فصلنامه سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی
سال چهاردهم شماره 2 (تابستان 1402)

Drought is one of the most important natural disasters with devastating and harmful effects in various economic, social, and environmental fields. Due to the repetitive behavior of this phenomenon, if the appropriate solutions are not implemented, its destructive effects can remain in the region for years after its occurrence. Most natural disasters, such as floods, earthquakes, hurricanes, and landslides in the short term, can cause severe financial and human damage to society, but droughts are slow-moving and creepy in nature, and their devastating effects appear gradually and over a longer period of time. Therefore, by modeling drought, it is possible to provide plans for drought preparation and reduce the damage caused by it. In this study, computational intelligence algorithms of Multi-Layer Perceptron neural network, Generalized Regression Neural Network, Support Vector Regression with support kernel, and Support Vector regression with the proposed kernel (Support Vector) Regression New kernel has been used to model the drought using the Standardized Precipitation Index. The modeling results, in most cases, showed better performance of the proposed SVR_N model than other models. The values of RMSE and R2 were 0.093 and 0.991, respectively, and the GRNN, MLP, and SVR models performed better in modeling after SVR_N, respectively. Modeling of drought phenomenon in modeling is supported by vector regression method.

Soil is a natural environment for plant growth and due to its importance, the study of heavy metal distribution for soil contamination monitoring and environmental quality maintaining is necessary. This investigation was performed to study the spatial variability of some heavy metals in some surface soils of the Shazand City. A total of 76 soil surface samples were randomly collected from a depth of 0-30 cm in the study area. After preparation of the samples, some physico-chemical characteristics and the total amount of, lead (Pb), cadmium (Cd), nikel (Ni), zinc (Zn) and copper (Cu) were measured. Mean concentrations of Pb, Cd, Ni, Zn and Cu were 31.20, 54.12, 23.11, 2.05 and 59.36 mgkg-1, respectively. Significant correlations were found between Ni with Cu (r= 0.41, p<0.01) and Zn (r= 0.5, p<0.01). Also, a significant correlation was obsrved between Cu and Zn (r= 0.4, p<0.01). The spatial variability of these variables was examined by variogram models and ordinary kriging. The results of geostatistical analysis showed that the spherical model was the best model for describing the spatial variability of all studied elements. Strong spatial dependence (the ratio of nugget to sill less than 0.25) was obtained for the lead element. According to the standard limits, Pb and Cd showed the highest pollution in rangeland and agricultural land use in the study area, respectively. It’s recommended to increasing soil quality and reduces it’s degradation from pollutants applied proper environmental proceeding.

Land subsidence occur due to the natural and human activities. One of the main causes of subsidence is the excessive usage of groundwater resources. In the study area, due to the dry and desert area, low rainfall and lack of permanent rivers, the most use of groundwater resources is that in this area we see many wells for agricultural use. Various types of deformation surveying technique have been used widely to measure the land subsidence pattern.This research is based on radar remote sensing method and field study. First, satellite data and images were examined and processed using the remote sensing method. To investigate the temperature, the radar interference technique was used to process the radar images at specific intervals. The images used relate to the ASAR Envisat satellite in the C band from 2003 to 2010 and the pair of images of the Sentinel 1 satellite in the C band from 2019 to 2020. The results of this technique show that the highest amount of subsidence related to the agricultural lands in the center and north of the plain is about 13.4 cm in the period 2007 to 2010 and also in the period 2007 to 2008 about 13.1 cm. We see the subsidence meter. In the new study of Sentinel1 satellite images in the period 2019 to 2020, this area has also experienced a subsidence of 5.9 cm, which indicates the continuation of subsidence in this region. To verify the results, statistics related to piezometer wells were used and the results show a decrease in groundwater level in the study area in the period 2007 to 2010.

Factors affecting the distribution of plant species include climate, soil properties, topography, land use, and total biological relationships. Climate is one of the most important factors in the distribution of plant species. The present study was conducted to predict the geographical distribution of Acacia tortilis (Forssk. Hayne), to find important environmental factors and to investigate the tolerance of the species to environmental factors in Hormozgan province. According to the purpose, vegetation information and habitat factors such as elevation, climate, geology and soil were collected. Vegetation sampling was done randomly-systematically by plotting along 4 transects of 200-1000 m. Map of environmental variables was prepared using GIS. Then, prediction maps related to species distribution were prepared using entropy modeling method. The accuracy of the obtained prediction models was evaluated using AUC statistics. In general, the results showed that the variables of total rainfall in the hottest season, seasonal changes of rainfall (coefficient of variation), total rainfall of the lowest rainfall of the year, average temperature of the driest season, average of the warmest season are the most important climatic characteristics. They affect the distribution of Acacia tortilis (Forssk. Hayne).characteristics. They affect the distribution of Acacia tortilis (Forssk. Hayne).The results of this study can solve the problems facing habitat management. In fact, when a species is threatened by habitat destruction, by recognizing the factors to which the species is highly dependent, conservation plans can be proposed according to the habitat needs of the species.

Global climate change and overpopulation increased the need for groundwater resources. Land subsidence due to groundwater extraction and water depletion is one of the environmental hazards that threatens the communities. This research has been done as a case study in Semalghan plain, one of the agricultural plains in North Khorasan province. According to the information of the Regional Water Organization of this province, most of the groundwater resources have been extracted from the deep wells of this plain and groundwater resources are in a critical situation. In this study, to investigate land subsidence due to groundwater depletion in Semalghan plain radar differential interferometry technique of Sentinel-1 satellite Descending data from 2014 to 2020 have been produced and analyzed. Also, to monitor vegetation changes Landsat-8 satellite images and study of aquifer interaction behavior was applied. Groundwater depletion maps have been produced using GIS and piezometric data of plain, and their relationship with subsidence was assessed. The result maps showed a direct relationship between water table head fall and subsidence in the Semalghan aquifer during the study period. The maximum subsidence which occurred in this plain in 2019 is equal to 34 cm and a minimum of 18 cm in 2015 and 2018 respectively. Finally, the relationship between groundwater level decline and land subsidence was studied and the behavior of the aquifer was investigated. The results showed the elastic behavior over the Semalghan aquifer and around Chamanbid city. Furthermore, by processing Landsat-8 images from 2015 to 2020, the temporal-spatial relationship of vegetation changes with subsidence was studied. The results showed that the destruction of vegetation has occurred by 5.9% in some areas in the plain due to groundwater level decline which has occurred all over the plain and subsidence in the region and revealed a high temporal-spatial correlation.

Iran's location in the desert belt of the northern hemisphere, rainfall irregularities, multiple droughts and poor vegetation have caused the phenomenon of dust in the country. This phenomenon is one of the most important environmental hazards that causes a lot of damage every year in different parts, especially in the southern and southwestern provinces of the country. The present study investigates the causes of dust storms in Hormozgan province in the period from 2002 to 2009. In this study, which was conducted using the statistics of the Meteorological Organization of Iran in five synoptic stations of Hormozgan province, dust days with two sources near and outside the synoptic stations were extracted and after annual and monthly monitoring of storms, using climatic elements. And Plant Index (EVI) was investigated for dust storms using multivariate regression. The results show that dust with a source close to the station causes the most dust days in the region, so that more than 65% of dust days caused by dust originates near the station and the most occurrence of dust occurred in the warm season. Overall, the results of this study indicate that the simultaneous use of ground measurements and satellite images has a good ability to monitor and investigate dust storms. Due to the fact that most of the dust in this area is close to the station, it is possible to reduce the significant volume of these storms by biological and mechanical measures and to deal with wind erosion.