Urban Heat Island Effect: Analyzing Land Cover Change & Temperature in Islamabad & Rawalpindi via Remote Sensing

This MS Thesis presentation investigates the critical relationship between urban development and the Urban Heat Island (UHI) effect in the twin cities of Islamabad and Rawalpindi, Pakistan.

Key Highlights:

Timeframe Analysis: Uses Landsat 5 TM and ASTER satellite imagery from 1990 to 2007 to track land cover changes.

Methodology: Applies advanced remote sensing techniques, including calculating Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI).

Findings: Reveals how the conversion of vegetation and bare land into built-up areas directly increases local temperatures, creating significant heat islands.
Relevance: Essential for urban planners, environmental scientists, geographers, and policymakers focused on sustainable development and climate change mitigation in South Asian cities.

This research provides a robust scientific framework for understanding the environmental impacts of rapid urbanization and offers insights for creating cooler, more sustainable cities.

Keywords: Urban Heat Island, Remote Sensing, GIS, Land Cover Change, NDVI, NDBI, Land Surface Temperature, Islamabad, Rawalpindi, Urbanization, Environmental Science, Sustainable Development, Climate Change, Geography Thesis.

1. Relating Urban Heat Island Effect and Land Cover Type in Islamabad & Rawalpindi through Image Analysis Presented by Arooj Fatima MS 2006

2. INTRODUCTION Urban Development & Urbanization A major type of land cover change in human history drastically effecting our environment. The Urban Heat Island (UHI) Effect Phenomenon of higher atmospheric and surface temperatures occurring in urban areas as a result of radical urbanization. Cont..

3. INTRODUCTION Detection Through Remote Sensing Spectral characteristic is the physical basis of Remote Sensing (reflectance and absorption feature). Satellite thermal remote sensing can effectively depict the patterns of the thermal environment and spatial coverage of extensive urban areas on a repeated basis (Du and Chen, 2005) . Cont..

4. INTRODUCTION This study would primarily utilize thermal infrared imagery from Landsat 5 and ASTER TIR imagery to examine the changes in land use/ cover type in Islamabad and Rawalpindi (Twin cities) during summer time between the period of 1990 to 2007 and the impact of such changes on the spatial pattern of temperature, thus evaluating the UHI effect. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) will be used to extract land use/cover information and would then be analyzed with the surface temperature retrieved from respective satellites and ambient air temperatures from weather stations.

5. PROBLEM STATEMENT The growth of urban population is a subject of increasing concern among social scientists and government planners in the developing world. Changes in land use/ land cover pattern in relation to urban growth have made city environments congested, mainly due to lack of proper planning. This noticeably impacts the local temperature of an area, creating heat islands that are now abundantly prevalent in almost all densely populated cities around the globe.

6. OBJECTIVE The objective of this study is to investigate the relationship between urban temperature and land cover type in order to estimate urban heat island effect. This will be accomplished through a review and analysis of relevant meteorological data for daylight hours and calculation of surface temperature during the months of May and June.

7. HYPOTHESIS Relationships between average ambient air and surface temperature and land cover type will be explored to investigate the hypothesis: “Change in land cover type induces change in urban temperatures, all other factors being constant."

8. LITERATURE REVIEW Voogt and Oke (2002) reviewed the use of thermal remote sensing in the study of urban climates, focusing primarily on the urban heat island effect. This included the advancements made in the application of urban thermal remote sensing to the study the climate of urban areas, improvements in the spatial and spectral resolution of current and next generation satellite-based sensors, more detailed surface representations of urban surfaces and the availability of low cost, high resolution portable thermal scanners. Dousset and Gourmelon (2003) studied and analyzed the summertime microclimate of the Los Angeles and Paris metropolises by deriving some parameters governing the surface heat fluxes, constructing statistics of thermal infrared images, and using a GIS to combine them with a land cover classification from SPOT-HRV multispectral images, and with data from intensive in-situ experiments.

9. LITERATURE REVIEW Chen et al. (2006) determined the urban heat island effect and intensity in Pearl River Delta (PRD) in Guangdong Province, southern China. The study selected Landsat TM and ETM+ images from 1990 to 2000 in the PRD to get the brightness temperatures and land use/cover types. Additionally, Shenzhen city in Guangdong Province was also taken as an example to analyze the temperature distribution and changes within a large city. Yuan and Bauer (2007) compared the normalized difference vegetation index (NDVI) and percent impervious surface as indicators of surface urban heat island effects in Landsat imagery by investigating the relationships between the land surface temperature (LST), percent impervious surface area (%ISA), and the NDVI for the Twin Cities, Minnesota and metropolitan area.

10. STUDY AREA Islamabad & Rawalpindi  Twin Cities Criteria for Selection Experienced increase in population due to rural-urban migration. Planned and Unplanned

11. Astronaut photograph taken August 21, 2003 with a Kodak DCS760 digital camera equipped with a 180 mm lens. http://earthobservatory.nasa.gov Weather Stations

12. DATA REQUIREMENTS Two Landsat 5 TM (1990-2000) and two ASTER images (2001-2007) - (SUPARCO, LP DAAC) IKONOS - ( land use/ land cover classification reference ) Average data for daily minimum and maximum ambient air temperatures from 4 weather stations, wind direction and wind speed - (Meteorological Department of Pakistan)

13. PROPOSED METHODOLOGY CLIMATE DATA SATELLITE IMAGES INPUT DATA Temperature Data Wind Data Radiance at Sensor LC Classification Brightness Temperature NDVI, NDBI Image Processing ANALYSIS Factors Influencing UHI Primary Secondary Correlation

14. PROPOSED METHODOLOGY Radiance at Sensor Retrieval of brightness temperature Landsat 5 ASTER R TM6 = V ( R max – R min ) + R min 255 ( Chen et al. 2002) Radiance at the Sensor = (DN – 1) UCC ( Abrams and Hook, 2001). Landsat 5 ASTER (Chen et al. 2002) (Alley and Jentoft-Nilsen , 1999) UCC is the Unit Conversion Coefficient for each ASTER Channel in W/(m 2 sr m)/DN

15. PROPOSED METHODOLOGY Indices Landsat 5 ASTER NDVI = (0.76-0.9 µm ) – (0.63-0.69 µ m) (0.76-0.9 µm) + (0.63-0.69 µm) NDVI = (0.78-0.86 µm ) – (0.63-0.69 µm ) (0.78-0.86 µm) + (0.63-0.69 µm) NDBI = (1.55-1.75 µm ) – (0.76-0.9 µm ) (1.55-1.75 µm) + (1.55-1.75 µm) NDBI = (1.6-1.7 µm ) – (0.78-0.86 µm ) (1.6-1.7 µm) + (1.6-1.7 µm)

16. PROPOSED METHODOLOGY

17. PROPOSED METHODOLOGY Relating land type and temperature Major Land cover types: - Vegetation - Water - Built-up area - Bare land. This study will attempt to isolate and eliminate the effects of mountains and water bodies that may have on inland weather stations. The mean ambient air temperatures among four weather stations surrounded by different land types and brightness temperature of various land types would be compared. A regression model between the temperature and land type will provide the relationship between these variables.

18. ANTICIPATED OUTCOMES Spatial distribution of temperature in Twin Cities. Land cover type in the study area. Relationship between the temperature variation and land cover type.

19. TIMELINE

20. BUDGETING COST PER SCENE (in Dollars) COST PER SCENE (in Rupees) LANDSAT TM 5 ---- Rs 500 ASTER $ 85 Rs 5211 Total Cost = Rs 11422 only

21. REFERENCES Chen, L. X., Zhao, M. H., Li , X. P. and Yin, Y. Z. (2006). Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Rem. Sens. Env . 104: 133–146. Chen, Y., Wang, J. and Li, X. (2002). A study on urban thermal field in summer based on satellite remote sensing. Rem. Sens. Land Resources. 4: 55−59.   Dousset , B, and Gourmelon , F. (2003). Satellite multi-sensor data analysis of urban surface temperatures and landcover . ISPRS J. Photogr . & Rem. Sens. 58: 43– 54.   Dua , P and Chen, Y. (2005). Some key techniques on updating spatial data infrastructure by satellite remote sensing imagery. Proceedings of the ISPRS Workshop on Service and Application of Spatial Data Infrastructure, XXXVI(4/W6), Oct.14-16, Hangzhou, China.   Goldreich , Y. (1995) Urban climate studies in Israel—a review. J. Atm. Env . 29: 467–78.   Gomez, F., Gaja , E. and Reig , A. (1998). Vegetation and climatic changes in a city. Ecol. Engr. 10: 355–60. Jung, A., Kardevan , P. and Tokei, L. (2005). Detection of urban effect on vegetation in a less built-up Hungarian city by hyperspectral remote sensing. Phy . Chem. Earth. 30: 255–259.  

22. REFERENCES Landsberg , H.E. (1981). The urban climate. Academic Press, New York.   Oke , T.R. (1978). Boundary layer climates. Methuen,London .   Shirazi , A. S. (2006). Patterns of urbanization in Pakistan: a demographic appraisal. Voogt , J. A., & Oke , T. R. (2003). Thermal remote sensing of urban areas. Rem. Sens. Env . 86: 370−384. Weng , Q., Lu, D. and Schubring , J. (2004). Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Rem. Sens. Env . 89: 467–483. Weng , Q. and Yang, S. (2004). Managing the adverse thermal effects of urban development in a densely populated Chinese city. J. Env . Mang . 70: 145–156. Yuan, F. and Baver , E.M. (2007). Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Rem. Sens. Env . 106: 375–386.

23. THANKYOU