How can i classify the temperatures of a land surface temperature map to find urban heat islands( I need precise ranges or ranges)?
I'm doing research for my degree thesis in architecture on the urban heat islands of the city of Naples - Italy.
I'm reclassifying the Land surface temeperature map in gis and I am looking for a method to classify the temperatures on the ground in a precise way, according to the classes that allow me to locate the heat islands.
I just found one of my presentations on "using GIS technology to detect the most suitable experiment areas for global warming research" in 2005 at the GeoTech Event, in Vancouver, Canada. The method is "Weighting Factors for Aggregation".
There are no universal thresholds to classify urban heat islands because each city will experience its own specific climate and environmental constraints. Since you are dealing with a single city, I suggest you start by determining the lowest and the highest temperatures in your data set, and then divide that range (Tmax - Tmin) in 3 equal intervals. For instance, if Tmin = 18 and Tmax = 24, your intervals would be 18 to 20, 20 to 22 and 22 to 24 C. In that case, all areas falling in the lower interval could be labeled 'cool', the areas falling in the next class could be called 'warm', and those belonging to the higher interval could be indicative of 'hot' conditions.
You should make sure that your data set covers a region around the city large enough to include agricultural fields or forests: those areas would provide you with a baseline environmental temperature away from the urban center. An obvious extension of this approach would be to map the city areas in single degree intervals, from blues through to greens, yellows, oranges and red. That will clearly indicate which areas are hotter.
A better approach would be to start from a preliminary question: why do you need to make a map of the urban heat island effect of Naples in the first place? If you were concerned by the health effect of temperature on morbidity and mortality of the inhabitants, for instance, then your temperature thresholds should be driven by medical rather than purely statistical considerations. Similarly, if your underlying concern were energy expenditures (cooling during the summer or heating during the winter), then your thresholds should relate to the corresponding critical rates of energy expenditure. In other words, your approach should depend on your ultimate goal.
Remember also that
- the urban heat island is quite time-dependent: it varies with cloudiness and synoptic conditions such as sea-breeze on a daily time scale, it is much more noticeable during the winter than the summer, and it may evolve on longer time scales, depending on the rates of urbanization and industrialization;
- land surface temperature is quite dependent on altitude, so you might want to acquire a topographic map of your area and look at the correlation between these two parameters;
- a land use map of the region will be useful to properly interpret your results, as the hotter area may not be the city center but some industrial area, depending on their relative rates of energy consumption.
I hope these comments may help you in your work. Michel.
It is important to give your definition of the hot spot or hot island. Then you can divide them into several classes. Based on the temperature conditions, label your result of a hot spot or hot island on the GIS map.
I have only the LST map of the city of Naples as input data and from this map I want to obtain the hot areas (heat isand). Is there a precise way to reclassify the LTS according to temperature ranges? an equation for which I will have a legend like:
There are no universal thresholds to classify urban heat islands because each city will experience its own specific climate and environmental constraints. Since you are dealing with a single city, I suggest you start by determining the lowest and the highest temperatures in your data set, and then divide that range (Tmax - Tmin) in 3 equal intervals. For instance, if Tmin = 18 and Tmax = 24, your intervals would be 18 to 20, 20 to 22 and 22 to 24 C. In that case, all areas falling in the lower interval could be labeled 'cool', the areas falling in the next class could be called 'warm', and those belonging to the higher interval could be indicative of 'hot' conditions.
You should make sure that your data set covers a region around the city large enough to include agricultural fields or forests: those areas would provide you with a baseline environmental temperature away from the urban center. An obvious extension of this approach would be to map the city areas in single degree intervals, from blues through to greens, yellows, oranges and red. That will clearly indicate which areas are hotter.
A better approach would be to start from a preliminary question: why do you need to make a map of the urban heat island effect of Naples in the first place? If you were concerned by the health effect of temperature on morbidity and mortality of the inhabitants, for instance, then your temperature thresholds should be driven by medical rather than purely statistical considerations. Similarly, if your underlying concern were energy expenditures (cooling during the summer or heating during the winter), then your thresholds should relate to the corresponding critical rates of energy expenditure. In other words, your approach should depend on your ultimate goal.
Remember also that
- the urban heat island is quite time-dependent: it varies with cloudiness and synoptic conditions such as sea-breeze on a daily time scale, it is much more noticeable during the winter than the summer, and it may evolve on longer time scales, depending on the rates of urbanization and industrialization;
- land surface temperature is quite dependent on altitude, so you might want to acquire a topographic map of your area and look at the correlation between these two parameters;
- a land use map of the region will be useful to properly interpret your results, as the hotter area may not be the city center but some industrial area, depending on their relative rates of energy consumption.
I hope these comments may help you in your work. Michel.
I just found one of my presentations on "using GIS technology to detect the most suitable experiment areas for global warming research" in 2005 at the GeoTech Event, in Vancouver, Canada. The method is "Weighting Factors for Aggregation".
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