فصلنامه سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی
سال سیزدهم شماره 3 (پاییز 1401)

Background and Objective:

 Soil as a heterogeneous natural resource and the largest organic carbon storage in terrestrial ecosystems is composed of complicated processes and mechanisms. The necessity of accurately estimating soil properties on the national and regional scales for improving soil management, and understanding their influence on agriculture have resulted in attracting researchers’ attentions to this field. Soil Organic Matter (SOM) is considered as an indicator of soil quality in fertility and food production. It is also considered as a key variable in environmental and agricultural issues. Thus, using rapid and cost effective and more accuracy estimation of the SOM content in soil resources assessment and management can be helpful. In precision agriculture, the scale of soil data required for management of lands and products is very large. The scale of collecting filed data usually cannot fulfil those needs. Sampling, preparing and analyzing the large number of soil samples as well as producing the distribution map for large areas are very difficult. In addition, traditional laboratory methods of soil analysis are boring, time-consuming, and costly. In fact, they need specialized laboratory operators. The aim of the present study is to compare the performance of the two Partial Least Squares Regression (PLSR) and Boosted Regression Tree (BRT) for predicting SOM using VNIR spectrometry data. With the use of combining Wavelet transform and diagnosis of independent bands, noises existing in soil spectroscopic data has reduced. In addition, independent and effective spectra and bands in spectroscopy of SOM were selected. Consequently, in the present research, Wavelet-PCA-PLSR and Wavelet-PCA- BRT models were developed and performance were assessed.

Materials and Methods:

 42 surface (0-30cm) soil samples in the heterogeneous areas of urban-agricultural regions in Tehran province were collected. Soil Organic Carbon (OC) measured using Walki Black method and the samples’ spectrums were measured by ASD FieldSpec-3 spectrometer. First and second derivitation of spectral reflectance and absorbance were calculated. To reduce noises and smooth the spectrum, Sym8 matrix function of wavelet transform was used, wavelet transform is conducted to show and reconstruct characteristics in the spectrum. Principal component analysis and Hotelling's T2 test with 95% confidence level were used for outlier detection. PLSR and BRT was conducted onreflectance, absorbance and their first and second derivatives, at five levels of wavelet transform. Then, by comparing the results, the appropriate model was selected via validation. For doing the PLSR in nonlinear data, Kernel functions were used. When using numerical samples, regression trees are used instead of decision trees. But their processes are the same. In regression trees, the greedy algorithm was used. Therefore, by answering the binary question through which node the maximum data about respons variable is obtained, the root node and its two children are obtained. Producing the structure of trees is recursively repeated and a typical stopping criterion is considered. The stopping criterion can be as achievement to a split which cannot be divided and provides fewer data, or when data in the node contain 5% of the total data. Moreover, the tree size should be minimized. For splitting the node, the Ginny factor, entropy factor, etc. were used for minimizing those factors. In addition, the total square error is calculated in each branches and those with minimized values are selected. In addition, in the regression tree, the pruning process is employed for over-fitting. The BRT consists of the two regression tree and boosting techniques for improving the predictability of each of them. For calibration and validation of the model, 30 and 12 soil samples were randomly selected, respectively and R2 and RMSE were used for quantify the accuracy of models. Moreover, to select the best production factor of the PLSR mode, explained variance residual values and RMSE of validation were considered. Finally, soil organic matter map was produced using Landsat OLI satellite imagery and the proofed method for the study area.

Results and Discussion :

The SOM value acceptably, the creation of continuous mappings with more accuracy based on noise reduction and retention of suitable data have always received researchers’ attentions. The present study tried to find the better method such a more accurate quantization of SOM using soil spectroscopic data. Using wavelet transform and outlier removal based on Hotelling's T2 via the PCA, the suitable data were extracted for producing the more accurate quantization. In this method, independent and effective bands or spectra remain in the model, while Lin et al. used wavelet transform and correlation techniques for selecting appropriate bands in estimating SOM. Since the soil reflectance is more complex and affected by several factors, using correlation method in these heterogeneous areas such as the area studied in the present study does not lead to acceptable results. Considering the data values, the unsupervised PCA method calculates principle components and eigenvalues and eigenvectors. It also tries to maximize the covariance matrix based on Singular Value Decomposition (SVD). SOM estimation models were developed using the PLSR and BRT for reflectance and absurbance spectra and their first and second derivation. Based on the results, the BRT method with RMSE and R2 values as 0.58 and 0.94, respectively leads in the better results for the data of the second derivation of reflectance. Moreover, values of RMSE and R2 in the PLSR were obtained as 1.0338 and 0.938, respectively for the data related to the second derivation of reflectance. However, comparing RMSE of the BRT and PLSR shows better results of the BRT model.

Conclusion:

 In that field measurements of chemical properties of soil such as organic matters are critically time-consuming and costly. Furthermore, measuring those properties is not possible in the large samples. So, the results of the present study indicate that in heterogeneous agricultural-urban areas, potential of the developed models such as wavelet-PCA-PLSR and wavelet-PCA-BRT can be used for estimating SOM. Meanwhile, these two algorithms do not make distributional assumptions and therefore, there are no strong assumptions about normality. Using continuous functions and satellite imagery, the map of the level of SOM in large scales can be prepared in order that it can be utilized in studies such as cultivation potential, soil fertility, and sustainable development of soil.

Background and Objective:

 North Karun watershed is one of the important sub-basins of the Great Karun River basin. In recent years, the occurrence of severe downstream floods in this basin has caused a lot of human and financial losses. Estimating the amount of runoff produced by rainfall is the main step in conducting a study on flood control and mitigation. Runoff estimation is one of the most important steps in the study of watershed hydrology for flood management, water resources management and soil conservation activities. Runoff is produced as a result of excess rainfall on soil infiltration and surface maintenance and depends on various factors such as physical characteristics of the basin, rainfall and infiltration. The rainfall-runoff relationship has been studied by scientists and researchers and many models have been proposed to simulate this process. One of the basic models in this field is the curved number method model that was proposed by the US Soil Conservation Service and was named the Soil Conservation Service Curve Number or SCS-CN. The SCS-CN model is one of the simple and empirical models in the field of rainfall-runoff that is widely used in estimating runoff height around the world. The curve number (CN) of each basin indicates the hydrological behaviour and runoff generation capacity of that basin during rainfall and its value is estimated from the standard table in which the soil properties with a hydrological factor that indicates the minimum infiltration rate in the long-wet state. It is the duration of the soil, it is expressed. Accordingly, the US Soil Conservation Service has divided all soils into four main groups, A, B, C and D, with high, medium, low and very low infiltration rates, respectively. Due to the high time consumed in calculating this method, traditionally and manually, researchers used remote sensing and geographical information systems technologies to calculate it. To do this, they designed an extension called ArcCN-Runoff that can be added to the GIS environment. The purpose of this study is to generate a curve mapping (CN) and estimate the runoff height in the North Karun Basin using Remote Sensing (RS) and Geographic Information System (GIS) technologies and the SCS-CN method.

Materials and Methods:

 North Karun watershed is one of the most important watersheds in the country in providing water resources, which is located in the geographical position of 49o 35' to 51o 47' E longitude and 30 o 28' to 32 o 40' N latitude. This basin has an area of 23299.31 Km2, which is located in Kohgiluyeh and Boyer-Ahmad, Chaharmahal and Bakhtiari, Isfahan, Khuzestan and part of Fars province. In order to produce curve number maps and estimate runoff height, first land use maps were generated using a supervised classification method using Landsat 8 satellite images of OLI sensors related to 2017 and the maximum likelihood algorithm was obtained. Then, the soil layer and soil hydrological groups of the basin were prepared based on the global soil map produced by the Food and Agriculture Organization (FAO) and the SCS standard table for different soil hydrological groups. By combining land use maps and soil hydrological groups with the Perform Intersect command in the ArcCN-Runoff GIS environment, a curved number (CN) map was generated and edited and finalized based on the SCS-CN table. Finally, by generating a spatial distribution map of precipitation by IDW method at the basin level, runoff height or excess precipitation height was obtained by the SCS-CN method. The final runoff height map was divided into five categories: very low, low, medium, high and very high.

Results and Discussion :

The results showed that a large area of the basin has a slope of more than 30%. Slope can be considered the most important physiographic factor in runoff production in watersheds. According to the land use map, the highest level of use is related to the oak forests of Zagros and rangelands. Due to the type of oak forests that have low density and also poor pastures in the basin due to overgrazing, runoff production is high in these uses. The curve number map also indicates that the maximum and minimum curve number values are in basins 98 and zero. In areas with high curve numbers, the potential for runoff production is high and these areas are related to areas with poor vegetation in the basin. Areas with high curve numbers and high runoff production are mostly related to the calcareous formations of the basin, including the Bakhtiari and Asmari formations, which have formed the high altitudes of the basin, including the Dena Mountains. Also, areas with medium and low CN are mostly related to forest and pasture uses, in which the potential for runoff production is moderate. On the other hand, due to the fact that most of the precipitation in the basin heights is snow, there is an opportunity to penetrate into the soil and as a result, the amount of runoff production is less. The calcareous formations in the basin, which form most of the Zagros highlands, penetrate rainfall through pores and fractures and feed groundwater, which is why we often encounter a large number of springs in these areas. On the other hand, the results of the runoff map indicate that the Zagros heights, which have a high slope, play a major role in runoff production. These areas are mostly located in the southern and southeastern areas of the basin, as well as northwest of the basin.

Conclusion :

A large area of the basin has the potential to produce moderate runoff, which can lead to flooding downstream of the basin. Therefore, it is necessary to implement conservation and watershed management measures in the branches and areas where the flow occurred. Due to the fact that the basis of calculations in preparing maps of curve number and runoff height are raster layers, each pixel of which has a value, the calculations are done and the results are much closer to reality and save time and money.

Background and Objective :

Land surface temperature (LST) has become an important issue in the world today, as it affects the climate and environment at the local, regional and global levels, and these changes in land surface temperature are mainly caused by it arises from urbanization, and human activities and extreme Landuse and Land-cover (LULC) changes. Due to the limitations of meteorological stations, remote sensing can be used as the basis of many meteorological data. One of the most important practical aspects of remote sensing in climate studies is the estimation of surface temperature. In this research, the temperature of the earth's surface between 1990 and 2018 was extracted from the images of TM and OLI sensors of the coastal lands of Bushehr, using the Stefan-Boltzmann method.

Materials and Methods:

 The land study area of Bushehr city, which is on the northern coast of the Persian Gulf, with dimensions of 20×8 km2 an area of 1011.5 km2 and with an average minimum temperature of 18.1oC and an average maximum temperature of 33 oC, relative humidity between 58-75% and the average annual rainfall is 272 mm, it’s located in the geographical location of 50°50' to 10°51 E longitude and 28°40' to 29°00' N latitude. The data used in this research include; Landsat 8 (OLI) data in 2018 and TM data in 1990, which were downloaded from the United States Geological Survey (USGS) data center (https://earth explorer.usgs.gov). In order to calculate the parameters related to temperature extraction, the meteorological data of the synoptic stations located in the studied area were used. After taking the images, due to the larger range of the images, the images were cut (Resized) and then the geometric correction of the images was done using topographic maps on a scale of 1/25000 and all the images were adjusted to the UTM coordinate system of the 39 N were adapted. In geometric correction, the RMS error of all images was less than 0.5 pixels. In order to compare the results of Stefan-Boltzmann method for extracting LST with ground data, thermal map data obtained was compared with soil temperature data (obtained from meteorological stations in the selected area). In order to evaluate the Stefan-Boltzmann method from ground data, the Mean Absolute Error (MAE) index statistical method was used.

Results and Discussion: 

 The average minimum and maximum Land surface temperature (LST) extracted from the 1990 TM image was 26.5 and 45 °C, respectively, and for the 2018 OLI image, it was 30.1 and 48.6 °C, respectively. The results showed that the Mean Absolute Error (MAE) index values for TM and OLI sensors are to 7.1 and 5.6, respectively. The results of the research showed that the Stefan-Boltzmann method provided a reliable result in estimating the Land surface temperature.

Conclusion:

 This research aims to extract LST by Stefan-Boltzmann method. The results of this method were estimated using the Mean Absolute Error (MAE) statistical index for the study period (1990-2018). Applying the MAE on the produced thermal maps, it was found that the Stefan-Boltzmann method is suitable for future research in the fields of thermal remote sensing by observing the results of using the MAE index on thermal maps. Therefore, we conclude that the Stefan-Boltzmann method is suitable for estimating the surface temperature of the land in coastal areas. Finally, it is suggested that for quantitatively describing LST patterns a GIS/RS-based method, and methods such as spatial autocorrelation and semivariance are used.

Background and Objective :

Karst is a composite image of all the roughness, shapes, pores, and phenomena caused by water corrosion, above and below the surface, in various soluble geological formations, which cover about 15% of the world's exposed rocks. Despite the importance of karst areas in the past, today, the study, identification, spatial analysis and management of these areas are of very interest to geologists, hydrologists, hydrogeologists, eco-tourists, geotourists and environmentalists at various scales. Karst phenomenon also has a special place in various dimensions from the point of view of geology and geomorphology, because the causes and methods of dissolution processes and forms and their extension in rocks and minerals are of great importance to East Mazandaran, especially in the study area, is geologically one of the areas with karst process potential and has been less studied due to environmental conditions, especially vegetation and access roads. This study is focused on identifying karst areas and their extent using RS and GIS method in the Zalem-Rud Sari basin in Mazandaran province in northern Iran. In order to identify karst areas and physicochemical characteristics of existing aquifers, it was first necessary to identify karst areas and then assess their physicochemical status. In the first step, using Landsat, ASTER and SRTM satellite data, geological maps, weather information and field visits, factors affecting karstification such as rock types, Fracture lineaments, vegetation, climate, condition of the drainages and the slope of the topography are extracted. It should be noted that in this step, to obtain a better result, fuzzy logic and Hierarchical Analysis Process have been used. In the second step, in order to investigate the physicochemical condition of the aquifer, the chemical parameters of some springs are analyzed and the dissolution parameters, saturation indices, the origin of water-soluble components and the general flow system in the existing aquifers are determined.

Materials and Methods:

 In this study, the composition and distribution of rock, based on field visits and georeferenced geological maps of one hundred thousandths of Behshahr and Sari, have been done. Three methods of manual, automatic and semi-automatic have been used to extract the lineaments due to fractures. In the manual method, the lineaments are highlighted and extracted by applying High Pass filters, PCA and the color combination of Landsat 8 satellite data. In the automatic method, the Segment Tracing Algorithm (STA) in PCI software is used. In the STA algorithm, linear pixels are identified based on the degree of gray difference and then converted to vectors based on RADI, GTHR, LTHR, FTHR, ATHR and DTHR parameters. The Normalized Difference Vegetation Index (NDVI) was used to detect and extract vegetation status on OLI sensor data from the Landsat 8 satellite. To generate DEM, photogrammetric techniques were performed on a pair of stereo images of ASTER sensor in Idrisi software based on parametric variables of external and external orientations and ground control points (GCPs). The topographic slope of the area has been calculated by DEM and based on the degree in GIS and its map has been prepared. Based on the weather data of the General Meteorological Department of Mazandaran Province, the weather condition is determined by the isothermal, isohyetal and isoevaporation curves and the type of weather is determined by the De Marton method.

Results and Discussion :

The result of the above activities has been the production of lithological maps, density and distance from fracture lineaments, density and distance from drainages, topographic slope, rainfall and vegetation. Due to the different criteria used in the generated maps and the need for a single, comparable and proportionate criteria for combining information layers, fuzzy logic has been used. In this case, all layers, except lithology, which has a definite boundary and is a function of Boolean logic, are fuzzy and then extracted as fuzzy layers in GIS. On the other hand, because the weight and effectiveness of the eight factors affecting the occurrence of the karst phenomenon are not the same, the Hierarchical Analysis (AHP) method has been used to determine the preference and prioritization of these factors. And the participation rate, in other words, the weight of each criterion with an incompatibility coefficient of less than one percent has been calculated in Expert Choice 12 software. Finally, by the weighted linear combination (WLC) method, fuzzy raster layers based on their effective weight are combined with Compromise Operator or Gamma and the karst distribution map is extracted with different probability coefficients in the study area. In the next step, in order to investigate the physicochemical status of the aquifers and evaluate the karst phenomenon, 18 springs with equal flow and more than three litres per second were selected and evaluated using SPPS, RockWorks2016 and PHREEQC2.6 software. In addition to extracting hydrogeochemical tables and graphs, the saturation index of various minerals has also been calculated.

Conclusion:

 The results of this study show that the karst phenomenon has the most spread in the central part and the lineaments resulting from fractures have played the most role in the formation of this phenomenon. The water type of these springs is calcium-magnesium bicarbonate and according to the Gibbs diagram, the role of aquifer rocks in determining the chemical composition of water is very clear. Also, the saturation indices of minerals and the type of flow in groundwater reservoirs are diffuse and diffuse-duct flows. To this research, the use of new remote sensing technology and GIS increases accuracy and speed and reduces costs in karst studies.

Background and Objective:

 One of the issues that occur due to groundwater abstraction is land subsidence. This situation is now reported in many arid and semi-arid regions of Iran, especially in Yazd province. In addition, in recent decades, heterogeneous development of agricultural lands and uncontrolled extraction of groundwater from the reservoirs of Herat and Marvast aquifers in Yazd province have caused the occurrence of land subsidence around agricultural lands. The rising metal wall of deep wells and the horizontal cracks on the ground directly indicate the degree of subsidence. It is necessary to identify and identify areas that are prone to subsidence due to the risk and danger to life. On the other hand, we must note that The effects of subsidence may be accelerated by other natural activities in the area such as volcanic activity, earthquakes and landslides, and due to the seismicity of many areas of our country, attention to this phenomenon is of particular importance. Today, one of the most accurate and cost-effective methods for detecting ground surface movements is the radar interference technique. By comparing the phases of two radar images taken from the same area at two different times, this method will be able to determine changes in the earth's surface with accuracy and spatial resolution in centimetres and even millimetres in that time interval. In this article, for the first time, we tried to monitor the subsidence of land subsidence in Herat and Marva's aquifers by using Sentinel-1 satellite images and open source software. In this research, we try to achieve the following goals by using the time series data of the Sentinel-1 sensor, which has not been used in the study of subsidence of the studied areas. The aim of this study is to implement the technique of interferometry with permanent distributors using the integrated SNAP2StaMPS package. Another goal can be to estimate the subsidence rate by processing a set of Sentinel-1 sensor images in the period 20/02/2017 to 10/02/2019, approximately two years of time series. The ultimate goal was to investigate the data potential of this sensor in time series analysis and monitoring of changes due to land subsidence.

Materials and Methods :

Herat and Marvast aquifers, in fact, the study areas in this study include Herat and Marvast aquifers located in Yazd province. Herat and Marvast aquifers are geologically part of the Zagros (ophiolite, radiolarite) and Sanandaj-Sirjan zones. The study areas are located in the 2nd-degree catchment area of Abargoo and Sirjan deserts with code 44 and an area equal to 57125 square kilometres. In this study, 24 data related to Sentinel-1 sensor were processed in one-dimensional mixed image level, high pass, VV polarization and segment number 93 related over a period of approximately two years in both study areas. In general, most of the process of processing and analyzing the time series of interferometers with permanent distributors (PSI) in this paper was done by two open source software, Snap and Stamps. Finally, to automate the single-reference interferometry steps, a set of code written in the Python programming language called SNAP2StaMPS was used, which is well designed based on the graphs of the Snap software.

Results and Discussion:

 One of the results of interferometric processing based on the innovative SNAP2StaMPS algorithm in this research was the production of interference maps from which the topographic phase has been omitted. Other results of the standard deviation scatter index for the average displacement map of both Herat and Marva's aquifers were 4.19 and 3.65 mm per year, respectively. Also, the main results of this study are the estimation of the average displacement map of the Herat aquifer between -40.33 to 11.46 mm per year and for Marva's aquifer between-39.79 to 10.63 mm per year in terms of satellite visibility during the study period (2017 to 2019). For this purpose, areas were randomly selected and areas based on subsidence field evidence in both study areas were selected. Hajiabad Naseri and, 6) Marvast city, related to the Marvast aquifer can be named. In this paper, due to the lack of specialized tools to evaluate and validate the only way to review the results, its compliance with ground subsidence evidence, time series diagrams and hydrograph of the aquifer unit. According to the hydrograph results of Herat and Marvast alluvial aquifers, the groundwater level in the Herat aquifer has decreased by about 5.5 meters during the 8-year period from 2011 to 2019, based on data from 28 observation wells. This hydrograph shows a drop of groundwater of about 7 meters over an eight-year period. The time series results obtained from the interferometry of the images used in this paper show the slope of the fitting line, which actually shows the amount of displacement (up or down), indicating a downward trend in the second area (white aqueduct of Herat aquifer) and Fifth (Shuran aqueduct from Marvast aquifer) shows its amount equal to about 5 and 7 cm, respectively. These results have a significant relationship with the hydrograph of the unit of both aquifers.

Conclusion :

In this study, for the first time, to estimate the rate of subsidence in Herat and Marvast aquifers in Yazd province, the technique of interferometry with permanent dispersants was used using Sentinel-1 sensor data and SNAP2StaMPS open source package. Also, the potential of Stamps and SNAP software for radar interferometry processing was investigated, and also the details of the implementation of the Step to Stamps software package were shown. In general, based on the processed outputs of this package and the results of validation, it is possible to understand the ability of the automated method presented in this study to monitor subsidence and use this algorithm in other study areas.

Background and Objective:

 Due to the increasing degradation at the level of the natural ecosystem, the amount and location of land use changes and predicting its future growth trend, I can provide the information I need to planners and managers. In this study, in order to change the current changes and predict the future in the Siahkal range, forecasting and changing the nose were done with Landsat images. There are various methods for predicting land use change. Processes for predicting and modelling land use change, such as urban growth and development, deforestation, etc., are considered powerful tools in managing natural resources and changing the state of the environment. This change reflects how humans interact with their environment, and its modelling has had an impact on settlement and macro-planning. In this research, due to the high capabilities of remote sensing and modelling tools and predicting changes in change using automatic-Markov cells in forests in northern Iran.

Materials and Methods:

 In this research, Landsat 5 images, 2000 TM sensor, Landsat 7 ETM+ sensor 2010 and Landsat 8 OLI sensor 2018 are used. In the preprocessing stage, errors in raw data such as radiometric, atmospheric, geometric, etc. errors are corrected. Was significant but had a radiometric error. 84 points are used for forest use, 76 points for thin forest water, 31 points for consumption and 2 required sensitivities to indicate a specific level of land cover. Land cover is defined into five classes: dense forest, semi-dense forest, sparse forest, urban area and agricultural area. The ENVI Remote sensing Software defines four types of kernels for the support vector machine in the SVM classification section: Polynomial, Sigmoid torsion, and FBCTION (RBF). According to the best kernel studies for land use classification, the radial kernel (RBF) has been proposed. In the present study, this kernel was used for classification. The classification of the appropriate band composition that you want to separate these classes for visual interpretation was selected by the spectral mean plot. This is done by the complex OIF index. After the extraction of land uses by the method, the results were evaluated accurately. Maps are prepared by land use, then with the GPS position of the earth, the map of the situation in the visible area and using the formed error matrix of kappa weakness and its overall accuracy obtained for this work, 200 points are randomly created on the images. The use of these points was determined by field visits and topographic maps of the surveying organization. Land use classification models are prepared, for modelling and land use changes are entered into office software to design land use changes in the required years. Degree of land use change modelling The LCM model was used in the Idrisi software environment. The Markov-CA model is a combination of automated cells, Markov chains, and multi-purpose land allocation. The Markov model also shows each user by generating a set of status probability images from the transfer probability matrix. In the last step of the structural model, using the transfer area matrix in the CA Markov model, a simulated simulation of future land use can be obtained. In this research, the land use map of 2010 and 2018 was used to predict the 2028 map. And in order to accurately review the forecast by CA Markov using the user map for 2000 and 2010, the map for 2018 has been predicted and increased by the map obtained from the classified level for this year.

Results and Discussion:

 The classification accuracy test was obtained using the Kappa coefficient index and overall accuracy. Kappa coefficient and overall accuracy were 0.88 and 0.89 for the image of 2000, 0.91 and 0.92 for the image of 2010, and 0.93 and 0.95 for the image of 2018, respectively. The images are categorized as entered into the software and processed by changing the LCM. Changes in the LCM model showed that during the years 2000 to 2018, more changes were related to the conversion of semi-dense forest land with an area of 42104.27 hectares. Urban land use change has also increased in the years of many studies and amounted to 148.14 hectares. The table of the probability of land use changes in the Markov production model and with the production map at this stage, for the years of Markov forecast studies for 2018 and 2028 showed that in 2028 the urban class area increased to 21293.1 hectares and the valuable land use area of dense forest to 2189.97 hectares will be reduced.

Conclusion:

 In order to prevent the uncontrolled expansion of cities, residential areas and the destruction of forest areas and vegetation, management measures should be taken and management decisions should be made. The level of dense and semi-dense forests in areas with high slopes will decrease further by 2028. Urban land use changes have also increased in the study years and amounted to 148.14 hectares. The results of surveying the area of forecasting classes showed that in 2028, the area of urban classrooms will increase to 21293.1 hectares and the valuable land use area of dense forests will decrease to 2189.97. The ability of the vector machine model in determining land cover/land use, vegetation and forest cover in different regions of Iran has been proven by other researchers. Remote sensing tools can be an important arm in information production in natural resource management.

Background and Objective:

 Land surface temperature is a vital indicator for studying environmental changes, hydrological conditions and the energy balance of the earth, which can also be used to monitor the temperature changes of cities. The lack of meteorological stations in most parts of the country, including the study area, has created information limitations in the field of surface temperature data. There are also a large number of non-remote sensing users who need LST maps, and most of them are not familiar enough with LST computing software and inevitably have to spend a lot of time mapping to prepare their maps. This process can be time-consuming even for remote sensing professionals if the number of images is high. The use of valid data for validation that has the least time difference with the satellite passes time is very important in estimating the accuracy of the results. By reviewing internal research similar to the one under study, most internal studies used only meteorological station data to validate the results, the data recording time at these stations is different from the satellite passes time. In this study, due to the large area of the study area and the insufficient number of meteorological stations, in addition to the surface temperature data measured in synoptic stations, the land surface temperature in two ground stations was recorded simultaneously with the satellite. Creating a graphical user interface (GUI) to automatically calculate the surface temperature of Ardabil city with two single-channel and RTE algorithms and use the results to evaluate the temperature changes of land uses

Materials and Methods:

 In this study, in order to automatically calculate the land surface temperature of Ardabil city from three types of data: Landsat 5 and 8 satellite images, land surface temperature data recorded at two meteorological stations in the study area and also due to an insufficient number of stations Meteorological data land surface temperature data measured with digital thermometers are also used as the satellite passes. After preparing thermal and multispectral images, first MODTRAN web computing software was used to model the atmospheric transferability and atmospheric coefficients were extracted. Then, to create graphical user interfaces and automatic calculation of LST, land surface temperature with two algorithms single-channel and RTE method with Landsat 5 and Landsat 8 satellite images for two dates: 31/07/2000 and 21/08/2019 in MATLAB software were coded and using these codes, graphical user interfaces were created for each algorithm and finally, an automatic land surface temperature calculator application was produced. Also, the land use map of Ardabil city for both mentioned dates was classified and extracted using a random forest algorithm in the Google Earth engine system environment with 7 classes. This algorithm has a much better performance compared to traditional methods such as maximum likelihood due to its hierarchical structure in selecting each pixel to the appropriate class. To validate surface temperature maps from two types of surface temperature data recorded in two meteorological stations and surface temperature recorded by a digital thermometer that simultaneously passes the satellite in two points of the homogeneous non-urban environment with agricultural use (alfalfa) and Bayer that product It was harvested, used. To evaluate the accuracy of land use maps, using Google Earth, which has a better spatial resolution than the image used, 248 ground control points were obtained from pure pixels of different land uses and used in the validation process. Also, statistical parameters such as error matrix, overall accuracy and kappa coefficient were applied to the output of both land use maps.

Results and Discussion :

Using the codes written in MATLAB software, graphical user interfaces (GUI) were created and then the automatic LST calculator application was produced. The output of the application was surface temperature maps with single channel algorithms and radiation transfer equation (RTE) for 31/07/2000 using thermal image (band 6) of Landsat 5 satellite TM and 21/08 / 2019 was created by the 10 TIRS sensor band of Landsat 8 satellite. After comparing the output maps with the meteorological station and ground station data, the results showed that the single-channel method had the lowest temperature deviations compared to the stations in both years. After preparing LST maps and selecting the optimal algorithm (single channel), land use maps of Ardabil city were prepared using a random forest algorithm in the GEE platform. Statistical evaluations of the classification results showed that for 2000, the highest pixel interference was related to the middle and poor rangeland class, which has a 16-pixel displacement with residential and rainfed agricultural classes. Due to the improved spatial resolution of the Landsat 8 satellite compared to the Landsat 5, followed by better class separation, this pixel displacement in the 2019 user map shows a smaller value. The most common error was related to the aquaculture class, which had a displacement of 10 pixels with rich rangeland and rainfed agriculture classes. Finally, using the LST map and land use map, the temperature changes of the land uses over a period of 19 years were evaluated. By entering the input images and atmospheric parameters in the application, the land surface temperature was calculated with two one-channel algorithms and the RTE method. Evaluation of output maps with meteorological and terrestrial data showed that the single-channel algorithm with a difference of +2.5 and -2 with stations 1 and 2 for the year 2000 and with a temperature difference of +1.3, +0.9, -1 and -0.9 with stations 1, 2, 3 and 4 in 2019, respectively, had higher accuracy than the RTE method. Also, the results of validation of land use maps showed an overall accuracy of 0.95 and a kappa coefficient of 0.94 for 2000 and overall accuracy of 0.96 and a kappa coefficient of 0.95 for 2019.

Conclusion :

Assessing the relationship between land surface temperature and land use maps showed that despite the significant physical growth of the urban sector over a period of 19 years, except for residential areas, all land uses in 2019 compared to 2000 with an increase in average surface temperature. It seems that factors such as the expansion of agricultural lands with irrigated cultivation around the urban area up to a radius of 10 km and the entanglement of these farms with the urban sector have a great impact on the temperature adjustment of the urban sector. In 2000, these lands were mainly under cultivation of rain-fed crops, and by solving the water problem (digging deep wells and water transfer projects), they became orchards and irrigated farms such as potatoes. Due to the high water requirement, these products also have high greenery, and this factor has increased the rate of evapotranspiration, followed by cooling of the cultivation area and the urban sector. Among other classes, in both years of water use, the lowest and the use of barren lands had the highest average surface temperature. The generated application can be run on any operating system that supports the exe format, and the user by specifying atmospheric parameters can automatically estimate the LST. This application can also be used in various sectors such as agricultural systems, and climate and water resources management.