دانشگاه کاشانمهندسی اکوسیستم بیابان2538-633610شماره 7 انگلیسی20220121Investigating the Status of Desertification Vulnerability in Joghatay County, IranInvestigating the Status of Desertification Vulnerability in Joghatay County, Iran121114080FAمصطفیدستورانیdepartment of remotsensing/ geographical and enviromental college, hakim sabzevari university. sabzevar, iranJournal Article20211024This study sought to investigate the vulnerability of the Joghatay County, Khorasan Razavi, Iran, to desertification using several remote sensing products. To this end, sixMODISpotential indicators, including enhanced vegetation index (EVI), Vegetation Condition Index (VCI), salinity index (SI), Synthetized Drought Index (SDI), Temperature Condition Index (TCI), and precipitation rate of March 2020,were applied. These layers were then normalized and weighted using the Min-Max approach and Analytical Hierarchy Method, respectively. Finally, the vulnerability map was prepared via the weighted average method. The study’s results indicated that the majority of the study area (67.5%) fell within the low to moderate vulnerability classes. However, the high-risk class area should be taken into account seriously, as it covers 365 km<sup>2</sup> (~21%) (one-fifth) of the whole study area.Moreover, the mountain foothills in the south and north of the area were classified within the high desertification vulnerability class, possessing the lowest vegetation density and highest temperature values. Nonetheless, the central areas with the greatest vegetation density formed the lowest desertification vulnerability as expected. The comparison of the ground truth values (some 200 points of the study area were randomly visited, and each of them was assigned a 0 or1 score)and the rated scores revealed more than 75% compatibility.Therefore, it could be argued that lack of vegetation due to climatic and edaphic measures and anthropogenic factors are responsible for the Joghatay region’s high vulnerability to desertification.This study sought to investigate the vulnerability of the Joghatay County, Khorasan Razavi, Iran, to desertification using several remote sensing products. To this end, sixMODISpotential indicators, including enhanced vegetation index (EVI), Vegetation Condition Index (VCI), salinity index (SI), Synthetized Drought Index (SDI), Temperature Condition Index (TCI), and precipitation rate of March 2020,were applied. These layers were then normalized and weighted using the Min-Max approach and Analytical Hierarchy Method, respectively. Finally, the vulnerability map was prepared via the weighted average method. The study’s results indicated that the majority of the study area (67.5%) fell within the low to moderate vulnerability classes. However, the high-risk class area should be taken into account seriously, as it covers 365 km<sup>2</sup> (~21%) (one-fifth) of the whole study area.Moreover, the mountain foothills in the south and north of the area were classified within the high desertification vulnerability class, possessing the lowest vegetation density and highest temperature values. Nonetheless, the central areas with the greatest vegetation density formed the lowest desertification vulnerability as expected. The comparison of the ground truth values (some 200 points of the study area were randomly visited, and each of them was assigned a 0 or1 score)and the rated scores revealed more than 75% compatibility.Therefore, it could be argued that lack of vegetation due to climatic and edaphic measures and anthropogenic factors are responsible for the Joghatay region’s high vulnerability to desertification.https://deej.kashanu.ac.ir/article_114080_a8efc6a6e6cbc90b0ec7f4fe4bbba016.pdfدانشگاه کاشانمهندسی اکوسیستم بیابان2538-633610شماره 7 انگلیسی20220121Monitoring meteorological drought with SPI and RDI drought indices and Forecasting Class Transitions Using Markov Chains in southern IranMonitoring meteorological drought with SPI and RDI drought indices and Forecasting Class Transitions Using Markov Chains in southern Iran2613114081FAمریمحیدرزادهDepartment of Water Science and Engineering, Minab Higher Education Center, University Of HormozganامیرسالاریFaculty of Agricultural and Natural Resources, University of Torbat HeydariehJournal Article20211209The uncertainty found in standard methods of drought monitoring has made it necessary to compare the accuracy of the drought's monitoring methods. This study examined the Standardized Precipitation Index (SPI) and Reconnaissance Drought Index (RDI) to determine drought severity in 12 meteorological stations in southern Iran from 1975-2015 and predict draught transition from one to another class to another, using the Markov Chain model. According to the coefficient correlation analysis results between the precipitation data collected from the meteorological stations and the drought index, it was found that 3-month SPI and RDI have a high correlation with precipitation data. On the other hand, the analysis of the 3-month SPI and RDI index in all stations showed that the highest probability belonged to normal and near-normal classes, with their mean average probability values being 0.73 and 0.27 for SPI and 0.70 and 0.30 for the RDI, respectively. Moreover, based on RDI and SPI, the most probability rate of the drought occurrence in most of the stations belonged to normal and moderate drought classes, confirming a high correlation between meteorological drought and short-term drought index. According to the results, it is recommended that in the analysis of drought features, their characteristics such as vulnerability, resiliency, and reliability should be examined based on the climate type. Furthermore, to reduce the harmful effects of drought, necessary measures must be taken, especially in managing water resources.The uncertainty found in standard methods of drought monitoring has made it necessary to compare the accuracy of the drought's monitoring methods. This study examined the Standardized Precipitation Index (SPI) and Reconnaissance Drought Index (RDI) to determine drought severity in 12 meteorological stations in southern Iran from 1975-2015 and predict draught transition from one to another class to another, using the Markov Chain model. According to the coefficient correlation analysis results between the precipitation data collected from the meteorological stations and the drought index, it was found that 3-month SPI and RDI have a high correlation with precipitation data. On the other hand, the analysis of the 3-month SPI and RDI index in all stations showed that the highest probability belonged to normal and near-normal classes, with their mean average probability values being 0.73 and 0.27 for SPI and 0.70 and 0.30 for the RDI, respectively. Moreover, based on RDI and SPI, the most probability rate of the drought occurrence in most of the stations belonged to normal and moderate drought classes, confirming a high correlation between meteorological drought and short-term drought index. According to the results, it is recommended that in the analysis of drought features, their characteristics such as vulnerability, resiliency, and reliability should be examined based on the climate type. Furthermore, to reduce the harmful effects of drought, necessary measures must be taken, especially in managing water resources.https://deej.kashanu.ac.ir/article_114081_8fffece23decaae17d1af35e2fba9212.pdfدانشگاه کاشانمهندسی اکوسیستم بیابان2538-633610شماره 7 انگلیسی20220121Investigating Short to Long-term Effects of Ground-based Agents on Dust Pollution Variations in Iranian Arid and Semi-arid RegionsInvestigating Short to Long-term Effects of Ground-based Agents on Dust Pollution Variations in Iranian Arid and Semi-arid Regions4627114082 10.22052/deej.2022.114082FAزهرهابراهیمی خوسفیAssistant Professor, Faculty of Natural Resources, University of Jiroft , Kerman,IranJournal Article20211123This study sought to investigate change patterns in the standardized surface soil moisture (SSM), standardized land surface temperature (SLST), and standardized normalized difference vegetation index (SNDVI) in Iranian arid and semiarid regions using the Mann-Kendall test. To this end, the temporal response of dust occurrences to terrestrial factors variations in 1, 3, 6, 9, and 12-month time-series was identified at different lag times using the cross-correlation (CC) method. The standardized dust concentration (SDC) in dusty days was also considered as a criterion for evaluating the performance of dust storms during the study period (2010-2018). The study's results indicated dust storms' decreasing and increasing trends from 2010 onwards in Iran's arid and semiarid regions, respectively. Moreover, the trend of SSM changes was found to be significantly positive at different time series (Z>+1.96) in both regions. A similar trend was also observed for SNDVI in long-term series across the study areas. However, while the SLST variations showed meaningful positive trends in the arid regions at various time series (Z>+4.5), it only showed a significant positive trend in the 1-month series (Z=+2.12) in semi-arid regions.<br />Furthermore, according to the strongest CC values, the temporal response of dust storms to changes in vegetation, LST, and SM occurred at 6, 12, and 3-month time series with different time lags in arid regions. Nonetheless, the temporal responses of dust events to vegetation and LST variations in the semi-arid regions were found at 12-month time series with a 1-month and 5-month time lags, respectively.This study sought to investigate change patterns in the standardized surface soil moisture (SSM), standardized land surface temperature (SLST), and standardized normalized difference vegetation index (SNDVI) in Iranian arid and semiarid regions using the Mann-Kendall test. To this end, the temporal response of dust occurrences to terrestrial factors variations in 1, 3, 6, 9, and 12-month time-series was identified at different lag times using the cross-correlation (CC) method. The standardized dust concentration (SDC) in dusty days was also considered as a criterion for evaluating the performance of dust storms during the study period (2010-2018). The study's results indicated dust storms' decreasing and increasing trends from 2010 onwards in Iran's arid and semiarid regions, respectively. Moreover, the trend of SSM changes was found to be significantly positive at different time series (Z>+1.96) in both regions. A similar trend was also observed for SNDVI in long-term series across the study areas. However, while the SLST variations showed meaningful positive trends in the arid regions at various time series (Z>+4.5), it only showed a significant positive trend in the 1-month series (Z=+2.12) in semi-arid regions.<br />Furthermore, according to the strongest CC values, the temporal response of dust storms to changes in vegetation, LST, and SM occurred at 6, 12, and 3-month time series with different time lags in arid regions. Nonetheless, the temporal responses of dust events to vegetation and LST variations in the semi-arid regions were found at 12-month time series with a 1-month and 5-month time lags, respectively.https://deej.kashanu.ac.ir/article_114082_d33b5d2bfb550241f35063f25d811135.pdfدانشگاه کاشانمهندسی اکوسیستم بیابان2538-633610شماره 7 انگلیسی20220121Investigating the Impact of Land-use and Climatic Factors on Land Degradation in North-East of IranInvestigating the Impact of Land-use and Climatic Factors on Land Degradation in North-East of Iran6847114083FAعلیآذرهAssistant Professor, Department of Geography, University of Jiroft, Kerman, Iran.Journal Article20211201Vegetation is one of the most important factors in assessing land degradation. On the other hand, remote sensing of vegetation changes can provide useful information for ecosystem management. Therefore, this study sought to investigate the trend of changes in vegetation and its correlation with land-use and climate change in northeastern Iran. To this end, the data regarding the NDVI and EVI which were extracted from the MODIS satellite and MOD13A2 product from 2000 to 2017 were used to study vegetation changes, and data obtained from the MODIS MCD12Q1 product from 2001 to 2017 were used to investigate the land-use changes. Moreover, the meteorological stations' data were examined to evaluate the trend of climate factors in the region.<br />The study's results showed that the trend of changes in both NDVI and EVI was significantly negative. Furthermore, the land-use analysis showed that the agricultural and rangeland area decreased and the urban and barren land area increased significantly. The temperature also increased significantly during the period while the precipitation decreased slightly. Moreover, it was found that there was a significant correlation between land-use classes, NDVI, and EVI and that the correlation between precipitation and NDVI was significant at 95% (R=0.53). on the other hand, the investigation of the relationship between climatic factors, land use, and vegetation indices based on the Pearson correlation coefficient indicated that the land-use had a higher correlation with vegetation indices compared with that of the climatic factors. Therefore, it could be argued that degradation can be affected by human activities which in turn leads to land-use changes and the overuse of water and soil resources. The degradation can also be influenced by climate change, leading to a decrease in the available water supply to be used by natural vegetation. However, land-use and human activities were found to have more influence on NDVI, EVI, and land degradation.Vegetation is one of the most important factors in assessing land degradation. On the other hand, remote sensing of vegetation changes can provide useful information for ecosystem management. Therefore, this study sought to investigate the trend of changes in vegetation and its correlation with land-use and climate change in northeastern Iran. To this end, the data regarding the NDVI and EVI which were extracted from the MODIS satellite and MOD13A2 product from 2000 to 2017 were used to study vegetation changes, and data obtained from the MODIS MCD12Q1 product from 2001 to 2017 were used to investigate the land-use changes. Moreover, the meteorological stations' data were examined to evaluate the trend of climate factors in the region.<br />The study's results showed that the trend of changes in both NDVI and EVI was significantly negative. Furthermore, the land-use analysis showed that the agricultural and rangeland area decreased and the urban and barren land area increased significantly. The temperature also increased significantly during the period while the precipitation decreased slightly. Moreover, it was found that there was a significant correlation between land-use classes, NDVI, and EVI and that the correlation between precipitation and NDVI was significant at 95% (R=0.53). on the other hand, the investigation of the relationship between climatic factors, land use, and vegetation indices based on the Pearson correlation coefficient indicated that the land-use had a higher correlation with vegetation indices compared with that of the climatic factors. Therefore, it could be argued that degradation can be affected by human activities which in turn leads to land-use changes and the overuse of water and soil resources. The degradation can also be influenced by climate change, leading to a decrease in the available water supply to be used by natural vegetation. However, land-use and human activities were found to have more influence on NDVI, EVI, and land degradation.https://deej.kashanu.ac.ir/article_114083_8d1b649a80fec53e7c2a95233efc8b2d.pdf