Estimation of the Relationship between Urban Vegetation and Land Surface Temperature of Calicut City and Suburbs, Kerala, India using GIS and Remote Sensing data

Chaithanya V.V., Binoy B.V., Vinod T.R., (doi: 10.23953/cloud.ijarsg.112)

Abstract


Land surface temperature was the most important determining factor for the analysis of urban heat island. The Vegetation content in urban areas has a great influence on the temperature variation of those areas. Satellite remote sensing is very much helpful in the analysis of urban heat islands by using Land Surface Temperature. By analyzing the Landuse, Temperature and NDVI from satellite images the temperature and the variations was studied. Landsat 7 images are used in the calculation of Temperature, NDVI and Landuse. Land surface temperature of Calicut Corporation was estimated by using Mono Window Algorithm from land sat 7 ETM images. Landsat images for the years 2003, 2008 and 2015 were down loaded from the USGS earth explorer web site. Temperature band or band 6 was used for the land surface temperature estimation. NDVI was derived from the band 3 and band 4 using the ERDAS imagine for the year 2003, 2008 and 2015. Supervised classification was done to classify the images in different land use categories like vegetation, built up and water bodies. From the temperature map a gradual increase in land surface temperature was noticed from 2003 to 2015. This is due to the decrease in urban vegetation as observed in the landuse. A negative correlation was obtained by correlating the NDVI with the temperature. The landuse changes between these three years are analyzed. The vegetated area was reduced in the year 2015 because of increasing the built up areas.


Keywords


NDVI; LST; LULC; Brightness Temperature; Thermal Band

Full Text: PDF

Refbacks

  • There are currently no refbacks.




Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

*2016 Journal Impact Factor was established by dividing the number of articles published in 2014 and 2015 with the number of times they are cited in 2016 based on Google Scholar, Google Search and the Microsoft Academic Search. If ‘A’ is the total number of articles published in 2014 and 2015, and ‘B’ is the number of times these articles were cited in indexed publications during 2016 then, journal impact factor = A/B. To know More: (http://en.wikipedia.org/wiki/Impact_factor)