object-oriented

This article focuses on analyzing snow cover changes in the western watersheds of Lake Urmia (Zola Chay, Nazlou Chay, Shahr Chay, and Barandoz Chay) using object-based image analysis methods applied to satellite imagery. The study utilizes Sentinel-2 and Landsat-8 satellite images to extract the extent of snow cover for days with maximum snowfall. This data was then processed using object-based methods, and the future state of snow cover in these four watersheds for the year 2029 was simulated with 80% accuracy using the CA-Markov model.In addition, the depth of snow was examined using FLDAS_NOAH data and analyzed through the normalized residual mass curve method in Excel. The study reveals fluctuations in snow cover and depth across all four sub-watersheds between 2016 and 2023, showing an overall decreasing trend. The highest snow cover occurred in 2018, 2019, and 2021. In terms of snow depth, the maximum and average values reached their peak in 2020, with figures of 2.28 meters and 0.68 meters, respectively. Conversely, the lowest maximum snow depth was recorded in 2016 and 2018, at 1.07 meters and 1.1 meters. Moreover, the CA-Markov snow cover map for 2029 indicates a relatively increasing trend in snow cover. Despite the region's water scarcity issues, the findings of this study suggest that other factors are contributing to the intensification of water shortages and the reduction of Lake Urmia's water level. Identifying and examining these factors is crucial for effective water management in the region.

One of the important conditions for optimal use of land is obtaining information about landuse patterns and their changes over time. Landuse is usually defined based on human use of land, emphasizing the role of land in economic activities. Today, remote sensing technology is considered as the main element in landuse monitoring. The aim of the current research is to extract landuse maps for the years 2000 and 2021 in FirozabadKhalkhal region and to investigate the changes made in the studied time period in the region using the images of ETM and OLI sensors of Landsat. Also, checking the capability of basic pixel and object-oriented methods for landuse classification is another purpose of this study. In the current research, the object-oriented technique nearest neighbor algorithm and the vector machine method supporting the pixel-based algorithm have been used for landuse classification. Then, to verify the accuracy of these two methods, the overall accuracy and Kappa were extracted. The results of this evaluation show the high accuracy of the object-oriented method in extracting land use classes. Based on the results of the detection of landuse changes in the studied time period, the highest amount of changes occurred is related to the use of good pasture to poor pasture with a value of 51.72 square kilometers, followed by forest to good pasture with a value of 30.11 and the lowest changes It is related to the use of pasture and water with the amount of 0.03 square kilometers. The reasons for these changes are the increase in population, indiscriminate grazing of livestock, incorrect and illegal use of different lands. The use of more parameters such as scale, shape, compactness, color, texture, smoothness criterion and pattern for landuse classification in the object oriented technique can be considered as an innovation of the present study.

Land surface temperature (LST) is one of the important parameters in the study of climate change and physical processes of the earth's surface. Rising land temperature causes the phenomenon of heat islands, which is caused by changes in land-use and land cover in urban areas and today has become a major environmental concern. Ahvaz metropolis, as the capital of Khuzestan province in southwestern Iran, has undergone many changes in land-use and land cover in recent years and has enjoyed significant population growth. In recent years, the pattern of unbalanced urban development in Ahvaz has led to the destruction of agricultural lands, vegetation and gardens around the city as one of the most important factors balancing the land surface temperature. This has led to an increase in land surface temperature and the formation of heat islands. The purpose of this study is to investigate land-use changes in Ahvaz and its effects on spatio-temporal patterns of land surface temperature and heat islands in the years 2002-2021.

First, the land-use changes were studied and then, in order to evaluate the relationship between the changes in each Land-use and the land surface temperature, the maximum temperature in each land-use was determined and then the amount of changes was investigated. Landsat 7 ETM+ (2002) and Landsat 8 OLI/TIRS (2013/2020) images downloaded from the USGS. Object-oriented method was used for classification. Educational samples were implemented on the image surface and their corresponding objects were selected as educational samples for each class. The classification was done by SVM algorithm and the user maps were classified into four classes including vegetation, barren areas, constructed areas and water areas. In this study, estimating the LST was performed base on separate window Algorithm and with Landsat 7 ETM+ band 6 and Landsat 8 OLI/TIRS band 11 data. In this Study, LST was estimated using Split-Window (SW) algorithm on Landsat-7 ETM+ and Landsat-8 OLI/TIRS Thermal Infrared (TIR) bands. Then the NDVI values, Vegetation Proportion and Radiated Power were obtained. Finally, The LST value was extracted based on degrees Celsius. In this study, Pearson correlation coefficient was used to investigate the relationship between LST and air temperature. RMSE values was used to compare the estimated temperature by SW algorithm and the measured temperature by Field research. Also, two indices UHII and UHIII were used to calculate the intensity of heat islands. These indices evaluate the LST using the values of vegetation in one area.

According to the results of this study, Kappa coefficients and overall accuracy were 74% and 76% for year 2002 map, 78% and 85% for year 2013 map and 88% and 93% for year 2020 map, respectively. The area of built-up and barren areas has increased and the area of vegetation and water areas has decreased. The rate of increase in land-use of the constructed areas from 2002 to 2020 was about 579.69 hectares and the decrease in vegetation cover was 3200.15 hectares. The barren areas have increased by 2521.66 hectares from 2002 to 2020. Pearson correlation coefficient between air temperature and LST values of 0.65% is obtained, which shows a positive correlation. The RMSe values in comparison with LST and measured temperature by Field research for the images of 2002, 2013 and 2020 was 1.79, 1.66 and 0.98 degrees, respectively. According to the results, the LST values in year 2002 fluctuated about 24.48-42.55, in year 2013 about 26.32-44.47 and in year 2020 about 28.13-46.65 degrees Celsius. The results of LST show an increasing trend of temperature during the period 2020-2020. After estimating the LST, the maximum temperature of each user was determined, which showed the increasing trend of temperature in all applications. The decreasing trend of vegetation has a direct effect on increasing LST in this land-use. The temperature of this land-use has increased by 6.32 degrees in 2002-2020. Over a period of 18 years, the LST in built-up areas and barren areas has increased by 4.10 and 5.26 degrees, respectively. In order to study the spatio-temporal patterns of LST and heat islands, first with NDVI index, vegetation status in each year was divided into three classes of low, medium and high vegetation and then LST values in each category were determined. According to the results, the highest temperature occurred in the floor with low vegetation. The correlation between the two variables LST and NDVI was significant and regression between them showed an inverse correlation that indicated a negative relationship between LST and vegetation. Therefore, vegetation plays an important role in reducing the intensity of the heat island. UHIII index values in the low vegetation class from 0.63 to 0.67, in the medium vegetation class from 0.57 to 0.61 and in the high vegetation class from 0.51 to 0.54 it is arrived. The highest intensity of heat islands in 2002, 2013 and 2020 were in the southern, eastern and northwestern parts of the city, respectively.

According to the research results, land-use changes in the study period have caused an increase in land surface temperature. The highest temperatures have occurred in built-up areas and barren areas; this is due to the increase in the area of built-up and barren areas. Decreased vegetation has a direct effect on increasing LST in this land-use. The NDVI, UHII, UHIII indices and LST values were used to study the spatio-temporal patterns of land surface temperature and heat islands. The results indicate high temperatures in the vegetation with low vegetation. Due to the correlation of NDVI values with land surface temperature and the intensity of heat islands, the necessity and importance of vegetation protection as a very important variable to regulate the air temperature in the city is essential.

Land use classification has always been one of the most important applications of remote sensing, and for this reason, different methods have been developed. With the passage of time, more advanced methods with higher accuracy were created, which increased the accuracy and performed better in extracting classes that were spectrally closer to each other. Algorithms for detecting changes in remote sensing images are divided into two categories: pixel-based and object-oriented, based on the minimum processing unit. The purpose of this research is to evaluate and prepare a land use map of the Nirchai watershed in Ardabil province using the object-oriented method. Land use classification including segmentation of image data was done using multi-scale segmentation algorithm in eCognition software environment. Then these parts were selected and classified and evaluated using the object-oriented nearest neighbor algorithm. The results showed that the object-oriented classification has an overall accuracy of 99 and a kappa coefficient of 0.88%, which indicates the high accuracy of the object-oriented method in classification. Also, the land use map showed that the use of water areas and poor pastures occupied the least (70 hectares) and the most (8069 hectares), respectively.

In this study, 5 main DEM derivatives of 12.5 m ALOS satellite (slope layer, slope direction layer, curvature layer, cumulative flow layer and altitude layer) as well as Sentinel-2 satellite images and NDVI vegetation index were used as auxiliary layers. Segmentation in this area was performed using segmentation multi resolation method. In this segmentation, the height layer was given a value of 3, the curvature layer was given a value of 2, and the other layers were given a value of 1. Then, using Layer Values ​​and Geometry algorithms and assign class commands, landforms located in the western and southwestern slopes of Zagros (Aligudarz city area) have been classified. The results showed that the use of Layer Values ​​and Geometry algorithms and assign class commands have a good ability to isolate and classify landforms, so that 8 types of landforms (slopes, ridges, water areas, precipices, peaks, ridges, lowlands and lowlands) Kappa coefficient was 0.87 and overall accuracy was 91.71%. The ridge landforms form the largest part of the region and are the dominant landforms of the region and have a good distribution in different parts, but the peak landforms with the minimum area have formed only a limited part of the study area.

Land use reflects the interactive characteristics of humans and the environment and describes how human exploitation works for one or more targets on the ground. Land use is usually defined on the basis of human use of the land, with an emphasis on the functional role of land in economic activities. Land use, which is associated with human activity, is undergoing change over time. Land use information and land cover are important for activities such as mapping and land management. Over time, land cover patterns and, consequently, land use change, and the human factor can play a major role in this process. Today, satellite-based measurements with geographic information systems are increasingly being used to identify and analyze land-use change and land cover. With regard to the problems of changes and transformations in the studied area, remote sensing can allow managers to categorize images and evaluate land use changes, in addition to saving time and costs, which allows planners to make plans based on changes, more resources are lost. To be prevented.

In order to classify and detect the marginal land of the river, TM and OLI image images were selected for a specific month (August, August) for the years 1987 and 2017. The purpose of this study was to investigate the changes occurring in the studied area with an emphasis on agricultural lands. To do this, the images before processing in the ENVI software took radiometric, atmospheric and geometric corrections on them. After that, the main components of the river route were extracted. Five basic algorithms were used to classify the base pixel, but eCognition software was used to classify the object. Supervised classification identifies homogeneous regions with examples of land use and land cover, in which pixels are assigned in known information classes. Education is a process that determines the criteria for these patterns. Learning output is a set of spectral signatures of proposed classes. The first step in object-oriented classification is the segmentation of the image and the creation of distinct objects, consisting of homogeneous pixels. The main purpose of image segmentation is to combine pixels or small objects to create large image objects based on the spectral and spatial characteristics of the image. In order to evaluate the accuracy and compare the resulting maps, the overall accuracy and Kappa coefficient are used. When the sampling of pixels is done as a spectral or informational class pattern, the evaluation of the spectral reflection of classes and their resolution can also be done. An algorithm with the highest accuracy and accuracy will be the basis for the detection. Detection of changes, which leads to a two-way matrix and shows variations of the main types of land use in the study area, was carried out in this study. Pixel-based cross-tabulation analysis on pixels facilitates the determination of the conversion value from a specific user class to another user category and areas associated with these changes over the given time period.

The results showed that the object-oriented method is more accurate than the base pixel algorithms for providing user-defined maps. The amount of accuracy in the method based on object-oriented classification depends largely on choosing the appropriate parameters for classification, defining the rules, and applying the appropriate algorithm to obtain the degree of membership. The Kappa coefficient for each image is approximately 0.90. So these maps are the basis for the discovery of change. According to the results, the agricultural and residential lands have been increased and this increase has been accompanied by a decrease in rangelands. A general overview of this 30-year period shows that the arable and dry farming, respectively, increased by 2418.79 and 719.61 hectares and the rangelands had a decrease of 2848.86 hectares. However, the residential class and human effects show an increase of 428.88 hectares or a growth of 178.87%, which indicates the importance of agriculture in the studied area.

Identifying and discovering land cover changes can help planners and planners identify effective factors in land use change and land cover, and have a useful planning to control them. For this reason, maps are needed with precision and speed, and object-oriented processing methods make this possible with very high precision. The results of this study, in addition to proving the precision and efficiency of object-oriented processing in land cover estimation, between 1987 and 2017, have witnessed a decrease in the area of rangeland lands and, on the other hand, agricultural and residential lands, which is indicative of the overall trend Destruction in the area through the replacement of pastures by other uses such as rainfed farming.

The snow cover is one of the quickest changing phenomena on earth that considerably affects the climate, amount of radiation, the balance of energy between atmosphere and earth, hydrology cycle and also, biogeochemical as well as human activities. Precise estimate of snow cover is regarded as one of the fundamental operations in precipitation. Thus, monitoring the snow-covered surfaces is of specific importance from the perspective of climatic, ecologic and hydrologic studies. Researchers believe that remote sensing data can lead to assess the snow-covered areas better than traditional topography methods. Therefore, nowadays, in efficient management of water resources, applying remote sensing data aims to achieve exact information on snow-covered areas operationally. Satellites are suitable tools to measure the mentioned areas since high snow reflection creates a good contrast with other natural surfaces except clouds. This research is conducted to compare the performance of Cornell functions of support vector and object-oriented Fuzzy operators in estimating the desired areas in Almabolaq Mountain, Asadabad.

The data used in this research are the bands with 10 m spatial segregation of 2B Sentinel satellite including bands 2, 3, 4 and 8 on 6th March 2020. To classify Cornell functions of support vector machine and compute their accuracy, ENVI software was implemented. The eCognation software was used to partition and categorize those with the same object-oriented Fuzzy operators. Separating similar spectral sets and classifying those with the same spectral behaviour are regarded as satellite information classification. In other words, categorizing the photo pixels, and allocating one pixel to one class or phenomenon are the mentioned classification. Support vector machine is one of the most common classifiers in learning machine, which divides data using an optimum separation super plate. One of the important advantages of support vector machine is the ability to deal with high dimensional data using almost less training samples for remote sensing applications. Objective analysis is an advanced technique of image processing which is used to assess the digital images and typical conflicts of basic pixel classification based on different methods. Traditionally, pixel-based analysis is done by available data of each pixel whereas object-based analysis considers a set of similar pixels called objects or image objects. It regards adjacent pixels with the same information value as one distinct unit called piece or segment. In fact, pieces are the areas produced by one or few homogeneous criteria in one or few dimensions of a specific space so that the pieces have extra spectral information in each band, mean, maximum and minimum amounts, variance, etc. as compared to single pixels. Combined object-oriented and Fuzzy methods provide the classification of image pieces with a specific membership degree. In this process, image pieces with different membership degrees are classified in more than one class and according to the membership degree, image piece classification is done leading to the increased final precision.

In the research, after preparing satellite images in SNAP software using Sen2Cor, radiometric correction was conducted on the images. To prepare the classification map of Cornell functions of support vector machine, TIFF satellite images were called by ENVI software. Using the shape file of the case study, the area cutting operation was done. Afterwards, two classes of snow and non-snow regions were created to pick up the training points and based on imagery processing, training points were specified for each class. To classify support vector machine algorithm, linear, polynomial, radial and sigmoid Cornell functions were applied and classification maps were separately produced. To draw the classification map of object-oriented Fuzzy operators, satellite images pre-processed in previous stages were called by eCognation software and then they were defined as a project. Afterwards, two mentioned classes were defined to do the classification process and for each class, the desired Fuzzy operator was determined. For suitable classification, it was done in various scales and weight coefficients of shape and compactness. Scale 75, shape 6.0 and compactness 8.0 presented suitable classification. After selecting the training samples, parameters of lighting, mean and standard deviations were chosen as distinct features of classes for object-oriented classification. Using the nearest adjacent neighbor algorithm, object-oriented classification was done for each of the Fuzzy operators. After drawing the snow-covered areas through Cornell functions of support vector machine and object-oriented fuzzy operators, the accuracy of classification was computed.

The results indicate that AND algorithm showing the logic share and minimum return value out of Fuzzy values is of the highest accuracy (98%) and to classify digital images, the object-oriented processing methods of satellite imagery enable more precision due to the data related to texture, shape, position, content and geometrical features as compared to Cornell functions of support vector machine.

Land use reflects the interactive characteristics of humans and the environment and describes how human exploitation works for one or more targets on the ground. Land use is usually defined based on human use of the land, with an emphasis on the functional role of land in economic activities. Land use, which is associated with human activity, is changing over time. Land use information and land cover are important for activities such as mapping and land management. Over time, land cover patterns and, consequently, land-use change, and the human factor can play a major role in this process. Today, satellite-based measurements with geographic information systems are increasingly being used to identify and analyze land-use change and land cover. Therefore, accurate detection of changes in land surface properties, especially LULC changes have become a key issue for monitoring local, regional, and global resources and environments, Providing a basis for a better understanding of the interactions between humans and natural phenomena and the proper management and use of these terrestrial resources. About the problems of changes and transformations in the studied area, remote sensing can allow managers to categorize images and evaluate land-use changes, in addition to saving time and costs, which allows planners to make plans based on changes, more resources are lost, to be prevented.

The Gamasiab River originates from calcareous springs located 21 kilometers southeast of Nahavand in Hamadan province from the northern slopes of the Greene Highlands known as the Mirab Gamasiab. This river enters Kangavar, Harsin, and Bistoon Kermanshah from the east-west direction of Nahavand and then enters the Faraman area by going around Bistoon and continues its north-south direction after receiving other branches and water. The surface currents of the adjacent basins join the Gharasu. For this study, an approximately 80 kilometers interval from the Gamasiab River and its adjacent lands 5 kilometers from each side was selected. Three images of Landsat for TM, ETM+ and OLI sensors were selected for monitoring of the river adjacent lands and vegetation indices for the years 1987, 2000, and 2017, respectively. NDVI is the normalized difference vegetation index and is the most common vegetation index. SAVI Soil- Adjusted Vegetation Index by Huete (1988) has been developed to use soil optical properties on the canopy reflectance capability. This index has added a factor of L (soil texture correction factor) to the NDVI equation. Radiometric and atmospheric correction images are performed before applying spectral indices. In the process of atmospheric correction, the first step is to calculate the radius value, and from the radius value, the reflectance value is calculated. There are two advantages to using reflectance values compared to radiative values: first, the effect of the cosine angle of the different solar angles can be measured relative to the time difference between the data harvesting, and second, the different amounts of solar radiation outside the atmosphere caused by the differences. The band is spectral, corrected. Atmospheric correction is done to eliminate the effects of the transmission and absorption of electromagnetic waves in the visible and infrared range. In general, each of the terrestrial features has a special spectral sign (spectral signature). These spectral signatures depend on many factors, such as sensing properties, differences in radiation and reception angles, atmospheric and topographic conditions, and imaging time. Because of the factors mentioned above, digital numbers (DN) cannot represent the actual conditions of spectral reflection of the Earth. The purpose of radiometric correction is to remove or neutralize the above effects of the image. After the indexes are applied, the land units have to be separated so that they are threshold on them, which means that we separate the pure classes. So, values between -1 to 0 are considered as wet and water body, values between 0 to 0.3 as soil, and values between 0.3 to 1 as vegetation.

Due to the lack of user interference against the object-oriented and pixel-based classification algorithms, this process (applying spectral indices) uses spectral information of the bands used, as accurate as the radiometric resolution of the sensors used. The results showed that for the NDVI index in 1987 the amount of water land fields was 13% and this value decreased by 0.63% to 12.57% in the year 2000 and 18.71% in the whole study area in 2017. These figures for the SAVI index in the year 1987 amounted to 10.42%, for the year 2000 the value was 10.93%, and for the year 2017 amount was 16.54% of the total area studied. What is certain is the slight change in 2000 relative to 1987 for both indices. Both NDVI and SAVI show an increasing trend of vegetation cover (water land fields) in 2017. These figures show the unprecedented use of river water and groundwater in recent years. Excessive use of groundwater resources results in land-use changes and subsequently physical, chemical, and even biological changes in water resources and land surface. For soil class, it is clear that both NDVI and SAVI indices show slight changes from 1987 to 2000, and for 2017 both indices show decreases in soil class compared to 1987 and 2000. The results show the inefficiency of indices in river and water body extraction in the study area. Principal components analysis was used for river extraction. Consequently, by comparing the indices with the corresponding PCAs it can be said that the river is properly extracted using PCA which can lead to even better results for the wider rivers.

Identifying and discovering the land cover changes can help planners and planners identify effective factors in land-use change and land cover, and have useful planning to control them. High accuracy maps are required for this purpose. The use of spectral indices makes this possible with very high accuracy. The results of this study, in addition to prove the accuracy and efficiency of spectral indices for estimating land cover, showed that during the years of 1987, 2000 to 2017 the soil class reduce and, on the other hand, increased water land fields a general trend This illustrates the general trend of degradation in the region through the replacement dry-land fields than water land fields.

Today, the use of remote sensing methods for measuring land surface temperature has got more popular. Because remote sensing provides the opportunity to estimate the temperature in every region accurately. This study aimed to estimate land surface temperature using remote sensing in the south of Urmia Lake, compare them with observed data, and analyze the relationship between estimated land surface temperature and vegetation cover.

Changes in temperature of the surface of the earth were investigated from 2000 to 2017 as well as their relationship with changes in vegetation and land use in agricultural regions. Then, thermal bands of Landsat 7 and OLI were used for measuring land surface temperature and converting DN to radians and brightness temperature. Moreover, NDVI was used for calculating emissivity and determining land use based using an object-oriented method. Then, the relationship between vegetation and land surface temperature was investigated using regression analysis.

The results showed that the observed and estimated land surface temperature had increased from 2000 to 2017 due to the changes in land use and vegetation cover. According to the results of linear regression analysis, there is a significant relationship between estimated and observed land surface temperature (R2 = 0.72). Furthermore, there is a significant negative relationship between land surface temperature and vegetation cover.

The results showed that remote sensing methods provide accurate results in estimating the surface temperature. Understanding the surface temperature and its relationship with various land uses helps planners and experts to make managerial decisions to protect natural resources and agricultural lands.

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.

To assess environmental changes, monitoring systems and remote sensing satellites provide powerful tools that make the assessment of environmental change trends easier by multi-temporal comparisons. In recent decades, remote sensing data and GIS techniques for various aspects of urban spatial expansion and urban dispersal such as mapping (for expansion pattern), control (for process pattern recognition), measurement and evaluation (for analysis), and modeling (for Expansion simulation) are used. The object-based analysis is one of the emerging advanced techniques in the classification of satellite images. The object-oriented classification uses a segmentation process and a learning algorithm to analyze the spectral, spatial, and textural properties of the pixels. Along with the object-oriented classification method, Google Earth Engine, with extensive support for free satellite data and images, enables the classification and processing of high-speed satellite imagery that can be used in the monitoring and mapping land use.

In the present study, the digital data of the Landsat satellite provided by GEE are used. The data do not require pre-processing and initial correction (geometric, radiometric, etc.) and are readily available for processing. Landsat image types (1 to 8) can be summons with any processing level in GEE. In this study, atmospheric correction images of the Surface Reflectance Tier1 are used. This dataset is modified for atmospheric errors and includes OLI / TIRS sensors for Landsat 8. With simple coding patterns in GEE, the images of 1999, 2009, and 2019 are corrected for the processing step. GEE has provided a modern set of pixel-based classification that can be used for monitoring and mapping. By analyzing the corrected image of 1999, 2009, and 2019 and capturing the training samples, the images are classified with the support vector machine algorithms, random forest, and minimum distance. To perform object-oriented analysis and classification, images are segmented using the multiresolution segmentation algorithm in specialized recognition software. Geometric properties of land use classes (including shape, size, texture) are used for segmentation. By analyzing the results of the segmentation of images with different scale parameters, the optimal values ​​of scale, shape, and compression for the images used are obtained. In this study, based on spatial resolution and image quality, four land use and land cover classes were considered in Zanjan urban areas. These classes include built-up, irrigated and urban green areas, dry farming, and rangelands. By selecting the above classes, training samples for multi-temporal images (1999, 2009, and 2019) are prepared. The nearest neighbor algorithm is used to classify images based on the object-oriented method. In this process, the maximum difference index of mean and NDVI vegetation index are also applied for each of the classes to reduce class mixing and improve the classification accuracy of influential parameters such as normalized difference built-up index (ndbi), mean and standard deviation of each band, area, the ratio of length to width, compaction, and brightness. Statistical parameters of kappa coefficients and overall accuracy are used for the accurate assessment of the classified images. To understand the changes in the area, after producing the land use maps and assessing them, the classification methods are used to evaluate the land use changes that occurred in the period 1999 to 2019. 

After the classification of Landsat 5 and 8 satellite images, land use maps of 1999, 2009, and 2019 are prepared using object-oriented and pixel-based methods. Since in this study the parameters and characteristics of mean and standard deviation of bands, NDBI, NDVI indices, etc. are used to improve the results of the algorithm nearest neighbor object-oriented method, the results of image classification accuracy assessment show that the object-oriented method is weaker in separating rangelands and built-up in 1999 and 2009 than the support vector machine classification method. However, the object-oriented classification results for 2019 show the best performance of all the utilized classification algorithms. Due to the better results of the support vector machine classification method for 1999 and 2009 and the object-oriented method for 2019, the results of these methods are used in the assessment of land use changes in the study area. According to the results, significant changes have occurred in the region from 1999 to 2019. During this period, the land (mainly Zanjan) showed an increase of 5036 hectares. Also, the results show that Zanjan has grown and expanded into rangelands and dry farming in the suburbs over the period 1999 to 2019.

Comparing the results of classifier accuracy assessment, the nearest neighbor object-oriented classification algorithm for 2019 showed better performance in terms of kappa coefficient and overall accuracy than other algorithms. Also, by comparing the results of the assessment of the classification maps of 1999 and 2009, the support vector machine algorithm showed the best performance compared to other classification algorithms in the study area. The support vector machine was the basis for the assessment of changes. Based on the results of land use changes assessment, in recent years, significant land use changes have occurred around Zanjan city. The reason for the increased land area in Zanjan in 2019 (26.12%) is the increase of population and the development of new settlements in the suburbs, and consequently, the reduction of agricultural rangelands.

With their dynamic nature, water resources are essential fortheenvironment and play a vital role in human life, development of communities, and climate change. Water bodies have been declining over time due tothe rapid growth of urbanization, excessive abstraction of water, damming, increasing demand for agricultural products, pollution anddegradationofthe environment. Therefore, monitoring water bodies and retrievingrelated information are essential for management of environmental issues and decision making in this field. Accurate recognitionof water bodiesiscrucialin many applied fields, such as environmental monitoring, production of land cover and land use maps, flood risk assessing and monitoring, and drought monitoring.Modern methods such as object-oriented processing take advantage of remote sensing capabilities to make accurate and precise recognition of water bodies possible. Classical methods on the other hand, cannot accurately classify satellite imagery with similar spectral information merging into each other. This reduces the accuracy of pixel-based classification methods. Therefore, object-oriented processing of satellite images is used in the present study to obtain precise maps for the identification of waterbodies.

A part of Aji Chai River, near the city of Khajeh in Harris County, has been selected as the study area. The total study area included 28 square kilometers. Based on the aim of the present study, the study area was selected in a way to contain linear features, arable lands, and other topographical and human-madefeatures (shading factor) which interfere with the extraction of water bodies and reduce the classification accuracy. Object oriented methods (the closest neighbor and fuzzy object-oriented methods) were used in the present study to identify and extract water bodies from high resolution images (Sentinel 2A imagery).

Different functions used in OBIA techniques,such as GLCMtextual features, average number of bands in the image, geometric information (shape, compression and asymmetry), and normalized difference vegetation index(NDVI) were used in the present studyto precisely extract land cover. Moreover, algorithms with the highest membership degree in the class of water bodies were considered as effective factors in classification. Usual methods of extracting and monitoring water bodies use spectral information of pixels, and therefore, have limited ability in distinguishing water bodies from linear features, such as roads, clouds, shaded regions, and residential areas. These methods also have limited capabilities in mountainous areas, especially when they are required to separate water from snow. In other words, these methods cannot separate water bodies from regions with lower albedo. Therefore, the present study takes advantage of object-oriented methods (the nearest neighbor and fuzzy methods) and evaluate their effectiveness in the extraction of water bodies.

In this study, the nearest neighbor and fuzzy object-oriented methods were used to extract water bodies and their efficiencies were compared. To improve the results in the nearest neighbor method, the separation space between the samples was optimized using the FSO algorithm, then the water bodies were extracted with 95% accuracy and a Kappa coefficient of 93%. Findings of the present studyindicated that this method cannot distinguish water bodies from shaded regions, and linear featuressuch as roads, and residential areas, and categorizes these features as water bodies, which reduces the accuracy of the final results. In the next step, water bodies were once more extracted using object-oriented fuzzy model. In this method, membership degrees were first calculated for each sampleand then applied in the classification procedure. High accuracy of the results of this method (overall accuracy of 98% and a kappa coefficient of 96%) indicated the superiority of this method over the previous one (nearest neighbor). In this method, water bodies are completely distinguished from linear features such as roads, as well as shaded regions, clouds and residential areas. The results of this study can be generalized to other rivers and water bodies. Compared to classical methods, object-oriented methods are more time efficient and accurate.

Monitoring land use and vegetation changes plays an important role in urban management. The purpose of this study was to investigate the changes in vegetation cover of Ahvaz metropolis pertaining to land use changes. First, satellite imagery was prepared through object-oriented vector machine-based algorithm, classified method, and land use maps. To increase the accuracy of the maps, three normalized indices of vegetation difference, soil effect modifier and relative vegetation were used separately in the classification. The results showed that soil effect modifier index had higher capability and its maps with the highest kappa coefficients and overall accuracy were entered into land change modeling to detect changes. Changes in the next 10 years were also predicted using the Markov chain automated cell model. The results of the changes showed that the vegetation has a decreasing trend, so that 1339.65 hectares the vegetation in the period 1989-1989 and 1860.50 hectares in the period 2019-2002, have reduced. The most changes are related to the conversion of vegetation into man-made areas with 686.44 hectares in the period 1989-1989 and 1032.51 hectares in the period 2019-2002. The least changes are related to the conversion of vegetation into water areas with 7.18 hectares in the period 1989-2009 and 9.33 hectares in the period 2019-2002. The results of forecasting changes by 2029 also confirmed the reduction of vegetation. In 10 years, 785.77 hectares of vegetation will be reduced and the area will reach 2923.24 hectares.

For landscapes and natural phenomena, the face of the earth has always been subject to change. The increase in population and the excessive use of land has increased the pressure on the environment.Therefore, in order to optimize the management of natural areas, awareness of land use is considered as an urgent requirement In this study, a pixel-based classification approach based on ENVI 5.3 software and an object-oriented approach using eCognition software was used to prepare the land use map of Susangerd County with Landsat 8 satellite OLI sensor. In order to compare the results, both methods used the same educational data for classification. Then, the most important methods for assessing accuracy including precision and kappa coefficient of classification were extracted and it was determined that the maximum optimal algorithm in the base pixel classification method compared with other algorithms, 9% show better results. But in contrast to the object-oriented classification method, about 1% (in particular, precise and precise classification) results in higher accuracy in the classification of images. The amount of accuracy in the object-oriented classification-based method depends largely on choosing the appropriate parameters for classification, defining the rules, and applying the appropriate algorithm to obtain the degree of membership.

Extended Abstract Introduction Currently, two general methods are used for classification of digital satellite images: pixel-based and object-oriented processing. Unlike pixel-based Methods, object-oriented techniques employ different geometric, spatial, spectral, and form-based algorithms, and selecting the most efficient algorithm in this process requires a lot of experience in image processing. In addition, multiple algorithms usually offer different results and this in many cases makes the selection of efficient algorithms difficult. In general, pixel-based classification includes supervised and unsupervised methods. Examples of these methods include maximum likelihood, neural network and support vector machine. Maximum likelihood method is one of the most effective methods used for image classification. Object-oriented methods take advantage of knowledge-based algorithms, and thus overcome problems pixel-based method faces because of not using geometric and textual information. In order to achieve high classification accuracy, two methods of pixel-based and object-oriented classification are compared in this research. On the one hand, integrated planning and management of urban areas, and on the other hand, collecting reliable information regarding land use makes this kinds of studies indispensable. Materials&Methods Present study seeks to extract urban land use map. Thus, necessary data was received from Sentinel-2. Moreover, ENVI 5.3, eCognation 9, SNAP, ArcGIS 10.3, Google Earth, and land-use data were also used to process images and analyze data. In SNAP, atmospheric correction process was performed on images collected from the study area using SEN2COR plug-in. Samples collected from each class of Sentinel-2 satellite image were mapped on the image area. Pixel classification algorithms, support vector machines, maximum likelihood, artificial neural network, Minimum Distance to Mean (MDM), parallelepiped and Mahalanobis distance were used. Finally, land use classes (residential, gardens and green spaces, wastelands and passageways) in the study area were mapped using different classification algorithms. For object-oriented classification using nearest neighbor algorithm, the satellite image was first segmented in eCognation software using the Multiresolution Segmentation Algorithm. Parameters such as scale, shape and compactness were also studied in the image segmentation stage. Through trial and error, an appropriate value was selected for parameters used in segmentation. For practical comparison of the results, the same educational data was used in both object-oriented and pixel-based classification methods. Then, the most important methods for assessing accuracy including overall precision and kappa coefficient were extracted. Results & Discussion As one of the most important methods used for extracting information from remotely sensed images, classification allows users to produce various types of information such as coverage maps, and land-use maps. Classification of satellite data includes segregation of similar spectral sets and classification of sets with the same spectral behavior. Regarding the resolution of images used (10 m) in this study, only 4 land-use classes possessed the required resolution capability for pixel-based classification of Sentinel-2 satellite images. These classes include built-up (residential) area, waste land, urban green space and street network. In this regard, support vector machine, maximum likelihood, artificial neural network, Minimum Distance to Mean, parallelepiped and Mahalanobis distance were used for classification. Classification results indicate that compared to other pixel-based methods, maximum likelihood method and Minimum Distance to Mean method show a precision of 85% or higher. In present study, geometric properties of land use classes (including scale, shape, and compactness) were used for segmentation and this process was performed by multiresolution method. For this purpose, results of image segmentation process were analyzed based on different parameters (with different scales) and spatial resolution of the image. In this way, appropriate values for segmentation were selected based on the specific features of the study area (an urban environment) through trial and error. Then, the proper image segmentation was selected and prepared for the classification stage using the above mentioned parameters. In the next step, 20 effective parameters including statistical indices, mean score of bands, NDVI index, standard deviation of the bands and geometric index were used for classification. Conclusion The present study took advantage of six pixel-based methods (Support Vector Machine, Maximum Likelihood, Neural Network, Minimum Distance to Mean, Parallelepiped, and Mahalanobis) along with object-oriented classification method to produce a land-use map for Zanjan city. The accuracy of classification in different methods were compared and statistically analyzed using overall accuracy coefficient, kappa coefficient, user’s accuracy, and producer’s accuracy. The results of statistical analysis of the accuracy coefficients indicated that Minimum Distance to Mean and Maximum Likelihood method -with a Kappa coefficient of 90% and 85% respectively- are acceptable methods for land use mapping. Moreover, comparing pixel-based and object-oriented methods, it is possible to conclude that object-oriented approach with a Kappa coefficient of 0.95% and overall accuracy of 97.9% shows a higher potentiality. Nearest Neighbor algorithm is one of the most important reasons for achieving this high accuracy in object-oriented classification. In addition to the spectral information, this method uses information collected about issues like texture, form, position, and content for the classification process. Methods used in this study prove the accuracy of objective-oriented technique by employing effective parameters and developing rules to modify the initial classification of object-oriented technique. Another advantage of object-oriented method (as compared to pixel-based methods) is that apart from spectral information and statistical data, it is possible to apply several other indicators such as shape, texture, color, dimensions and altitude of the phenomena in the final land use map produced by this method. Finally, it should be noted that object-oriented classification has been developed for high resolution spatial data.

Wind erosion as an “environmental threat” has caused serious problems in the world. Identifying and evaluating areas affected by wind erosion can be an important tool for managers and planners in the sustainable development of different areas.  nowadays there are various methods in the world for zoning lands affected by wind erosion. One of the most important methods is the use of satellite images and various classification methods. Satellite imagery with features such as wide coverage, repeatability and continuous updating is particularly important in determining land cove.  classification methods include pixel based, object oriented and map decision tree. Field studies on the spatial development of wind erosion sites are difficult and expensive to replicate and monitoring studies in these areas are not possible. the purpose of this study is to evaluate the classification methods in the detection of the Sarakhs plain sandy zones in order to identify endangered sources of these zones.

In this study, the Landsat ETM + satellite data was used from USGS web site and all the processed satellite images was done with ENVI software and Arcmap 10.3 GIS. After pre-processing the images, including geometric, atmospheric and radiometric corrections, the land use map was prepared using a supervised classification method in six classes. These classes include agricultural lands, barren lands, sand dunes, wind deposition, lakes and rangelands. Classification was performed based on all the algorithms of pixel based, object oriented and map decision tree methods. These algorithms include maximum likelihood, minimum distance, neural network, and support vector machine in the pixel based method and The object-oriented approach used the nearest-neighbor algorithms on the scales of 1, 3, 5, 7 and the support of vector machine. The final classification was done by a decision tree method map. Parameters used for validation of the results include total accuracy, kappa coefficient, accuracy matrix of the producer and the produced, quantity and allocation disagreement.

The results of the classification show that the number of pixels in the training samples is 25049 pixels obtained by random sampling. The number of ground points used to estimate the overall accuracy of the produced maps are 90 control points from Google Earth satellite imagery, 50 control points from 1: 50000 topographic maps and 45 field control points. The evaluation of classification methods showed that higher accuracy percentage for decision tree method is 87%, kappa index 82%, Quantity disagreement 6.7% and allocation disagreement 5.6% Compare to other methods. These coefficients in the pixel based method are respectively 83%, kappa coefficient 78%, quantity disagreement 10.4% and allocation disagreement 6.1% And in object-oriented method, the overall accuracy is 80%, Kappa index 75%, quantity disagreement 83% and allocation disagreement 7.6%. The least producer and user accuracy in all three methods is related to sand dune class and the highest amount of quantitative disagreement is assigned to the pixel based method for the class of wind deposition and rangelands, In the object-oriented method, it is related to the class of agricultural lands and sand dunes, and in the decision-tree method, it is related to the classes of agricultural lands and rangelands. This may be due to the lack of acceptable separation of the sand dune class.

In this study, it was assumed that the object-oriented classification method would more accurately classify sand dunes and zones but since the sand dunes of Sarakhs do not follow specific morphology and geometry and they are more longitudinal Therefore, the classification of these zones was performed better with the pixel based method. But the land use, such as agricultural that follows geometric shapes, was more accurately classified in the object-oriented method. The area of sandy lands, including hills and sandy zones, was estimated to be about 1349 km2. Most of these lands are located in the central part of the study area in the vicinity of biological and physical elements. Also, the comparison of the area maps shows that the area of land using water levels and agricultural lands are close to each other. And the area differences are mostly related to rangelands, barren lands and sandy areas. Based on the results of this study, it can be suggested that decision tree method is more suitable than pixel based and object oriented methods for classifying land cover and detecting sandy zone changes and the most important reason is the use of both algorithms.

Land use reflects the interactive characteristics of humans and the environment and describes how human exploitation works for one or more targets on the ground. Land use is usually defined on the basis of human use of the land, with an emphasis on the functional role of land in economic activities. In this research, a pixel-based method and object-oriented method for mapping the map of Maragheh city using ASTER sensor images in a 17-year time series were compared between 2000 and 2017. Also the effect of land use changes on erosion was studied. In order to compare the results, both methods used the same educational data for classification. Then, the most important methods for assessing accuracy including precision and kappa coefficient of classification were extracted, and it was determined that the result of the object-oriented classification was better than the 3% Offers. The increase in accuracy in a method based on object-oriented classification largely depends on choosing the appropriate parameters for classification, defining the rules, and applying the appropriate algorithm to obtain the degree of membership. After classifying and comparing the extracted maps, we attempted to reveal the changes that occurred during this period and it was determined, that the rangeland and Bayer classes have a decreasing trend and dense garden classes and water with increasing trend. According to the user-mapped maps of the two methods of maximum-likeness and object-oriented classification and comparison and adaptation of these maps with ground-based properties, the results of the object-oriented classification method were confirmed. According to the results of the study, with object-oriented method, in the Maragheh area, densely populated gardens, gardens, housing, housing, agriculture, industry, and communication in the object-oriented method have increased, and agronomic, pasture, dryland and Bayer uses Reduced area. This indicates the importance of agriculture and horticulture in this city. In 2000, 9.08 percent and 15.88 percent of study area are located in very high-risk and high-risk erodibility and these values for 2017 are 13.66 and 29.76 percent respectively. Also results showed that dense and low dense agricultural land, residential and industrial land use have been increased. The increase in accuracy in a method based on object-oriented classification largely depends on choosing the appropriate parameters for classification, defining the rules, and applying the appropriate algorithm to obtain the degree of membership. After classifying and comparing the extracted maps, we attempted to reveal the changes that occurred during this period and it was determined, that the rangeland and Bayer classes have a decreasing trend and dense garden classes and water with increasing trend. According to the user-mapped maps of the two methods of maximum-likeness and object-oriented classification and comparison and adaptation of these maps with ground-based properties, the results of the object-oriented classification method were confirmed. According to the results of the study, with object-oriented method, in the Maragheh area, densely populated gardens, gardens, housing, housing, agriculture, industry, and communication in the object-oriented method have increased, and agronomic, pasture, dryland and Bayer uses Reduced area. This indicates the importance of agriculture and horticulture in this city. In 2000, 9.08 percent and 15.88 percent of study area are located in very high-risk and high-risk erodibility and these values for 2017 are 13.66 and 29.76 percent respectively. Also results showed that dense and low dense agricultural land, residential and industrial land use have been increased. The increase in accuracy in a method based on object-oriented classification largely depends on choosing the appropriate parameters for classification, defining the rules, and applying the appropriate algorithm to obtain the degree of membership. After classifying and comparing the extracted maps, we attempted to reveal the changes that occurred during this period and it was determined, that the rangeland and Bayer classes have a decreasing trend and dense garden classes and water with increasing trend. According to the user-mapped maps of the two methods of maximum-likeness and object-oriented classification and comparison and adaptation of these maps with ground-based properties, the results of the object-oriented classification method were confirmed. According to the results of the study, with object-oriented method, in the Maragheh area, densely populated gardens, gardens, housing, housing, agriculture, industry, and communication in the object-oriented method have increased, and agronomic, pasture, dryland and Bayer uses Reduced area. This indicates the importance of agriculture and horticulture in this city. In 2000, 9.08 percent and 15.88 percent of study area are located in very high-risk and high-risk erodibility and these values for 2017 are 13.66 and 29.76 percent respectively. Also results showed that dense and low dense agricultural land, residential and industrial land use have been increased.