جستجوی مقالات مرتبط با کلیدواژه « تصاویر لندست 8 » در نشریات گروه « جغرافیا »

Urban Heat Island (UHI) is an important factor for heat change and balance in global studies and is considered as a proxy for climate change. Studying this phenomenon and investigating its mechanism are very important in urban planning. During the last two decades, the great need for earth surface temperature information for environmental studies and land resource management activities has turned earth surface temperature remote sensing into one of the most important scientific issues (Sobrino et al., 2004). In a research, Hay et al. (2018) studied the effect of the topographical factor on LST in mountainous areas. The results of their research showed that there was a high correlation between topography parameters and surface temperature. For the area studied in this research, the relationship between LST and altitude was inversely linear. Tran et al. (2017) used Landsat 5,7,8 in order to determine the relationship between land cover change and land surface temperature of the inner city of Hanoi (plain and flat area). The results revealed that: a) LST depends on the non-linear method of LULC types; b) Foci analysis by using the Getis Ord Gi* statistic allows analysis of LST pattern change over time; c) UHI is affected from both the urban perspective and type of urban development; d) investigation of LST pattern prediction and UHI effect can be done by the proposed model by using nonlinear regression and simulated LULC change scenarios. In a research in a city of China, the harmful effects of land cover and land use changes on the Earth's surface temperature were investigated through vegetation indicators based on three image sensors of TM and ETM+. For this purpose, the researchers obtained the Temperature Vegetation Index (TVX) from the images. Their results showed that land use change is an important factor for increasing the Earth's surface temperature. It also showed high temperature in areas with scattered vegetation and low temperature in areas with dense vegetation. It has been used in the statistical period of 2013-2015 in the city of Isfahan as well. The results of this research showed that there was a sharp thermal gradient due to the presence of Urban Cold Islands (UCI) between the center and suburbs. The largest UCI was identified in Region 6 (Ahmadi et al., 2016).

Surface temperature is considered to be a substantial factor in urban climatology. Italso affects internal air temperature of buildings, energy exchange, and consequently the comfort of city life. An Urban heat island (UHI) is an urban area with a significantly higher air temperature than its surrounding rural areas due to urbanization. Annual average air temperature of an urban area with a populationof almost one million can be one to three degreeshigher than its surrounding rural areas. This phenomenon can affect societies by increasing costs of air conditioning, air pollution, heat-related illnesses, greenhouse gas emissions and decreasing water quality. Today, more than fifty percent of the world’s population live in cities, and thus, urbanization has become a key factor in global warming. Tehran, the capital of Iran and one of the world’smegacities, is selected as the case study area of the present research. A megacity is usually defined as a residential area with a total population of more than ten million. We encountered significant surface heat island (SHI) effect in this area due to rapid urbanization progress and the fact that twenty percent of population in Iran are currently living in Tehran.SHI has been usually monitored and measured by in situ observations acquired from thermometer networks. Recently, observing and monitoring SHIs using thermal remote sensing technology and satellite datahave become possible. Satellite thermal imageries, especially those witha higher resolution, have the advantage of providing a repeatable dense grid of temperature data over an urban area, and even distinctive temperature data for individual buildings.Previous studies of land surface temperatures (LST) and thermal remote sensing of urban and rural areas have been primarily conducted using AVHRR or MODIS imageries.

Recently, most researchers use high resolution satellite imagery to monitor thermal anomalies in urban areas. The present study takes advantage of themost recentsatellite in the Landsat series (Landsat 8) to monitor SHI, and retrieve brightness temperatures and land use/cover types.Landsat 8 carries two kind of sensors: The Operational Land Imager (OLI) sensor has all former Landsat bands in addition of three new bands: a deep blue band for aerosol/coastal investigations (band 1), a shortwave infrared band for cirrus detection (band 9), and a Quality Assessment (AQ) band. The Thermal Infrared Sensor (TIRS) provides two high spatial resolution thirty-meter thermal bands (band 10 and 11). These sensors use corrected signal-to-noise ratio (SNR) radiometric performance quantized over a 12-bit dynamic range. Improved SNR performance results in a better determination of land cover type. Furthermore, Landsat 8 imageries incorporate two valuable thermal imagery bands with 10.9 µm and 12.0 µm wavelength. These two thermal bands improve estimation of SHI by incorporating split-window algorithms, and increase the probability of detectingSHI and urban climatemodification. Therefore, it is necessary to design and use new procedures to simultaneously (a) handle the two new high resolution thermal bands of Landsat 8 imageries and (b) incorporate satellite in situ measurement into precise estimation of SHI.Lately, quantitative algorithms written for urban thermal environment and their dependent factors have been studied. These include the relationship between UHI and land cover types, along with its corresponding regression model. The relation between various vegetation indices and the surface temperature was also modelled in similar works. The present paper employ a quantitative approach to detect the relationship between SHI and common land cover indices. It also seeks to select properland coverindices from indices like Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Build-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), Bare soil Index (BI), Urban Index (UI), Index based Built up Index (IBI) and Enhanced Built up and Bareness Index (EBBI). Tasseled cap transformation (TCT) which is a method used for Landsat 8 imageries, compacts spectral data into a few bands related to thecharacteristics of physical scene with minimal information loss. The three TCT components, Brightness, Greenness and Wetness, are computed and incorporated to predict SHI effect.The main objectives of this research include developing a non-linear and kernel base analysis model for urban thermal environment area using support vector regression (SVR) method, and also comparing the proposed method with linear regression model (LRM) using a linear combination of incorporated land cover indices (features). The primary aim of this paper is to establish a framework for an optimal SHI using proper land cover indices form Landsat 8 imageries. In this regard, three scenarios were developed:  a) incorporating LRM with full feature set without any feature selection; b) incorporating SVR with full feature set without any feature selection; and c) incorporating genetically selected suitable features in SVR method (GA-SVR). Findings of the present study can improve the performance of SHI estimation methods in urban areas using Landsat 8 imageries with (a) an optimal land cover indices/feature space and (b) customized genetically selected SVR parameters.

The present study selects Tehran city as its case study area. It employs a quantitative approach to explore the relationship between land surface temperature and the most common land cover indices. It also seeks to select proper (urban and vegetation) indices by incorporating supervised feature selection procedures and Landsat 8 imageries. In this regards, a genetic algorithm is applied to choose the best indices by employing kernel, support vector regression and linear regression methods. The proposed method revealed that there is a high degree of consistency between affected information and SHI dataset (RMSE=0.9324, NRMSE=0.2695 and R2=0.9315).

In this research, the relationship between land use and land surface temperature and spatial correlation in Ardebil city using Moran index has been investigated. For this purpose, Landsat 8 satellite imagery (OLI) was used from 2015 to 2018. Initially, the relevant images were taken and the necessary pre-processing was applied. Then the classification was made using the object-oriented method and the nearest neighboring algorithm and the surface temperature was extracted by the algorithm (SW). The results showed that the temperature in the urban areas was 43 ° C and 45 ° C in 2015 and 2018, respectively, due to the absorption of heat and urban pollution. The use of water was 35 ° C in 2015, and 37 ° C in 2018, that eliminates more heat from the water and has a lower temperature. The results also showed a strong relationship between land use and temperature. Finally, Hot Spot Analysis was used for hot and cold clusters of Ardebil thermal islands. The analysis of spatial correlation with global Moron indexes showed that the surface temperature of Ardabil has a spatial structure or, in other words, the surface temperature of the earth is distributed in cluster form. Hot Spot Analysis has been a clear confirmation of the concentration and clustering of the thermal islands of Ardabil in space with increasing period of time.

Gavkhoni wetlands in terms of geography conditions, environmental and three factors hydrological , biological and ecological gives rise to the rarest wetland of iran in recent due to climatic conditions change, pollution and moving smooth has been subjectedchange and evolution .In this study has tried that by identification the process of change land use in this range in 17 years period as well as the changes the primary element of climate (temperature and precipitation) in synoptic stations of the Isfahan and Varzaneh, can be identified the desertification in the study area and in some way to help the environmental management this hole. Therefore at first the ETM satellite images area of study during the 1990, 1999 and 2006 prepared and by use of ENVI and GIS were analyzed.Untill obtained the information needed to evaluate quantitatively the changes. The results showed that due to the destruction of wetlands, related phenomena like variety and vegetation density and catchment area has declined .continuing to analyze desertification, view of climate first by using Thornthwaite method experimental procedure was evaluated evaporation and transpiration changes in Stations of Varzaneh and isfahan, finally were calculated the process of this change and temperature and precipitation by using Mann-Kendal and Sen's Estimator. The results showed components, temperature and evaporation and transpiration have significant rising trend in most months of the year, especially summer and autumn. On the other rainfull parameters in the two season, However, no clear trend , but it has fallen In fact, all these factors intensified the desertification of the area studied.

Gavkhuni playa has illustrated on its bed the occurrence of climatic-environmental happenings as an evidence and record of the Quaternary in Iran and enjoys a special position in Iran`s geomorphological domains. In the other hands, the domain of the Quaternary, diversity and stability of the archeological morphic forms portray the predominance of climatic cold and warm different phases and the reserve variations of matter and energy at different geomorphic levels.Study of Gavkhuni paleogeomorphology will help us identify and reconstruct the predominance of the past conditions and, at the same time gain a correct and true understanding of such domains and compare. Our behavior with the geomorphic nature of the region in the course of proper management and planning. In this paper, employing Landsat satellite images and performing pre-processing operations, mosaic and analysis of the images of the region of under - study, we reconstructed the domain of Gavkhuni in different phases of progression and regression. Then we proceeded to identify separative and compare the domains with the paleogeomorphic forms. In another word, employing processing various methods and the region altitude digital model to combine with the paleo domains and levels of the great Gavkhuni,we would be able to identify and separate the playa consecutive levels in different phases and compare paleo forms including mega deltas and terraces of lakes remained from the previous levels with the reconstructed domains.At the end, we both prepared paleo thematic maps, identified and reconstructed the great Gavkhuni domain upto four various phases. This method has been used for the first time in the domain of Gavkhuni playa and is recommended as a reliable method for studying other playa of Iran.