hooshang seifi

The environmental consequences of land subsidence are destructive, costly and irreparable, and include creating cracks on the surface of the earth, damaging human structures such as building foundations, streets, bridges, roads, and power transmission lines. Sewage is destruction of irrigation systems and fertile agricultural soils and damage to ancient sites. Remote sensing methods, unlike mapping data and topographical maps, which are in physical contact with terrestrial phenomena, are without the slightest interference on terrestrial phenomena, and measuring and evaluating changes in phenomena are evaluated from a distance. Short receiving time and high spatial accuracy of radar images have made it used as a general and widely used tool to estimate land subsidence. According to the statistics announced in the country of Iran, the adverse effects caused by land subsidence are not a low number and are rapidly developing and spreading in different regions of the country. Leave irreparable damage. Ardabil plain, with its rich underground water resources and good soil, has always been of interest in the last half century and has been a suitable place for providing drinking water and agriculture. With the boom of agriculture from the 1960s onwards and as a result the excessive harvest from the aforementioned table since 1363, the aforementioned source began to decline and the continuation of this situation in the following years caused this plain to become more critical.

In order to carry out this research, the data of 22 piezometric wells in the Ardabil plain have been used. The time period used in this research is a 7-year period from 1395 to 1402. The method used for the data of this section is the BRF method, as one of the methods of radial functions, which is used due to its low error value and high accuracy. SAR images of the European Space Agency's Sentinel 1 satellite in SLC format and with vv polarization have been used to find out the changes in land subsidence in the Ardabil plain. The images used by the Sentinel 1 satellite (in c-band with a wavelength of 5.6 cm) are in the group of sensors with medium resolution in terms of spatial resolution. The radar interferometry method provides the possibility of producing a digital model of the ground height, whose optimal height accuracy for c-band data with a wavelength of 5.6 cm is about 5 meters. This method is able to reveal surface changes in the ground in different intervals with millimeter accuracy by using at least 2 or more radar images. In this method, an artificial interferogram is produced with the help of digital elevation model of the earth and conversion of height into phase, and in this way, with the help of reverse DEM data, the phase effect caused by topography is calculated and removed from the phase difference values. The remaining phase difference belongs to the effect of surface displacement and atmosphere.

The results of the investigation of piezometric wells in the area of Ardabil Plain show that the maximum drop in the underground water level is 48.77 meters in the southeast of Ardabil Plain and the lowest drop in the underground water level is 1.57 meters in the north. Eastern Ardabil plain was calculated. The amount of fluctuations in the water level of piezometric wells shows that the highest amount of fluctuations was in the area of Pirqavam wells, Arallovi Bozor and Khalil Abad lands. The lowest fluctuation was also observed in the area of Agchechai wells, Nojedeh. In the studied time period, the water level of Khalilabad piezometric well in 2015 was 2.96 meters, while in 1402, the water level in this area reached 46.3 meters. During 7 years, the water level has dropped by 43 meters, which indicates a critical situation in this sector. The lowest fluctuation of the water level is also for the Aghche-chai well. The land subsidence map of Ardabil plain shows that: the south-eastern parts of Ardabil city and also to some extent in the southern part have suffered ground subsidence due to the extraction of underground water. In the next order, the western parts of Ardabil city are prone to land subsidence. Based on this map, the maximum amount of ground subsidence has been calculated at around 598 mm in the area of Khalil Abad well. In the area of Khalil Abad well, the situation of underground water level drop is very critical and it has dropped by 43 meters during 7 years from 1395 to 1402. The overlapping results of the underground water level and co-depth curves with the results of radar interferometry show the accuracy of the findings in this section. Overlapping the location of the historical sites with the land subsidence map shows that, first of all, Tapraqlu hill (first millennium BC) is in the area of land subsidence with a rate of 250 mm.

The results of the application of this method revealed a very high level of land subsidence for the Ardabil plain (598 mm during a 7-year period). The southeastern areas of Ardabil plain have found a critical situation in recent years due to unprincipled exploitation and lack of proper management. Overlapping the location of the historical sites with the land subsidence map showed that Taparaglu hill with a rate of 250 mm and Ozrik Tepe-si with a rate of 69 mm are in the area of land subsidence, respectively. The three historic sites of Qala-Bovini, Niar Tepesi and Tezre Tepesi are also located in the area prone to land subsidence with rates of 27 mm, 46 mm and 49 mm, respectively. The cause of land subsidence in the Ardabil plain, according to the studies conducted on the changes in the underground water level, is the excessive exploitation of the underground water resources for the cultivation of agricultural products and providing the possibility of compaction of the underlying layers. In general, it can be said that the research results indicate the involvement of historical sites in the area of land subsidence.

Accordingly, the study of land use change, given the significant effects that this phenomenon has on human life and the environment, seems necessary. At present, remote sensing as an ideal resource provides users with the necessary and sufficient facilities to be aware of land use changes and human land use, by extracting and updating land cover maps. Accordingly, the present study was conducted to investigate land use changes by object-oriented classification in the Quchan-Shirvan catchment, located between the provinces of North Khorasan and Khorasan Razavi during the years 1998 to 1397. For this purpose, first, TM and OLI images of Landsat 5 and 8 satellites have been prepared for the desired years, and through these images, land cover maps of the study area with object-oriented classification method in six classes of residential areas, irrigated lands. , Rainfed lands, fallow lands, water levels and pastures were obtained. Finally, through land use maps, land cover changes during the studied years in terms of area quantity, spatial distribution and trend of changes were examined using the LCM model. The results of this study showed that during the study period, fallow and rainfed land uses have a decreasing trend and residential uses, irrigated lands, rangelands and water levels have an increasing trend; As the most changes related to rainfed land use decreased by 23,729 hectares, and pasture land use increased by 35,935 hectares, are among the major changes in the study area.

It is very matter to study and measure snow covers as one of the important sources of water supply. Due to the hard physical conditions of mountainous environments, there is no possibility of snow measurement. the use of  remote sensing with regard to low costs, up-to-date and extensive coverage in this field can be proven to be a good way to identify in snowflake areas. the main objective of this research is to estimate the surface coverage of Sabalan mountains using satellite images of OLI and TIRS sensors and using the object-oriented classification method. The classification of satellite digital images is one of the most important methods for extracting information, which is currently done with two pixel-based and object-oriented processing methods. The base pixel method is based on the classification of numerical values of images, and the new object-oriented method, which, in addition to numerical values, uses content, Texture, and Background information also in the image classification process. Therefore, in the present study based on the precision of the object-oriented classification, the object-oriented techniques were used to extract the surface of snow cover. In this study, due to the use of high resolution spatial resolution (Landsat 8) and the new method of classification of images, the snow surface was characterized by Normalized Difference Snow Index (NDSI), Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Brightness with a total accuracy of 91 percent, to 2142.62 square kilometers for the range Sabalan mountains have been extracted and the results can be used as alternatives to snowflake stations.

Presently, population growth, urban development, the importance of agriculture in economic development, the need for supplying water demands of this sector and improving public health have multiplied water consumption as compared to the past. For appropriate and optimal use of water resources, it is necessary to know the amount of available water in the area, its temporal and spatial changes, and the exact planning for maintenance and utilization of the available water. Accordingly, studying and measuring changes in snow levels, as an important source of water in mountainous areas, is very important. Snow cover is one of the important parameters involved in the amount of snowmelt. Due to the difficulty of monitoring and measuring snow cover level in mountainous basins, satellite images are used as alternatives to monitoring and ground operations in the preparation process of snow cover map. In this regard, the use of satellite imagery and remote sensing, due to low cost, up-to-date and extensive coverage, is a major breakthrough which can be used to identify snowy areas and evaluate changes in that method. Detailed analysis of snow-related issues requires a set of snow measurements and observations. OLI and TIRS sensors with various advantages like appropriate number of bands, referable spatial resolution, and sequential time series are considered to be an appropriate tool for this purpose. The main objective of this research is to estimate snow coverage of Sahand Mountain using satellite images received from OLI and TIRS sensors and by object-oriented classification method.   The present study use images received from OLI and TIRS sensors, and Landsat 8 satellite on 08/02/2017 (Pass and Row no. 34-168), as well as Digital Elevation Model based on data received from Aster Sensor and Terra satellite with a resolution of 28.5 meters to produce snow coverage map. Geo TIFF satellite data were originally requested from the American Geological organization and received from USGS site. Envi 5.3, eCognition 9.1, and ArcMap 10.4.1 software were used for processing and preparation of images, as well as classification and extraction of the final maps. In order to classify and extract snow cover surface with high precision, NDSI, NDVI, LST, and Brightness algorithms were used along with fuzzy algorithms.   Classification of satellite digital images is one of the most important methods for extracting applied information, which is currently performed by two general methods of pixel-based processing and object-based processing. The former method is based on the classification of numerical values of images, and the latter use not only numerical values, but also content, texture, and background information in the image classification process. Recent researches have processed image pixels and have only applied NDSI algorithm to estimate snow cover level. Therefore, pixels recognized as snow in such researches may contain snow cover and other land uses, which reduces the precision of snow cover extraction and makes the process of extracting all snow covers difficult. Extraction of snow cover using MODIS images is one of such researches. Due to low spatial resolution of these satellite imageries, extraction of mountainous valleys snow cover, as well as the separation of snow cover from the cloud cover is done with very low accuracy. Therefore, due to higher accuracy of object-oriented classification as compared to pixel-based classification, object-based techniques were used to classify and estimate snow cover. In object-oriented method, pixels are classified based on shape, texture and gray tone of the image. Thereby, pixels change into image objects and resolves the pixel blend problem. Therefore, by assigning each object to a specific land use, classification accuracy increases. Also, using complementary algorithms such as Brightness and NDVI along with the NDSI algorithm will improve the accuracy of findings as compared to other recent research. Therefore, using Landsat 8 satellite images and the new method of image classification, the present study extract snow cover from different domains of the study area. The snow cover in valleys was also extracted with appropriate and acceptable accuracy using different algorithms.  Using LST algorithm in object-oriented processing method, detecting and separating snow cover from cloud cover was made possible. In this way, a satisfactory result was obtained from the snow cover. Finally, snow cover for Sahand Mountain Range was calculated to be 1882.88 km2. The results can be used as an alternative to snow measurement stations.   Based on research findings, using Landsat 8 satellite imagery and object-oriented processing methods for image classification have the necessary efficiency in extracting snow cover in mountainous regions. Given the precise estimation of the snow surface and the low cost of using this type of satellite imagery, it is possible to use this type of images and check the snow cover with great confidence. While ground observations are expensive due to impassibility of mountainous regions, and also they are not sufficiently precise.

Snow cover in mountainous basins is known as an important and reliable fresh water resource. Due to the physical conditions of the mountainous environments, it is believed that snow can measurement is still challenge based on traditional methods and techniques. In order to deal with this issue, remote sensing satellite images can be considered as efficient method for identifying snow covers and computing their depth and volume. Within the current research, the Landsat 8 satellite images were employed to detect snow cover surface for the Yamchi basin using NDSI indices and object based image analysis methods. The ground control points were applied to compute the accuracy of results while the overall accuracy was computed to be about 90%. In order to measure the snow cover depth and compute water discharge, the Sentinel A1 and D-InSAR satellite imagery were employed. In context of detection snow cover using Landsat 8 images, the satellite images for September indicated no snow cover for the study which accordingly employed as basic data for comparing against winter season satellite images and accordingly computing the snow cover and depth using the interferometry approaches. The results were verified by ground data.The correlation coefficients were computed to be about 85%. With confidence in the accuracy of the maps, the snow volume was also obtained through surface and snow depth maps. Accordingly, the correlation between snow depth of ground control points the obtained snows depth from differential radar interference were employed to compute water discharge of the study area.