جستجوی مقالات مرتبط با کلیدواژه « vegetation index » در نشریات گروه « جغرافیا »

Since the land use changes take place on a large and wide scale, remote sensing technology, geographic information system and updated systems such as Google Earth Engine system are essential, efficient and valuable tools for monitoring changes. Therefore, the purpose of this research is to investigate and evaluate the relationship between land surface temperature, land use and vegetation index in Khiauchai watershed, Meshginshahr in Ardabil province. In this regard, Landsat satellite images were used to prepare land use maps. In the next stage, by performing atmospheric and radiometric corrections, training samples from the region were carried out. In this direction, in order to increase the accuracy of the work from Google Earth images and a field visit to the area covered by land use maps using the Support Vector Machine (SVM) classification method in 6 user classes (residential, snow, water, pastures, water and rainfed agriculture) were prepared. In the next step, the values of kappa coefficient and overall accuracy were calculated for the years 2011 and 2011, which were equal to 0.89, 91.61 and 0.94, 94.16% respectively. In the next step, vegetation index maps and ground surface temperature of the studied basin were prepared in the Google Earth Engine programming environment. The results showed that the average temperature of the earth's surface for the month of June 2013 indicated that the maximum temperature with 38.71 degrees Celsius occurred in the northern parts of Khiavchai basin and the minimum temperature occurred in the southern areas with an average temperature of 15.87 degrees Celsius. . On the other hand, the average temperature of June 1401 showed that the thermal core is relatively concentrated in higher latitudes and this shows a direct relationship with the vegetation index.

Leaf area index (LAI) derived from remotely sensed images is considered as an important index for spatial modelling of vegetation productivity. Traditionally, the spectral vegetation indices (VIs) derived from the red (R) and near infrared (NIR) reflectance values have been utilized to statistically estimate LAI. However, most of these VIs saturate at some level of LAI. This limitation was over-come by using the reflectance spectra in the red-edge region. Therefore, it is necessary to evaluate the capability of different VIs derived from RS data to estimate the LAI of silage maize.  For this purpose, five field sampling campaigns which were near-simultaneous with Sentinel II over-passes were conducted by the Space Research Center, Iranian Space Research Center and totally 234 samples were collected from the silage maize fields, in Magsal, Qazvin.  Then, 13 VIs from the time series of Sentinel-2 imagery were computed and employed to statistically estimate the LAI values. The results showed that Enhanced vegetation index (EVI) with  outperformed other VIs to estimate LAI of silage maize. Moreover, the  values of non-linear regression models were higher that the liner ones.

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

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

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

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

Drought is one of the most destructive climatic phenomena whose impact is more significant at the regional scale. The importance of drought in an area such as Maharloo Bakhtegan is more important due to the strategic conditions of the region, the extent of agricultural lands and the existence of Maharloo and Bakhtegan lakes. The purpose of this study was to assess drought using remote sensing, spatial statistics, and GIS in the Maharloo-Bakhtegan basin. In this study, to evaluate the effect of drought on vegetation, Madis images were used to calculate the normalized vegetation index (NDVI) and then the changes in vegetation area were investigated. The results showed that the area of vegetation decreased in the studied years. Reasons for these changes can be attributed to reduced winter rainfall, land-use changes, increased off-season grazing, and bushes. The results indicate the drought in the region. Also to study drought in Maharloo-Bakhtegan basin, meteorological data for estimation of normalized precipitation index (SPI) and spatial statistical analysis, from 6 selected synoptic stations (Shiraz, Zarghan, Droodzan Dam, Eqlid, Neyriz, and Abadeh) used from 1999 to 2018. The results of the Moran index for drought showed that the values for different years have a positive coefficient and close to one during the statistical period, indicating that the SPI drought index data have a cluster pattern. The results of the Z standard score and P-value also confirmed the clustering of the spatial distribution of drought.

Due to the importance of meteorological data and limitations of data gathering from ground stations, remote sensing can play an important role in the preparation of these data. The purpose of this study was to quantitatively evaluate the Land Surface Temperature (LST) obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor images for estimating the maximum and minimum daily air temperature in the Taleghan watershed. For this purpose, the maximum and minimum daily air temperature data of three existing ground stations for the period 2009 to 2015 were obtained. Day and night LST and Normalized Difference Vegetation Index (NDVI) values ​​of MODIS were also prepared. The relationships between each of the effective variables and maximum and minimum daily air temperature in ground stations have been extracted using multiple linear regression method. The results showed that there was a significant correlation between maximum and minimum daily temperature of ground stations with day and night LST and NDVI from MODIS sensor. Therefore, these variables were used in regression relationships. The results of validation showed that the established relationships with all effective variables had the most accuracy. Therefore, the best model for estimating the maximum daily air temperature had , NSE and RMSE values ​​of 0.74, 0.74, and +4.7, respectively and for estimating the minimum daily air temperature had 0.71, 0.72 and +2.9, respectively. Therefore, by converting the surface temperature obtained from MODIS sensor images, the air temperature can be estimated with high accuracy on a daily and monthly scales for various studies.

Today, soil erosion is one of the major problems of watersheds and agricultural areas and natural resources, which causes land degradation and decreases soil fertility. Therefore, the purpose of this study is to investigate the relationship between vegetation and geomorphic indices with the values of erosion and sediment in the watershed of Koot-e-Tootraghi basin, which was done by using the capabilities of GIS to extract the geomorphic characteristics of the basin. For this purpose, erosion and sedimentation rates were calculated using the modified Psiac model (MPSIAC). Also, in order to extract physiographic and geomorphic features including: TWI topographic moisture layers, SPI current strength, SLOPE slope, domain curvature, profile curvature and sub-basin plan curvature, from the height digital model with a spatial accuracy of 30 meters, as well as other layers used in the MPSIAC model including1:25000 topographic maps, 1:100000 geological maps were used. According to the box diagram, the indices related to curvature have little changes in the studied area. Also, the indices related to curvature have little changes in the studied area. Based on the results, there is a positive and significant correlation of 0.47 (p-value less than 0.01) between the standard index of vegetation cover and topographic humidity index. In addition, there is a significant correlation (0.63) between waterway power index and slope. It was also found that the relationship between the slope and the normalized index of vegetation has an inverse and significant relationship (0.48) (p-value less than 0.01.).

The life and development of human societies requires energy production and consumption, the need to pay attention to the environment and the fatalities of fossil fuels for energy production has focused on the use of energy from renewable energy sources. New energies are important because they are a good alternative to fossil fuels. Fossil fuels cause environmental pollution and air pollution. Geothermal plants, on the other hand, also have environmental impacts on vegetation around the power plants. In this research, various vegetation indices have been used to investigate the potential damaging effects of Meshkinshahr geothermal power plant on vegetation around the site of the power plant. Investigations using Landsat 7 and 8 satellite images and ETM + and OLI sensors from the target area and extracting the SAVI index in two periods of 2000 and 2017 showed that the vegetation index of the areas around the site of the power plant Geothermal has dropped to 6.68 square kilometers. Residential, soil and water areas also increased 53.69, 14.26 and 0/255% respectively. Comparison of two classified images showed that the lagoon class in 2000 had an area equal to 11.18 km2, down from 95.91 km2 in 2017. In addition, the class of residential areas in 2000 has an area of 95.74 km2, in 2017, it is up to 120.41 km2, the soil class ranges from 219.59 km2 to 405.82 km2 and the water class ranges from 0.43 to 0.69 km2 has risen.The life and development of human societies requires energy production and consumption, the need to pay attention to the environment and the fatalities of fossil fuels for energy production has focused on the use of energy from renewable energy sources. New energies are important because they are a good alternative to fossil fuels. Fossil fuels cause environmental pollution and air pollution. Geothermal plants, on the other hand, also have environmental impacts on vegetation around the power plants. In this research, various vegetation indices have been used to investigate the potential damaging effects of Meshkinshahr geothermal power plant on vegetation around the site of the power plant. Investigations using Landsat 7 and 8 satellite images and ETM + and OLI sensors from the target area and extracting the SAVI index in two periods of 2000 and 2017 showed that the vegetation index of the areas around the site of the power plant Geothermal has dropped to 6.68 square kilometers. Residential, soil and water areas also increased 53.69, 14.26 and 0/255% respectively. Comparison of two classified images showed that the lagoon class in 2000 had an area equal to 11.18 km2, down from 95.91 km2 in 2017. In addition, the class of residential areas in 2000 has an area of 95.74 km2, in 2017, it is up to 120.41 km2, the soil class ranges from 219.59 km2 to 405.82 km2 and the water class ranges from 0.43 to 0.69 km2 has risen.

Soil erosion is one of the environmental problems that is a threat to natural resources, agriculture and the environment, and in this regard, assessing the temporal and spatial amount of soil erosion has an effective role in management, erosion control and watershed management. The main aim of this study was to estimate soil erosion in Amoqin watershed and its relationship with well-known vegetation-based and topographic-related indices. The meteorological data has been used to determine the rainfall erosivity. The rainfall erosivity index was calculated using the modified Fournier index during the 10-year available recorded rainfall data. The value of LS factor has been calculate using digital elevation model. Meanwhile, C and P factors were determined based on the utilization scheme and condition of the study area. Data were analyzed and processed using ArcMap 10.1, ENVI 5.3, and Excel software. In this study, RUSLE model was used to estimate soil erosion, in GIS environment. According to the results, the amount of factor R in Amoqin watershed varies from 12.32 to 50.52 MJ/ha/h per year. The variation of soil erodibility index (K) over the study area is between 0.25 to 0.42. The amount of LS factor varies between 0.19 and 0.38, which is more in high slopes, especially around the waterways and uplands of the study area. The variation of C factor was estimated to be around -0.18 to 0.4. In general, it can be said that the central part of Amoqin watershed has less C value due to the greater area of agricultural activities and the highest amount is related to western areas, especially southwest areas because existing the rangeland areas. Due to the lack of protective measures in the study area, the amount of factor P was considered as unity for the whole region. The base layers of RUSLE factors were obtained and overlayed in GIS to calculate the soil loss in tons per hectare per year. The map of annual soil loss indicate that the erosion amounts varies between 1.21 to 5.53 tons per hectare per year in different parts of the study area. According to the results, the vegetation factor with a coefficient of determination 0.47% had a significant correlation with soil loss. The stream power index with the coefficient of determination of % 0.07% had the lowest correlation with soil erosion values.

The knowledge about spatial relationships of vegetation with sediment yield (S.Y) in watersheds is essential for optimal controlling and managing of water and soil resources. The purpose of this study was to recognize and determine the relationship between vegetation cover and annual S.Y of Sabalan catchments, N.W Iran. In this regard, the possibility of presentation of regional S.Y estimation models based on correlation and regression analysis were considered. Independent variables include average, minimum and maximum NDVI (Normalized Differential Vegetation Index), extracted through the processing of Landsa.t 8 satellite imagery in the ENVI environment. The dependent variable, which was the annual S.Y, was obtained through calculations on sediment load data of 10 sample hydrometric stations. Results of correlation test showed that there was a strong and significant relationship between the average NDVI and the annual S.Y in the catchments (r = -0/758). This inverse relationship emphasizes the importance and apparent role of vegetation in reducing sediment production and transporting in the region. Moreover, the results of regression tests showed that is possible to present the regional estimation models regards to annual S.Y by fitting linear, quadratic and power functions. In this regard, the quadratic model has the highest performance and explain 75% of the annual S.Y variance. The linear relationship was also able to explain 57% of the annual sedimentary variance.

Awareness of the effect of soils on the dynamics of water resources is extremely important to improve management in the face of stress; In recent years, concerns about climate change have led to concerns about preserving the soil bed and regulating activities that are least harmful; Therefore, in a recent study aimed at quantifying the spatio-temporal sensitivity of soil during the Quaternary period due to fluctuations in vegetation index and water resources to the importance of soils and zoning of ecological response to changes, in this regard using the Kendall (nonparametric) time series method. Vegetation and irrigation changes on Quaternary soils compared to other soils in Iran were studied. In the study, Modis data and MERA version 2 model in the period 2018 to 2021 were used for 36 months. The results of the study showed; The average volume of water stored in Iranian soils is 345 ml / m2. According to the results of water reserves in Quaternary regions with extremely humid climate 20% and semi-humid regions 18% and also Mediterranean regions 8% - semi-arid regions + 12%, arid regions + 21% and desert regions with + 25% relative to the same climate They themselves have a change in volume. Also, the shores of Lake Urmia and Khuzestan province have the effect of soil type on cover and water resources with high sensitivity and an area of 650,000 hectares. There are 22,000 hectares in Golestan plain under threat and it shows high sensitivity. Recent results indicate the high sensitivity and fragility of Quaternary soil areas in the face of climate change, so planning and studying the country's planning with respect to soil potentials is extremely important.

Studies show that landing area crops and crop growth curves for countries have provided them with several million new jobs and billions of dollars in gross income, with more than 50% of them being farmers. Due to the growth curve of crops in 8 plots of land applied to Landsat 8 images, alfalfa (105) had the best, most lively and least stressful vegetation, and alfalfa (303) had the least vegetation at peak vegetation growth and also The highest coefficient of variation was in its growth curve. According to the results obtained from the crop area of NDVI image, we found that wheat (304) had the highest difference and wheat (108) had the least difference. In the year 98, the total area of these 8 plots is 82.775 hectares and according to the results of the company was 89 hectares. In other words, the area calculated from the NDVI image in 1398 was only about 7% lower.

Drought is a natural phenomenon that has a significant impact on agriculture and also influences different aspects of people's lives in both arid and semi-arid regions. Vegetation cover is one of the living components of the ecosystem and plays an important role in many ecosystem processes that are strongly affected by climatic events such as drought. In the present study, the status of vegetation changes in relation to drought index has been investigated in the Ilam catchment (Ilam city). In this research, the 30-year precipitation of synoptic stations of Ilam, Mehran, Dehloran, Sarableh, Ivan, Darrehshahr and Abdanan were applied to calculate the standardized precipitation index (SPI). The Landsat satellite images were used to extract the standard vegetation difference index (NDVI) and also to investigate the detection of vegetation changes in the study area. Besides, the relationship between the SPI drought index and NDVI vegetation index was performed by the Pearson correlation method between raster layers of NDVI and SPI. The results of the SPI index showed that the drought in the years 2003 and 2008 occurred with more intensity than other years. Moreover, the vegetation classification map obtained from the NDVI index showed a decreased trend in the level of vegetation from 1988 to 2018 which is occurred mostly in the dense vegetation category (15959 ha in 1988 and 6492 ha in 2018). Based on the results of the present study, it is concluded that the changes in vegetation over time is directly related to the severity of drought, which should be considered by managers and decision-makers in the natural resources for vegetation management.

Studying the spatial pattern of thermal islands with satellite imagery is one of the major challenges in the urban development process of developing countries. If this growth is dispersed and unplanned, many problems and problems will arise in the management and urban planning process.  The purpose of this study is to identify the distribution and temperature difference of different regions of Qom city with respect to its suburbs in order to discover thermal islands and to map its temperature. In this study, using remote sensing and Landslide and OLI and TIRS satellite imagery during six consecutive months from January 1979 to June 1398 cold months to hot months, we investigate the LST surface heat distribution in Qom and its lands. Its margin was scattered in relation to the use and NDVI vegetation index factor. During the research, it is suggested that the thermal islands in Qom be prevented by preventing the destruction of agricultural fields and the construction of urban parks in the desert and open urban areas.

The risk of fires in natural areas is one of the most important climatic hazards in Lorestan Province. The current study aims to analyze the temporal series trend of wildfire events in natural areas and to reveal the spatial-temporal pattern of wildfire related to the type of land cover. In this regard, the data of MODIS fire product as well as land cover and vegetation product of MODIS during the 2000-2020 were used. Cross-tabulation matrix analysis and spatial correlation matrix were used to reveal the relationship between wildfire events and land cover. The findings showed that more than 70% of the total frequency of wildfire in natural resources (wildfire code 2) in Lorestan Province is related to June and then July. The results of cross-tabulation matrix analysis revealed that 3 uses of rangelands with medium canopy, low density forest lands and rainfed agricultural lands accounted for 0.75 of wildfires. More than 70% of the annual frequency of fire events in the two forest land cover classes was in June and July (June and July), which is significantly correlated with the density of vegetation in these classes two months ago (April and May). While the frequency of fires in rainfed agricultural lands was generally concentrated in August and September, it did not show a significant correlation with vegetation of any month. Practically, it can be said that the presence of vegetation density during April and May in the forest floors is one of the most important factors related to wildfire in following two months, June and July. Therefore, focusing on management over a 62-day period, from May 15 to July 16, 55% of fire incidents can be controlled in the area of ​​pastures and forest floors.

Application of the remote sensing methods in crop area mapping on a large spatiotemporal scale serves is as an alternative to costly time-consuming field data gathering methods. So far, some methods have been developed for wheat and rice area mapping using the images from optical and radar sensors. Some of these methods are appropriate for humid climates with several cloudy days, while others use complex processes in terms of combining both optics and radar images. Meanwhile, methods based on the unique variation of the vegetation index time series belongs to each crop are relatively simple methods that can be used for crop area mapping. The objective of this study is to improve one of the proposed methods for rain-fed wheat area mapping, in which a step-by-step elimination algorithm of non-wheat pixels was applied to MODIS images. The Improved algorithm took advantage of both MODIS and Landsat Images in terms of their high temporal and high spatial resolutions, respectively. The mentioned process could detect rain-fed wheat areas from the pastures and heterogeneous areas with higher accuracy in comparison with the previous algorithm. The overall accuracy, Kapa index, and F1 score for the final rain-fed wheat map was 92.5%, 0.67, and 0.71 respectively.

Iranian oak has been affected by oak canopy level dieback in recent years. This phenomenon has led to damage a vast part of the oak forests in the Zagros arena. As to the suitable temporal and spacial resolution of the recent satellite images, it seems promising to detect the forest dieback by remote sensing. The spatial capabilities of Spot6 images with pan and spectral resolution of 1.5 and 6 m, respectively in detecting the drying of oak trees was investigated. The forest area was located on ​​Kuhmareh district of Shiraz in Fars province. The values of different indices such NDVI, EVI, TDVI, SAVI, RNDVI, OSAVI, DVI, MSR of each tree stocks was obtained and the corresponding quantity of dryness was determined at the filed. The best correlation was obtained between TDVI and the observed data. A non-linear function was built based on TDVI standard deviation to predict the dryness of more than 30% as y=17.92(x-0.06)-0.32 with an R2 = 82%. Monitoring forest areas to understand the decline or recovery of trees will be of great help to the forest management community. Therefore, using the results of this study can be a proof to compare the current situation with future periods.

One of the major environmental issues and requirementsof the contemporary worldis the acquisition of knowledge and related technologies. Urban Heat Island (UHI) refers to the occurrence of higher surface temperature in urban areas compared to the surrounding rural areas due to high urbanization. Urban Heat Island (UHI) is an important ecological effect of rapid urbanization. While the temporal and spatial importance of UHIs and their causes have been discussed in previous studies, precise identification of the morphology and shape of the earth and its relation with UHIs have not been studied. Urban heat islands occur primarily due tourban developmentand changes in land surface. This has created unfavorable conditions and many problemsfor citizens. Vegetation cover can reduce the effect of heat island. Satellite data can be used to determine the distribution of urban heat islands, but new methods of measurement are still needed to get better results.Ground data can also help in validation of remote sensing analysis. The present study has investigatedurban heat islands occurring in the city of Tehran and its suburbs due to urbanization and traffic.

The present study has been carried out in Tehran, the capital city of Iran, located in the northern part of the country,on the southern slopes of the Alborz Mountain Range, along 51⁰ to 51⁰ 40′ easternlongitudeand 35 ⁰ 30′ to 35 ⁰ 51′ northernlatitude. According to the latest population and housing census in 2011 performed by the Statistical Center of Iran, Tehran has a population of 8,154,051 and still is the most densely populated city of Iran with a clear demographic difference with other cities of the country. The study area borders with mountainous areas of the north and desertsof the south, thus the southern and northern regions of the study area have different climates. The northern regions have cold and dry climates, while the southern parts suffer from hot and dry climates. The elevation varies from 900 to 1800 meters. This huge difference inelevationis due to the vast area of the city. In Tehran metropolis, the average annual temperature varies between 18 and 15 ° C, and different parts of the city have an average temperature difference of 3 ° Cdue to the elevation difference in the city. Average monthly relative humidity including minimum and maximum relative humidity recorded at Mehrabad station shows that in in the morningof July to January, humidity changes from at least 38% to a maximum of 79%. Midnight relative humidity varies from 15% to 18% in June to 47% in February. The annual rainfall in Tehran is mainly influenced by the difference in elevation and varies between 422 mm in the north and at least 145 mm in the southeast. The number of rainy days also follows the same pattern and varies between 89 days in the north and 33 days in the south. Also in this urban area, 205 to 213 days of each yearhave a clear sky with some cloud. In this exploratory study, Landsat 8 satellite images for Tehran were obtained and processed (geometrical, radiometric and atmospheric corrections).  The Operation Land Imager(OLI)with its three new bands: a deep blue band for coastal / aerosols studies (band 1), a short-wave infrared band for cirrus cloudsdetection and Band Quality Assessment (Band 9), and an Infrared Thermal Sensor (TIRS) which offers two high resolution thermal bands (approx. 30 m) (band 10, 11) were used. In addition, two of the valuable thermal bands at 10.9 µm and 12.0 µm have Landsat 8 images. In this study, spectral reflections of all terrestrial members of spectral phenomena were obtained based on the total wavelengths of Landsat 8 (wavelengths of 430-2290 nm). For UHI estimation,surface temperature can be obtained from the two thermal bandsand improved using split-window methods.The relation between thermal islands can be calculated using air pollution ground data. The present study tries to select suitable indices such as Normalized Difference Vegetation Index (NDVI). The vegetation index (NDVI) of land surface was calculated using spectral bands.  

The LST map was produced using Landsat OLI 8 satellite images. Temperature in this map was obtained using standard deviation from the classified values,and areas affected by the UHI were identified subsequently. According to the LST map, the surface temperature varies between 21.5 ° C and 57.9 ° C. On the day of imaging, the lowest average temperature of water was 35 ° C and the maximum average temperature of bare lands was 48 ° C in the study area.  

It is recommended to use spectral reflectance measurements such as field spectroradiometer in natural conditions to evaluate the spectral reflectance accuracy. At a later stage, spectral reflection of different phenomena can be used to classify satellite images and examine their relationship with the urban heat islands

Nowadays, many factors such as destruction of the urban green areas and increasing the environmental pollutants in urban areas cause the solar radiation confinement and formation of urban heat island phenomenon. This has dangerous consequences, such as stagnation and lack of the air flow, transmission of air pollution and dispersed toxic particles in urban areas. The main purpose of this research is to analyze the vegetation status and land surface temperature in Urmia city, investigate the formation of urban heat islands and analyze their temporal and spatial variations. For achieving these purposes, Landsat 4 and 8 satellite images for 1989 and 2017 years were used. In this study, after applying the necessary corrections to the images and training them, land use change detection  was accomplished. By using the red and the near infrared bands of the images, the vegetation index (NDVI) was calculated and then. Ground surface temperature (LST) was calculated by the image thermal bands. The results of this research showed that the temperature of Urmia city and its suburbs increased during the 28 years (1989-2017). It was also found that increasing temperatures within Urmia city was mainly due to the loss of the vegetation areas and to the conversion of the green area to urban lands by urbanization processes. The results showed that the vegetation index (NDVI) in Urmia city decreased as it reashed to-0.053 from 0.023 in the wasteland, which indicate the disappearance of thin green cover (about 9.24% of the vegetation cover of Urmia city disappeared during 1989–2017).

 Soil moisture is considered to be a key parameter in meteorology, hydrology, and agriculture, and the estimation of its temporal-spatial distribution contributes to understanding the relations between precipitation, evaporation, water cycle, and etc. Soil moisture reduction results in the creation of centers susceptible to dust storms. With socio-economic impacts ranging from urban to intercontinental and from a few minutes to several decades, this can challenge regional development. The first estimate of potential dust sources is derived from the soil properties. With the reduction of surface soil moisture and the wind speedcrossing a certain threshold level, wind erosion process can cause the formation of dust storms. Field studies have proved that increasing the moisture content in soil from zero to about 3%, reduces the dust concentrationsignificantly. To understand the climatology of dust and develop related numerical predictive methods, continuous recording of dust storms is essential, which requires effective and continuous monitoring of the variations in surface soil moisture. Remote sensing technology is an effective method for calculating soil moisture. This technology was first used for the estimation of energy flux and surface soil moisture in the 1970s. To extract the surface soil moisture content, some remote sensing methods use surface radiation temperature and some others apply water transfer (soil/vegetation/air) (SVAT) model. Various indices have been developed for soil moisture monitoring, such as soil moisture (SM), soil water index (SWI), Temperature-Vegetation-Dryness Index (TVDI), Soil Moisture Index (SMI) and Perpendicular Soil Moisture Index (PSMI), all of which combine vegetation and surface temperature variables.

Soil moisture is considered to be a significant parameter in the exchange of mass and energy between the Earth surface and the atmosphere. Lack of soil moisture or decreased moisture in soil is considered to be a factoraccelerating the process of dust storm formation. During the previous decades, water stresses on the ecosystem of Hour-al-Azim have transformed this wetland into one of the main dust centers in the southwest Iran. Hour-al-Azim is one of the largest wetlands in southwestern Iran. This wetland is shared between in Iran and Iraq. It is located between N 30° 58´- N31° 50´ and E 47° 20´- 47° 55´. The Iranian part of this wetland encompassed an area of 64,100 ha in the 1970s, while in the 2000s, the area has decreased to only 29,000 ha. The present study aims to monitor the spatial-temporal variability of soil moisture in Hour-al-Azim wetland and to investigate the relation between these changes and dust storms in the southwest Iran. To reach this end, we used 8-day images obtained from the Aqua satellite in the period of 2003 to 2017 and also annual frequency of dust storms with a visibility of less than 1000 m in the period of1987–2017. A database consisting of 189 images of the red band, near-infrared band, and ground surface temperature (LST) was created, which contained 4 images per year (one image per season). The resolution of the red / near-infrared band data and daily LST values were 231.65 and 926.62 meters, respectively. Then, soil adjusted vegetation indices (SAVI) and perpendicular soil moisture index (PSMI) were extracted. SAVI index is used to reduce the effect of background soil on vegetation cover in semi-arid and arid environments with less than 30% vegetation cover.Compared to NDVI, SAVIwith L = 0.5reduces the effect of soil changes on green plants. In the next step, a trapezoidal method was used to calculate the PSMI index. In order to investigate changes in the soil moisture content of the Hour-al-Azim wetland, three time series obtained from regional mean of SAVI, LST and PSMI remote sensing indices and a time series consisting of the number of days with dust storms observed in the 9 stations were evaluated using simple linear regression test. 

Extracting Soil Adjusted Vegetation Index indicated that in the study period, the highest values of this index was observed with a regional mean of 0.15 on 4/7/2014 and the lowest values was observed with a regional mean of 0.08 on 1/1/2005. Land Surface Temperature survey showed that during the study period, the highest values of this index was observed with a regional mean of 54.42 ° C on 7/4/2010 and the lowest values was observed with a regional mean of 17.28 ° C on 1/1/2007. The regional mean of Perpendicular Soil Moisture Index indicates that despite winter is considered to bethe wettest season of the region, PSMI index with a regional mean of 0.2 has experienced the driest soil moisture conditionsat the beginning of winter (1/1/2016),while it had experienced the wettest soil moisture conditionsin the same season on 1/1/2009 with a regionalaverage of 0.13. 

Finding of the present study indicate an increasing trend in the range of remote sensing indicators. The range of SAVI index is increasing, which means that the density of vegetation in the Wetland is decreasing. Perpendicular Soil Moisture Index values also show an increasing trend, indicating a decrease in soil moisture content. As a result of the decrease in soil moisture, the vegetation density also has decreased and the land surface temperature has increased. Results of statistical tests indicate the role of changes in environmental conditions of Hour-al-Azim wetland in the frequency of dust storms. Using findings of the present study, or studies such as Kim et al. (2017), it is possible to take advantage of soil moisture variations for the prediction of dust generation, its emission, and spread level.

Soil salinity and soil salinization as one of the problems facing agriculture and natural resources are of great importance which needs to be prevented with proper knowledge. In this regard, it is important to obtain information about soil salinity and vegetation, such as their amount and distribution. The use of satellite data enables extensive study of soil salinity and vegetation. Since vegetation in most arid and semi-arid regions is strongly influenced by soil properties such as salinity, therefore, this study investigated the effects of Interceptor Drain on soil salinity and vegetation changes using remote sensing capabilities in a 15-year interval. Results showed that construction Interceptor Drain in Salt Marsh Qazvin plain had no effect on soil salinity changes and vegetation cover. According to the results of correlation test between measured soil elements and satellite image bands, bands 5 and 7 were highly correlated with soil SAR (Sodium Adsorption Ratio) index prior to drainage construction and thus, the two bands after drainage construction had a significant correlation with soil EC (Electrical Conductivity) index. In fact, indices including red and infrared bands showed a significant relationship with soil salinity parameters. Also the results of correlation test of remote sensing indices and ground data in the salinity area showed that SI (Salinity Index) index had a highly significant correlation with soil salinity data.

One of the methods of salinity monitoring is ground-based data, which can be time-consuming and costly, especially if large-scale monitoring is performed over multiple time periods. Using Remote Sensing method and georeferenced and laboratory data, soil salinity changes over time can be monitored. The spectral reflectance of a variety of salts at the soil surface has been studied in several studies which has been used as a direct indicator in remote sensing. However, when the soil moisture is high or the salt layer is not visible at the soil surface or the salt is mixed with other soil components, the direct salinity detection approach will become more complex. However, vegetation and saline-friendly plants can be used as a sign of soil salinity for indirect detection and identification of saline areas based on spectral reflectance of plants. The purpose of the present study was to investigate the trend of soil salinity and vegetation changes using remote sensing capabilities in two intervals before and after construction of Interceptor Drain in Salt Marsh Qazvin plain. Therefore, the trends of soil salinity and vegetation changes over a 15-year period have been studied.

The study area is located in a part of Qazvin province, 150 km northwest of Tehran and the major cities adjacent to the area in Qazvin are Takestan  in the west, Abike in the north and Dansfahan in the southwest. Due to the geological conditions of the bedrock and deposition of sediments and groundwater discharge from Qazvin and Hashtgerd Plains, the marsh has been formed which has become saline due to years of severe evaporation. Most saline species have little growth in the salinity range and only in the months when rainfall increases and it is moderately saline, the plants with moderate to low salinity resistance and with a low vegetative period have a short time. Then, with increasing salinity, the soil is seated and dried; only saline-resistant plants in this area survive for the remainder of the year.In this study, spatial and temporal variations of vegetation and soil salinity were investigated using Landsat 7 satellite images. After calculating salinity and vegetation indices, the spatial variability map of soil salinity and vegetation index was prepared. In this study, Landsat satellite images during years 2004 to 2018 were used to study the trends of soil salinity and vegetation changes in Salt Marsh Qazvin plain. After analyzing the remote sensing indices in the study area, the data from satellite images and georeferenced data were compared. The soil salinity and vegetation indices used in the study included 6 soil salinity indices and 5 vegetation indices. The soil samples used in this study were related to 99 observation wells dug in the Proximity of Interceptor Drain of Salt Marsh Qazvin plain during 2010 and 2012.

According to the results of correlation test between measured elements in soil and satellite image bands, bands 5 and 7 were highly correlated with soil SAR index before drainage construction, and thus two bands after drainage construction, with soil EC index. The use of different soil salinity and vegetation index equations gives the results the differential preferences to achieve adequate soil salinity and vegetation index estimation on a large scale using remote sensing data. The evaluation of different soil salinity indices was based on the Statistical test. Statistical test results comparing mean at seasonal variations scale showed that remotely sensed indices related to soil salinity monitoring including BI, SI, SI1, SI2 and SI3 indices showed significant response to seasonal changes in surface soil condition. On the other hand, indices related to monitoring of vegetation status showed a significant difference in vegetation status from spring to summer. The results of correlation test of remote sensing indices and ground data expressed that in the salinity range of SI index there was a significant correlation with soil salinity data. Mean correlation results demonstrated that GVI had a high negative correlation with all salinity indices, but there was less correlation between salinity and other vegetation indices. The satellite images used in this study indicated the highest correlation with soil salinity in the dry months of the year and the correlation between the two factors was reduced in the months with precipitation. Soil salinity estimation based on SI index has been investigated in several cases which revealed that this index is accurate in estimating surface soil salinity in arid and semi-arid regions.

The present study showed that Interceptor Drain of Salt Marsh Qazvin plain had no effect on soil salinity and vegetation changes in the region, so none of the 11 indices derived from satellite images had significant changes in the period before and after drainage. In other words, soil salinity and percentage of vegetation in the Salt Marsh Qazvin plain have not changed significantly over a period of 15 years. However, due to the dryness of the area and the lack of rainfall as well as the incidence of drought, the fresh water content for natural leaching of adjacent drainage lands is limited and the drainage increases the intensity of groundwater flow and reduces soil salinity by creating a hydraulic gradient. Strengthening of vegetation in the area is affected by drainage. As the Qazvin Plain is one of the agricultural hubs of the country, hence the expansion of saline lands is one of the biggest threats to agriculture in the region. Since deep soil salinity status is one of the important factors in the establishment of vegetation in the region, studies combining remote sensing method, geophysical data and simulation models can lead to a better understanding of the status of the area.