فصلنامه سنجش از دور و GIS ایران
سال پانزدهم شماره 2 (پیاپی 58، تابستان 1402)

The increase in urban population following migration from villages and sometimes the uneven development of villages and the transformation of villages into cities are problematic factors in the environmental structure of developing countries, resulting in ecological changes, especially the destruction of natural landscapes. And reducing them is in the interest of man-made landscapes and disrupting spatial patterns of natural cover. The purpose of this study is to evaluate the ecological changes and measure the patterns governing the landscapes of Varzeqan city using quantitative and qualitative metrics of land appearance in the years 1363, 1381, 1398. The nature of research is developmental-applied and descriptive-analytical. Data were collected through library studies and field studies to calculate the detection of changes, and to measure the quantitative and qualitative metrics of the landscape. After preparing the land cover maps in ENVI software environment, TerrSet software was used to calculate the detection of changes and quality indicators of the land appearance and Fragstats software was used to calculate quantitative metrics. The results showed that the most changes are related to vegetation classes, especially at low density level and in the second interval, which in addition to reducing the area, also includes increasing the number of spots. And has the largest share in becoming barren and pasture lands. Also, two quantitative metrics of spot number (NE) and landscape percentage (PLand) of quantitative metrics and patch area index and patch compactness (Patch Compactnees) more favorably show the changes and patterns governing the appearance of a land. they give.

One of the new and unique sections, especially in internal studies, is the quantitative examination of unevenness. The scientific and quantitative study of topographic position has always been one of the topics that have received little attention in domestic research. So, classification and identification of different morphometrically distinct regions are necessary. Thus, the present study aims to classify landforms in the northwest of Fars province, Kharestan region and investigate its factors affecting. In this regard, the Topographic Position Index (TPI) method was used in the first stage to classify landforms, followed by the CORINE method to determine erosion risk classes. Additionally, Landsat 8 satellite images from June 2017 were used to determine the normalized differential vegetation index (NDVI). The next step was to determine the relationship between different types of landforms and terrestrial factors such as height, slope, slope direction, topographic wetness index (TWI), Terrain Ruggedness Index (TRI) and NDVI. Finally, the status of different landforms was determined based on erosion risk classes. Results showed ten different types of landforms existed within the study area. Small plains (1.18%) were the lowest in the study area, while waterways (27.71%) and high peaks (27.48%) were the highest. The TWI was significantly correlated with landform classes at 95% level. Most of the region (91.71%) had NDVI classes of 0.1 to 0.3. Stream and u-shaped valleys were found to have higher NDVI values. Real erosion risk was classified into three classes: low, medium, and high with areas of 31.14, 31.11, and 37.78%. There were 44, 57, and 59% erosion levels in the low, medium, and high erosion classes, respectively.

The world is warming and the world's population is moving to cities. These two truths do not seem to be related; But a phenomenon called urban heat island connects the two. UHI is one of the most common urban climate phenomena in which some urban areas, especially urban centers, become several degrees warmer than the surrounding areas. Studying this phenomenon and examining its mechanism is very important for urban planning. In the present study, in order to estimate LST, four single-channel Landsat algorithms, single window, Planck equation and radiation transfer equation in QGIS software environment between 2000 and 2019 in summer and winter seasons in Birjand city have been used. The effect of land use change on the thermal island has also been investigated. In the present study, ground surface temperature in Birjand city was first extracted using Landsat 7 ETM + satellite imagery and Landsat 8 TIRS / OLI sensors in 2000 and 2019 by four methods. In order to investigate the general ability of algorithms to calculate the surface temperature, the statistical indices of mean square error, Nash-Sutcliffe coefficient, mean absolute error and coefficient of determination were used. The results showed that the Landsat single-channel algorithm for calculating the surface temperature in Birjand is more accurate than other algorithms.

Construction violations are considered one of the most important challenges of modern urbanization due to their widespread level and long-term and stable effects on the profile of cities. Construction violations are an important issue for municipalities that can threaten building structures in a city. The traditional methods that are used today to control constructions are very time-consuming and expensive. The main goal of this research is to provide a new framework for quick and low-cost estimation, in revealing and monitoring constructions and identifying unauthorized urban buildings using Sentinel-1 satellite images in the period from 2017 to 2022 and spatial information systems. For this purpose, in the first step, based on the analysis and processing in SNAP software, the Sigma-Notch dispersion coefficient of the images was extracted and separated into two floors of buildings and non-buildings, and a threshold limit of more than 0.01 was obtained. Then, by using pixel based algorithm, the binary image of building and non-building was prepared as zero and one, and based on the difference between the two images, the area where the construction was done was determined. After revealing the changed construction areas, they were classified into three classes (building, under construction, and other lands) using maximum likelihood classification algorithms and random forest, and were evaluated with a field survey map and unlicensed parcels. The results showed that the number of unlicensed buildings using the maximum likelihood algorithm, random forest and field sampling is 97,135 and 48, respectively; Also, the accuracy of the maximum likelihood method was 0.89% and the kappa coefficient was 0.83% compared to the random forest method with the overall accuracy of 0.86 and the kappa coefficient was 0.81%.

This study aims to evaluate and compare coastline displacement and erosion intensity on the Caspian Sea's southern shores in the largest ports of Northern Iran, including Amirabad, Fereydunkenar, Nowshahr, Anzali, and Astara. Landsat satellite images were used to estimate the morphological status of the coasts in terms of erosion, sedimentation characteristics, diversity of existing coastal landforms, and changes in the GIS environment were used by digital coastline analysis software (DSAS) over the years 1995 to 2021. It has been indicated that the southern shores of the Caspian Sea differed in how they responded to the construction of port structures and Caspian Sea level (CSL) changes, and the areas of Amirabad and Astara ports had the highest displacements as well as accurate measurements of sedimentation and erosion rates, respectively. Given this situation, the beaches overlooking the ports of Nowshahr and Anzali have had significant changes in sedimentation, while the coast of Fereydunkenar had a very slow erosion rate. The northern ports of Iran, as well as changes in the Caspian Sea level (CSL), have direct physical impacts on the adjacent coasts. The management of concentrated sediment resources on the coast is a reliable solution to reducing erosion rate very effectively and the use of sand resources to mitigate coast erosion.

Since climate change is one of the most important and biggest threats to nature and biodiversity, it makes it difficult to manage and protect species. Predicting and determining its effects will considerably help to provide appropriate protection solutions as well as management plans. In the present study, the impacts of climate change on the distribution of Mesopotamichthys sharpeyi species were forecasted by using the MaxEnt model in the R software environment. The environmental variables included slope, temperature annual range, flow accumulation, annual precipitation, annual mean temperature, and upstream drainage area. According to the results, the performance of the model in predicting the species was excellent (0.989) based on the AUC (Area Under the Curve) criterion. Moreover, the annual mean temperature and slope have been the most important environmental variables in determining the distribution of this species, respectively. In addition, the distribution range of this species will decrease in both the optimistic (RCP 2.6) and pessimistic (RCP 8.5) scenarios of 2050 and 2080. In conclusion, in order to protect this species, it is necessary for decision-makers to identify and implement appropriate actions in order to adapt the effects of climate change and reduce the related threats.

Industrial activities and urban traffic contribute to increased air pollution in large cities, resulting in a rise in various diseases among the population. Consequently, studying and investigating polluted areas is crucial for effective city management. This study aims to examine the air pollution levels in Tehran, Isfahan, and Qom cities, focusing on NO2, CO2, CO, and CH4 pollutants, during two distinct periods: pre-COVID-19 (2018-2019) and during COVID-19 (2020-2021), across all four seasons. By employing the Pearson correlation method and RBF neural networks (radial basis function neural network), the relationship between temperature and pollutants was explored. The findings reveal higher levels of air pollution in Tehran and Isfahan compared to other regions. Moreover, the study demonstrates a significant reduction in pollution during the COVID-19 era compared to the pre-COVID-19 period. Additionally, the regression analysis highlights a strong correlation between temperature increase and pollution levels (R2=0.981). Furthermore, the RBF method exhibits high accuracy in predicting air pollution levels (R2 = 0.85, RMSE = 0.08). In conclusion, this research underscores the urgent need for comprehensive measures to mitigate air pollution, particularly in highly polluted areas, and emphasizes the role of temperature as a crucial factor affecting pollution levels.