نشریه سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی
سال پانزدهم شماره 3 (پاییز 1403)

Among environmental changes, water plays a very vital role in the political, social and economic issues of countries, which can be used as one of the most practical sources of water supply available to humans and animals. Investigating the fluctuations of the water level of the lakes in terms of the importance, location and nature of these water bodies has become especially important in recent years. Lake Urmia with an area of 51200 square kilometers is of special importance as the largest internal lake of Iran and the 20th lake in the world. Landsat-8 and Sentinel-2 satellite data for the years 2013 to 2021 were used to investigate and evaluate changes in the water level of Lake Urmia, vegetation and soil around it. First, radiometric and atmospheric corrections were made on the images, and then, while using Gram Schmidt and LMVM integrators to increase spatial resolution, NDWI, AWEI, WI2015 and NDVI indices were extracted in order to differentiate the lake water level from non-water. paid. Finally, by combining the indicators with each other and by sampling the training and test samples, in order to classify the images, supervised classifiers such as Maximum Likelihood, Support Vector Machine, Neural Network and Minimum Distance to Mean were used. Also, in order to improve the results, the output of the classifiers was merged using the majority voting method. The results of the research showed that the majority voting method was chosen as the most suitable classification method with the highest level of accuracy. The water level of Lake Urmia, the vegetation and soil around it have also undergone significant changes during the years 2013 to 2021, so that in 2021, compared to 2020, the water level decreased by 29.89%, and the vegetation increased by 16.08%. And the soil has increased by 17.50%.

Soil pollution and heavy metal accumulation are one of the most important environmental problems to plants, animals and human health. In this research, distribution maps of heavy metals (Cd, Pb, Ni, Zn, Fe, Mn and Cu) were prepared and the pollution status was assessed in the arable land of the Alborz Dam downstream basin.For this purpose, 215 surface composite soil samples were provided in depth of 0-30 cm. Cd, Pb and Ni were extracted by 0.05 M Na2EDTA extractant and the other elements including available Zn, Cu, Fe and Mn extracted by DTPA extractant and measured using AAS. After checking the normality of the data, the best semivariate model was selected based on the lowest fitting error value. Spatial distribution maps were prepared by conventional kriging method in GIS environment. The results showed that the best semi-variable models for the studied data were spherical and exponential models. The average concentration of harmful heavy metals to plants, including Cd, Pb and Ni in the soils of the region is 0.6, 11.5, and 4.9 mg/kg, respectively, which is less than the maximum permissible concentration. The mean concentration of other heavy metals that are required by the plant, including Zn, Cu, Mn and Fe, in the soils of the region is 1.13, 3.9 and 14.8 mg/kg, respectively, which is not only lower than the maximum permissible concentration but in some areas, Zn and Mn deficiency was observed. The correlation between the three elements Cd, Pb and Ni is significant and positive. There is a negative and significant relationship between soil lime and Cd, Pb and Ni. The higher the amount of lime in the soil, the less mobility of these elements which causes the precipitation of heavy metals in the soil.

In optimal planning and management of natural resources, knowledge of how land cover changes and land use and the factors that cause it are very necessary. In this field, remote sensing data have high potential to study temporal and spatial changes in land cover and land use. The purpose of presence study is prediction and assessment of the trend of land use changes and land cover in Zahak area of Sistan plain. For this purpose, land use and cover maps were prepared from landsat satellite images using support vector machine method of supervised classification in 1987, 2001 and 2018. Then, using the land use map in 1987 and 2001, land cover in 2018 was predicted. Land use maps for 2001 and 2018 and land cover for 2030 was predicted using integrated method of Markov chain and automated cells. To analyze the trend of land use changes and land cover since 1987- 2001, 2001- 2018 and 2018- 2030, Land change modeler was used. Results indicated that areas of watery agriculture 2013 hectares, tree cover 1117 hectares, water areas 2391 hectares and barren lands 9535 hectares has decreased since 1987- 2001. Also, the mulching area uses area 192 hectares, and sand dunes 14864 hectares were increased. During the period 2001- 2018, the areas of watery agriculture land uses 3533 hectares and barren lands 3707 hectares has decreased and uses area tree cover 313 hectares, water areas 5385, mulching area 247 hectares, and sand dunes 1295 hectares were increased. In the forecasting the time period 2018- 2030, the area of uses watery agriculture will be 1098 hectares, sand dunes 527 hectares, and barren lands 2020 hectares are reduced. In this forecast, land use of tree cover 16 hectares, water area 3607 hectares, and mulching area 23 hectares will increase.

Extensive exploitation of groundwater resources in the Mashhad-Chenaran aquifer in the last 50 years (1970-2021) has led to a sharp decline in groundwater levels. Negative effects of subsidence due to the severe development of groundwater resources have occurred in some parts of the plain since the late 1980s and in recent years has covered the entire plain. In this study, subsidence of the entire Mashhad-Chenaran plain has been investigated using Sentinel-1A radar image data between 2014 and 2020 using Small Baseline Subset(SBAS) Interfergrametry method. Examination of subsidence maps shows that there are three subsidence areas including area 1 in the southeast of Mashhad, area 2 in the northwest of Mashhad and area 3 in the northwest of the plain. The maximum annual sitting rate in the two subsidence zones is equal to 19 cm per year and in the two subsidence zones 1 and 3 are 8.9 and 12.1 cm per year, respectively. The results of the time series rate estimated using radar image processing at the location of Golmakan GPS stations and Toos power plant with the actual measured values show the high compliance of these two time series. Adaptation of 30-year groundwater level drop curves and subsidence map shows that the triple subsidence zones correspond to areas with groundwater level drop of more than 30 meters. Also, to investigate the relationship between subsidence and groundwater level drop, correlation coefficients and determination coefficients for each well have been calculated using groundwater level and subsidence data at the location of selected observation wells. Based on the obtained correlation coefficients, the selected wells are divided into two groups with a correlation coefficient of more than 0.8 (75% of wells) and between 0.46 and 0.008, which indicates a good correlation between subsidence rate and groundwater level drop in the most observation wells.

Heat capacity is a physical quantity of the surface that is directly related to the amount of heat energy required to change the surface temperature. Land surfaces with a high of thermal capacity are a sign of moderate climatic conditions and the presence of the low ones is known as a reason for the occurrence of desert climate conditions. When uniform heat energy absorption is occurred by different surfaces, their temperature changes can be inversely related to the heat capacity. However, due to obstacles and shadows, the sunlight as the most important factor in the reception of heat energy during the day does not receive uniformly to the surface of the earth. In this article, by adjusting the shadows effect the differences in sunlight energy received by different parts of land surfaces are modeled. Then, by calculating the day and night land surface temperatures a method has been proposed for estimation of relative heat capacity. In this method, the time series of MODIS images are used to reduce the destructive effects of atmospheric conditions in the estimation of land surface temperature. The percentage of shadow’s presence in each position is also estimated through the spatial analyses on digital elevation models. The proposed method has been calibrated through the ground truths identified with expert knowledge about the soil properties. The results demonstrate that the efficiency of the calibrated method reaches the overall accuracy of 93% in a relative assortment of land surfaces in terms of their heat capacities.

Therefore, the purpose of the current research was to investigate the health of Koozeh Topraghi watershed using the Vigor, Organization and Resilience (VOR) model during the years 2000, 2010 and 2021. Ecosystem resilience is considered as the potential of an ecosystem to respond to external disturbances by restoring its original structure and function. Each type of land has a different ability to recover, depending on its nature.To calculate resilience, a coefficient was determined for all types of land uses according to the type of service for each user. The results showed that in Koozeh Topraghi watershed, the Vigor index has the mean and standard deviation of 0.88±0.05 in the first year, 0.81±0.08 in the second year, and 0.17±0 in the third year. Were 0.68. The Organization index had the mean and standard deviation of 0.57±0.08 in the first year, 0.70±0.08 in the second year, and 0.68±0.07 in the third year. Also, the resilience index had the mean and standard deviation of 0.28±0.22 in the first year, 0.30±0.25 in the second year, and 0.29±0.25 in the third year. Also, the watershed health index (WHI) in 2000 was equal to the numerical value of 0.49 and in 2010 it had an increasing trend and reached 0.85, but in 2021 it decreased to 0.47. It can be concluded that the health of the watershed has decreased slightly in the studied period. Various reasons, inappropriate land use especially pasture production for agriculture, and changes in climatic and hydrological components, have caused inappropriate health conditions in the Koozeh Topraghi watershed.