مجله مدیریت بیابان
پیاپی 21 (بهار 1401)

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

Meteorological drought is a complex natural disaster that occurs everywhere in the world. Predicting the occurrence and severity of drought can be effective in managing water crises and their impacts. The purpose of the current study is to select the most appropriate model from statistical models and artificial intelligence (artificial neural networks) to predict drought in Isfahan synoptic station during the span period of 1990-1920 using the Z-Score index (ZSI). In this study, the capability and efficiency of the SARIMA stochastic linear model and three advanced learning machine models of the Feedforward Neural Networks (FNNs), Multi-layer Perceptron (MLP), and Extreme Learning Machines (ELM) were evaluated based on Root Mean Squared Error (RMSE), Mean Absolute Square Error (MASE) and Mean Absolute Error (MAE). The results showed that among the many models made, the Feedforward Neural Networks (FNNs) model with RMSE of 0.33, MASE of 0.02, and MAE of 0.22 were selected as the best model. Using the superior model, precipitation for the period of 2025-2021 of Isfahan synoptic station was predicted, then based on the ZSI drought index, drought intensity of forecast precipitation data in 3, 6, 9, 12-month time scales, 18, and 24 months was calculated. The results of drought severity predicting showed that severe drought in 3 and 6 month time scales in 2021 and 2023 and in 9 and 18 month time scales in 2024, moderate drought in all time scales in 2024, and weak droughts occurred at the 3, 6, and 24-month time scales in 2024 and 2025, respectively. Overall, the results showed that the use of feed neural network model was more efficient. Since predicting drought at all time scales can reveals drought more accurately, this predicting in turn to facilitate the development of water resources management strategies for management of drought is effective.

TWater deficit is considered as one of the limited factors in agriculture in the arid and desert regions of the country. The cultivation of drought tolerant plants such as safflower (Carthamus tinctorius L.) and using methanol to increase the drought resistance of these plants is an appropriate solution for agricultural development in these regions. For this purpose, an experiment was carried out in the design of split plots in a randomized complete block with three replications in the growing season of 2019-2020 in Iranshahr. Experimental treatments were consisted of three drought stress levels including no stress, moderate stress and severe stress, as the main plots, and the four methanol levels including no use, using 10, 20 and 30% volume of methanol as sub plots. The results showed that the highest values of the studied traits were obtained from no stress treatment and foliar application of 30% volume of methanol. However, for the 1000 grain weight, grain and oil yield, there were no statistically significant differences between 30 and 20% by volume of methanol.  For all the studied traits, there was no statistically significant difference between the absence of stress treatment and the absence of methanol use, with moderate stress treatment and foliar application of 30% methanol volume. The lowest values of the traits studied were achieved by severe stress treatment without foliar application.

The present study aimed at preparing the potential distribution map and identifiying the environmental requirement of Prosopis farcta L. using tree-based and regression methods in the marginal rangeland of Niatak river in Sistan region. For this purpose, species presence data was recorded randomly. Environmental variables were prepared using field sampling and digital elevation model. In order to achieve the pseudo-absence points, first habitat modeling was performed using the domain model, then pseudo-absence points were prepared using the prediction map obtained from this method. Species distribution modelling was conducted using random forest (RF), classification and regression trees (CART) and generalized additive model (GAM). The accuracy of the models used was evaluated using the area under curve criterion. Result showed that the RF with area under curve 0.98 has the highest accuracy. Generalized additive models and classification and regression trees were ranked after RF. The highest and lowest values of kappa index were assigned to the RF with 0.75, and GAM with 0.43 Kappa value. Accordingly, the RF model is the most accurate model in predicting the potential habitat distribution. Analysis of the variable’s importance showed that in the studied scale, edafic factors and distance from the river have greater effect on species distribution than other factors. So that, in all models used, acidity and electrical conductivity were identified as the most important variables. In general, it is suggested that habitat development plans for Prosopis farcta should be planned in the central and marginal parts of the Niatek river due to better suitability of these regions for species distribution.

The purpose of current study was to study the physiological and phonological traits of Salivia eremophila Boiss. As a native and medicinal species in three rangeland sites in Yazd province (Iran). For this perpose three ares (Damaghan, Tange-Chenar and Ghavam Abad) and three sites in each area, were investigated in terms of Soulobe carbohydrates, Proline, Chlorophyll and Carotenoids in aerial parts of Salivia eremophila in two phonological stages: Flowering and seeding. Plant sampling was done based on randomized-systematic method in 1M2 plots, along with 50 m2 transects. Also, phenolocical diagram of Salivia eremophila was figured based on consecutive field investigations. Data were analyzed at level of 5%. The results showed that there are significant differences in interaction effects among phonological stage, site and area. site No.1 of Tange-Chenar had significant difference in Chlorophyll (a & b) with the other sites (p<0.05). As for Carotenoids, There were significant difference between sites and areas, in which site No.3 of Damgahan in seeding stage, and site No.1 of Tange-Chenar had the higher rates. Also, the rate of Proline in site No.1 of Damgahan (in flowering stage) was higher than the other sites and areas and it showed significant difference. The amount of Soulobe carbohydrates had significant difference in sites and phonological stages in general (except flowering stage). In overall, the climatic conditions of the sites of studied areas are almost similar in terms of annual rainfall, as well as rangelans type and altitude, and this can affect the occurrence of low changes in plant nutrients

Dust, the main pillar of air pollution, has always been an important subject of study on several levels. Over the last few years, it has become increasingly concentrated in various regions. The present study, which uses Google Earth engine system and MODIS satellite data with 8-day temporal resolution and spatial resolution of 1km and 250m, as well as statistical methods such as correlation and averaging of Triple Exponential Smoothing (TES), to monitor and predict spatial and temporal changes of airborne dust in Qazvin province. For this purpose, using Aerosol Optical Depth Index (AOD) images, Optimal Vegetation Index (EVI) and Modis Heat Island (HI) change index, and preparing their temporal and spatial monitoring maps during the statistical period 2015-2020 and predict to 2030, the relationship between these factors was examined. The results of the present study showed an increase in the quantity of airborne dust in Qazvin province from 0.461 in 2015 to 0.603 in 2017. his rate was then reduced by 0.493 in 2018 and by 0.575 in 2019. The quantity of airborne dust, then fell slightly to 0.5366 in 2020. The results showed a negative relationship between precipitation, relative humidity and vegetation, and a positive relationship between wind speed, freezing days, temperature, variations in temperature islands and hours of sunshine with AOD. The present study also examined changes in airborne dust concentrations in two high- and low-risk classes, and predicted a high-risk class for 2030. This finding is helping policymakers and planners reduce dust pollution in cities through vegetation management and reduce the heat islands, as well as implement climate programs to manage precipitation and moisture in cities.