In the Aran Road, the largest area with 310.79 thousand hectares is related to the class without change. The area of the additive class in this area is equal to 9.85 hectares due to the natural regeneration of the arable in some places. Also, the level of change in the decay class is equal to 40.91 hectares, due to the continuation of recent drought in the region, road construction and degradation of vegetation covers.<br />In Fakhre area, due to the implementation of the combat to desertification plans in the sequestration area, which is a suitable place for cultivating the species of halophytes, and the root of halophytes can easily use the water resources in the depths of the sand dunes, the highest amount of natural regeneration of gazelles and the largest area of density change is related to the incremental class with an area of 315.83 hectares and the area of the floor without change in this region is 140.27 hectares. Also, due to the recent drought in the whole region and the destruction of wood of woody trees, for the production of charcoal, the decreasing floor density changes are equal to 44.26 hectares. In the Rijen area, the largest area is unchanged, with an area of 555.17 hectares. Due to the implementation of the combat to desertification plans in the sequestration zone that caused the natural regeneration of ghosts, the area of change in the incremental class is equal to 434.26 hectares. The level of condensation changes in this area is 13.77 hectares, due to its close proximity to the Rijen countryside, the destruction of foodstuffs by local livestock is the same as camel and goat, in addition to the trafficking of wood by the profitable people to produce coal, the trees are dying. The results of the sustainability assessment show that the existing designs in the Fakhre and Rijen areas have excellent sustainability scores and existing plans in the Aran Road have a good sustainability score and also existing schemes in the Ab Shirin region have a moderate sustainability score.<br />Discussion and Conclusion: Field surveys from different parts of the combat to desertification plans show that in the Rijen region and the Fakhre, natural regeneration is a species of fungus, although natural regeneration is not observed in many of the floodplain projects in the freshwater region. The results of the field study indicate that from the direction of the Ab Shirin area towards the Abu Zaid Abad area, the vegetation density is increased, because the region of Rijen and Fakhre due to the seismic region of the conditions are better than the areas of transportation and harvesting in the successful implementation of halftone projects has been. Halibut plans have more reproductive power in the sequestering areas, in other words, the roots of these species can more easily penetrate the soil and use the water resources of the sand dunes. In sum, it is possible to say that the best territory for implementation of the combat to desertification projects through the implementation of hunting projects for the development of environment friendly plant species, is the range of sand dunes.<br />According to the studies carried out in the combat to desertification plans implemented in the study areas, it can be stated that although there may be a species of feces in the area of carriage and harvest, but is special in seismic areas, because in the area sequestration of sand dunes such as intakes and has a high permeability are good, as a result of evaporation at their surface is negligible and provides a suitable bed for the establishment and stability of the species of fungus, and also makes it more regenerative. Halibut plans in the Ab Shirin region due to the planting of halophilia in the transport areas, which were mainly soil salinity, and also the root of the plants had no proper penetration in the depth of the earth, lacked the necessary regeneration in many areas that in the long run the stability of Haloxylon planes endangers the work. However, in Rijeg and Fakhre regions due to de-drainage projects and hunting in places of sequestration, the herbaceous species have a greater regeneration than Ab Shirin schemes and increased the density of the coatings in these areas. The process in long-term can have more sustained effects on the combat to desertification plans in the regions. <br /> Introduction: Currently, desertification is a catch-up of many countries in the world, including developing countries. This problem is seen not only in dry and semi-arid areas, but also in parts of the semi-humid areas. Desertification involves processes that are both natural causes and human inferiority. Land degradation is referred to as desertification due to one or a combination of processes, such as wind erosion, water erosion, degradation of vegetation, degradation of water resources and soil salinity which exacerbates their environmental or human factors. In this regard, human factors play a key role in the emergence of the phenomenon of desertification, because they act as stimulants in addition to their direct role in harming the environment and provide stimulation and enhancement of environmental factors. The phenomenon of desertification has high severity in areas with high desertification potential. Therefore, coping with this phenomenon, especially in the mentioned areas, is very useful in protecting the ecosystem. In this regard, it is possible to reduce the severity of this phenomenon by preventing its development and advancement by providing appropriate managerial approaches and methods.<br />Effectiveness of implementation of desertification plans on the area under the project management can be investigated from five dimensions or aspects of water quality, water erosion, wind erosion, salinisation of water and soil resources. Because it is based on the foundation of validated and documented researches carried out in this regard. The type of effects that desertification has on the environment, or in other words, the kind of feedback that is expected from the implementation of a natural resource plan in the desert area and desertification control, is necessarily available in one of the five dimensions.<br />Sustainability measures in the territories affected by desertification plans include vegetation cover, erosion, wind erosion, water drainage and water resources. In this regard, plants play an important role in terms of the ecological structure of each area, soil conservation, moisture storage and increased permeability of descendants. The structure and composition of each plant community are largely controlled and influenced by environmental factors. In fact, these factors cause the establishment of different plant species in different habitats or prevent vegetation from settling in a place. In this research, an effective evaluation of the combat to desertification plans implemented in Aran and Bidgol City has been done based on vegetation criteria. Therefore, the objectives of this research are to identify combat to desertification plans implemented in Aran and Bidgol region based on the identification of plans, evaluation of combat to desertification plans based on the quantitative and qualitative index of vegetation, and ultimately determining the degree of stability of the ecosystems and the fate of the planting hands.<br />Materials and Methods: Aran and Bidgol City with an area of 6051 km2 located in northern Isfahan Province. The city is bounded to the north by the Salt Lake and the provinces of Semnan and Qom, from the west to Kashan, from the south to Natanz and from the east to Ardestan. The total area of the city covers about 1900 km2 of the desert and sand dunes. In order to carry out this research, firstly, the plans for desert restoration in Aran and Bidgol were identified based on their identity. For this purpose, descriptive identification and desertification studies were received by referring to the Natural Resources Department of Isfahan Province, which included the combat to desertification plans implemented in Ab Shirin areas, Nasr Abad, Siazgeh, Aran and Bidgol Road, Abu Zaid Abad, Fakhre and Rijen. Then, with reference to executive areas, four projects such as Ab Shirin, Aran Road, Fakhre and Rijen were used to harvest a series of vegetation parameters such as plant height, density, canopy percentage and length of collision, to evaluate the combat to Desertification plans and desert restoration projects. The field sampling method is a linear transect that has similar results to the Quadrat method. The length of the trench was selected according to the type of vegetation in the area between 10 and 100 m. Field observations also include canopy components, plant height and crop density. In order to measure the canopy cover percentage in the large diameter range, the species encountered with the transect were determined and measured using tape measurements. Indicators used to evaluate the combat to desertification plans include canopy cover, species diversity and rangeland quality. In this regard, the percentage of canopy is defined according to four classes and, depending on the percentage of canopy in each region, the necessary scoring is required. The number of plant species per unit area is also scaled according to the species in the design. Also, the quality of rangeland based on the current prevalence of perennial plants in terms of rangeland value and prevention of wind erosion in vegetation composition is compared with the pre-implementation conditions. Sustainability of the combat to desertification plans is also based on the final score of the ecosystem from the perspective of various indicators. One of the main criteria for determining the effectiveness of desalination plans is vegetation index, which itself is influenced by various indicators and according to the scores that can be taken, the severity of the desertification and the rate of success of the implemented projects can be measured. According to the information obtained in the field surveys in different designs, as well as the scores given on canopy indexes, variety and quality of the rangelands, it is possible to evaluate effectively the implementation of the combat to desertification projects in four study areas.<br />Findings and Results: The Ab Shirin area with a mean height of 176.88 cm is the highest plant species and the Aran Road with an average height of 143.4 cm is the shortest plant species. The maximum range of altitude variations is related to the Ab Shirin area, and the other three regions show small and close variations. The Rijen area with the average density of 5626 species per hectare is the densest area of the project, and the Aran Road with an average density of 828.8 species per hectare has the lowest density in the project areas. Also, the maximum range of density changes is related to Ab Shirin area. The results of the correlation between the measured density and NDVI with a coefficient of determination of 0.779 and a sig. of 0.048 at the level of 95% are significant. In the Ab Shirin area, due to the implementation of the combat to desertification plans, the area of incremental changes is equal to 4775.14 hectares. This increase in the density of cover is observed in some areas of the region, in which their designs have been implemented. In addition, the area of the fallen floor in this region is equal to 8591.86 hectares due to the recent drought in the region, as well as the land use change and smuggling of wood for the coal to produce coal in this area, and the area of change of the class without change in this region is 32094.6 hectares. In the Aran Road, the largest area with 310.79 thousand hectares is related to the class without change. The area of the additive class in this area is equal to 9.85 hectares due to the natural regeneration of the arable in some places. Also, the level of change in the decay class is equal to 40.91 hectares, due to the continuation of recent drought in the region, road construction and degradation of vegetation covers.<br />In Fakhre area, due to the implementation of the combat to desertification plans in the sequestration area, which is a suitable place for cultivating the species of halophytes, and the root of halophytes can easily use the water resources in the depths of the sand dunes, the highest amount of natural regeneration of gazelles and the largest area of density change is related to the incremental class with an area of 315.83 hectares and the area of the floor without change in this region is 140.27 hectares. Also, due to the recent drought in the whole region and the destruction of wood of woody trees, for the production of charcoal, the decreasing floor density changes are equal to 44.26 hectares. In the Rijen area, the largest area is unchanged, with an area of 555.17 hectares. Due to the implementation of the combat to desertification plans in the sequestration zone that caused the natural regeneration of ghosts, the area of change in the incremental class is equal to 434.26 hectares. The level of condensation changes in this area is 13.77 hectares, due to its close proximity to the Rijen countryside, the destruction of foodstuffs by local livestock is the same as camel and goat, in addition to the trafficking of wood by the profitable people to produce coal, the trees are dying. The results of the sustainability assessment show that the existing designs in the Fakhre and Rijen areas have excellent sustainability scores and existing plans in the Aran Road have a good sustainability score and also existing schemes in the Ab Shirin region have a moderate sustainability score.<br />Discussion and Conclusion: Field surveys from different parts of the combat to desertification plans show that in the Rijen region and the Fakhre, natural regeneration is a species of fungus, although natural regeneration is not observed in many of the floodplain projects in the freshwater region. The results of the field study indicate that from the direction of the Ab Shirin area towards the Abu Zaid Abad area, the vegetation density is increased, because the region of Rijen and Fakhre due to the seismic region of the conditions are better than the areas of transportation and harvesting in the successful implementation of halftone projects has been. Halibut plans have more reproductive power in the sequestering areas, in other words, the roots of these species can more easily penetrate the soil and use the water resources of the sand dunes. In sum, it is possible to say that the best territory for implementation of the combat to desertification projects through the implementation of hunting projects for the development of environment friendly plant species, is the range of sand dunes.<br />According to the studies carried out in the combat to desertification plans implemented in the study areas, it can be stated that although there may be a species of feces in the area of carriage and harvest, but is special in seismic areas, because in the area sequestration of sand dunes such as intakes and has a high permeability are good, as a result of evaporation at their surface is negligible and provides a suitable bed for the establishment and stability of the species of fungus, and also makes it more regenerative. Halibut plans in the Ab Shirin region due to the planting of halophilia in the transport areas, which were mainly soil salinity, and also the root of the plants had no proper penetration in the depth of the earth, lacked the necessary regeneration in many areas that in the long run the stability of Haloxylon planes endangers the work. However, in Rijeg and Fakhre regions due to de-drainage projects and hunting in places of sequestration, the herbaceous species have a greater regeneration than Ab Shirin schemes and increased the density of the coatings in these areas. The process in long-term can have more sustained effects on the combat to desertification plans in the regions. <br /> https://deej.kashanu.ac.ir/article_112645_80feb4d7d3d80739d3c5171602db686a.pdfUniversity of KashanDesert Ecosystem Engineering2538-633671920220827Using Data Mining Algorithms in Separation of Sediment Sources in Nodeh Watershed, GonabadUsing Data Mining Algorithms in Separation of Sediment Sources in Nodeh Watershed, Gonabad819411264610.22052/deej.2018.7.19.49FAMehdiBashiriMahsaAriapourAliGolkarianJournal Article20220827Introduction: Reduction of sediment supply requires the implementation of soil conservation and sediment control programs in the form of watershed management plans. Sediment control programs require identifying the relative importance of sediment sources, their quantitative ascription and identification of critical areas within the watersheds. The sediment source ascription is involves two main steps so that in the first, several diagnostic tracers are selected for obvious and significant separation of potential sources of sediment and in the second step selected tracers for potential sources of sediment are compared, with corresponding values extracted from the sediment samples taken in the watershed outlet. Also, due to the large amount and complexity of data available, nowadays in geo- and environmental sciences, we face the need to develop and incorporate more robust and efficient methods for their analysis and modelling. Therefore recent fundamental progress in data mining algorithms can considerably contribute to the development of the emerging field - environmental data science.<br /> Methodology: According to what was said, in this research, the data mining algorithms used to separate sediment sources in the Nodeh watershed of Gonabad located in Razavi-Khorasan province by using the geochemical (includes the 21 elements of Mg, Sr, Mn, Ba, Zn, Y, V, Ti, Pb, P, Na, Li, K, Cu, Cr, Co, Ce, B, Ca, Al and Fe), granulometric (includes the D90, D50, D10, percent of sand, percent of silt, percent of clay, skewness and kurtosis and the diameters less than 1, 2 and 4 millimeters and less than 500, 250, 125 and 63 microns) and lithological variables (includes the quartz, tuff, laterite, dacite, andesite, dolomite, calcite, andesitic tuff, lithic andesite and salt). A set of 11 classification algorithms includes the decision tree, random forest, regression methods, discriminant analysis, local linear model tree, nearest neighbor analysis, support vector machine, logistic regression, artificial neural network, pattern recognition and group method of data handling programmed in the MATLAB software and the results compared based on the coefficient of determination and mean squared error.<br /> Results and Discussion: Study of geochemical element concentrations in 7 geological units showed that the Ca, Fe, Mg and Al elements have the highest and B and Co have the lowest concentrations within the soil samples. Overall evaluation of classification algorithms in training stage showed that the discriminant analysis, random forest, k nearest neighbor and support vector machines with linear, polynomial, multiple and RBF kernels with maximum values of the coefficient of determination (R2=1) and minimum values of the mean squared error (RMSE=0) are the most accurate algorithms in sediment source separation but the regression trees method has the worst performance. Also, at testing stage, the support vector machines with RBF kernel was the most accurate and the classification trees with maximum error rate was the most inaccurate algorithm. Also, entrance of geochemical and granulometric variables lead to the highest and lowest accuracy in the sediment source separation, respectively. Using the geochemical variables for the separation of sediment sources, types of support vector machines, nearest neighbor analysis, discriminant analysis and the random forest algorithm had the highest coefficients of determination and lowest error values in the training and testing stages. By entering the lithological variables, the random forest algorithm had the highest accuracy for the sediment sources classification in the training and testing stages and the discriminant analysis and support vector machines were located thereafter. Finally, fitting the classification algorithms using granulometric variables showed that the support vector machines had highest accuracy in the training and testing stages of models and the random forest and nearest neighbor analysis were ranked thereafter.<br /> Conclusion:Totally, due to the proper accuracy and performance of data mining classifier algorithms, application of these methods in the natural sciences is suggested especially in the large amounts of data. These algorithms are used to find patterns in large sets of data and help classify new information. Especially, the support vector machines that are supervised classifier algorithms and besides that, in the natural sciences have successful results. In the watershed management considering the time and cost, sediment source ascriptions are difficult to obtain using monitoring techniques, but data mining procedures, have emerged as a potentially valuable alternative. Therefore, application and evaluation of these methods are suggested for further studies and natural sciences data.Introduction: Reduction of sediment supply requires the implementation of soil conservation and sediment control programs in the form of watershed management plans. Sediment control programs require identifying the relative importance of sediment sources, their quantitative ascription and identification of critical areas within the watersheds. The sediment source ascription is involves two main steps so that in the first, several diagnostic tracers are selected for obvious and significant separation of potential sources of sediment and in the second step selected tracers for potential sources of sediment are compared, with corresponding values extracted from the sediment samples taken in the watershed outlet. Also, due to the large amount and complexity of data available, nowadays in geo- and environmental sciences, we face the need to develop and incorporate more robust and efficient methods for their analysis and modelling. Therefore recent fundamental progress in data mining algorithms can considerably contribute to the development of the emerging field - environmental data science.<br /> Methodology: According to what was said, in this research, the data mining algorithms used to separate sediment sources in the Nodeh watershed of Gonabad located in Razavi-Khorasan province by using the geochemical (includes the 21 elements of Mg, Sr, Mn, Ba, Zn, Y, V, Ti, Pb, P, Na, Li, K, Cu, Cr, Co, Ce, B, Ca, Al and Fe), granulometric (includes the D90, D50, D10, percent of sand, percent of silt, percent of clay, skewness and kurtosis and the diameters less than 1, 2 and 4 millimeters and less than 500, 250, 125 and 63 microns) and lithological variables (includes the quartz, tuff, laterite, dacite, andesite, dolomite, calcite, andesitic tuff, lithic andesite and salt). A set of 11 classification algorithms includes the decision tree, random forest, regression methods, discriminant analysis, local linear model tree, nearest neighbor analysis, support vector machine, logistic regression, artificial neural network, pattern recognition and group method of data handling programmed in the MATLAB software and the results compared based on the coefficient of determination and mean squared error.<br /> Results and Discussion: Study of geochemical element concentrations in 7 geological units showed that the Ca, Fe, Mg and Al elements have the highest and B and Co have the lowest concentrations within the soil samples. Overall evaluation of classification algorithms in training stage showed that the discriminant analysis, random forest, k nearest neighbor and support vector machines with linear, polynomial, multiple and RBF kernels with maximum values of the coefficient of determination (R2=1) and minimum values of the mean squared error (RMSE=0) are the most accurate algorithms in sediment source separation but the regression trees method has the worst performance. Also, at testing stage, the support vector machines with RBF kernel was the most accurate and the classification trees with maximum error rate was the most inaccurate algorithm. Also, entrance of geochemical and granulometric variables lead to the highest and lowest accuracy in the sediment source separation, respectively. Using the geochemical variables for the separation of sediment sources, types of support vector machines, nearest neighbor analysis, discriminant analysis and the random forest algorithm had the highest coefficients of determination and lowest error values in the training and testing stages. By entering the lithological variables, the random forest algorithm had the highest accuracy for the sediment sources classification in the training and testing stages and the discriminant analysis and support vector machines were located thereafter. Finally, fitting the classification algorithms using granulometric variables showed that the support vector machines had highest accuracy in the training and testing stages of models and the random forest and nearest neighbor analysis were ranked thereafter.<br /> Conclusion:Totally, due to the proper accuracy and performance of data mining classifier algorithms, application of these methods in the natural sciences is suggested especially in the large amounts of data. These algorithms are used to find patterns in large sets of data and help classify new information. Especially, the support vector machines that are supervised classifier algorithms and besides that, in the natural sciences have successful results. In the watershed management considering the time and cost, sediment source ascriptions are difficult to obtain using monitoring techniques, but data mining procedures, have emerged as a potentially valuable alternative. Therefore, application and evaluation of these methods are suggested for further studies and natural sciences data.https://deej.kashanu.ac.ir/article_112646_5cacbda78a70acb6f3612aaa203279a0.pdfUniversity of KashanDesert Ecosystem Engineering2538-633671920220827Introduction of the simplest spectral ratios in order to detect some chemical properties of soil in arid regions using remote sensing technique (Case study: Dareh Anjir Kavir)Introduction of the simplest spectral ratios in order to detect some chemical properties of soil in arid regions using remote sensing technique (Case study: Dareh Anjir Kavir)9510511264710.22052/deej.2018.7.19.59FAZohreEbrahimi KhusfiMohammadJavadGhanei BafghiJournal Article20220827Introduction Understanding the spectral reflectance of different soils and other surface elements forms the basis for analyzing the process of interpreting remote sensing data. Spectral properties of the various phenomena of the Earth's surface are not constant and are changing, based on the complex time and space conditions. Determination of soil chemical properties using remote sensing techniques mainly affects their spectral reflectance, which is itself influenced by the amount and type of salts in the soil, the amount of moisture, the color and roughness of the soil surface. The use of various satellite image processing techniques, especially spectral ratios, is one of the most common methods for detecting the phenomena of the earth's surface. The purpose of this study is introduce the simplest and most suitable spectral ratios prepared using ASTER sensor data for the enhancement of saline, TNV, alkali and gypsiferous soils in the spatial range of the Dareh Anjir Kavir in Yazd province. Mterials and methods To determine the salinity, alkalinity, gypsum and TNV values of soils, in the spring of 2007 according to the time of the image, by placing a systematic random sampling grid on the image of the area, the location of 42 sampling points was determined and sampled. Soil samples were analyzed after the transport to the laboratory and the values of mentioned parameters were determined. The geometric correction of the ASTER images was carried out using the image to image method. The FLAASH algorithm was used in ENVI4.7 software to obtain ground reflection and atmospheric correction of images after converting the digital value recorded to radians. Then, 6 spectral ratios in the near visible and infrared spectral range, 30 spectral ratios in the middle infrared range and 20 spectral ratios were produced in the thermal infrared range. A point map prepared from the location of the profiles was crossed with all the information layers obtained from the spectral ratios and the value of the reflection of each pixel was extracted. By stepwise multi-variable regression, correlation coefficients and models related to each component were calculated. The extracted models were validated based on the higher values of the corrected explanatory factor and F factor as well as lower standard error. By fitting a straight line between observational and estimated values at the test points, the resultant coefficient was considered as the accuracy of the selected model for the study area. Results The initial results obtained from the establishment of regression relations between salinity, alkalinity, gypsum and TNV components with spectral ratios calculated based on the different band ratios showed that between three components of salinity, alkalinity and gypsum with spectral ratios has been established only one statistically significant relationship. But there are three significant relationship between the TNV component with band ratios that due to the existence of a significant correlation between the band ratios introduced in model number (3), we can not be cited statistically to the application of this model in the identification of TNV soils in the study area. Also, the results showed that the band ratios (b8/b6) and (b8/b5) can be used for enhancement of saline and alkaline soils with relatively high precision and band ratios (b11/b12) and (b9 / b8) in order to characterize the calcareous soils of the study area. Also, the results indicated that the only optimum spectral ratio for separating of gypsum soils from other soils is the band ratio is (b11 /b 13). The correlation coefficient between observational and estimated values at the test points (14 samples) for the mentioned components was relatively good and was estimated to be 0.66, 0.55 and 0.47, respectively. Also, the results indicated that the only optimal band ratio for separating of gypsum soils from other soils is (b11 to b13). Discussion and Conclusion The importance of a band in a region and its inappropriateness in another region is due to the varying degrees of salinity, alkalinity and humidity or different amounts of cations and anions, as well as different climatic, geographic and geological conditions which has led to a difference in the spectral reflectance of phenomena. Since, salinity, TNV and alkalinity models have been able to justify 66%, 55% and 47% of salinity, TNV and alkalinity changes in the study area with acceptable accuracy, By completing and expanding the research, can be done soil zoning in terms of the characteristics studied without the need for sampling. This technology, while providing more precision, can minimize sampling costs. Of course, considering other factors affecting the spectral reflection of different soils and the use of satellite data of other sensors that have a higher spatial resolution than ASTER sensor, or combining them with the data of the ASTER, can produce more precise models. Therefore, the use of ASTER sensor data can have acceptable performance in detecting the chemical properties of soil in arid areas.Introduction Understanding the spectral reflectance of different soils and other surface elements forms the basis for analyzing the process of interpreting remote sensing data. Spectral properties of the various phenomena of the Earth's surface are not constant and are changing, based on the complex time and space conditions. Determination of soil chemical properties using remote sensing techniques mainly affects their spectral reflectance, which is itself influenced by the amount and type of salts in the soil, the amount of moisture, the color and roughness of the soil surface. The use of various satellite image processing techniques, especially spectral ratios, is one of the most common methods for detecting the phenomena of the earth's surface. The purpose of this study is introduce the simplest and most suitable spectral ratios prepared using ASTER sensor data for the enhancement of saline, TNV, alkali and gypsiferous soils in the spatial range of the Dareh Anjir Kavir in Yazd province. Mterials and methods To determine the salinity, alkalinity, gypsum and TNV values of soils, in the spring of 2007 according to the time of the image, by placing a systematic random sampling grid on the image of the area, the location of 42 sampling points was determined and sampled. Soil samples were analyzed after the transport to the laboratory and the values of mentioned parameters were determined. The geometric correction of the ASTER images was carried out using the image to image method. The FLAASH algorithm was used in ENVI4.7 software to obtain ground reflection and atmospheric correction of images after converting the digital value recorded to radians. Then, 6 spectral ratios in the near visible and infrared spectral range, 30 spectral ratios in the middle infrared range and 20 spectral ratios were produced in the thermal infrared range. A point map prepared from the location of the profiles was crossed with all the information layers obtained from the spectral ratios and the value of the reflection of each pixel was extracted. By stepwise multi-variable regression, correlation coefficients and models related to each component were calculated. The extracted models were validated based on the higher values of the corrected explanatory factor and F factor as well as lower standard error. By fitting a straight line between observational and estimated values at the test points, the resultant coefficient was considered as the accuracy of the selected model for the study area. Results The initial results obtained from the establishment of regression relations between salinity, alkalinity, gypsum and TNV components with spectral ratios calculated based on the different band ratios showed that between three components of salinity, alkalinity and gypsum with spectral ratios has been established only one statistically significant relationship. But there are three significant relationship between the TNV component with band ratios that due to the existence of a significant correlation between the band ratios introduced in model number (3), we can not be cited statistically to the application of this model in the identification of TNV soils in the study area. Also, the results showed that the band ratios (b8/b6) and (b8/b5) can be used for enhancement of saline and alkaline soils with relatively high precision and band ratios (b11/b12) and (b9 / b8) in order to characterize the calcareous soils of the study area. Also, the results indicated that the only optimum spectral ratio for separating of gypsum soils from other soils is the band ratio is (b11 /b 13). The correlation coefficient between observational and estimated values at the test points (14 samples) for the mentioned components was relatively good and was estimated to be 0.66, 0.55 and 0.47, respectively. Also, the results indicated that the only optimal band ratio for separating of gypsum soils from other soils is (b11 to b13). Discussion and Conclusion The importance of a band in a region and its inappropriateness in another region is due to the varying degrees of salinity, alkalinity and humidity or different amounts of cations and anions, as well as different climatic, geographic and geological conditions which has led to a difference in the spectral reflectance of phenomena. Since, salinity, TNV and alkalinity models have been able to justify 66%, 55% and 47% of salinity, TNV and alkalinity changes in the study area with acceptable accuracy, By completing and expanding the research, can be done soil zoning in terms of the characteristics studied without the need for sampling. This technology, while providing more precision, can minimize sampling costs. Of course, considering other factors affecting the spectral reflection of different soils and the use of satellite data of other sensors that have a higher spatial resolution than ASTER sensor, or combining them with the data of the ASTER, can produce more precise models. Therefore, the use of ASTER sensor data can have acceptable performance in detecting the chemical properties of soil in arid areas.https://deej.kashanu.ac.ir/article_112647_61d76751917b9c898f8e7f8149c66708.pdf