While, taking the advantage of using MODIS high temporal resolution data alone might be useful to select appropriate images for LST processing in large study areas, but it will not be helpful in studying small areas of interest such as coastal reclaimed areas because of the data coarse spatial resolution. However, this solution in many cases is inapplicable as it is time intensive demanding large computing resources. Other efforts have been made to fuse coarse spatial resolution and high temporal resolution data such as MODIS (Moderate Resolution Imaging Spectroradiometer), with moderate spatial resolution and low temporal resolution data such as Landsat and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data, to overcome cloud cover and long time span of moderate spatial resolution data problems.
#Zhongshan aqua extreme lighting series#
While some other studies sacrificed the time series analysis by compose images of many years-sometimes 30 years divided into two periods-to do a reliable average of LST values. Some studies revealed LST changes based on only a single image per period, raising doubts on their results reliability. Monitoring temporal LST and SUHI changes is challenging due to the seasonal variance in temperature, and even daily changes within the same season. Hence, monitoring surface temperature in urban areas might help planners abate and mitigate potential thermal stress threats to people living and working in those areas. SUHIs induce thermal stress on human bodies especially in summer seasons, and increase the intensity and duration of heat waves, which could result in rising death rates during heat waves in coastal areas. LST increased in urban areas of many countries of the world, due to land cover changes from natural environments into urban impervious surfaces (UIS), resulting in the creation of new surface urban heat islands (SUHI). The wide coverage, cost efficiency, and time series of observations over the entire globe are the main advantage of remote sensing data, which allows the usage of land surface temperature (LST) as a variable to study rapid urbanization, destruction of vegetated areas, and climate change at either local or global scale. This method offers more robust detection of surface urban heat islands than original LST in newly developed coastal areas. Additionally, LSTn revealed pronounced differences between the temperature of impervious surfaces and other land cover types. In contrast to the original LST, results show that LSTn can reduce seasonal variability when monitoring temporal change in surface temperatures. Original LST and LSTn values were calculated for years 1987, 1997, 2007, and 2017. This method was applied in the Lingding Bay area, Guangdong Province, Southern China. To address this problem, we propose a new method for temporal monitoring of surface temperature based on LST normalization (LSTn) deploying the average open water temperature to normalize LST when monitoring temporal change in the surface temperature of newly coastal reclaimed areas. However, the cloud cover in thermal remotely sensed images and the coarse resolution of passive sensor system significantly limits data availability of same season for comparative temporal analysis in many parts of the world. The temporal analysis of land surface temperature (LST) has generally been studied using data from the same season, as temperature varies greatly over time.
0 kommentar(er)
