Climatology
Zeynab Jawanshir; Khalil Valizadeh Kamran; Ali Akbar Rasuly; Hashem Rostamzadeh
Abstract
Introduction Water management has always emphasized the need to abandon water storage in reservoirs and pursue a policy of limiting water consumption. Spatial-spatial information on evapotranspiration helps users understand the evacuation and depletion of water due to evaporation and establish the relationship ...
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Introduction Water management has always emphasized the need to abandon water storage in reservoirs and pursue a policy of limiting water consumption. Spatial-spatial information on evapotranspiration helps users understand the evacuation and depletion of water due to evaporation and establish the relationship between land use, water allocation, and water consumption. Evapotranspiration is the second element of the water cycle (after precipitation) and its accurate estimation on a regional scale is necessary to design appropriate management strategies. Evapotranspiration is a function of the amount of energy available for vegetation and its exchange. Because of this dependence, it can be estimated using the principle of energy conservation. Due to the limited number of meteorological stations in the country and the high cost of collecting ground data, the cost-effectiveness of the use of satellite data is one of its advantages, and the possibility of retrieving data from all levels of the region at one time is its next advantage. Having timely information makes horizontal monitoring of meteorological and environmental parameters possible. The ability of remote sensing to measure some terrestrial parameters has had an important effect on estimating actual evapotranspiration. The SEBAL model is one of the remote sensing algorithms that calculate plant evapotranspiration based on the momentary energy balance at the level of each pixel of a satellite image. The study area of the current research was the eastern cities of Lake Urmia. The reason for studying this section was the impact of recent droughts on these areas and the reduction of surface and groundwater, which has increased the need to manage water resources in these areas. Methodology In the first step of radiometric corrections, the amount of spectral radiance in the thermal band and at the next step, the reflectance in the visible bands, near-infrared, and short-wavelength infrared bands were calculated. As mentioned above, in the SEBAL model, actual evapotranspiration is calculated through satellite imagery and meteorological data is calculated using the surface energy balance. When satellite imagery provides information for its transit time, SEBAL calculates the instantaneous evapotranspiration flux for that time. Landsat 8 images for 2017-2016-2014-2013 years and meteorological data such as Minimum temperature, maximum temperature, dew point temperature, evaporation pan data, sunny hours, and wind speed were analyzed using ENVI 4.8 - Excel 2013- Arc GIS 10.3 software. Results and Discussion SEBAL is an image processing model that measures evapotranspiration and other energy conversions on the Earth's surface using digital data measured by remote sensing satellites that emit visible, near-infrared, and thermal infrared radiation. This method uses surface temperature, surface reflection, and normalized plant differential index (NDVI) and their internal relationships to estimate surface fluxes for different types of land cover. In this section, using the values obtained from latent heat flux and evaporation heat flux, first, the amount of instantaneous evapotranspiration for each pixel was calculated. Then, using Ref_ET software, the total 24-hour evapotranspiration was calculated and the daily evapotranspiration rate was obtained for the whole image. Conclusion The results showed that there was a good correlation between the values estimated by the remote sensing algorithm (SEBAL) and the FAO-Penman-Monteith method as well as the evaporation pan method. The difference between the amount of SEBAL and the FAO-Penman-Monteith method in the reference plant was less than 4.21 mm/day; the largest difference was related to the 22nd of October. In total, SEBAL and Penman-Monteith methods had an average absolute difference of 4.28 mm/day. According to the results of this study, it can be observed that using the SEBAL model, the actual evapotranspiration and water needs of crops and even orchards and rangelands can be calculated on a large scale. This case could prove the suitability of this model for estimating actual evapotranspiration at different levels of the farm and irrigation networks. Therefore, remote sensing has a very high potential to improve the management of irrigation resources in very large areas using various algorithms and providing an estimate of the amount of ET with minimal use of ground data. Using remote sensing technology and GIS, acceptable results can be obtained in estimating the actual evapotranspiration rate, especially in large areas. If the parameters of the energy balance equations and Penman-Monteith could be calculated from satellite images spatially, with a suitable plant coefficient, the two methods would have similar results in estimating the rate of evapotranspiration. Using this method, the plant coefficient, which is one of the important factors in calculating the evapotranspiration of plants, can be accurately determined.
Urban Planning
Hassan Mahmoudzadeh; azar puyanjam; fatemeh amanzadeh
Abstract
Introduction One of the emerging environmental hazards caused by the expansion of urbanization is the "thermal island" phenomenon, in which urban areas have a distinct climate compared to rural areas, and the city center has higher temperatures than its surrounding areas. This phenomenon occurs when ...
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Introduction One of the emerging environmental hazards caused by the expansion of urbanization is the "thermal island" phenomenon, in which urban areas have a distinct climate compared to rural areas, and the city center has higher temperatures than its surrounding areas. This phenomenon occurs when a large percentage of natural surface coverings are destroyed and replaced by buildings, roads and other installations. The thermal island phenomenon has been studied and recorded in various cities around the world for more than 150 years. And it generally appears as the surface of the earth shifts from natural to non-perishable. Surface temperature is considered one of the most important parameters in identifying a city's climate that directly controls the effect of the city's heat island. And more recently, many regional studies, such as global climate change, hydrological and agricultural processes, urban land use and land cover, and soil moisture assessment, have been identified as important factors. Traditionally, urban heat islands have traditionally been studied using meteorological station data, or vehicle surveys, but today to reduce the weakness of these methods and to study them more closely, Satellite and remote sensing data are used more frequently because of more spatial resolution than terrestrial weather data. Remote sensing images, because of their wide coverage, timeliness and ability to obtain information in the thermal range of the electromagnetic spectrum, are a useful source of heat mapping and estimation of Earth's radiant energy. Methodology Split-Window algorithm is one of the most important methods for estimating surface temperature which is better than other methods for calculating surface temperature. An important feature of this algorithm is the elimination of atmospheric effects. Since this algorithm does not require accurate information on atmospheric profiles during satellite acquisition, it is widely used in several sensors to retrieve Earth's surface propagation capability. The sensors used in this algorithm include the Multi Spectral Sensor and the TIRS Thermal Sensor. The following are the cases: Due to the lack of a database to measure the Earth's surface propagation capability with Landsat 8 satellite images, the C coefficients through various numerical simulations It was obtained from atmospheric and surface conditions.In this study, Landsat 8 images with 7/15/2015 Landsat 8 (OLI and TIRS) images and land use maps were used to analyze the thermal islands. After processing the images, a separate window algorithm was used to calculate the surface temperature and the maximum likelihood method was used to classify the images. Discrete Window Algorithm is a mathematical tool that uses ground information, thermal sensor brightness temperature (TIRS), ground emission capability (LSE) and fractional green vegetation factor (FVC) obtained from OLI and temperature multispectral band. Estimates the surface of the earth. Image analysis was performed in ENVI 5.3 and ArcGIS 10.5 software environments. Result and Discussion Surface temperature is one of the main factors in the study of cities. Because only two or three degrees differs from the air temperature of the lower layers of the urban atmosphere, which is the center of the surface energy balance, which determines the climate between buildings and affects the comfort of urban dwellers. In the present study, preliminary processes such as radiometric, atmospheric and geometric corrections were carried out and then high atmospheric radii were converted to surface radiation and then calculated by vegetation index, vegetation fraction index, radiation power and water vapor column, temperature. Ground level in the study area was obtained using a separate window algorithm. Conclusion The results of thermal extraction showed that maximum temperature was related to low density vegetation, residential, industrial, industrial, asphalt-concrete and brick-iron frameworks. Minimum temperatures are also visible in green, brick-wood and clay-wood. The results of this research for planners and experts at the regional level to obtain information on the status of land surface temperature and their relationship with land use can pave the way for management decisions to conserve natural and agricultural resources. It is suggested that due to the moderating role of vegetation, vacant land and the wilderness be changed to uses such as parks and landscapes, and in addressing other uses, the reasons for residential and industrial and workshop areas should be taken into account, and the surface temperatures of buildings most The city has its own surface area and has the highest amount of radiation reflection can be reduced by planting vegetation on the roofs of buildings known as green roofs. High resolution satellite images are also recommended for land use mapping.
Geomorphology
Mohammadreza Rezaei Moghaddam; Khalil Valizadeh Kamran; Soghra Andaryani; Farhad Almaspoor
Volume 19, Issue 52 , June 2015, , Pages 163-183
Abstract
Land use and land cover maps are necessary for planning and natural resources management. In the way, remote sensing data have special place because of providing update data, repetitive covers and low cost images. Therefore Optimum Land Image/ Thermal Infrared Sensor were used to map land-use and land-cover ...
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Land use and land cover maps are necessary for planning and natural resources management. In the way, remote sensing data have special place because of providing update data, repetitive covers and low cost images. Therefore Optimum Land Image/ Thermal Infrared Sensor were used to map land-use and land-cover in 1 and 2 level. Because of, this images are new thus radiometric correct was used ERDAS software model maker. Also Normalize Difference Vegetation Index (NDVI), Bare Soil Index (BI) and Principal Component Analyze (PCA) were used as inputs to improve classification accuracy. On the other hand kernels functional and polynomial ranks of Support Vector Machine method evaluated in side others bands and the best result of SVM method compared with Artificial Neural Network (ANN). The results indicated that SVM method has accuracy: 92% with Kappa Coefficient: 0.91 and ANN method has accuracy: 89% with kappa coefficient: 0.87 also SVM method has a good performance in the regions that, classes show similar spectral behavior.
