Extreme Temperature

Land surface temperature (LST) is a significant parameter in urban environmental analysis. Current research mainly focuses on the impact of land-use and land-cover (LULC) on LST. Seldom has research examined LST variations based on the integration of biophysical and demographic variables, especially for a rapidly developing city such as Beijing, China. This study combines the techniques of remote sensing and geographic information system (GIS) to detect the spatial variation of LST and determine its quantitative relationship with several biophysical and demographic variables based on statistical modeling for the central area of Beijing. LST and
LULC data were retrieved from a Landsat Thematic Mapper (TM) image. Building heights were delimited from the shadows identified on a panchromatic SPOT image. The integration of LULC and census data was further applied to retrieve gridbased population density. Results indicate that the LST pattern was non-symmetrical and non…

The purpose of this study was to determine the relationship between the absence of trees and the presence of micro-urban heat islands. The objectives were to determine the usefullness of Landsat TM bands 3 and 4 for tree cover mapping, and the uisefullness of TM band 6 in identifying micro-urban heat islands. All 7 bands were utilized in this study to provide the full spectral curve for differentiating all cover types. When micro-urban heat islands were determined and maopped in relation to tree cover, the final objective of this study was to conbine the informationn into a GIS to obtain a map or blueprint of the exact location of micro-urban heat islands in the Dallas area.

Background: Extreme heat events are the number one cause of weather-related fatalities in the United States. The current system of alert for extreme heat events does not take into account intra-urban spatial variation in risk. The purpose of this study is to evaluate a potential method to improve spatial delineation of risk from extreme heat events in urban environments by integrating socio-demographic risk factors with estimates of land surface temperature derived from thermal remote sensing data.

Results: Comparison of logistic regression models indicates that supplementing known socio-demographic risk factors with remote sensing estimates of land surface temperature improves the delineation of intra-urban variations in risk from extreme heat events.

Conclusion: Thermal remote sensing data can be utilized to improve understanding of intra-urban variations in risk from extreme heat. The refinement of current risk assessment systems could increase the…

Part of China has been hit by low-temperature and blizzard since 2 January. The snow disaster lead to the most tremendous snow in Beijing and Tianjin since 1951 and it is bound to move to the south of China during 4-6 January 2010.

Source: www.disasterscharter.org

Due to low temperatures in the country 5,960 people have stayed in the 15 shelters opened by the Government response body since November 2009.

Source: www.glidenumber.net

Emergency was declared for 4 municipalities in Zacatecas after severe frosts during Dec 2009.

Source: www.glidenumber.net

Cold wave in early January has caused at least 100 deaths in India and 18 deaths in southern region of Nepal.

Source: www.glidenumber.net

Cold Wave in early January has claimed at least 18 lives in Nepal's southern region.

Source: www.glidenumber.net

In Warsaw, police officials said 15 people froze to death on 19 Dec alone, bringing to 57 the number of people who have died in Poland from exposure to the severe weather since early December.

Source: www.glidenumber.net

According to the Ministry of Civil Affairs announced (MCA ), heavy snow has left 21 people dead in north and central China. Mmore than 9,000 buildings collapsed and about 190,400 hectares of crops were affected, causing direct economic losses of around 4.5 billion yuan (about 659 million U.S. dollars).

Source: http://www.glidenumber.net/glide/public/search/details.jsp?glide=18965&record=3&last=6.