landsat images

Urbanization changes natural landscapes to human-made spaces and uses. With the expansion of cities, many of these spaces give way to roads, buildings and urban facilities and cause changes in different levels of the city, and these changes have very important effects on weather conditions (Shamsipour et al. 2013: 59). )The development of urbanization is one of the effective factors in increasing the air temperature in urban areas, which causes the creation of thermal islands in these places compared to the surrounding environment. This factor can have a negative effect on air quality and endanger the general health of society. (Mousavi Baighi et al., 2010. 190). What is considered as a fundamental defect in monitoring the temperature of the earth's surface is the lack of sufficient meteorological stations to know the temperature values. Today, this shortcoming has been solved by remote sensing and it can cover a large area of the earth's surface.

The study area is Amol city. The city of Amol is located in the Mazandaran province and the sides of the Heraz River with a height of 76 meters above sea level at 52 degrees and 21 minutes east longitude and 36 degrees and 25 minutes north latitude and at a distance of 70 kilometers west of Sari, the capital of the province, 18 kilometers south of the Caspian Sea and 6 It is located one kilometer north of Alborz mountain and 180 kilometers northeast of Tehran.In this research, Landsat 8 satellite images and Landsat 5 satellite images were used for 1990 in order to extract the land use map and surface temperature of 2020. In order to remove the effect of cloud cover from the images as well as the high intensity of sunlight, the desired images were taken from the summer season. Google Earth software was used for better accuracy of images, ENVI 5.3 software was used for atmospheric and radiometric corrections, and finally ARC GIS 10.8 software was used to prepare relevant maps.Using the atmospheric correction model (FLAASH), the data were qualitatively controlled and the radiometric error of the satellite images was corrected. In order to obtain a statistical set that represents the spectral pattern of land cover, training data must be selected before supervised classification of images. At this stage, information from the uses and topographical maps of the region were prepared using the visual interpretation of the images for all five floors, to prepare educational data for use in supervised classification operations. Maximum likelihood classification method was used for land use classification. This method is considered a part of the supervised methods for classification and for this purpose it uses a set of training data. In this method, after evaluating the probabilities in each class, the pixels are assigned to the classes that have the most similarity, and if the probability values are lower than the introduced threshold, they are considered as unclassified pixels.After that, the brightness temperature of the sensor is done by converting the digital values of band 6 in Landsat 4 and 5 and also band 10 in Landsat 8 to spectral radiance and converting the spectral radiance to the brightness temperature of the sensor in terms of Kelvin.Then, red and near-infrared bands were used to calculate NDVI to obtain the normalized vegetation difference index. After calculating NDVI we need to get Emissivity. Emissivity is the amount of reflection of a phenomenon relative to the black body. Then the land surface temperature (LST) is calculated. By using LST, it is possible to calculate the temperatures near the surface of the earth. In order to know and evaluate the correctness and accuracy of the classification, the user's accuracy, overall accuracy and Kappa coefficient were calculated in 1990 and 2020.

In this research, in the first step, the classification and the resulting changes were done in a specific time frame in Amol city and its surroundings. The classification results indicate that the classification in both periods, especially in 2020, was highly accurate, and its kappa coefficient and overall accuracy were at their highest coefficient, i.e. 100.After classification, the changes obtained in the area were examined for a period of 30 years and the changes were extracted for each land use in terms of hectares. The change of use from agriculture to the city and also from the city to roads and streets have the most changes. These changes indicate that the increase in urban use has caused a decrease in agricultural use and the size of urban areas has increased.Using Landsat satellite images, the temperature of the earth's surface has been studied in relation to land use and the results showed that the temperature is different in different uses. The highest temperature recorded for the years 1990 and 2020 in Amol city is related to urban use, the recorded temperature of which is 32.6 and 40.5, respectively, which shows the concentration of heat in urban areas. Urban use has the highest temperature due to the presence of man-made factors and heat absorbers such as asphalt, concrete and the presence of machinery. Also, the presence of tall buildings acts as a barrier to the heat escaping to the surroundings and in some way traps the heat inside the cityWith the development of urbanization in Amel city, a significant part of the area of natural and forest areas has been replaced by industrial areas, buildings and other infrastructures. The lowest temperature recorded in Amol city is related to forest use with 23.8 and 28.4 degrees Celsius. In forest areas, due to high albedo, high humidity and more open space, the temperature is lower and heat absorption is low there.The relevant researchers and experts in the region can use the results of this research to obtain information about the temperature of the earth's surface, land use, and also the changes that have occurred in the region, In order to predict the future situation of the region, they will take appropriate and correct policies.

The shoreline is a very dynamic environment. Its situation is influenced on sea level changes, erosion and sedimentation processes, and its displacement has overshadowed human life, communications, and coastal facilities. The purpose of this study is to measure and compare the shoreline changes in the Gorganroud Delta and Sefidrouad Delta by The Digital Shoreline Analysis System (DSAS). In this study, after applying NDWI Index and high pass filtering, the shorelines extracts from the TM and OLI sensors for the years 1986, 2000, and 2015. In order to measure the shoreline changes, transects are considered at intervals of 50 meters perpendicular to the baseline. This step is done in Arc GIS software by DSAS extension.
The results show that the effect of sea level changes on displacement of shoreline is not the same in the both of studied areas. By the increase of sea level in 2000, the shoreline moved landward, while in 2015, due to the drop in sea level, shoreline moved seaward. During this 30-year period, the total rate of change are estimated in Gorganroud Delta and Sefidroud Delta 104/85 and -2/1 in meters/year respectively. With respect to these measurement, the Gorganroud Delta has shown remarkable retrogression and the Sefidroud Delta has shown little change. Also, the shoreline changes in the two above mentioned deltas indicate that the amount of sediment load transported by rivers plays an important role in controlling the shoreline.