جستجوی مقالات مرتبط با کلیدواژه « change detection » در نشریات گروه « جغرافیا »

The change and evolution of land and water erosion zones through time shows the stability of natural lake ecosystems. Therefore, monitoring the fluctuations of the lake boundary through the preparation of remote sensing data through time, with high spatial and temporal resolution, can reveal the effects of natural hazards, including drought. Compared to other methods, the remote sensing methods due to their ability to easily and cheaply access data, their high accuracy and comprehensiveness, wideness of satellite images and their spectral diversity are considered an effective tool in water resource management and lakes monitoring. Therefore, the importance of monitoring the changes in water-land erosion areas of Lake Neor can provide a suitable platform for making efficient management decisions and prioritizing executive programs to deal with the water shortage crisis. This research has been conducted with the aim of evaluating the changes in water and land erosion areas of lake Neor (as the largest fresh water lake in Ardabil province and one of the most important tourist attractions of this province) in relation to climatic factors during a period of 32 years. This issue has been less discussed in the studies conducted in the region.

 Most of the data of this research are time series of Landsat 5 and 8 images during the period of 1988-2020, which were obtained from the website of the US Geological Survey. The map of the studied basin was extracted using ASTER DEM and ARC HYDRO tool. Atmospheric and geometric corrections on the images were applied in ENVI 5.3 software. Water areas were extracted using AWEI, NDWI and NDVI indexes. ARC GIS 10.2 software was used to get the final outputs of the maps. The 32-year climate statistics of the synoptic station of Ardabil city were obtained from the Meteorological Organization of the country in order to investigate the causes of changes in water erosion zones.

 The application of three different water indicators on the images indicated that the water area of the lake has decreased from 1988 to 2020 . The largest water area obtained from the AWEISH index in 1992 is 4.1 square kilometers and the smallest lake area in 2019 is 2.2 square kilometers and 2005 with 4.08 square kilometers. The lowest area of the lake according to this index is related to the year 2019 with an area of 2.28 square kilometers and in 1991 with an area equal to 2.26 square kilometers. These numbers have been repeated in other indices with a slight difference. This great fluctuation of the water level of the lake during three decades can have several reasons. In this study, rainfall changes during the studied years have been considered as the main cause. The comparison of the fluctuations of rainfall with changes in the lake level shows the water of the lake has prograded a lot towards the land in the rainy years (1992 and 2005). Also, the performances of three water indexes (general accuracy and Kappa coefficient) were evaluated in the form of Support Vector Machine (SVM) algorithm and the Minimum Distance method. The overall accuracy and Kappa coefficient in all three indicators is higher than 0.92 which indicates the used methods in the extraction of water have high compliance with the land use.

In this research, the surface dynamic changes of Lake Neor in relation to climate fluctuations (precipitation) during the period of 1988-2020 were studied using multi-temporal data. In order to detect the temporal-spatial changes of the lake water level, three different indices of quantitative water extraction (AWEISH, NDVI and NDWI) were used. The performance of each of them (general accuracy and Kappa coefficient) was evaluated in the form of Support Vector Machine (SVM) algorithm and the Minimum Distance method. It was found that the AWEISH index had the lowest error compared to other indices used in the detection of water areas. The results of monitoring three decades of changes in the area revealed that the annual average of the water area of the lake has experienced a decreasing trend. This downward trend has become more intense during the last years of this period due to climate changes and occurrence of recent droughts. In order to optimally manage this lake, considering its environmental importance and tourist attractions, the boundaries of the lake's bed and privacy should be determined by the relevant organizations such as Department Of Environment, Water Resources Management Company and remote sensing researchers. Another suggestion in this regard is to use these indicators in places with diverse geomorphological, climatic and environmental conditions simultaneously to be able to have a better assessment of their accuracy in detecting water areas.

Along with the ever-increasing urban population, the amount of construction in the city space has been developed. The development of construction in the horizontal space and regardless of the existing restrictions has led to environmental, economic and legal problems for the citizens. Achieving the amount, intensity and direction of construction development from the past to the present and predicting the construction situation in the future is the first step towards the scientific and practical management of the physical development of urban construction, planning and providing suitable solutions in order to create a balance between allocation Spatial-spatial construction and all kinds of legal, economic and environmental considerations. Data and information extracted from satellite images, while showing the historical changes of urban construction, are used as the main, necessary and necessary input data for models to predict its future state. In this research, satellite images of TM, ETM+ and OLI sensors of Landsat satellite were used in the time periods of 1997-2007 and 2007-2017 related to the city of Sari. After performing geometrical corrections, city area maps were prepared. Then, by using the effective parameters in urban construction changes, using the Cellular Automata(CA) Markov Model, the accuracy of the simulations was checked. Finally, for validation, the simulated maps and the ground reality map were matched with each other. The simulation of the construction development process in 2027 using the CA-Markov model showed that if the existing management regulations continue, this area will decrease from 4617.90 hectares in 2017 to 4357.44 hectares in 2027. But the examination of change maps and stability maps showed that new areas will be under construction between 2017 and 2027, which were mainly used for agriculture and barren land.

This paper evaluates land-use changes in Sracheshmeh copper mine using temporal Landsat satellite imageries during period of 1972 to 2017. This aim was accomplished based on maximum likelihood classification method and post classification change detection method. The study area was classified considering 4 main classes including mining area, industrial area, bare land and vegetation class. The results were validated by comparing to real data classes using overall accuracy and kappa coefficient methods with 95% and 0.94, respectively. Land use changes in Sarcheshmeh mining region were investigated through comparison between pair images of time period of 5 years including 1972-1984, 1984-1988, 1988-1993, 1998-2003, 2003-2008, 2008-2013, 2013-2018 and time periods of 16 years including 1972-1988, 1988-2003, 2003-2018 and time period of 48 years from 1972 to 2018. years indicating severe increase in mining and industrial classes to 15.4 and 3.6 km2, respectively. Based on the results, experts and managers can identify the amount of changes among different land-use classes and establish development and environmental policies toward sustainable development in mining areas.

Drying up of Urmia Lake has great influence on the surrounding environment. The salinity of the soil on the edge of the lake, the increase of salt particles and the reduction of the usual agriculture in this area are among the effects of this drought. With proper planning and extensive studies, it is possible to turn the created deficiencies into opportunities and take maximum advantage of them. One of the parameters that can be checked using satellite data is soil salinity. Soil salinity is the accumulation of dissolved salts in the form of electrical conductivity, and it can be measured.  In this study, the changes in land use and vegetation from 2000 to 2016 have been investigated using Landsat 5 and 8 images. The analysis of the images has been done using object-oriented methods, and the soil salinity has been calculated using salinity indices and during the studied years. Soil salinity index as well as other indices such as plant indices has been used in the classification and examination of changes. Land use changes were obtained by object-oriented method in Ecognation software and calculations related to the amount of changes is done using Arc map and Idrisi software. The study was carried out on 4 periods of 2000, 2006, 2010 and 2016. The images related to each year are first classified into 9 classes of water, salt marsh, new salt, pastures and weak pastures, city, agriculture, barren land and garden by object-oriented method. After classification, the amount of changes during these years has been obtained.

Most of the study area is located in pass 168 and row 34, and only parts of the lake in the northwestern part of the study area are outside this image, so to get the desired result, we have to mosaic the images of passes 168 and 169 with rows 33 and 34. Processing is divided into three stages: pre-processing, processing and post-processing.Segmentation is the first step in processing images in object-oriented methods. Segmentation is the integration of objects from small to large based on the parameters of color, shape, density, smoothness and scale. In this study for processing have used from ecognation Software. The method used to classify images is the thresholding method. In order to improve the classification method, salinity indices and vegetation indices have used in this study.

The amount of water in the lake has decreased drastically between 2000 and 2016, and it has decreased from 54.39% range to 29.26%. By examining the changes, it can be seen that other land uses have been reduced, and salt marshes have been added.  The comparison of land uses from 2000 to 2016 shows that the amount of salt marshes has increased from 12.37% in 2000 to 13.84% in 2006, 24.7% in 2010 and 33.25% in 2016. Studies show that the intensity of changes in the amount of water and salinity between the years 2006 to 2010 and 2010 to 2016 is much more intense than the period 2000 to 2006. It seems that before 2006, unprincipled exploitation of lake water and underground water was less than after 2006, or the environment had the ability to recover the amount of water harvested. As it can be seen from the map of 2000, the amount of new or wet salt around the lake is very small, which has increased with the decrease of the lake's water. The survey of gardens in the studied area shows that the area occupied by gardens has increased from 2000 to 2010, but the results of changes in use from 2010 to 2016 show that the area of gardens has almost halved (1099 pixels from the image in 2010 And 667 pixels in 2016) that one of the factors of this reduction can be soil salinity.Another thing that can be seen by studying the maps is the distribution of new salt in the areas far away from the lake, which at first was only attached to the water at the edge, but over time, when the amount of this type of salt increased, due to the occurrence of salt storms, it was spread to further areas. If measures are not taken to control this amount of salt and soil salinity that has occurred after that and will increase over time, we will witness more severe problems in the region.

Rapid urbanization in recent decades has been a major driver of ecosystems and environmental degradation, including changes in agricultural land use and forests. Urbanization is rapidly transforming ecosystems into buildings that increase heat storage capacity. Loss of vegetation and increase in built-up areas may ultimately affect climate variability and lead to the creation of urban heat islands. The occurrence of natural disasters such as flood, earthquake … is one of the most effecting factors on the changes in intensity of urban heat islands. So far, a lot of research has been done on how it is affected by various types of natural disasters such as floods, earthquakes, droughts and tsunamis.Two major environmental challenges for many cities are preventing flooding after heavy rains and minimizing urban temperature rise due to the effects of heat islands. There is a close relationship between these two phenomena, because with increasing air temperature, the intensity of precipitation increases. Drought is also a phenomenon that is affected by rainfall, temperature, evapotranspiration, water and soil conditions. One of the major differences between drought and other natural disasters is that they occur over a longer period of time and gradually than others that occur suddenly. Another natural disaster is the tsunami, which increases the area of water by turning wetlands into lakes, thereby increasing the index of normal water differences, which has a strong negative relationship with surface temperature. Ecosystems in urban areas play a role in reducing the impact of urban heat islands. This is because plants and trees regulate the temperature of their foliage by evaporation and transpiration, which leads to a decrease in air temperature.Applying the locked down of the Covid-19 pandemic since the spring of 2020 has led to the global restoration of climatic elements such as air quality and temperature. In this study, the effects of Covid-19 locked down on the intensity of urban heat islands due to the limitations in industrial activities such as factories and power plants and the application of new laws to reduce traffic in Tehran were investigated. In this regard, the Landsat-8 satellite taken from a part of Tehran city has been used.

In order to investigate the effects of locked down in the spring of 2020 on the intensity of urban heat islands; the status of UHI maps in Tehran during the same period of locked down in three years before and one year after has been studied. The proposed method in this paper consists of two main steps. The first step is to generate UHI maps using land surface temperature (LST), normalized difference vegetation index (NDVI) and land use / land cover map analysis. In the second step, in order to analyze the behavioral changes in the intensity of urban heat islands during locked down and compare it with previous and subsequent years, changes in the intensity of UHIs are monitored.UHI maps consist of three classes of high, medium and low intensities urban heat islands, which are based on performing the rule based analysis on land surface temperature characteristics and normal vegetation difference index derived from Landsat-8 satellite images as well as land use / land cover map. LULC maps are produced by support vector machine classification method consisting of three classes of soil, building and vegetation. In order to calculate the spectral features used in the rule based analysis, atmospheric and radiometric corrections must first be made on the red, near-infrared, and thermal spectral bands of the image captured by the Landsat-8 satellite. Then, vegetation spectral indices including NDVI and PV indices are generated.

The capability of the proposed algorithm in this paper is first evaluated in the whole area covered by satellite images taken from the city of Tehran, and then in three areas including residential, industrial and green spaces. The data used inhis article are images taken by the OLI sensor of Landsat-8 satellite in the spring of 2017-2021.in the first step of the proposed method, maps of urban heat islands are generated based on multi-temporal satellite images of Landsat-8 taken in the years 2017to 2021 in the MATLAB programming software. Then, by comparing pairs of UHI maps in each of the residential, industrial and green space study areas, the trend of changes in the intensity of UHI is analyzed and the effects of locked down application in 2020 are evaluated.The results of changes detection in urban heat islands in the period under consideration in this study showed that the percentage of areas that are in the class of high UHI in 2020 due to locked down of pandemic Covid-19 compared to the average of three years before that is 55.71%, has a decrease of 17.61%. The percentage of areas in the class of medium UHI intensity in 2020 due to locked down compared to the average of three years ago, which is 39%, increased by 4.8%, and in 2021 this amount again has decreased to less than the average. Also, the percentage of low intensity UHI class in 1399 compared to the average of three years ago, which is 5.3%, has increased by 12.8%.

In this study, the effect of locked down application due to the Covid-19 virus pandemic, which was applied in Iran in the spring of 2020 is investigated on the intensity of  urban heat islands in a part of Tehran city and three selected areas with residential, industrial and green space. Detection of changes in the intensity of urban heat islands was done based on the post-classification method and on the UHI classification maps related to the years 2017 to 2021. In order to produce UHI maps, in addition to the land surface temperature, the amount of vegetation index and the type of land use / land cover class were also used in the form of a set of classification rules.Comparing the results of the study areas of residential, industrial and green spaces, it is important to note that the rate of reduction of the area of UHI with high intensity in the residential area is 5.25% more than the industrial area and 6.1% more than the green space. However, the reduction of locked down restrictions in 2021 had the greatest effect on the return of the area of ​​the high UHI class and caused the area of ​​this class to increase by 23% compared to 2020. These results indicate the fact that restrictions on the activities of industrial units such as factories and power plants and the application of new laws to reduce traffic, despite the same weather conditions in an area have been able to significantly reduce the severity of urban heat islands.

Coastal areas has been important for mankind because of the abundance of potential possibilities for development since long time ago and this can be the reason for concentration of %75 of the world's population at radius of 200 km from the coasts.The purpose of this study is the change detection of land use in coastal areas of assalouye by using landsat images in two 16-year periods (2984-2000-2016). Land use maps for these years was extracted from landsat satellite imagery and was corrected by using of available maps and also google earth images and was verified through the error matrix approach, kappa coefficient and overall accuracy. kappa coefficient ( 0.94; 08808 and 0.9517) and overall accuracy (96.56%; 95.4952% and 93.5883% ) was obtained for 1984, 2000 and 2016 years respectively.The results show that increase in area in the residential areas use unit (126.36 square kilometer ) and vegetation (6.13 square kilometer) and decrease in area in the bayer areas use class (-125.37) and water range (-7.15 square kilometer) has been created during 32 years. Bayer lands has the most changes and residential areas unit as the most development.

The main aim of this paper is to detect the ten-year changes in urban green spaces of Tehran metropolis, from 2010 to 2019, using the time series of Landsat 5, 7 and 8 images. The change detection was done in both annual and ten-year scale and the results are analyzed in two spatial scales; City level and municipal district-level. Detection of changes was done by a post-classification approach. The innovation of the study is efforts to reach the best results in the image classification step, for which in addition to optical and thermal bands various features including some vegetation indices, water   and built-up index, image texture components, and principal components were used. Three classification methods including maximum likelihood, artificial neural network and support vector machine were implemented. The results indicated that the support vector machine has had the best result with 91.06% mean overall accuracy. The change detection showed a 10.58% decrease in the Tehran green spaces in the period under review. The greatest decrease, about 7.46 Km2, occurred in the period 1390-91 and the largest increase was 7.61 Km2 in the period 1394-95. Among the 22 municipal districts, regions 1 and 22 with 5.2 and 2.37 Km2, respectively, have had the highest decrease in urban green space, and regions 2 and 19 with 0.5 and 0.47 Km2, respectively, have had the highest increase.

Land use change detection requires the use of fast and appropriate methods to collect information and integrating information layers. Extracted data from satellite images and combine them with field data can provide accurate and better information for multilateral decisions. Arvand Free Zone with an area of 37219 hectars is located in northwest of Persian Gulf, in confluence of two rivers Karoon and Arvand and including the cities of Khorramshahr, Abadan and Minoo Island. In this study images of TM in 1990, ETM in 2002 and OLI in 2015 were used. For preparing the landuse/cover map maximum likelihood algorithm of supervised classification method was used. Ultimately, five types of landuse (Agriculture and gardens, saline solis, grassland and barren lands, residential and industrial facilities, water levels) identified. The results have been expressed wide changes in the study area. Grassland and barren lands had decreasing process in the study period of time.By the year 2002 ,agriculture and gardens had increasing trend then to 2015 had decreasing trend. Residential and industrial facilities from 12 percent of the total area in 1990 to 14.7 percent in 2002 and 29.6 percent in 2015 have increased. According to the results that Arvand Free Zone in the study period of time had many landuse changes on accordance with economic, geographic and strategic special situations, in area landuse management planning for changes should be considered.

Mangrove forests are one of the most important tropical forests found along the tidal coast. These forests are one of the most vulnerable marine ecosystems that are changing over the years due to human activities. Therefore, their prompt and timely monitoring should be the goal of environmental planners. The aim of this study was to investigate the 30-year changes of mangrove forests in the northern part of Qeshm Island during 1986, 2000 and 2020. Based on the research objectives, the mangrove cover map was extracted by applying SVM algorithm to Landsat 5 and 8 images in the instantaneous environment of Google Earth Engine. The results of 30 years of change showed that mangrove forests during the research periods (1986 to 2020) have an increasing trend, so that the area of mangrove forests in 1986 increased from 5130.78 hectares to 5471.87 hectares in 2000, which in fact amounted to 6.23% The area of forests has increased. However, the area of these forests reached 5967.13 hectares in 2020, which is an increase of 14.02% compared to the mentioned area in 1986. the results of this study show that the increase of mangrove forests during the study periods has been the result of artificial afforestation by the natives in order to preserve this marine ecosystem in the region, which has led to the prosperity of tourism industry in Qeshm region. What has accelerated the processing process in the current study is the use of the SVM algorithm and the Google Earth Engine system.

Damage caused by the sand dunes movement is one of the most important environmental and socio-economic issues in desert region. erosion and wind processes study began with the work of Bagnold (1954). After significant advances in laboratory and physical approaches to the elements and forces involved in wind erosion and at the contemporary with the development of remote sensing tools and data and changes in methods and algorithms for interpreting aerial photographs and satellite images, the rapid emergence of planetary geomorphology and the search for analogies and similarities on other planets. Rapid developments in the geomorphology of wind processes took place. Using Landsat, ASTER and Quick bird images and LIDAR data, many studies have been done to classify sand dunes. After the launch of two ERS remote sensing radar satellites in 1991 and 1995, the value of CCD Was considered. But in Iran, most of the studies conducted in the desert region, such as the Damghan playa, have studied the changes in long-term periods, which are mainly of sediment origin and classified sand dunes using multispectral satellite data. The aim of this study was to use Sentinel-1 IW SAR time series data in arid regions to detect surface changes in the short term due to wind morph dynamic activity and on the other hand to evaluate the effectiveness of using both radar and optical data and Object-oriented classification model in the events and morphological changes detection of sand surfaces and forms. the results obtained from the processing of remote sensing data and classification and achieving the dimensions of sand dune mobility with the results of wind data analysis will be evaluated and verified.Materials and methodsDamghan plain located in Damghan plain with longitude 54 10 to 54 40 east and latitude 36 36 to 36 10 north has a hot and dry desert climate and an average rainfall of 100 mm per year, which due to the desert nature, is prone to wind morph dynamic performance. Therefore, in the present study, we aimed to evaluate the mobility of sand dunes as part of the natural hazards active in the region. The research method is library, remote sensing and surveying. Data analysis is based on two main concepts; segmentation and classification. Initially, based on geological and topography maps and field survey, geomorphology maps were created. Then based on the prepared and adapted maps and field surveillance, sandy forms were limited. Then, in order to determine the working units, using the CCD technique with Sentinel-1 radar images, the active and inactive parts of the sand forms were detected. Two radar interfrograme (Master and slave) related to the two dates of 14/05/2017 and 22/03/2018 were used to extract the CCD (based on phase difference).Finally, with the identification of work units, automatic detection and extraction of sand dunes was targeted, and for this purpose, the bottom-up hierarchical object-oriented method and top-down classification using the growing region technique was used. Also, by extracting sand dunes using object-oriented classification, the values and direction of moving the dune were extracted using Guy, 1995 optimized model and the corresponding sand rose were drawn. Wind rose analysis and drawing related to wind statistics of Damghan synoptic station (the closest meteorological station to the study area) in the statistical period of 1384-96 was also performed with the aim of verifying the findings of the previous step.Result and discussionA: Extract sandy formsImage enhancement is the first step in preparing an image for the extraction of image elements (including sand dunes). Due to the importance of the dune slip face, in the process of identifying the displacement and sand dunes movement, and its lower compaction coefficient than other parts of a sand dune, in order to detection This enhance method, by using the most abundant discontinuity search, distinguishes brighter borders that forming sand dune steep slope from other parts of dune and around environments. The output of this filter is an image in which the sand dunes slip face, with different radiometric intense, is marked from the surrounding sand surfaces.B: Detecting and extracting sand dunesIn order to evaluate the displacement amount and direction, the object-oriented classification paradigm was used to automatically detect the edges as dune front. Instead of just evaluating pixels, the spatial pattern of objects and forms is also considered. Therefore, the initial segmentation was performed using a scale factor "100" that determined the maximum heterogeneity in the diagnosis of the forms. in addition to using radiometric values, classes can be formed based on geometry and related elements. The rules used are Brightness and Compactness. First, by analyzing the values of average brightness with a threshold of 165, the overall sand dune pattern as the first layer was created. Then, using the Compactness rule, the pixels that were recognized as the dune slip face by the spectral feature in the previous step were eliminated from the classes.C: calculation the sand dunes amount and direction displacement.Sand dunes displacement calculated by considering the end edge, as the progressive edge at successive times and measuring the distance between two consecutive lines in two consecutive years. To evaluate the dunes movement direction, the axis of symmetry of each hill was selected as the main axis and the initial and final point of this line on the downwind front of sand dunes in both the first and last years were considered. The azimuth line or the direction of movement relative to the north was drawn and this angle was calculated and its sand rose with an angle of 135 degrees was drawnConclusion This study, suggested a new approach to detect sand dynamics using radar InSAR techniques and object-oriented classification using high resolution optical images. The results of InSAR processing, and CCD technique, was able to recognize active and inactive sand dunes dynamic, and display them in continuous numerical values (fully active to fully stabilized hills).The application of OBIA on Bird’s eye and Geo eye images (2003-2016) results, indicates that the 22.4 m movement of hills is mainly in the southwest direction in a period of 13 years and 1.7 m for each year. The result of comparing wind rose (wind data analysis) and sand rose (sand movement data analysis) shows a significant relationship between 80% of northwest-southeast wind frequency in relation to 135 ° azimuth for 75% of sand dunes movement and 15% of north-south wind frequency in relation to 180° azimuths of 25% of sand dunes movement.

Land use change due to human activities is one of the important issues in regional and development planning. Given the advantages and capabilities of satellite data, this technology can be of great help in identifying and detecting these changes. The purpose of this study is to detect land use changes in Kamyaran city using satellite images over a period of 35 years. In this study, data from 1984 TM sensor, 2000 ETM + sensor and 2019 Landsat OLI sensor were used.  Initially, preliminary preprocessions including radiometric, atmospheric and geometric corrections were performed on the raw data. Land control points were used for training, accreditation and to prepare land use map. Land use class was determined by field survey and using Google Earth images in 9 land use classes of agricultural lands, forests, gardens, rich and wooded pastures, medium rangelands, residential areas, water area, barren lands and rock outcrops. Next, the neural network method was used to monitor the images in ENVI 5.3 software. The evaluation results showed that the overall accuracy and kappa coefficient of OLI classified images are 94.3 and 0.92%, ETM + 92.6 and 0.91% and TM 90.3 and 0.87%, respectively. The results showed that forest lands and rich and wooded pastures decreased significantly during three time periods, which decreased by 11.64 and 19.12 percent, respectively. So that rich and wooded pastures have an increasing trend until 2000 and in the next period until 2019 has a decreasing trend. Residential lands, water areas and gardens increased by 2.27%, 0.57% and 3.98%, respectively. Due to the growing trend of population and urbanization, the results of this study provide the necessary information to make basic decisions in the development of management policies for planners and regional managers for the sustainability and evaluation of natural resources.

Land-use change due to human activities is one of the important issues in regional and development planning. The aim of this study was to detect land-use changes using Landsat TM, ETM+, IRS and ASTER satellite imagery.

In this quasi-experimental study, land-use changes in the Haraz watershed over a 23-year period were evaluated. For this study, images of 1992 TM, ETM + 2002, and IRS and 2015 ASTER of Landsat satellite were used, and after performing the necessary actions in the preprocessing stage, a supervised classification and change detection map was prepared. Idrisi Andes, ArcGIS, ENVI, Edrisi Andes, and Statistica software were used. Also, to check the significance of the changes, the chi-square test was used in SPSS software.

The overall accuracy and Kappa coefficient were 0.84 and 0.79 for TM, 0.86 and 0.80 for ETM+, and 0.95 and 0.90 for IRS results respectively. The results showed that rangelands, forest, agricultural lands, and water-covered areas had converted to garden regions, building zone, and no vegetation. The results showed that 2.21%, 1.29%, and 7.01% increased in garden regions, building zone and no vegetation and 5.06%, 4.50% and 0.85% decreased in rangelands, forest, and agricultural lands during 23 years, also water-covered areas decreased.

The land use classes in the Haraz watershed have changed a lot during the time series 1992-2015. During this period, the area of natural lands (forests and pastures) with a rate of change of about 828 hectares per year and the same amount has been added to the area of the garden and residential lands.

Specifics regarding land cover and land use is an essential element of the planning process, as it can undoubtedly lead towards the debate around the present plans and patterns and the necessity to modify land use included in a regional plan. In this research, land use maps were prepared using Landsat TM (2000), (2008) and OLI (2016) satellite imaged. Land cover mapping was conducted after pre-processing and processing satellite images, creation of training samples and assessing maps accurate was done by coefficient kappa and overall accuracy. Supervised classification technique with maximum likelihood method were used to show the land use map. In this research, we use the 2000 and 2008 land cover maps to predict the 2016 land cover map and then use the 2008 and 2016 land cover maps to predict the 2024 land cover map.According to the results, with passing time the area of built-up area and mountainous increased with the passage of time while the dense poor rangeland, rich rangeland and agriculture area decreased during the period 2000-2016. The results of predicting changes in the time interval 2016-2024, showed that 55/0 of agriculture, 82% of rich rangeland, 80% of poor rangeland, 51% of built-up, and 0.97 of mountainous will remain unchanged

Information obtained from change detection processes in urban regions has a remarkable effect on urban planning and management. Due to the variety of land coversin urban regions, they are considered as a complex region extracting information from which is quite challengeable. Hence, independent application ofoptical and radar data in changedetection may result in improper recognition of some altered regions and falsification ofobtained results. These two sensors record different kinds of information from different phenomenonat the earth’s surface, and thus can be considered as complementing each other. So, the fusion of these two data sources (radar and optical) can improve the detection of altered area. Radar data do not depend on the sun and atmospheric conditions and has thus gained much attention. In fact, radar data provide information on the spatial and geometrical characteristics of the geographical features, while optical sensors are sensitive to the reflectance of different surfaces at visible and infrared wavelengths.Therefore, the surface reaction is different in optical and radar data. Application of radar data in urban regions is limited merely due to the dependence of the intensity data (i) on the incidence angle and the speckle noise.On the other hand, independent application of optical data cannot produce accurate results in urban regions due to the spectral similarity of materials. And since the nature of these two types of images is different, it seems that their fusion improves and increases the accuracy of the information collectedfrom urban areas.  

Considering thebenefits of optical and radar data integrationas well as the application of unsupervised techniques in change detection studies, the present research has developed an unsupervised method for the integration of optical and radar data in order to detect changes. The area under study is a region located in the northwestof Mashhad city in northeastern Iran which has experienced considerable changes in its land cover from 2016 to 2018. Optical and radar dataare used toevaluate the proposed method. Optical data consists of a pair of multispectral imagesacquired from Sentinel-2 in 9/2016 and 9/2018. Radar data consists of a pair of SAR imagesacquired from Sentinel-1 in 9/2016 and 9/2018. The proposed method was used to integrate radar and optical data with the aim of obtaining a single band image with a higher information content. This method is an effective solution used to integrate data and reduce data dimensions from n to one dimension. In this method, necessary preprocessing was first performed on the radar and optical data, and then the characteristics extracted from optical and radar images were integratedpixel-to-pixel. technique was used to integrate these characteristics and detect changes. Generally in this method, input is divided into two categories of radar and optical data. The optical characteristics include spectral indices calculated from different bands at t1 and t2. These indices include NDVI, ARVI, SAVI, NDWI, NDBI, which are efficient for studying and identifying three types of land cover: vegetation, water and residential areas. In fact, to reduce the effects of topography and image brightness and to increase the possibility of detecting and segregating geographical features, the spectral indices were used as the input of optical part. Normalized ratio images obtained from the VV and VH polarizations of the radar images at t1 and t2 were considered as the input of radar data part. Then, a weight was estimated for each feature entering the segment using the PSO algorithm. Since the present study seeks to estimate the optimal weight of characteristics extracted from optical and radar images and ultimately to combine these features and obtain a single-band image, each particle in this algorithm contains the n weight of the extracted features from the images. OTSU thresholding techniquewhich is the relation used for inter-class variance maximization is also used as thecost function to assess the particles. In this function, the weight of each characteristic should be selected in a way that the inter-class (two classes of altered and unaltered regions)variancereaches its maximum value and the most optimal threshold limit can be estimated. The output of the proposed method will be a single-band image with higher information content. After applying the OTSU threshold limit, two classesof altered and unaltered regions are formed. The proposed method was also compared with other unsupervised change detection methods.

Findings of the present study indicate high efficiency and accuracy of the method developed for changedetection. In this method, the ratio of pixels wronglydetected to the total number of evaluated pixels was 9.21% which is the lowest value. The overall accuracy and Kappa coefficients of the classification were respectively 90.79 and 0.819, which were the highest values compared to the other methods used in the present study.

Considering the benefits of optical and radar data integration, as well as unsupervised techniques application in change detection study, the present research has developed an unsupervised method for integration of optical and radar data andchangedetection. This unsupervised method for data integration is usedto achieve a single band image with higher information content. The technique makes it possible to integrate the optical and radar data and reduce data dimensions from n to one. For all input characteristics entering section, a weight was estimated using PSO algorithm. Since the proposed method is unsupervised, OTSU thresholding technique which is the relation used for inter-class variance maximization, is also used to assess the particles. The results have revealed high capability of the proposed method todetectchanges witha higher accuracy.

Today, the developing world is experiencing unprecedented growth that has a significant impact on land use intensification. Therefore, modeling and predicting growth patterns is crucial for natural resource planners and proponents to formulate a sustainable development strategy. The main purpose of modeling is to identify the factors and trends of future changes based on past changes. Monitoring the occurred changes in land units requires the use of rapid and appropriate methods to gather information and integrate layers of information. In the present study, based on image quality, the trend and rate of land use changes in Shush Township in a 30-year time series (1987, 2000 and 2017) have been investigated using Landsat satellite images and TM, OLI and MSS sensors. Different sections were processed and analyzed using ArcGIS, IDRISI and ENVI software. After classifying the images by the most similar supervised method, the classified maps were obtained with an average Kappa coefficient accuracy of 96.1%. The results of detection of changes showed that the largest decrease in area has occurred for uncovered land by 49078 hectares, and the highest increase was for agricultural land by 52691 hectares, which indicates the change of use of uncovered land in favore of agricultural land.

The urban heat islands are hazardous to the health of urban residence, their activities, lifestyle and the quality of their life. This phenomenon occurs, in particular, as a result of the urbanization process, land use/cover changes and the rate of impervious surface coverage. Since early 1970s, the urban heat islands have been studied using remotely sensed data; because this approach is cheaper, more efficient and faster than traditional techniques to detect the heat islands as well as to examine the severity of them. However, less attention has been paid on the relationship between urban heat island (UHI) and impervious surface patterns. Therefore, this work aims to study UHI based on the analysis of land-surface temperature (LST) and impervious surface patterns (ISP) retrieved from remote sensing data covering a 29-year period.

In this research, the city of Rasht as the center of Gilan province, Iran, is taken as the study area. Rasht is the largest city in the South Cost of Caspian Sea. In order to study the relationship between UHI and both LST and ISP, the time series of Landsat-5 / Thematic Mapper (TM) sensor, Landsat-7 / Enhanced Thematic Mapper Plus (ETM+) sensor and Landsat-8 / Operational Land Imager (OLI) sensor as well as Thermal Infrared Sensors (TIRS) of Landsat-8 from 1989 to 2018 have been utilized. Then preprocessing of satellite images including geometric correction and image referencing, radiometric corrections, and atmospheric corrections were applied on the images prior to other image processing steps. Then, by using the Normalized Difference Impervious Surface Index (NDISI), the impervious urban surfaces classified. The Otsu thresholding method was employed to determine a threshold value for the separation between impenetrable surfaces (constructed) and permeable surfaces (not constructed) in each utilized image. The classification accuracy was evaluated considering 300 randomly selected points. After mapping land use change over the years from 1989 to 2018, the future land use changes in the impenetrable urban areas were simulated to the year 2036 using CA-Markov model. Finally, the relationship between urban impermeable coverage and thermal island intensity changes were studied.

The results of this study showed an overall accuracy of 84.5 percent to 90 percent for impervious surface classification using the NDISI method and the Otsu threshold. The results of the CA-Markov's model also indicate overall accuracy of 83.6 percent for impervious surface prediction. The difference in CA-Markov's prediction map with a reality map was less than 8 percent; hence, CA-Markov can be considered as reliable method in predicting land-use change in Rasht. Moreover, the obvious peaks and valleys values can be seen in the histogram of NDISI index; therefore, the determined threshold has well been able to classify the impervious surface. The spatio-temporal change of impervious surface showed an increasing trend, more than double over the city, from 1989 to 2018. Moreover, the prediction results of CA-Markov model indicates that the impervious area would double again within the next 18 years. The highest levels of urban impervious are located at an average distance of 5,000 meters from the city center, which has had an important impact on the thermal island's severity. The relationship between impervious surface and thermal islands showed a positive and strong correlation coefficient between 0.69 and 0.89 for various years. Furthermore, the pattern of urban impervious surface growth and thermal island intensity changes coincided with each other. The spatio-temporal change of UHI showed that the spatial extent of heat islands in Rasht was increasing with time and the temporal trend of UHI was also increased. Moreover, the trend of heat island changes illustrated that area of regions with very low and low temperature were decreased. On the other hand, the coverage of regions with medium, high and very high temperature were increased.

The time series of Landsat images along with spectral indices are the convenient dataset to classify the impervious surface of the city with proper accuracy. The produced land cover map can also be employed as a proper input data for prediction models. The spatio-temporal analysis of urban heat island in Rasht illustrated that the urban heat intensity was increased. This trend was because of increasing rate of impervious urban surface. Ultimately, in order to control the heat island, it is required to prevent the unplanned urban construction and developments.

So far, many studies have been conducted to identify and detect land cover changes and land use changes. Optimizing land cover monitoring is an inevitable necessity in natural resource management. Given the evidence and effects of climate change in the region and the declining and increasing trend of some coverages and land uses can be effective in prioritizing conservation and zoning strategies. Factors such as land resources, environment, population, economy and society must be taken into account for sustainable development. Any type of development, regardless of quantity or quality, has specific environmental effects (1). Over time, dam construction has made some changes in the land cover - use of dams, in this study we aim to detect these changes. Therefore, for detecting cover changes - upstream and downstream of the Khordad 15 Khordad Dam during the period 201–990 1990, first visualize the changes using Googel Earth, then use Landsat 5 (1990 image) before. Dam Construction and Landsat 8 (Picture 2019) After dam construction and flooding using ENVI software, visual and automated changes detection are performed and the results of automatic image changes and classification in two classes Modified and unchanged Accuracy was 86.38% and was unchanged and modified by classification into three classes ( Decreased) reached 85.51% accuracy.Key words: Sustainable development,RemoteSensing,Dams,Change Detection,Classification.

In changes detection process, the choice of information extraction method plays an important role in the quality of final changes detecting. In this study, Landsat 8 multi-temporal data fusion method based on spectral distortion was used to detect changes and to determine the range of floods. For this reason, both pre and post flood images were fused using the Gram Schmitt algorithm to increase spatial resolution of images. In the following, three algorithms, Gram Schmitt, IHS, PCA, were used to detect changes and determine the extent of flood. In this study, input of each algorithm was pre-flooded as a multicolor image and post-flood infrared image as a panchromatic image selected to determine the extent of flood using the spectral distortion generated in each algorithm. The results showed that the capability of data fusion method based on spectral distortion is very high in detecting of changes. The spectral distortion generated in IHS is the most accurate distortion and the output of this algorithm is highly consistent with the reference data. Also, the output of the Gram Schmitt algorithm has spectral distortions in the unchanged regions. The PCA algorithm, which is highly sensitive to inputs, distorts most image regions, which is not recommended for detecting changes based on spectral distortion.