In recent studies several factors such as landslides, volcanoes, glaciers, tectonic factors and karst forms such as flooded pulleys resulting of karsts development cause the formation of lakes in mountainous areas. The mountain lakes of Iran, including Tar and Havir lakes, are mainly formed by landslides. In this research investigation of water basins changes for (Tar and Hoyer lakes) by remote sensing method in the period from 2013 to 2022 have been done. It has been tried to select appropriate Landsat 8 satellite images in terms of cloud cover and image quality from the time when the lakes are full of water. The training and testing data were selected with the same distribution in the whole image to make the classifications more accurate. After estimate kappa coefficient and overall accuracy for all kind of supervised and unsupervised methods, the results obtained from the two classification methods of maximum likelihood and support vector machine with linear kernel from Tar and Havir lakes, the conclusion was that the classification method of support vector machine with linear kernel have better distinguish land and water areas and it is the best method for classifying water basins located in mountainous areas. The amount of water in Tar and Havir lakes had been increased from 2013 to 2017but in 2018, the water area of ​​both lakes decreased significantly, and in 2019, the largest water area for the lakes was observed and calculated. In 2020, there is no big change in the water area of ​​the two lakes, and from 2020 to 2022, the amount of water in the two lakes is decreasing.

In recent years removing environmental pollution has become one of the main concerns of human life. Air quality in cities depends on weather conditions and the amount of pollutants produced. Today, air pollution is one of the most complex problems of human societies, which has left many negative effects on the health of living beings, especially humans. In this research, the average monthly and annual concentrations of air contaminators in the air pollution measurement stations of the General Directorate of Environmental Protection of Tehran province in the years 2013 to 2019 were examined. By using google earth engine site [1].  Processes that GEE (Google Earth Engine) contains a consolidated resource of open-access RS (Remote Sensing) datasets, along with a variety of algorithms to extract information for Earth’s surface monitoring [2], and using maximum likelihood classification method it recognized that examining the spatial changes of pollutants in the areas where there was more construction, the amount of contaminators was also higher. In terms of time, however, no special changes have been observed for all gases. For AI (Aerosol Index) pollutant, no very specific changes were observed within the region. We have seen a growing trend for carbon monoxide pollutant. For the nitrogen oxide pollutant, we have not seen any growth during this period of time. For sulfur dioxide, we have seen a growing trend. Also, in terms of seasonal changes, air pollutants are more concentrated in winter.

Rapid economic growth has increased the speed of resource changes and many of these changes have rapid and harmful effects Natural environment such as agriculture, forest, water resources, value Cultural things such as historical landscape and health Humans have put Land use changes directly It changes the resources of the earth, which affects the temperature and humidity causes changes in the climate and weather of the region as well It reduces cultivated areas [1]. Considering that one of the main prerequisites for the optimal use of land, obtaining information One of the patterns of land use over time is map design. Related specializations are one of the most important goals in management it is considered natural resource [2]. In recent years, preparing land use maps by digital classification of remote sensing data have been adverted as appropriate alternative for using this type of maps. Remote sensing is a modern and useful technique in updating land use maps and detecting new changes. In this research ArcGIS pro used for classification that is one of the most accurate and updated software for remote sensing’s process for detecting 4 main type of classes in Tehran city in IRAN. standard accuracy in satellite image processing is important criteria in this study with standard kappa coefficient accuracy, and overall accuracy of data calculated for each maps, by considering 4 essential classes in a major city and converted to maps of changes in linear regression concluded that build-up class have a significant slope increase 3422/3 (hectares), plant class is improving during the study period as 2821/71 (hectares) but these increment are inhomogeneous, water class has sharp drop as 443.52 (hectares), Then the most of decrement is for the barren area which named soil class as 5800.48 (hectares). Part of accuracy in this research depends on severity of the numbers of test samples which given for classification that are more than5000 pixels to assessment reliable results. According to the standards of kappa coefficient that provided in USGS earth data site all off maps are acceptable.

No abstract

With more than 50 years of proximity to Tehran metropolis, the Kehrizak waste center has had destructive effects on the environment with the development of Tehran and the significant increase in waste production, including the change of land use units that are always affected by natural events, human actions, social issues and Economic. In this research, using Landsat 8 satellite images and remote sensing technique, the land use changes of Kahrizek waste center in thematic maps that are more accessible to users. Five vegetation indices NDVI, EVI, SAVI, LAI and VCI and six supervised classifications are investigated. Several methods were evaluated, parameters of overall accuracy and kappa coefficient. For this research, 4 classes of soil, water, building (urban area) and agricultural land were selected, and the results showed that the maximum likelihood classification method was the best method with an overall accuracy of 90.99% and a kappa coefficient of 0.85 and high similarity. Then, using the maximum likelihood classification method as the most accurate method, a user map was prepared from all images from 2011 to 2022. After calculating the area of ​​the floors, the results showed that the area of ​​the building floor increased by 71% and the area of ​​the agricultural land floor decreased by 80%.