نشریه کاربرد سنجش از دور و سیستم اطلاعات جغرافیایی در علوم محیطی
پیاپی 6 (تابستان 1402)

Air pollution is one of the most significant challenges in today's world, particularly in developing countries. Therefore, it is necessary to monitor and control the amount of pollutants that threaten human health. In recent years, remote sensing data related to the Sentinel 5 Tropomy sensor has been considered a rich and up-to-date source of information for monitoring and investigating the temporal-spatial changes of air pollutants. In this study, the Google Earth Engine system was used to obtain Tropomi sensor products to check the amount of nitrogen dioxide pollutants in East Azerbaijan province. For this purpose, the distribution map of the NO2 was produced in four consecutive years (1397-1400). Additionally, due to the effective role of motor vehicle traffic in increasing the amount of this pollutant on the one hand and the restriction of activities and traffic of cars during the spread of the Covid-19 virus on the other hand, the changes of nitrogen dioxide in different time periods were investigated to determine the effect of restrictions on the concentration of this pollutant quantitatively. Also, in order to investigate the impact of the reopening of schools and universities after the corona pandemic, pollutant distribution maps were produced and compared for April 1400 and April 1401. The results showed that the density of NO2 in the center of the province was higher than in other cities in all time periods, and the 14-day closure in 2019 had the greatest effect in reducing the pollutant, which was about 59% in Tabriz.

Flood zoning maps provide valuable information about the nature of flood and its effect on floodplain lands. A set of effective factors must be defined to map flood susceptibility or, in general, to develop a model for assessing natural disaster risk. The factors affecting flood were used in eastern catchment area of lake Urmia which include altitude, slope, distance from the river, topographic moisture index, topographic position index, roughness index, curvature level, topographic curvature section, total curvature, NDVI index, land use, lithology and rainfall according to the experiences of experts and researchers reported in previous studies. After preparing the effective layers on the flood and the point layer of the flood points, as well as performing the linear test, five methods including Multiple Linear Regression, Partial Least Squares Model, Quantile Regression, Ridge Regression and Robust Regression were used for modeling and predictions. Then the ROC curve was used to validate the results. The results of this validation showed that the partial least squares (PLS) and multiple linear regression (MLR) models with the maximum area under the curve (AUC) (0.983 and 0.997, respectively) and the lowest standard deviation (0.015 and 0.018, respectively) ) have performed better. Among these two models, PLS has slightly better results than MLR. Finally, a random forest model was used to determine the importance of the input factors, and it was found that the factors of height, distance from the waterway and slope percentage are the most influential factors on floods in the study area.

Over the past two decades, the expansion of Shahrebeez to the surrounding areas has caused the destruction of resources and environmental problems, including the disruption of the ecological balance, the increase in service costs, the construction of unsuitable land, the aggravation of air pollution, and the lack of attention to existing worn-out structures and progressing trends. Previously, environmental quality has been reduced. The purpose of this study was to analyze the changes in urban land use of Tabriz with satellite images to determine the nature and intensity of these changes and in which directions and in which cases they are more so that a solution to these problems can be considered. In this research, land use changes in Tabriz city in three periods (2000, 2005, 2010, 2015, 2020) were investigated using multi-temporal data of Landsat satellite images in ENVI 5.8 software, and the results were analyzed in Arc GIS 10.2 software. And its results were produced in the form of a map. The obtained results show that the lands built during the period of 20 cases of Matala, from 30% in 2000 to 0.53 in 2020, which has increased by 23.0% in this 2020 year. Undeveloped land has also reached 0.42 in 2020 from 62.00 in 2000, which has gone through a significant decrease and changes in the last 20 years and has decreased by 0.19. Vegetation uses have decreased from 11.0% during the studied 20-year period to 4% in 2020.

Understanding the impact of unmanned aerial vehicle (UAV) photogrammetric network design parameters on 3D reconstruction quality is crucial for achieving optimal spatial information production from UAV images in a UAV-based project, considering existing conditions and limitations. The purpose of this research is to assess the impact of ground control point network architecture and distribution on the accuracy of spatial information and maps generated from drone images. The design and distribution of the ground control point network have been examined in two modes and three distinct scenarios for this aim. Control points ranging from 4 to 42 points were designed just in the side models in the first case (A) and both around and in the center position of the photogrammetry block in the second case (B). According to the data, mode B had the highest measurement accuracy in both urban and non-urban regions, as well as the best results and the least error in each of those scenarios. The findings showed that the average accuracy loss in UAV-based outputs in the first scenario is at least 12% in the arrangement of A and B control points. Furthermore, the accuracy loss in the second and third scenarios is at least 0.59% and 0.53%, respectively, when compared to the first scenario. In general, it is not suggested to construct and expand control point distances more than 30 times GSD, and if these distances are exceeded, control points must be designed in central block models.

Dust at different levels in the city can be a good indicator of the presence of these heavy metals in the city, therefore the main goal of the present study is the spatial analysis of heavy metal contamination of surface dust in the northwestern area of Tabriz city. For analyzing, Moran's index, hot spot analysis and ANOVA test were used in Arc Gis10.8 and Spss20 software. The studied heavy metals included; lead, chromium, manganese, zinc, copper and iron. For this purpose, samples were taken from 12 stations in the area. After the initial preparation, the concentration of heavy metals in the samples was obtained using an atomic absorption spectrometer. Based on the findings of the Moran index, the research showed that the spatial distribution of heavy metal pollution follows a cluster pattern, . Moran's index analysis shows the highest z score of 204.24 and 140.12 respectively for zinc and copper metal, which shows the intense spatial concentration of these two polluting elements in the studied area.The results of the ANOVA test also showed that the level of heavy metal pollution changes with the type of use. In fact, it can be said that the spatial distribution of heavy metal pollution differs according to the type of urban use. The most observed pollutions were in commercial-service, administrative and law enforcement uses.

 the process of water scarcity in Iraq has intensified due to natural and political factors, especially in its central and southern parts. In this regard, the current research has evaluated the trend of land use and land cover changes in Babol province in the center of Iraq and analyzed the changes and developments in the surface water resources of this region. The current research is based on the supervised classification of satellite images of Babol Province, Iraq, and in this regard, ETM+ sensor images of Landsat 7 satellite in 2003, OLI-TIRS sensor images of Landsat 8 satellite in 2013 and Landsat 9 satellite in 2023 were used and the maximum likelihood technique was performed on them. According to the results, during the first ten-year period (2003-2013), the surface water area of the region has decreased from 729.44 square kilometers to 174.14 square kilometers, which means a 76.13% decrease in the area of this group of lands. In the second ten-year period (2013-2023), the extent of surface water has increased from 174.14 square kilometers to 825.61 square kilometers, which indicates a growth of 374.1 percent. The results of the research have determined that factors such as the development of agricultural lands, the growth of urban and rural constructions, and climatic factors have emerged as the main factors of land use change in Babol province, and the cause and origin of land use changes in Babol province is not only water shortage.