جستجوی مقالات مرتبط با کلیدواژه « Land surface temperature » در نشریات گروه « اکولوژی »

Remote sensing data play an important role in environmental planning and monitoring. The current study aimed to investigate the land surface temperature (LST) and the effect of environmental factors on the LST, to identify the temporal-spatial patterns and determine the hot spots in the period of 2013 to 2019, using Landsat 8 images. The effect of spectral indices: Normalized Difference Build-up Index (NDBI), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) on the surface temperature was investigated. Results indicated that the lowest average temperature has occurred in 2019 and the highest LST was in the 2017. The results of Moran's index correlation also showed that the most clustering pattern of LST, with the Moran value of 0.85 was obtained in 2019, the highest correlation between LST and NDBI, with the R value of 0.76 in the 2015, the highest correlation between LST and NDVI in the 2015 (R = -0.56), and the highest correlation between LST and NDWI in 2013 (R = -0.53). Rasht watershed in Guilan province is affected by human factors and land use changes. Therefore, it is recommended to increase the vegetation cover in urban areas, reduce the change of pasture to agricultural area, and reduce forest destruction.

The use of satellite data for rapid estimation of soil moisture (SM) and determination of environmental factors affecting it has been developed in recent years. The aim of this study was to investigate the effect of biophysical and topographic characteristics on spatial distribution of SM. For this purpose, SM was measured at 148 points in Balikhli-Chay watershed in Ardabil province and triangular method based on ASTER digital elevation model, land cover map and climatic data was applied for SM modeling. Surface biophysical properties including wetness, greenness, brightness, and land surface temperature and topographic variables (solar local incidence angle, elevation, slope, and aspect) were calculated. Model error in different months was determined using error statistics. According to the results, the average SM content in the region in July, August and September were 4.67, 6.22 and 4.66%, respectively. The lowest coefficient of determination (R2) and root mean square error (RMSE) of estimated and measured SM were related to September (0.78 and 1.44, respectively). The strongest linear relationship between SM and biophysical variables (topography) was related to July (with R2 and RMSE equal to 0.53 and 0.29, respectively). SM decreased with increasing land surface temperature and brightness, however increasing greenness, wetness, elevation and solar local incidence angle increased SM content. This study showed that the triangular model can be used to investigate the spatial distribution of SM using biophysical and surface topographic properties.

Soil temperature is a key factor that controls physical, chemical and biological properties of soil and its processes. Since soil temperature is measured at synoptic stations and data availability, especially in arid lands, is limited, capability of satellite images to estimate soil temperature at different depths evaluated in the Yazd-Ardakan basin, as the study area. Daily soil temperature at 5, 10, 20, 30, 50 and 100 cm depth measured at synoptic stations of Yazd, Meybod, and Mehriz for the periods of 2014 to 2016, and Landsat 8 satellite images of were used as the main data in this research. Then, using split-window surface temperature, Land Surface Temperature (LST) maps were estimated. Temperature trend from soil surface to a depth of 100 cm were examined seasonally. Using simple linear regression and artificial neural network techniques, the relationship between temperature of surface soil and soil temperatures at different depths were predicted. Results showed that the artificial neural networks had greater accuracy than the linear regression method in all seasons. The lowest accuracy of this method is related to the soil temperature at 5 cm depth. Artificial neural networks can be used for predicting of soil temperature till depth of 100 cm, using land surface temperature obtained by Landsat 8 images. To validate the results, soil temperatures at depth of 30 cm for 16 selected points in the study area were compared with estimated soil temperature using Landsat images and artificial neural network. Absolute error of measurements show that the maximum error was observed to depth of 30 cm (3.7 ℃). Therefore, using the measured soil surface temperature by applying the split-windows and artificial neural network can be used to predict soil temperature.