جستجوی مقالات مرتبط با کلیدواژه "landsat-8" در نشریات گروه "جغرافیا"

The purpose of this research is to investigate and analyze the physiographic characteristics and land cover of Koze Topraghi watershed in Ardabil province using geographic information system. In this regard, by using Landsat 9 satellite images, the topographic map of the area and the use of the digital elevation model of the land to prepare slope factors, slope direction, DEM, land use and vegetation. The results of the factors showed that the highest point of the studied area with a height of 2540 meters is located in the south of the studied area and the lowest point of the studied area with a height of about 1400 meters is located in the northern parts of the basin. In the study area, slopes of more than 70% are observed in the northeast, south, southeast elevations, and due to the lack of vegetation with low coverage, especially in the eastern parts, they are highly vulnerable to erosion. Low slope areas in the studied basin are mostly located in the central areas. The general direction of the slope in Kozetopraghi watershed is southeast and northeast. in the studied area, the slope decreases from east to west and from the heights to the center of the basin. On the other hand, in order to prepare and classify the land use map of the studied basin, the support vector machine method was used. The overall accuracy and Kappa coefficient for the land use map of the studied basin were equal to 99.93 and 0.99%, which had acceptable accuracy, in order to prepare the vegetation map for the year 1402, Landsat 9 satellite data was used and the factor map was prepared. the values of the vegetation factor in the basin ranged from -0.21 to 0.85, which often had more density in the southern parts.

The sun is known as the source of energy, the beginning of life and the source of all other energies. The global radiation of the sun is considered one of the fundamental structures of every climate. Therefore, knowing the characteristics and predicting these basic structures has a great impact on energy-related planning. The use of satellite images and remote sensing models as a suitable and low-cost tool for estimating solar radiation has been in recent years. In order to carry out this research, the images of 2020 Landsat 8 satellite OLI sensor and TIRS sensor and Sabal algorithm were used. ENVI software was used for geometrical, atmospheric and radiometric corrections of satellite images, as well as the execution of calculations related to the Sabal model, and ArcGIS software was used for creating a database, spatial analysis, cartographic operations and finally implementing the model. The results show that the average maximum incoming shortwave radiation was 901 watts per square meter on 08/09/2020 and the lowest value was 302 watts per square meter in February. Meanwhile, the lowest amount of net radiation on 10/28/2020 is 355 watts per square meter. The difference in the amount of net radiation reaching the earth in the studied area is caused by the difference in the angle of the sun and the number of sunny hours in different months of the year. Finally, it can be concluded that the solar radiation in the region has the necessary potential for the implementation of solar photovoltaic projects in the year under review. 

Land surface temperature (LST) is an important factor in the study of global warming and climate change. Natural and man-made activities, especially land use and land cover changes, have a significant effect on land surface temperature. The purpose of this study is to estimate the surface temperature using Split window algorithm in Tabriz and determine its relationship with land use changes. For this purpose, OLI and TIRS images of Landsat 8 in 2013 and 2019 were used and after performing various pre-processing stages, vegetation index (NDVI), water extraction index (MNDWI), urban development index (NDBI) and land surface temperature were estimated using multispectral and thermal bands. Land use maps were prepared using object-oriented classification method in eCognition software and then accuracy assessment coefficients were extracted. The results of this study showed that the most land use changes are observed in rangeland use, which in the 7-year period, most of it has changed to road and urban land use. The results also show a negative correlation between NDVI and surface temperature and the highest temperature occurs in areas with poor vegetation. Therefore, considering the role of vegetation in modulating temperature conditions, the need for vegetation protection seems necessary.

The Land surface temperature (LST) is an important Quantity that affects the cycle of energy and moisture between the earth's surface and the atmosphere. (xin et al, 2023). In fact, land surface temperature is an important index related to climatic, meteorological, hydrological, and environmental phenomena and processes and is widely used in climate change investigation, hydrological process modeling, drought monitoring, and fire risk assessment (Yang et al, 2020). One of the methods of measuring temperature is meteorological stations, which are less used due to problems such as lack of proper spatial distribution and difficult access to data, especially on a large scale. (Heidari and Akhundzadeh, 2019). Thermal infrared remote sensing is an efficient approach in this field and has attracted the attention of researchers. (Yi et al, 2023). Considering that Ardabil city, located in the northwest of the country and on the foothills of Sablan mountain, is considered one of the big cities of Iran and is affected by climate changes, including the increase in temperature (Malkian et al, 2018), it is necessary to investigate climatic factors, including temperature, in this region. The purpose of this study is to investigate the temperature in different land uses by LANDSAT9 satellites by applying the split window (SW) algorithm and Sentinel3 using the SLSTR sensor and its LST tool. Checking the temperature using two different images, by applying different algorithms and checking these temperatures in different uses in the study area can be considered as an innovation of the present study.

Ardabil city with an area of 2165 square kilometers is one of the big cities of Iran and the capital of Ardabil province. In this study, Landsat 9 and Sentinel 3 images from 20/7/2022 have been used to extract the land surface temperature (LST). For this purpose, the split window algorithm was implemented on the Landsat 9 image, and the LST algorithm of the SLSTR sensor was implemented on Sentinel 3. To check the results related to the temperature, the data from the meteorological station has been used. Also, the land use map of the region has been extracted using the Landsat 9 image on 5 residential and industrial floors, vegetation, agricultural land, water areas, and soil cover using the object-oriented technique to check the temperature in different uses.

The land use map of the studied area was extracted with overall accuracy and a kappa coefficient of 98 and 97%, respectively. Investigations showed that in Landsat image 9 agricultural land use with an average temperature of 35 degrees Celsius is the minimum temperature and soil cover use with an average temperature of 42 degrees Celsius is the maximum temperature. In the image of Sentinel 3, in the same way, agricultural land use and soil cover have minimum and maximum temperatures of 37 and 42 degrees Celsius, respectively. The minimum and maximum temperatures obtained by both sensors show close values with a difference of one degree in the maximum and 3 degrees in the minimum temperature. The temperature of the station related to Landsat 9 and Sentinel 3 is estimated to be 35 and 36 degrees, respectively, which are close to each other and show a difference of one degree. However, the temperatures obtained from the Landsat 9 sensor have a smaller difference from the temperature of the meteorological station, and this Temperature compliance is especially noticeable in the minimum temperature with a difference of 10 degrees Celsius. It should be mentioned that the spatial resolution of 30 meters of Landsat 9 and 1 km of Sentinel 3, also the location of the weather station should be considered.

The minimum and maximum temperature has been estimated using the split window algorithm on the Landsat 9 image, 22 and 51 degrees Celsius, and using the SLSTR sensor on the Sentinel 3 image, 25 and 52 degrees Celsius. The split window algorithm has high accuracy and capability and is considered one of the most effective and widely used algorithms in extracting land surface temperature in many studies (Grace et al, 2020), (Eon et al, 2023), (Zhang et al, 2019). SLSTR is also a high-precision infrared radiometer and enables more accurate LST measurements using common algorithms as well as the development of new algorithms (Kuppo et al, 2016), (Sobrino et al, 2015). The obtained temperatures show that reasonable and close values were obtained by both sensors and are by different uses. The results of this research show that the temperature calculated using the satellite images of both sensors is consistent with the data obtained from the meteorological station, however, the temperatures obtained from Landsat 9 show a greater agreement with these obtained data, especially in the minimum temperature. Regarding the maximum temperature and the station temperature, the difference between the Landsat 9 temperature and the station temperature is one degree less than the Sentinel 3 temperature. LST for each pixel in remote sensing is equivalent to the average temperature of different earth surface covers (Alavi Panah, 2017), Therefore, it seems that the high spatial and radiometric resolution of the Landsat 9 sensor has not been ineffective in the high accuracy of the obtained values.

In this study, the effects of land use changes with surface temperature of karaj cityand spatial autocorrelation have been discussed using the moran index. for this purpose, landsat OLI- TM satellite imagery has been used in 2000 and 2018. first, the images were taken and the required images were applied. then classification using object orinted method and nearest neighbor algorithm was developed and the earth surface temperature was extracted with split window algorithm (imagery). The results showed that the arid land has the highest temperature in 2000 and 2018, respectively, due to the lack of vegetation, the high capacity of absorption of heat by barren soil, and the use of blue zones in 2000 and 2018 respectively, has a temperature of 38 - 25 (° C), which is دفع and has lower heat temperatures. also, the results showed strong relationship between land use and temperature. finally, the hot and cold clusters of karaj city heat islands were extracted using the hot spots analysis index (hotspot). Space autocorrelation analysis with global Moran's indices showed that the Earth's surface temperature was cluster - like. the analysis of hot spots confirmed the focus and cluster of the heat islands of karaj city in space with increasing periods of time.

Urban heat islands (UHIs) are a growing concern in metropolitan areas, leading to temperature differences between urban centers and surrounding areas due to various ecological, morphological, topographical, and demographic factors. Major Iranian cities have undergone rapid land-use transformations and expanding urbanization, facing the challenge of UHIs.

  This study investigates UHIs in Tabriz, focusing on District 1, Neighborhood 1 as the first area and District 6, Neighborhood 4 as the second area. The study employed Landsat 8 remote sensing images to analyze UHIs in Tabriz, Iran. The images were acquired for the summer season, representing the period of maximum UHI intensity. The Normalized Difference Vegetation Index (NDVI) was calculated to assess vegetation cover, while land surface temperature (LST) was derived using the single-channel algorithm. Morphological indicators were extracted from OpenStreetMap data.Findings and

The study aims to extract, locate, and analyze UHIs in the study area using specific formulas and prepare relevant maps, focusing on ecological and morphological indicators. The minimum and maximum temperatures in District 1 were 15.88°C and 37.65°C, respectively, while in District 6, they were 24.11°C and 40.87°C. Analyzing the extracted maps based on the selected ecological indicators (vegetation cover, wind, solar radiation, and elevation) and morphological indicators (building height, building density, building façade, and building roofs) in both districts, we concluded that District 1 is cooler due to several factors: higher elevation, better reception of prevailing northeastern winds, more suitable street orientations, lower building heights (mostly one-story), and a higher prevalence of reflective roofs compared to District 6. Although District 6 has better vegetation cover and lower building density.

Atmospheric aerosols are a colloid of solid particles or liquid droplets suspended in the atmosphere. Their diameter is between 10-2 to 10-3 micrometers. They directly and indirectly affect the global climate by absorbing and scattering solar radiation, and they also have a serious impact on human health by emitting harmful substances. In addition, high concentrations of aerosols on a local scale due to natural or human activities have adverse effects on human health, including cancers, pulmonary inflammation, and cardiopulmonary mortality. Monitoring the temporal and spatial variability of high concentrations of aerosols requires regular measurement of their optical properties such as aerosol optical depth (AOD).

Algeria is a large country with little knowledge of the spatial and temporal diversity of AOD, and the low spatial resolution of existing products makes it very difficult to predict aerosols (airborne particles) at the local scale, especially in arid southern regions. As a result, AOD recovery with data with higher spatial resolution is crucial for determining air pollution and air quality information. Several AERONET stations have been installed in Algeria. The Tamanrasset_INM station has been selected based on its location and the availability of historical AOD data for the period (2015-2016).
In this study, Landsat-8 / OLI image from tile 192/44 was used for satellite images. To this end, 23 TOA-corrected L1G-level Landsat-8 / OLI cloudless scenes were downloaded from January 2015 to December 2016 in the study area. DN values ​​are converted to TOA reflections using the scaling factor coefficients in the OLI Landsat-8 metadata file. In this study, the minimum monthly reflectance technique was used to recover AOD in this area. As a result, LSR images were used in the recovery process in different months of 2015 and 2016. The process of selecting reference LSRs was initially based on the selection of clear, foggy / cloudless sky images. The selected images were then used to construct artificial images in which each pixel corresponds to the second lowest surface reflection of all selected monthly images to be the LSR pixel for the respective month. The AOD retrieval method developed in this study is based on a LUT, using the 6S radiative transfer model. The advantage of using the 6S model is its ability to estimate direct components and scattering using a limited number of inputs for each spectral band in the entire solar domain. The effect of the viewing angle is limited because Landsat data are usually obtained with a fixed viewing angle. Surface reflectance can be estimated from a pre-calculated LSR database. The accuracy of AOD recovery depends on the use of the appropriate aerosol model. A continental model was selected from the available aerosol models. Other atmospheric parameters such as ozone, carbon dioxide, carbon monoxide and water vapor are considered by default. The AOD values ​​used to make LUT are set as follows: 0.0, 0.05, 0.1, 1.5, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.2 and 1.5. The zenith angles of the sun and the sensor range from 0 to 70 degrees with a step of 5 degrees and the range of azimuth angles from 0 to 180 degrees with a step of 12 degrees. Using these parameters, the radiative transfer equation was run in forward to obtain the TOA reflection. Different combinations of input and TOA output parameters are stored in LUT. AOD retrieval is based on a comparison between the TOAs estimated with the model and the observed items using the best fit approach. Using such an approach, the estimated AODs are simulated in accordance with those used in the production of TOAs, using a competency function that minimizes the distance.

In this study, the AODs recovered at 550 nm in a 5-by-5-pixel window around the AERONET site were averaged. The considered AERONET values ​​are the average of all measurements taken within ± 30 minutes of image acquisition time. Observation regression results (AOD from Landsat 8 images and AERONET stations) showed that the correlation coefficient is about 84%. This study shows a good fit of the model on the research data and shows the high capability of the model. This study showed a strong recovery of AOD against AERONET data of more than 70% at . The differences can be attributed to a limited number of points or hypotheses related to the aerosol model used in this study. The assumption of using a pre-calculated LSR does not limit the accuracy of this method because we have shown that in arid regions where the change in land cover in different months of the year is small, a pre-calculated LSR image can be representation used the share of surface reflection in the radiative transfer model throughout the month.

In this study, an AOD derived from a high-resolution satellite at an urban scale was produced in the city of Tamanrasset, Algeria. The developed method assumes that the change in land cover is minimal and the temporal change in LSR is not significant. A pre-calculated LSR image is created to show the surface reflection in the retrieval process. Based on the 6S radiative transfer model, an LUT was constructed to simulate the TOA reflection of the built-in LSRs and a set of geometric and atmospheric parameters. The retrieved AODs were compared with the AERONET ground data. The results show that this approach can achieve reasonable accuracy in AOD recovery, which reaches about 70.9% at . In addition, this approach is suitable for estimating AOD in urban areas compared to existing AOD products with low spatial resolution. The results of this study show a 4% improvement compared to the results of Omari et al. (2019). The results of this study showed that ignoring the monthly changes in LSR values leads to good results in AOD recovery.

Flood is considered one of the most destructive natural hazards in the world, which causes a lot of damage all over the world every year. In Iran, due to special climatic conditions, the distribution of rainfall in terms of temporal and spatial conditions does not have a specific rhythm. Today, satellite images are one of the fastest and most accurate tools for flood monitoring. Therefore, in the present study, the spring flood map of 2019 Aqqala City was prepared using Landsat-8 images, and then, using the combined AHP-OWA model. The flood risk map was prepared in four classes: "high risk", "medium risk", "low frisk" and "no risk". Based on the gained results, the northern and northeastern parts of this region are detected as high flood risk, and the western and southern parts are in low and no flood risk conditions. Also, the 2019 spring flood of this region involved two parts the northern and southern. In terms of flood severity, most water accumulation was in the northern parts, but the severity of the damage was in the southern and northern parts. Due to the high density of land uses in the southern parts, the flood event in this part destroyed many agricultural, vegetation and residential lands. But in the northern parts, due to the large extent of barren lands and the low density of other land uses such as agriculture, residential and vegetation, the flood caused more damage to the barren lands. In general, based on the obtained results, the AHP-OWA model is considered one of the most effective models for flood risk studies.

In recent years, the technology of crop production has been greatly expanded using satellite data. Today, Landsat 8 and OLI sensor data, with a spatial resolution of 30 meters, allow the discovery of factors that control phenology on a local scale. In this study, the remote sensing indices - NDVI, EVI, Greenness, and Brightness - obtained from the OLI sensor and the GCC index obtained from digital camera images were used to estimate the phenological stages of the rapeseed plant. The Savitzky-Goli filter was used to remove outlier data and to produce smooth curves of time series of plant indices. The results showed that the curves obtained from the indices of NDVI, EVI, GCC show all four stages of remote sensing phenology – green-up, dormancy, maturity, and senescence - well, but the Greenness index did not show the dormancy stage well. The Brightness index curve shows the inverse behavior to other curves. According to Pearsonchr('39')s correlation test, GCC index data are correlated with NDVI and Brightness index data .we used the ratio threshold, rate of change and first derivative methods, to estimate "start of season" and "end of season" and the results showed that the first derivative and ratio threshold methods with an average difference of 18 and 19 days in the "start of the season"  and the rate of change method, with an average difference of 8 days, has the best performance in estimating the “end of the season”. Also, the Brightness index with an average difference of 16 days and the EVI index with an average difference of 7 days have the best performance in estimating "start of season" and "end of season", respectively.

The sun is known as the source of energy, the beginning of life and the source of all other energies. The global radiation of the sun is considered one of the fundamental structures of every climate. Therefore, knowing the characteristics and predicting these basic structures has a great impact on energy-related planning. The use of satellite images and remote sensing models as a suitable and low-cost tool for estimating solar radiation has been in recent years. In order to carry out this research, the images of 2020 Landsat 8 satellite, OLI sensor, TIRS sensor and Sabal algorithm were used. ENVI software was used for geometrical, atmospheric and radiometric corrections of satellite images, as well as the execution of calculations related to the Sabal model, and ArcGIS software was used for creating a database, spatial analysis, cartographic operations and finally implementing the model. The results show that the average maximum incoming shortwave radiation was 816 watts per square meter in July and the lowest value was 302 watts per square meter in February. Meanwhile, the highest amount of net radiation in June is in the range of 602-680 W/m2 with a value of 32%. The difference in the amount of net radiation reaching the earth in the studied area is caused by the difference in the angle of the sun and the number of sunny hours in different months of the year. Finally, it can be concluded that the solar radiation in the region has the necessary potential for the implementation of solar photovoltaic projects in the year under review.

Trend analysis of growth of cities and predicting their changes in the future are essential for spatial ‎planning. For this purpose, it is necessary to map build-up areas. In many areas, especially in arid ‎climate, it is not possible to separate the build-up areas from the surrounding land cover simply. That's ‎mean the usual methods of classifying satellite images or conventional indices can’t separate mentioned ‎classes with acceptable accuracy. Hence, many researchers have developed different spectral indices to ‎extract the build-up areas. The use of surface temperature changes to represent build-up areas using the ‎Local Climate Zones (LCZ) algorithm is less considered and is a relatively new method. Therefore, in ‎this paper, the separation of build-up areas from the other surrounding land cover was considered using ‎LCZ algorithm. There is no limit to the number of bands in this method, thus four series of Landsat ‎satellite images in the year 2020 were used and the LCZ algorithm’s accuracy was compared with the ‎latest automatic classified build-up indices including DBI, BLFEI, BAEI and BAEM. The results of this ‎study showed that the classification accuracy of the LCZ algorithm was 96%, while the BLFEI and ‎BAEM indices were not able to completely separate the build-up areas from other types of land cover. ‎The total accuracy of the BAEI index was 0.37. Therefore, the use of LCZ method has a high efficiency ‎compared to build-up indices, and it is recommended in arid and semi-arid zones.‎

With the expansion of the urban limits, some of the lands that were used for gardening years ago have been located within the urban limits. The difference between the value of garden land use and urban land use, such as residential and commercial, encourages gardeners to change their land use. Urban managers try to prevent this change of use by enforcing strict rules.Assessing the success of such plans requires examining land-use change in the urban over a long periodof time. The main purpose of this study is to detect abandoned urban gardens using Landsat satellite imagery. The second goal is to determine the extent of changes in urban gardens in the study area over the past 30 years. In this study, based on Landsat satellite images in 2018 and 1988 for the northern slope of Alvand Mountain in Hamadan province and the city of Hamadan, the normalized differential index of vegetation (NDVI) along with land surface temperature (LST) in 9 time periods per year was extracted. The results indicated a 4/75 ° C increase in LSTfor the region over 30 years. Also, the inverse relationship of LST with NDVI is confirmed. Based on the separation of urban gardens, a comparison was made between 2018 and 1988, which showed a decrease of 175 hectares of urban gardens in the study area, which is equivalent to a 49% reduction in urban gardens. In the main part of the research, based on the behavioral evaluation of urban gardens, in these two characteristics, a differentiation index for active and abandoned gardens is presented. Examination of the results based on ground truth data including 25 active gardens and 25 abandoned gardens suggested that the proposed method had an overall accuracy of 82% and a Kappa coefficient of 0/64.

The study area includes a part of the northern slope of Alvand Mountain, which is limited to the southern part of Hamedan and has a latitude of 34 degrees and 45 minutes to 34 degrees and 48 minutes north and a longitude of 48 degrees and 27 minutes to 48 degrees and 31 minutes east. Ground truth data including 25 active gardens and 25 abandoned gardens were collected as field visits using a Garmin GPSMAP 62s handheld navigator so that coordinates were collected by attending the location of abandoned and active gardens. The satellite data used in this study concern the time series data of Landsat 8 satellite OLI and TIRS sensors for 2018 and Landsat 5 satellite TM sensor for 1988.To achieve the first objective and separate active and abandoned gardens in 2018, the land surface temperature (LST) and the normalized difference vegetation index (NDVI) are calculated and the behavior pattern of these two components is examined during the year for active and abandoned gardens in nine periods according to the proposed method, a final index for separating active and abandoned gardens is presented based on the NDVI behavior pattern throughout the year. The time series of NDVI for each year is evaluated in 9 periods and garden maps are extracted in 1988 and 2018 to achieve the second objective and prepare the maps of 30-year changes in active gardens in the study area. The rate of change of area and the percentage of changes in the class of gardens are obtained by comparing the maps.

Since this study is conducted mainly to identify abandoned gardens in urban space, two criteria for assessing user accuracy and errors of commission in the abandoned garden class are very important. In other words, in this problem, the number of gardens that are properly divided into the abandoned garden class is important, and the proposed method provides an accuracy of 86%. The most important issue is the number of abandoned gardens that the proposed method has mistakenly labeled as active gardens, which is 14% in this method. Both accuracies provided are evaluated as acceptable. The overall accuracy of the proposed method is estimated at 82%, which is acceptable, indicating the efficiency of the proposed method.

One of the problems facing human societies today is the reduction of forests and gardens. Given the important role that trees play in improving the quality of human life, protecting them is one of the inherent duties of rulers. Various factors cause the destruction of trees, one of which is the development of urban areas in the vicinity of forests and gardens. Traditional methods of conserving natural resources and monitoring their changes have failed in practice. For example, in the study area, 49% of the tree-covered areas have declined over the past 30 years. However, the ban on construction in the area has always been emphasized by city managers in the years under study, and the inefficiency of the methods used has been proven by the statistics provided. New methods of monitoring changes based on satellite image processing can be alternatives to traditional methods due to their high accuracy and speed and significant cost reduction. The proposed index is recommended to be evaluated to separate active and abandoned gardens in other areas facing this problem using images with higher spatial resolution. In different cases of threshold limit, the overall accuracy of the proposed method is examined based on the ground truth data of the evaluator. At best, the separation of active and abandoned gardens is associated with an overall accuracy of 82%.

Alteration is the simplest, cheapest and most suitable means of mineral exploration. The best way to find changes is to use satellite data processing.
Asadi and Tabatabaei (2007) have used band ratio processing methods and false color images by using selected principal component processing (PCA) to identify the range of variations in different regions on Aster images. Gomez et al. (2005) visualized the lithological units of Namibian using the PCA algorithm on Aster data.
The exposed rock units in Muteh mining area include a series of sedimentary, volcanic, and volcanic-clastic metamorphic rocks that extends from the green schist facies to the border of green schist and amphibolites along the northeast-southwest direction. These units have been repeatedly penetrated by alkaline intrusions, especially acid and granite (Rashidenjad, Omran et al., 2002).
In general, the controlling elements of mineralization in Muteh area include structural factors (faults and fractures), alteration, and deformation. Field observations indicate the occurrence of vein mineralization and gold sulfide deposits in mylonite shear zones and fault zones in felsic to mafic metavolcanic host rocks.
Gold mineralization is mainly concentrated in highly altered metariolites containing iron and copper sulfides and within fractures as veins and deposits. Alterations in silica, sericite, and carbonation are also observed along with these sediments, which are studied as exploration keys (Moritz et al., 2006).
In this area, according to the lithology and distribution of alteration zones and the type of mineralization in Muteh gold mine, gold orogeny-type mineralizations are expected, which can be indirectly identified by recognizing the above alteration.

In this study, Aster satellite images have been used to identify, discover and separate alteration zones in ENVI 5.3 software. Also, Landsat 8 satellite images have been utilized for general investigation and identification of hydrothermal alteration zones and expansion of iron oxide minerals, and Sentinel 2 satellite data due to better spatial and radiometric resolution than the above data has been applied to increase the spatial resolution of these data and the spatial accuracy of the map from the extracted changes.
In order to validate between the field observations and spectral analysis, 24 rock samples were taken from the place of alteration, especially siliceous, argillic, and sercitic alteration around Senjedeh and Chah Khatoon deposits. 11 samples were sent to Zarazma laboratory for XRD analysis, and five samples were sent to Zarkavan Alborz Company’s laboratory for chemical analysis of 41 elements by ICP-MS method and gold element by Fire Assay method.

Considering the relationship between alteration zones and metal mineralization, it is very important to know and map these areas in the exploration of these deposits.
The results and images show that the methods used in determining and separating the altered areas in Muteh exploratory area are acceptable and the optimal and effective methods in this research, SAM and MF, have been introduced.
According to the field observations and surface sampling around Chah Khatoon and Senjedeh mineral deposits, as well as the investigation of changes, it was found that the most important changes in the region are: silicification, kaolinization, sericization, chlorination, alonation, pyrite, carbonation and so forth. This wide range shows the difference in intensity of alteration in different parts of the mineral reserve, which can be attributed to the system of joints, fractures and faults in the region.
According to the available evidence, the metariolite rock is highly silicified in the tensile zones or in places with dense seams, and the pyrite particles in the context of these rocks have turned into iron hydroxide.

By using satellite data processing, various data and information can be identified and extracted. Satellite data processing is done in two ways: visual and digital processing. By combining these two methods, the desired effects can be detected more accurately than the accuracy of satellite images. The visual method consists of preparing images of different color combinations by placing spectral bands in the red, green, and blue channels. Digital satellite image processing methods include band ratio, principal component analysis, least square regression method (Ls-Fit), spectral analysis, spectral angle mapping (SAM), and adaptive MF filter. The selection of the above methods was based on the type of information requested to extract data from images.
Aster sensor images have no blue band (spectral range 0.4-0.5 µm) and the color composition of its VNIR bands is a standard RGB (1,2,3) false color composition. In this color combination, vegetation is seen in red. Since the study area is located in a relatively arid environment without vegetation, vegetation cover was avoided in the spectral analysis.
The use and processing of Aster satellite data is one of the main features of this sensor; the use of unique spectral reflectance curves of alteration indicator minerals helped to identify and highlight these altered areas as well as finding the potential of areas prone to metal mineralization. Due to the high ability of Sentinel-2A images in identifying gossan and iron oxide ranges, the processing of these data was used to highlight these areas better.

According to the agreement of the results of geochemical and XRD studies with the distribution map of the alteration zones identified from the reference spectrum (USGS) and the spectral library (JPL), with the distribution map of lines and structural fractures of Muteh exploratory zone outside the pre-identified areas, new alteration zones were also introduced that require field research to confirm the results of stereo data processing.

The world is warming and the world's population is moving to cities. These two truths do not seem to be related; But a phenomenon called urban heat island connects the two. UHI is one of the most common urban climate phenomena in which some urban areas, especially urban centers, become several degrees warmer than the surrounding areas. Studying this phenomenon and examining its mechanism is very important for urban planning. In the present study, in order to estimate LST, four single-channel Landsat algorithms, single window, Planck equation and radiation transfer equation in QGIS software environment between 2000 and 2019 in summer and winter seasons in Birjand city have been used. The effect of land use change on the thermal island has also been investigated. In the present study, ground surface temperature in Birjand city was first extracted using Landsat 7 ETM + satellite imagery and Landsat 8 TIRS / OLI sensors in 2000 and 2019 by four methods. In order to investigate the general ability of algorithms to calculate the surface temperature, the statistical indices of mean square error, Nash-Sutcliffe coefficient, mean absolute error and coefficient of determination were used. The results showed that the Landsat single-channel algorithm for calculating the surface temperature in Birjand is more accurate than other algorithms.

The surface of the earth or the top of the earth at the level of cities and urban areas becomes significantly higher than the surrounding environment, which is known as the thermal component. If this phenomenon is higher than a certain limit, it will cause problems for the city residents. But one of the most important factors that can play a very important role in controlling this phenomenon is the presence of vegetation, which is mainly a significant part of this coverage in urban areas. In this study, the effect of vegetation cover on the amount of urban heat island in Isfahan City was investigated. For this purpose, the surface temperature of the city and its relationship with the surface cover were investigated using the land surface temperature (LST) products of the MODIS sensor in places with heat islands by applying the Normalized Difference Vegetation Index (NDVI) of Landsat satellite data during the period of 2001-2020. Examining the annual changes in temperature in Isfahan shows that during this period, the temperature is generally increasing, so 2011 with an average annual temperature of 37.21 degrees Celsius is the hottest year and 2012 is the coldest year with an average annual temperature of 33.7 degrees Celsius. The most apparent thermal islands were observed in areas with industrial use, high-traffic places with severe air pollution and population density, and places with weak vegetation, areas with dense and worn-out textures.

Urban heat islands could have destructive effects on the environment and increase energy consumption. The present study aimed to investigate the spatial autocorrelation of land surface temperature and its relationship with land use in Ahvaz city. Daily and Night Land surface temperature maps were extracted from the split window method for the Landsat 8 satellite image and MOD11A1 product, respectively. Land use maps of the study were extracted into five classes: Built-up area, vegetation, bare land, water, and Karun River. Based on the results, Global Moran's index on day and night was equal to 0.94 and 0.9, respectively, according to which Land Surface temperature is distributed in a cluster form. Results of Getis-ord GI statistics showed that the largest area of daily and nightly hot spots in the east of Ahvaz was obtained with 5050.53 and 8055.69 ha, respectively. The results of the Kruskal Wallis test showed that there was a statistically significant difference between the average temperature of land uses (p-value<0.05), so industrial areas and bare land had the highest temperature and the water bodies and vegetation had the lowest temperature. The results also showed that LST has a negative relationship with NDVI (r = -0.47); however, the average NDVI index in the study area was equal to 0.17 which indicated the poor quality of vegetation in Ahvaz. Therefore, it is necessary to take measures to improve the quality of vegetation and expand the urban green space and preserve water resources to adjust the land surface temperature in Ahvaz.

Rice is an important crop and the main food of more than half of the world’s population, which needs water and heat to grow. Thus, mapping and monitoring rice fields with efficient means such as remote sensing technology is necessary for food security and the lack of water sources. The phenology extracted from the time series of vegetation indices is used for monitoring and mapping the area under rice cultivation. In addition to the phenological curve, the LST time series map, which is calculated from Landsat 8 images and is related to the phenomenon of evaporation and transpiration of irrigated crops, can cause the separation of rice cultivation from rainfed crops, summer crops, water, etc. Therefore, in this study, the effect of the LST time series map is investigated map for improving the accuracy of rice field identification.

Since the planting to harvest period of rice is from May to October, in this study, the time-series maps of LST and NDVI for the 3rd of April, 21st of May, 6th of John, 22nd of John, 8th of July, 24th of July, 9th of August, 12th of October, and 28th of October have been calculated after download the Landsat-8 time-series in 2020 The ground truth map of the study area has been obtained from the US Department of Agriculture. To identify rice fields and calculate the LST and NDVI using the Landsat-8 images, initial pre-processing including radiometric and geometric corrections has been applied to these images first. After initial corrections and the calculation of NDVI and LST maps, to identify rice fields in the study area, machine learning algorithms such as Support Vector Machine, K-Nearest Neighborhood, Multilayer Perspective, Logistic Regression, and Decision Tree, have been proposed.

The results of the proposed method at the state of California showed that using the time series map of  Land surface temperature (LST) with the time-series map of  Normalized Difference vegetation Index, improved the results of identifying rice fields (the average Overall Accuracy= + 3/572% and the average kappa coefficient= +7/112%). Visual results showed that some cultivation such as tomato, corn, cucumber, fallow, and water were removed from the rice final map when using the LST time-series map with the NDVI time-series map. According to the numerical results, the Support Vector Machine algorithm (Overall Accuracy 94/28 and Kappa Coefficient 88/29), the Multilayer Perceptron algorithm  (Overall Accuracy 94/26 and Kappa Coefficient 88/21), and the K-Nearest Neighborhood algorithm (Overall Accuracy 93/71 and Kappa Coefficient 87/08) showed the highest Overall Accuracy and Kappa Coefficient compared to the Logistic Regression algorithm (overall accuracy 91/96 and kappa coefficient 83/54) and the Decision Tree algorithm (Overall Accuracy 91/34 and Kappa Coefficient 81/97), respectively.

Although, many methods have been proposed to identify rice fields from satellite images. But, the similarity of rice class with other classes is one of the main challenges related to rice identification. In this research, the effect of LST time series maps to improve the identification accuracy of rice fields in Landsat-8 time-series images was investigated. In this study, the effect of the time series map of land surface temperature index extracted from Landsat-8 images on improving the accuracy of identifying rice fields from other rice fields due to the evapotranspiration process using machine learning algorithms was investigated. The results showed the effectiveness of the proposed index in improving the identification accuracy of rice fields. One of the reasons for improving the accuracy of identifying rice fields is to extract the characteristics of the thermal growing season from the Earth's surface temperature time series (LST) maps along with the rice phenology curve. The results showed that due to the flooding of rice fields when using the NDVI time series map, water class and fields summer crops were identified as rice class. But, water and summer crops classes were removed from the rice final map using a land surface temperature time-series map with the extraction of thermal growth season characteristics. Therefore, the results showed that there was a direct relationship between LST time-series maps and rice cultivation.

Urban expansion leads to the creation of urban heat islands, which can create risks for human health and the environment. Knowledge of the state of vegetation, changes in land use and temperature of each region over time is very important and is used in micro and macro planning. Therefore, the purpose of this research is to investigate the relationship between temperature and vegetation in creating a thermal island in Isfahan city. In this regard, the use of Landsat ETM satellite images during the period of 1990-2019, LST and NDVI indices, and land use were studied and investigated. The results of the investigations show an increase in the size of the city and a decrease in the size of garden lands and marginal pastures of the city. Areas without vegetation are located in the urban area and residential areas and in the southern and eastern heights of Isfahan city. The maximum NDVI index is also in the western and southeastern areas of Isfahan city. The intensity of heat islands is more than other land uses according to the minimum and maximum level and average temperature in urban land and with the increase in the area of ​​residential uses and urban development, the intensity of heat islands has increased. On the other hand, the lowest temperature intensity of heat islands is observed in irrigated agricultural lands and gardens. In general, it can be said that in the city of Isfahan, the high temperature is mostly in the central core of the city, and the surroundings of the city are covered by the middle temperature layer. Low temperatures have also been observed in agricultural use. Parks and urban green spaces also have medium temperature classes due to their proximity to urban uses and being influenced by them. During the studied period, the largest area was related to average temperature and this trend will continue in the coming years.

The purpose of this study is to use modern methods to improve the accuracy of depth measurement, first by using the proposed methods based on image fusion of sentinel-2 and Landsat 8 images and then based on hybrid fusion methods. Thus, depth measurements were performed in two studied areas, namely Chabahar Bay and Mousa Estuary, based on different scenarios. These scenarios include the traditional ratio method, the proposed methods based on image fusion using three methods of Intensity-Hue-Saturation, Gram-Schmidt transformation, and Brovey transformation, and the proposed methods based on hybrid image fusion. In order to perform depth measurement based on the proposed hybrid fusion methods, the output of the image transformation using the principal component analysis method was merged with the input image of the best bathymetry map based on image fusion at another level of fusion. Chabahar Bay depth measurement results showed that in comparison with the traditional ratio model, the proposed models based on Brovey and Gram-Schmidt methods and based on a hybrid method resulting from the integration of principal component analysis output and Gram-Schmidt method have the best depth measurement results. Regarding the depth measurement of Mousa estuary, the proposed depth measurement method based on the combination of principal component analysis and Intensity-Hue-Saturation method has created the best result in comparison with the traditional ratio model. Also, the results showed that the appropriateness of the number and distribution of depth field points with the topography of the area has a great effect on the accuracy of depth measurement.

awareness of the type and percentage of land use and cover types is a fundamental requirement for understanding and management of a region. The growth of urbanization and increasing use of resources due to the development of the required facilities in urban life has changed the pattern of demand for resources and lands, changing the nature and quality of agricultural land, Historical and natural landscapes and surrounding urban areas through the transformation of these lands into residential areas. In recent decades, the suburban lands of cities have changed their use due to the urbanization process and the need of citizens for new residential areas and the surrounding lands, which are often high quality agricultural lands and gardens. This, along with things like industrialization and changing rainfall patterns, has destroyed the cover and natural environment of cities, and thus has posed many social and environmental challenges and endangered sustainable urban development, and as a result of this process, a lot of ecological pressure has been imposed on the natural ecosystem of the region. These changes are considered as one of the important and effective factors of social and environmental challenges. Today, remote sensing technology and GIS due to capabilities such as high monitoring power and resolution, frequent images, cost reduction, etc., To effectively identify and quantify land use changes and their effects on the environment and monitoring And rapid management of the growth and development of cities are used. In the present study, the aim is to evaluate the urban development of Ardabil in the last 30 years using remote sensing technology and satellite images.

Landsat satellite imagery was used to prepare land use maps for 1987, 2000 and 2017. In order to ensure the quality of data and bands, the images used in this research were first corrected for radiometric errors in ENVI 5.3 software environment. Then RVI, SAVI, NDVI, BI and IPVI indices were extracted. In the next step, maps related to filter texture, vegetation delineation and tasseled cap were prepared. At the end of this step, all the extracted layers were merged with the corrected image bands. Then satellite imagery using support vector machine algorithms, maximum similarity and artificial neural network with acceptable accuracy in six user classes (residential areas, covered agricultural lands, fallow, barren lands, urban forest and water) floor were classified. Then, to evaluate the classification accuracy, the overall accuracy and kappa coefficient were calculated for each of the maps.

According to the values of overall accuracy and kappa coefficient, which in 1987 for the support vector machine algorithm were 90% and 0.86, respectively, the maximum likelihood was 84.5% and 0.78, and the neural net was 90.5% and 0.87, respectively, in 2000. Overall accuracy and kappa coefficient for support vector machine algorithm 92% and 0.90, maximum likelihood 92.5% and 0.90 and neural net 92.6% and 0.90, and in 2017 overall accuracy and kappa coefficient for backup vector machine algorithm 90.6% and 0.88, maximum likelihood of 82.8% and 0.78 and for neural net were 88% and 0.85, it was found that the support vector machine algorithm has the highest accuracy compared to the other two algorithms. According to the results obtained from the study of satellite images classified by the support vector machine algorithm, the area of land built in Ardabil has increased from 20.023 square kilometers in 1987 to 41.554 square kilometers in 2017.

In general, it can be concluded that to evaluate the trend of urban sprawl and awareness of land use change patterns for optimal management and planning of cities, the use of satellite images, especially Landsat images is a suitable and low cost option. The results also showed that the rate of land use change to land uses is increasing and since land is the main element in urban development, so control how to use it and also calculate the real need of the city for land, to In order to provide different uses is effective. As a result, according to the findings of this study, in the absence of proper planning for this city due to favorable lands for urban development around the city, in the not too distant future, witness the destruction of agricultural lands around the city of Ardabil and conversion they will be residential areas.