University of Kashan Desert Ecosystem Engineering 2538-6336 8 Issue 3 in English 2019 03 21 Analysis and Prediction of Land Use Change in Yazd-Ardakan Plain Analysis and Prediction of Land Use Change in Yazd-Ardakan Plain 1 20 114062 FA Hassan Fathizad Department of management the arid and desert regions, College of Natural Resources and Desert, Yazd University, Iran Mohamadali Hakimzadeh Ardakani Desert management, natural resources, Yazd University, Yazd, Iran Rouhollah Taghizadeh Mehrjardi Agriculture and Natural Resources Department, Ardakan University, Yazd, Iran Hamid Sodaie Zadeh Department of arid and desert regions management, College of Natural Resources and Desert, Yazd University, Iran Journal Article 2019 01 30 Land use maps provide a large fragment of the information required by planners for basic decision-making. Detection of changes as well as prediction of land use changes play a critical role in providing a general insight into better management and conservation of natural resources. This study aimed to simulate land use and land changes using the automatic cell model and Markov Chain in a 30-year period (1986-2016) in the Yazd-Ardakan plain, Iran. In this regard, the object-oriented classification technique, Landsat satellite images (MSS) of 1986, Landsat (TM) of 1999, Landsat (ETM+) of 2010, and Landsat 8 (OLI) of 2016 were employed to create the land use maps, including seven land use types ( afforestation, agricultural land and garden, barren land, poor rangeland, residential land, rocky land and sand dune). To validate the model accuracy, the simulated land use map of 2010 was compared to the actual map obtained by mapping of the satellite image of the same year. The Kappa coefficient obtained showed that the CA-Markov chain model had a high ability (81%) in simulation of land use changes in the Yazd-Ardakan plain. Based on the results, it is likely that, at the interval of 2016-2030, 80% of afforestation land, 55% of agricultural land and gardens, 41% of barren land, 34% of poor rangeland, 47% of residential land, 43% of sand dune, will be 93% unchanged. Additionally, from 2016 to 2030, the conversion of barren lands to afforestation (55%) as well as poor rangeland to agricultural lands and gardens (43%) is highly probable. Based on the area obtained from each land use in 2030 compared to 2016, the areas of afforestation, agricultural land and gardens, residential land and sand dune will increase, and the barren land and poor rangeland will decline. The excessive growth of the population and the increasing need for food and new energy sources as well as the need for residential areas lead to unconventional and extreme exploitation of the natural resources of the Yazd-Ardakan plain. Land use maps provide a large fragment of the information required by planners for basic decision-making. Detection of changes as well as prediction of land use changes play a critical role in providing a general insight into better management and conservation of natural resources. This study aimed to simulate land use and land changes using the automatic cell model and Markov Chain in a 30-year period (1986-2016) in the Yazd-Ardakan plain, Iran. In this regard, the object-oriented classification technique, Landsat satellite images (MSS) of 1986, Landsat (TM) of 1999, Landsat (ETM+) of 2010, and Landsat 8 (OLI) of 2016 were employed to create the land use maps, including seven land use types ( afforestation, agricultural land and garden, barren land, poor rangeland, residential land, rocky land and sand dune). To validate the model accuracy, the simulated land use map of 2010 was compared to the actual map obtained by mapping of the satellite image of the same year. The Kappa coefficient obtained showed that the CA-Markov chain model had a high ability (81%) in simulation of land use changes in the Yazd-Ardakan plain. Based on the results, it is likely that, at the interval of 2016-2030, 80% of afforestation land, 55% of agricultural land and gardens, 41% of barren land, 34% of poor rangeland, 47% of residential land, 43% of sand dune, will be 93% unchanged. Additionally, from 2016 to 2030, the conversion of barren lands to afforestation (55%) as well as poor rangeland to agricultural lands and gardens (43%) is highly probable. Based on the area obtained from each land use in 2030 compared to 2016, the areas of afforestation, agricultural land and gardens, residential land and sand dune will increase, and the barren land and poor rangeland will decline. The excessive growth of the population and the increasing need for food and new energy sources as well as the need for residential areas lead to unconventional and extreme exploitation of the natural resources of the Yazd-Ardakan plain.

https://deej.kashanu.ac.ir/article_114062_cb328f8f7ac7b721c77a0407638d1ebc.pdf

University of Kashan Desert Ecosystem Engineering 2538-6336 8 Issue 3 in English 2019 03 21 Assessment of Seed Germination of Moringa peregrina under Drought and Salinity Stress and its Cardinal Germination Temperatures in Laboratory Environment Assessment of Seed Germination of Moringa peregrina under Drought and Salinity Stress and its Cardinal Germination Temperatures in Laboratory Environment 21 30 114063 FA Elham Elham Rafiei Sardoii Assistant professor, Faculty of Natural Resources, University of Jiroft, Kerman, Iran Abbas Hashemi Ph.D. student of seed science and technology, College of Agriculture and Natural Resources, University of Tehran Hamed Eskandari PhD Student, Faculty of Natural Resources, University of Tehran Hassan Khosravi University of Tehran Saeed Barkhori Assistant Professor, Faculty of Natural Resources, University of Jiroft, Kerman, Iran Journal Article 2019 02 11 The effect of different temperatures on germination was assessed in a laboratory study in the framework of complete randomized design with five repetitions to determine the specific temperature of Moringa peregrina seeds. Evaluation of germination response was conducted at constant temperatures of 5, 10, 15, 20, 25, 30, 35, 40 and 45 °C. Cardinal germination temperatures were fitted using three models, including beta, segmented and dent-like. Optimum and maximum temperatures of germination were calculated 17, 25-30 and 47 °C, respectively, based on the dent-like model, which was identified as the best model using statistical indicators. Then, to investigate germination and seedling growth response of Moringa peregrina toward different levels of salinity and drought stress at an optimum temperature, another test was conducted. In this experiment, seed germination was assessed in four levels of salinity and drought with the osmotic potential of 0, -4, -8 and -12 bar. The results indicated that seed germination speed and percentage were decreased due to drought and salinity stress. Generally, seed germination of Moringa peregrina was more sensitive to drought stress than to salinity stress. The effect of different temperatures on germination was assessed in a laboratory study in the framework of complete randomized design with five repetitions to determine the specific temperature of Moringa peregrina seeds. Evaluation of germination response was conducted at constant temperatures of 5, 10, 15, 20, 25, 30, 35, 40 and 45 °C. Cardinal germination temperatures were fitted using three models, including beta, segmented and dent-like. Optimum and maximum temperatures of germination were calculated 17, 25-30 and 47 °C, respectively, based on the dent-like model, which was identified as the best model using statistical indicators. Then, to investigate germination and seedling growth response of Moringa peregrina toward different levels of salinity and drought stress at an optimum temperature, another test was conducted. In this experiment, seed germination was assessed in four levels of salinity and drought with the osmotic potential of 0, -4, -8 and -12 bar. The results indicated that seed germination speed and percentage were decreased due to drought and salinity stress. Generally, seed germination of Moringa peregrina was more sensitive to drought stress than to salinity stress.

https://deej.kashanu.ac.ir/article_114063_7248363105296796672888dcc2d6c6a8.pdf

University of Kashan Desert Ecosystem Engineering 2538-6336 8 Issue 3 in English 2019 03 21 Spatial monitoring of drought in the Khatun Abad basin using SPI and remote sensing technique Spatial monitoring of drought in the Khatun Abad basin using SPI and remote sensing technique 44 31 114064 FA Mohsen Porkhosravani Associate Professor Department of Geography Shahid Bahonar University of Kerman, Kerman, Iran Sadegh Karimi Assistant Professor, Department of Geography and urban planning Shahid Bahonar University of Kerman. Kerman Iran Ali Mehrabi Assistant Professor, Department of Geography and urban planning Shahid Bahonar University of Kerman. Kerman Iran Amir Takin Mohebbi Kermani Geography department, Literature faculty, Shahid Bahonar university, Kerman, Iran Journal Article 2018 10 21 Drought is a natural and recurrent phenomenon. It is considered ‘a natural disaster’ whenever it occurs intensively in highly populated regions, resulting in significant damage (material and human) and loss (socioeconomic). In this regard, this research aims to evaluate the drought of the Khatun Abad basin using the combination of NDVI (Normalized Difference Vegetation Index) and LST (Land surface temperature) MODIS sensors and an SPI indicator. For this purpose, the VHI index was calculated from the combination of VCI and TCI indices based on the 18-year time series (2000-2017) in June. Finally, drought zoning maps based on the VHI index were produced in five classes: very intense, intense, median, and mild and without drought. The evaluation of the time series derived from the VCI and TCI indices shows that there is a significant relationship between NDVI and LST variations. The results show that an extreme drought class is observed in 2017, covering an area of 46 km² from the plain involved with the extreme drought. This is despite the fact that the highest levels of severe drought class occurred in 2008 with an area of approximately 900 km². The total severe and extreme drought classes are observed in 2007, 2008 and 2017. In 2017, a total area of approxiamtely 844 km² from the Khatun Abad basin was involved with the drought, reaching 902 km² in 2008 and 809 km² in 2007. According to the results, the lowest level of drought in Khatun Abad in 2009 was 34 km² classified as a severe and extreme drought. Additionally, the results of the data analysis using the index SPI show that, the most severe drought in the region occurred in 2008. As a result, 33% of the area was severely subjected to drought, and 65% was placed in the middle class drought. In general, the research results indicate that drought changes in the Khatun Abad plain are not logical, and in different years, different drought intensities have been observed. Drought is a natural and recurrent phenomenon. It is considered ‘a natural disaster’ whenever it occurs intensively in highly populated regions, resulting in significant damage (material and human) and loss (socioeconomic). In this regard, this research aims to evaluate the drought of the Khatun Abad basin using the combination of NDVI (Normalized Difference Vegetation Index) and LST (Land surface temperature) MODIS sensors and an SPI indicator. For this purpose, the VHI index was calculated from the combination of VCI and TCI indices based on the 18-year time series (2000-2017) in June. Finally, drought zoning maps based on the VHI index were produced in five classes: very intense, intense, median, and mild and without drought. The evaluation of the time series derived from the VCI and TCI indices shows that there is a significant relationship between NDVI and LST variations. The results show that an extreme drought class is observed in 2017, covering an area of 46 km² from the plain involved with the extreme drought. This is despite the fact that the highest levels of severe drought class occurred in 2008 with an area of approximately 900 km². The total severe and extreme drought classes are observed in 2007, 2008 and 2017. In 2017, a total area of approxiamtely 844 km² from the Khatun Abad basin was involved with the drought, reaching 902 km² in 2008 and 809 km² in 2007. According to the results, the lowest level of drought in Khatun Abad in 2009 was 34 km² classified as a severe and extreme drought. Additionally, the results of the data analysis using the index SPI show that, the most severe drought in the region occurred in 2008. As a result, 33% of the area was severely subjected to drought, and 65% was placed in the middle class drought. In general, the research results indicate that drought changes in the Khatun Abad plain are not logical, and in different years, different drought intensities have been observed.

https://deej.kashanu.ac.ir/article_114064_3941ca137106fe7959869cf7f55f8f3b.pdf

University of Kashan Desert Ecosystem Engineering 2538-6336 8 Issue 3 in English 2019 03 21 Analysis of the Trend of Dust Changes in Ardestan Region, Iran Analysis of the Trend of Dust Changes in Ardestan Region, Iran 45 54 114065 FA Mostafa Dastorani department of remotsensing/ geographical and enviromental college, hakim sabzevari university. sabzevar, iran Mehdi Jafari Department of Rehabilitation of Arid and Mountainous Regions, University of Tehran, Iran Journal Article 2019 01 18 Dust storms in central Iran are a natural hazard, and Tigris-Euphrates alluvial plain has been recognized as the main dust source in this area. In the present study, changes in dust events during the studied months, seasons and years (2000-2013) for Ardestan synoptic station, and their relationship to drought Standardized Precipitaion Index (SPI) were evaluated. The index is an standard indicator for precipitation. Additionally, non-parametric procedures in statistics, including Mann-Kendall and Sen-Stimator, were utilized to identify the changes trend in frequency of days with dust storms on monthly and annual scales. For this purpose, the statistics of selected station was utilized in a 14-year period. Codes extraction related to dust event (06 and 07) and data analysis were conducted using the MATLAB software, and to study the changes trend in monthly and annual time series, non-parametric test statistics were calculated, and then their significance was evaluated at 5 and 1 percentage error. The results showed that may and spring had the most dust events number compared to other months and seasons. Furthermore, results showed that there was a direct relationship between dust event and drought and years having intensive drought, resulted in more duct events. The results indicated that in Mann-Kendall procedure, of total 13 data series, annual data series and in San-Estimator method, August had positive significant trend at 1% probability level and in San-Estimator method, data series in April and June months had an increasing trend at 5% confidence level. The results of spatial analysis of anemometer data using WR plot showed that direction of dominant winds was toward south. The results showed that the integration of dust model and satellite images of dust could be used as an effective system to assess and alert the dust crisis rapidly. Dust storms in central Iran are a natural hazard, and Tigris-Euphrates alluvial plain has been recognized as the main dust source in this area. In the present study, changes in dust events during the studied months, seasons and years (2000-2013) for Ardestan synoptic station, and their relationship to drought Standardized Precipitaion Index (SPI) were evaluated. The index is an standard indicator for precipitation. Additionally, non-parametric procedures in statistics, including Mann-Kendall and Sen-Stimator, were utilized to identify the changes trend in frequency of days with dust storms on monthly and annual scales. For this purpose, the statistics of selected station was utilized in a 14-year period. Codes extraction related to dust event (06 and 07) and data analysis were conducted using the MATLAB software, and to study the changes trend in monthly and annual time series, non-parametric test statistics were calculated, and then their significance was evaluated at 5 and 1 percentage error. The results showed that may and spring had the most dust events number compared to other months and seasons. Furthermore, results showed that there was a direct relationship between dust event and drought and years having intensive drought, resulted in more duct events. The results indicated that in Mann-Kendall procedure, of total 13 data series, annual data series and in San-Estimator method, August had positive significant trend at 1% probability level and in San-Estimator method, data series in April and June months had an increasing trend at 5% confidence level. The results of spatial analysis of anemometer data using WR plot showed that direction of dominant winds was toward south. The results showed that the integration of dust model and satellite images of dust could be used as an effective system to assess and alert the dust crisis rapidly.

https://deej.kashanu.ac.ir/article_114065_054da8dc6d323007e8f74e8039c2718f.pdf

University of Kashan Desert Ecosystem Engineering 2538-6336 8 Issue 3 in English 2019 03 21 Evaluating Different Functions of Artificial Neural Networks for Predicting the Hourly Variations of Horizontal Visibility under Dry and Humid Conditions (Case Study: Zabol City) Evaluating Different Functions of Artificial Neural Networks for Predicting the Hourly Variations of Horizontal Visibility under Dry and Humid Conditions (Case Study: Zabol City) 55 69 114066 FA Zohre Ebrahimi Khusfi Assistant Professor, Faculty of Natural Resources, University of Jiroft , Kerman,Iran Mahbobeh Moatamednia Ph.D. of watershed Management Engineering, University of Hormozgan Journal Article 2019 02 06 The present research was conducted to compare different functions of two artificial neural networks (ANNs) including the multilayer perceptron (MLP) and radial basis function (RBF) in order to forecast the Horizontal Visibility (HV<1km) in Zabol city under dry and humid weather conditions. For this purpose, hourly data of horizontal visibility (HV), wind speed, relative humidity, temperature, and atmospheric pressure were used. Before importing these data to the ANNs, they were normalized and multicollinearity impact between the climatic variables was calculated using the variance inflation factor. In this study, 70% of data were used for data training and 30% for data testing. Accuracy of the models was estimated using the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and the correlation coefficient (R) between observed and predicted values of HV. The sensitivity of the output data was determined based on the most accurate model. The results showed that according to function MLP4, the prediction accuracy of HV was more than the accuracy of other functions of neural networks (ANNs) for both dry and humid climates. The mentioned error values were estimated at less than 0.5. Pearson correlation between observed and predicted values was estimated according to training data and testing data as 0.66 and 0.7, respectively. These coefficients were calculated 0.9 and 0.99 for humid and dry weather, respectively. Moreover, the wind speed and air temperature for dry and humid climate were identified as the most important factors effective on HV at the time of dust storm occurrence. The present research was conducted to compare different functions of two artificial neural networks (ANNs) including the multilayer perceptron (MLP) and radial basis function (RBF) in order to forecast the Horizontal Visibility (HV<1km) in Zabol city under dry and humid weather conditions. For this purpose, hourly data of horizontal visibility (HV), wind speed, relative humidity, temperature, and atmospheric pressure were used. Before importing these data to the ANNs, they were normalized and multicollinearity impact between the climatic variables was calculated using the variance inflation factor. In this study, 70% of data were used for data training and 30% for data testing. Accuracy of the models was estimated using the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and the correlation coefficient (R) between observed and predicted values of HV. The sensitivity of the output data was determined based on the most accurate model. The results showed that according to function MLP4, the prediction accuracy of HV was more than the accuracy of other functions of neural networks (ANNs) for both dry and humid climates. The mentioned error values were estimated at less than 0.5. Pearson correlation between observed and predicted values was estimated according to training data and testing data as 0.66 and 0.7, respectively. These coefficients were calculated 0.9 and 0.99 for humid and dry weather, respectively. Moreover, the wind speed and air temperature for dry and humid climate were identified as the most important factors effective on HV at the time of dust storm occurrence.

https://deej.kashanu.ac.ir/article_114066_aaf26163afcf5dcb2c1f5c6cc21fb064.pdf