Hadi Fadaei
Abstract
Extended Abstract Introduction 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 ...
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Extended Abstract Introduction 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. Method 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. Results 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. Recommendations 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
Mahdi Sedaghat; Hamid Nazaripour
Abstract
Extended Abstract Introduction 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 ...
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Extended Abstract Introduction 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. Materials and Methods 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. Results and discussion 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. Conclusion 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.
Behnam Bigdeli; Mohammad Javad Valadan zoj; Yaser Maghsoudi Mehrani
Abstract
Collecting information on the areas under cultivation of wheat and the amount of its products provides the successful and sustainable management in the economic policy-makingfor this strategic product. Introduction of high spectral and special resolution satellite data has enabled the production of such ...
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Collecting information on the areas under cultivation of wheat and the amount of its products provides the successful and sustainable management in the economic policy-makingfor this strategic product. Introduction of high spectral and special resolution satellite data has enabled the production of such information in a timely and accurate manner. Investigating the spectral reflection of plants using field spectrometry and forming a spectral library increases the possibility of differentiating various wheat cultivars and preparing their distribution map. For this purpose, the spectral behavior curves for 6 wheat cultivars named Bahar, Chamran, Pishtaz, Shiraz, Shiroodi and Yavaros, were measured at three stages of growth at the ‘Research Institute of Seed and plant improvement " of Karaj in Iran. Observations were obtained by the ASD FieldSpec®3 Field Spectrometerin the range of 350-2500 nm wavelength under natural light and natural conditions. In the pre-processing stage, three noisy ranges affected by water vapor were detected and eliminated to enhance the gathered data quality. Then,in order to qualitatively collect the data, wrong observations were excluded using statistical methods. This research was designed and implemented in two main steps. In the first step, the spectral response function of the OLI sensor installed on the Landsat 8 satellite was applied to the library's spectra. Then, using the spectral similarity criteria and the twenty seven important vegetation indices sensitive to chlorophyll concentration, photosynthesis intensity, nitrogen and water content in the crown of the plant, etc., the extreme final resolution of wheat cultivars under study, was estimated.In the second step, the classification of the identified farms was carried out by conducting a field survey of the studied area and obtaining satellite images of the target sensor using spectral library spectra. The results showed a significant separabilityof Yavarus wheat variety from other cultivars, both in field spectra and satellite images.
