جستجوی مقالات مرتبط با کلیدواژه « درجه حرارت سطح زمین » در نشریات گروه « منابع طبیعی »

Background and Objective :

Land surface temperature (LST) has become an important issue in the world today, as it affects the climate and environment at the local, regional and global levels, and these changes in land surface temperature are mainly caused by it arises from urbanization, and human activities and extreme Landuse and Land-cover (LULC) changes. Due to the limitations of meteorological stations, remote sensing can be used as the basis of many meteorological data. One of the most important practical aspects of remote sensing in climate studies is the estimation of surface temperature. In this research, the temperature of the earth's surface between 1990 and 2018 was extracted from the images of TM and OLI sensors of the coastal lands of Bushehr, using the Stefan-Boltzmann method.

Materials and Methods:

 The land study area of Bushehr city, which is on the northern coast of the Persian Gulf, with dimensions of 20×8 km2 an area of 1011.5 km2 and with an average minimum temperature of 18.1oC and an average maximum temperature of 33 oC, relative humidity between 58-75% and the average annual rainfall is 272 mm, it’s located in the geographical location of 50°50' to 10°51 E longitude and 28°40' to 29°00' N latitude. The data used in this research include; Landsat 8 (OLI) data in 2018 and TM data in 1990, which were downloaded from the United States Geological Survey (USGS) data center (https://earth explorer.usgs.gov). In order to calculate the parameters related to temperature extraction, the meteorological data of the synoptic stations located in the studied area were used. After taking the images, due to the larger range of the images, the images were cut (Resized) and then the geometric correction of the images was done using topographic maps on a scale of 1/25000 and all the images were adjusted to the UTM coordinate system of the 39 N were adapted. In geometric correction, the RMS error of all images was less than 0.5 pixels. In order to compare the results of Stefan-Boltzmann method for extracting LST with ground data, thermal map data obtained was compared with soil temperature data (obtained from meteorological stations in the selected area). In order to evaluate the Stefan-Boltzmann method from ground data, the Mean Absolute Error (MAE) index statistical method was used.

Results and Discussion: 

 The average minimum and maximum Land surface temperature (LST) extracted from the 1990 TM image was 26.5 and 45 °C, respectively, and for the 2018 OLI image, it was 30.1 and 48.6 °C, respectively. The results showed that the Mean Absolute Error (MAE) index values for TM and OLI sensors are to 7.1 and 5.6, respectively. The results of the research showed that the Stefan-Boltzmann method provided a reliable result in estimating the Land surface temperature.

Conclusion:

 This research aims to extract LST by Stefan-Boltzmann method. The results of this method were estimated using the Mean Absolute Error (MAE) statistical index for the study period (1990-2018). Applying the MAE on the produced thermal maps, it was found that the Stefan-Boltzmann method is suitable for future research in the fields of thermal remote sensing by observing the results of using the MAE index on thermal maps. Therefore, we conclude that the Stefan-Boltzmann method is suitable for estimating the surface temperature of the land in coastal areas. Finally, it is suggested that for quantitatively describing LST patterns a GIS/RS-based method, and methods such as spatial autocorrelation and semivariance are used.

Land surface temperature (LST) is an important factor in global warming studies. Therefore, considering its negative effects on human life, it is very important to study this phenomenon. The aim of this study was to investigate the effects of vegetation indices and surface characteristics of Sanandaj city on land surface temperature changes. For this purpose, the land surface temperature was calculated in ArcGIS and vegetation indices and the surface Sanandaj city were derived from Landsat images in ENVI software. The results of regression analysis and correlation of LST with synoptic station data were 0.45 and 0.20 respectively, which shows a positive relationship. The results of the overall accuracy and the indices shows that the correlation analysis the precise indices (enhanced buildup and bareness index, index-based built up the index, normalized difference built up the index, urban index), positive relationship and low precision indices (normalized difference bareness index, new built up the index, normalized difference water index, modified normalized difference water index) have the opposite correlation with land surface temperature. The results of the temperature situation in the vegetation classes indicate that the high temperature was observed in the low vegetation classes in the early summer of 1988, 1999, 2008, 2012, 2014 and 2015, and the lowest temperature in dense vegetation classes at the end of spring, 1989, 1993, 2000, 2007, 2010 and 2016. Therefore, the reduction of vegetation and growth of built-up areas has an important role in increasing the land surface temperature, and spectral indices to extract buildup areas from satellite images can provide acceptable results.

Land surface temperature is a key indicator of energy balance. Besides, it serves as input data for models of climate change, agriculture, meteorology, urban heat islands, choosing the best time to agricultural activities, study of volcanic and geothermal activity, and fire detection. In this study land surface temperature has been extracted by available methods using 4 images of TM and ETM+ sensors of Landsat in span years of 1985 to 2003. The methods of land lurface temperature extraction included landsat project science office, mono window, SEBAL, Stefan-Boltzmann and single channel. Because of the multiplicity of methods and the number of images used in this study using a statistical method is required. It is required to determine the most efficient extraction method of land surface temperature, which is close to the existing field data. The statistical indicator used in this study was a mean absolute error (MAE). The results indicated that Stefan-Boltzmann method was the best method for both TM and ETM+ sensors. The MAE values for TM and ETM+ were 4.3 and 6.8 respectively, which showed a minimum value among other results.