فصلنامه سنجش از دور و GIS ایران
سال پانزدهم شماره 4 (پیاپی 60، زمستان 1402)

Trend analysis of growth of cities and predicting their changes in the future are essential for spatial ‎planning. For this purpose, it is necessary to map build-up areas. In many areas, especially in arid ‎climate, it is not possible to separate the build-up areas from the surrounding land cover simply. That's ‎mean the usual methods of classifying satellite images or conventional indices can’t separate mentioned ‎classes with acceptable accuracy. Hence, many researchers have developed different spectral indices to ‎extract the build-up areas. The use of surface temperature changes to represent build-up areas using the ‎Local Climate Zones (LCZ) algorithm is less considered and is a relatively new method. Therefore, in ‎this paper, the separation of build-up areas from the other surrounding land cover was considered using ‎LCZ algorithm. There is no limit to the number of bands in this method, thus four series of Landsat ‎satellite images in the year 2020 were used and the LCZ algorithm’s accuracy was compared with the ‎latest automatic classified build-up indices including DBI, BLFEI, BAEI and BAEM. The results of this ‎study showed that the classification accuracy of the LCZ algorithm was 96%, while the BLFEI and ‎BAEM indices were not able to completely separate the build-up areas from other types of land cover. ‎The total accuracy of the BAEI index was 0.37. Therefore, the use of LCZ method has a high efficiency ‎compared to build-up indices, and it is recommended in arid and semi-arid zones.‎

Ground surface ozone is one of the most dangerous pollutants that has significant harmful effects on the residents of urban areas. The purpose of this study is to identify the factors affecting ozone concentration and modeling its changes using satellite data and different machine learning methods in Tehran. For this purpose, pollutant concentration and meteorological data were used along with the satellite product of land surface temperature (LST) in the period from 2015 to 2021. After calculating the correlation between ozone concentration and independent parameters, ozone concentration modeling was done in five different modes in terms of input parameters and learning method and applying data refinement. In the first and second mode, modeling was done using pollutant concentration and meteorological data through multivariate linear regression method. The only difference between these two modes is the filtering of the input data using the WTEST method in the second mode. In the third mode, the LST product was added to the input data, and in the fourth and fifth mode, ozone modeling was done using multilayer neural network and recurrent neural network, respectively. The comparison of the five modes showed that the modeling of the first to fifth stages with adjusted coefficient of determination of 0.5, 0.64, 0.69, 0.74 and 0.8 were able to recover the ozone concentration, respectively. It was also found that among different pollutants, nitrogen monoxide, nitrogen dioxide and nitrox have the greatest impact on ozone concentration, just as temperature, humidity and wind speed are the most influential among meteorological data. Although the use of WTEST statistics led to the identification and elimination of inconsistencies and errors in the observations of pollution measurement stations, the neural network learning method showed better performance in modeling than multivariate regression due to its less sensitivity to noise. As a notable result, adding the LST product to the input data brought a 5% increase in accuracy in estimating ozone concentration.

In order to know the site, obtaining soil strength parameters is necessary and at the same time costly and time-consuming. In this research, using 135 geotechnical boreholes drilled in Kermanshah, zonation of soil shear strength parameters (friction angle and cohesion) using ArcGIS software and ordinary kriging interpolation method (with spherical, exponential, and Gaussian semi-variograms), Up to a depth of 9 meters in three-meter intervals has been done and using the root mean square error (RMSE) and mean absolute error (MAE), the best model for predicting the characteristics has been selected. Based on the error evaluation indicators, the best variograms for zonation friction angle and cohesion at depths of 0 to 3 m is Gaussian, 3 to 6 m is exponential and 6 to 9 m are Gaussian and spherical, respectively. According to the results, mainly with increasing depth, angle of friction and cohesion (shear strength) has increased and the northern and southwestern parts of Kermanshah, compared to other regions, have soil with higher friction angle and lower cohesion (coarse-grained), and the northwestern parts of the city have clay and alluvial soils that the results are confirmed by the passage of the Qarasu river through this area and the location of the northern and southern regions of Kermanshah at the foot of the mountain.

The Central Plateau of Iran, due to climate changes and the reduction of available water resources on one hand, and the increase in population and the consequent increase in demand on the other hand, is facing a severe water crisis. The science of remote sensing and the availability of numerous satellite products have made it possible to monitor the process of changes in various environmental parameters, especially surface and underground water sources, with appropriate accuracy. For this purpose, using the Google Earth Engine system, 16 different satellite products including different environmental parameters such as precipitation, temperature, evaporation and transpiration, soil moisture, runoff, total water storage (GRACE), vegetation cover index and water surface area were received and prepared for the time period 2000-2022. Then, using the non-parametric Mann-Kendall test and the Sen’s slope estimator, the change trend of these parameters was investigated. According to the results, the changes in earth's gravity, which indicates the level of underground water, as well as the area of water surfaces, which indicates surface water resources, and soil moisture, showed a significant decreasing trend. On the other hand, maximum temperature, minimum temperature, potential evaporation and transpiration and NDVI index have a significant increasing trend. Despite the decrease in water surface area, the vegetation cover index has increased, which indicates the increase in the area under cultivation of agricultural products and excessive harvesting of underground water resources, which is also confirmed by the decreasing trend of the GRACE satellite product. The correlation coefficients between parameters with significant trends also showed that there is a significant correlation between GRACE and NDVI parameters, minimum temperature, maximum temperature, soil moisture and area of surface water bodies and and in future researches, satellite products related to these parameters can be used to evaluate the condition of surface and underground water resources in other regions and basins of Iran.

Wetlands as an integral part of the global ecosystem in flood prevention or mitigation, feeding aquifers and providing unique habitat for plants and animals and other services and benefits are key elements of a regional conservation strategy. Anzali International Wetland in Guilan Province is one of the 10 most valuable wetlands in the world, which has undergone many changes in land use and vegetation due to structural changes resulting from man-made processes, and its nature and ecological functions have been endangered. The purpose of this study is to investigate the application of remote sensing data in mapping changes in the spatial pattern of the landscape with the help of field work training areas at the bed of the wetland and to analyze the changes of territorial cohesion based on the metrics of the landscape. First, satellite data were analyzed and Sentinel-2 images from 2016 to 2020 were classified by training areas. Then, a map of land cover in 7 classes of agriculture, barren, reed, forest, rangeland, water and urban area was created for mapping and analysis of land use metrics. After extracting class-level and landscape-level metrics in Fragstats software and determining appropriate metrics using PCA method with R and Canoco software, LPI, LSI, ENN_MN, CA, TE, NP, SHAPE_MN, PARA_MN, IJN, ARE_ Applications were selected for better analysis of the area. Analysis of metrics indicates that, in general, the landscape is fragmented, more complex and irregular in form, and more discontinuous in terms of the degree of integration of structural elements.

Normally, images with a high resolution (temporal or spatial) are available, while there is a limitation in accessing images which are simultaneously high spatial and temporal resolution. While, in some applications, access to images with high spatial and temporal resolution is necessary. Therefore, this study was conducted to downscaling MODIS images to Sentinel-2 spatial resolution by STARFM, ESTARFM and FSDAF spatio-temporal downscaling algorithms in different land cover classes including urban, garden, pasture, agricultural and water classes. The study area was selected with a variety of land covers around the city of Mahabad, Iran. First, the corresponding visible and near-infrared bands in Sentinel-2 and MODIS were selected and necessary pre-processes such as geometric correction were done on these images. Then, Sentinel-2 images were simulated using downscaling algorithms. The results indicated the accuracy of downscaling in the urban, garden and pasture classes compared to the agricultural and water classes. So that the ESTARFM, FSDAF and STARFM algorithms averagely showed the coefficient of determination of 88.25, 87.25 and 86.5 for the urban class, the coefficient of determination of 83.75, 83.25 and 80.5 for the garden class and the coefficient of determination of 90.75, 70.5 and 87.5 for the pasture class in all bands.

Urmia Lake is one of the largest saltwater lakes in the world, which unfortunately is drying up and has caused many dangers and concerns, especially in relation to salt dust in its dried areas. Therefore, in this research, we tried to investigate the relationship between vegetation and dust in the cities around Lake Urmia. Salinity in plants causes physiological disorders, salt stress causes growth, photosynthesis, protein, respiration, energy production, premature senescence, water reduction in plants. Considering these effects, it was tried to evaluate the overall health of plants by using related indicators including NDVI, CIre, GCI, CRI2, NDWI, NDII, MSI, PSRI. These indicators evaluate the amount of plant water, plant water stress, photosynthesis capacity, plant growth and water deficit, the amount of chlorophyll, nitrogen and pigments, which are related to plant energy and health. According to these indicators, the health of plants is generally in a favorable condition, and mostly the highest numerical values of the indicators were assigned to gardens. Using Landsat and Sentinel 2 images and the NDVI index, the vegetation changes of the region were determined in the period from 2010 to 2020, and then using the MERRA-2 database, the amount of dust concentration was also extracted for the mentioned years. The results showed that the average NDVI in the studied area follows a constant trend with an overall average of 0.2957 and sometimes it increases or decreases due to the influence of external factors such as dust. Based on this, the highest (0.3495) average NDVI is related to 2018 and the lowest (0.2579) is related to 2013. Also, two methods of linear and logarithmic regression were used to investigate the relationship between vegetation cover and dust, and the results showed that based on the linear (0.7703) and logarithmic (0.7915) regression, the highest coefficient of explanation between the two mentioned indicators was in May. have been.