The Relationship Between Urban Land Cover And Surface Kinetic Temperature: A Case Study In Terre Haute, Indiana

This research examines the relationship between surface kinetic temperature (SKT), land cover, and the Normalized Difference Vegetation Index (NDVI) for a small city in the Midwestern United States. Color aerial photography was examined to create high resolution maps of land cover for 377 random quadrats. Average NDVI for each quadrat was determined from hyperspectral aerial imagery, whereas the average SKT of each quadrat was calculated from ASTER Data Product 8. Initial experiments using square sampling quadrats with areas ranging from 0.01 to 1.44 hectares demonstrate that the highest correlation exists between land cover and the ASTER SKT data with 1.1 hectare quadrats. Several regression analyses at this quadrat scale demonstrate that different land cover types either mitigate or compound the urban heat island SKT problem. In particular, increasing amounts of non-porous surfaces such as roofing and paving contribute more to higher urban SKT than increasing vegetation does to lowering urban SKT. Even though it was not originally meant as a measure of urban vegetation, NDVI explained almost as much variance in the SKT data as regression models employing different land cover percentages. ACknowledgmenTS The authors acknowledge the support of the National Science Foundation (Award number 0319145, Acquisition of AISA+ Hyperspectral Sensor) in providing funds to acquire the AISA+ hyperspectral sensor used in this research. The Indiana Space Grant Consortium provided funds for the Terre Haute data flight. InTRodUCTIon The United Nations estimates that almost 51% of the world’s population will be living in urban areas by 2010. This percentage is projected to increase to almost 60% by 2030, and most of this migration will occur in less developed regions (United Nations, 2005). While urban populations continue to grow, it is reasonable to assume that the importance of urban areas as human habitat and the physical and social The Journal of Terrestrial Observation | Volume 2 Number 2 (Spring 2010) A CAse study In terre HAute, IndIAnA | 47 processes contained therein will also continue to grow. Indeed, as the conditions of urban areas will impact over half of all people, our ability to map, monitor, and sometimes influence urban processes will be very important (Small, 2006). Urbanization, the complex interaction of various processes that transforms landscapes (Doygun et al., 2008), is one of the results of population increase and the migration of people from rural to urban areas (Guzy et al., 2008; Katpatal et al., 2008). Urbanization is one of the most dramatic forms of land cover transformation (Luck and Wu, 2002), and there is sufficient evidence that urban areas have created enormous impacts on the environment at many geographical scales (He et al., 2008). As urban areas expand, natural landscapes and open spaces are typically altered to include an anthropogenic landscape of buildings, roads, and other structures (Katpatal et al., 2008). One concern regarding this alteration is its effect on urban temperature. Impervious surfaces can raise urban air temperatures several degrees as compared to adjacent rural landscapes (He et al., 2007). This phenomenon is often referred to as the urban heat island. Urban heat islands represent human-induced urban/rural contrasts principally caused by the replacement of vegetated areas with non-evaporating and impervious materials such as concrete and asphalt (Pu et al., 2006). Such land cover replacement fundamentally changes the physical characteristics of the earth’s surface, including albedo, heat conductivity, and thermal capacity (Pu et al., 2006). In general, urban areas exhibit higher solar radiation absorption and a greater capacity to store heat. This heat is typically stored during the day and released at night (Weng and Yang, 2004). Small (2002) found that changes in urban reflectance have a strong influence on energy flux in the urban environment. In addition, Stathopoulou et al. (2007) found that urban materials with high albedo and thermal emittance values attain lower surface temperatures when exposed to solar radiation, which in turn reduces the transference of heat to the air. Hypothesis and Significance As detailed by Hart and Sailor (2009), urban heat islands can have thermal comfort consequences and impact human health. Therefore, our ability to measure the impact of specific land cover on the urban heat island effect is important for those charged with mediating urban temperature and those concerned with creating more “livable” urban areas. To this end, we examined the relationship between detailed urban land cover data derived from Digital Ortho-Photo Quarter Quadrangles (DOQQs), Normalized Difference Vegetation Index (NDVI) calculated from hyperspectral imagery, and urban surface kinetic temperature (SKT) derived from satellite remote sensing data. This research was based on two facts: (1) urban land cover contributes to urban SKT and (2) urban SKT can be accurately measured using satellite remote sensing techniques. It expands on previously published research that examined the specific role of vegetation in urban temperature (Weng et al., 2004; Hardin and Jensen, 2007). This study highlights the amount of SKT variation that can be ex48 | Jensen, Hardin, Curran, and Hardin The Journal of Terrestrial Observation | Volume 2 Number 2 (Spring 2010) A CAse study In terre HAute, IndIAnA | 49 plained by changing percentages of various urban land cover types and type combinations. The explanatory power of NDVI calculated using hyperspectral imagery is also examined. This research began with the hypothesis that land cover and its derivative measures (e.g., land cover ratios and NDVI) will account for much of the variation in remotely estimated urban SKT. This hypothesis led to three research objectives: 1. To determine the amount of variation in urban SKT explainable by specific land cover types. 2. To determine whether simple combinations (i.e., sum and ratios) of different land cover types explain more variation in urban SKT than simple percentages alone. 3. To determine the relationship between NDVI calculated from hyperspectral data and urban SKT. Remote Sensing of the Urban Heat Island Remote sensing data and techniques have proven to be reliable and accurate sources of information in urban areas, and many studies have used remote sensing data and techniques to study urban environments. Most urban remote sensing has been done using relatively coarse spectral resolution multispectral remote sensing data, such as, Landsat Thematic Mapper (Gatrell and Jensen, 2002), Landsat Thematic Mapper+ (Lu and Weng, 2004; Katpatal et al., 2008), Advanced Spaceborne Thermal Emission Radiometer (ASTER; Jensen et al., 2003; LaFary et al., 2008), and relatively fine spatial scale IKONOS and Quickbird satellite data (Doygun et al., 2008). Other studies have used hyperspectral data to study the urban environment (Jensen et al., 2009). Specifically, many urban heat island studies have been conducted using remote sensing data. Lo et al. (1997) found a strong relationship between the amount of vegetation present and irradiance recorded by the Advanced Thermal and Land Applications Sensor (ATLAS) in an urban area. This same concept was reinforced by Quattrochi and Ridd (1998), who found that vegetation—especially trees—have a significant mitigating effect on thermal radiation. Conversely, Stathopoulou et al. (2007) established that a negative Urban Heat Island (UHI) effect (i.e., urban land is cooler than adjacent rural land) could occur during the daytime because of the differing heating properties of each surface. This underscores the value of measuring precise land cover amounts when studying the UHI. The negative UHI effect was also noted by Xian and Crane (2006), who examined the UHI in both Tampa, Florida, and Las Vegas, Nevada, using Landsat data. They found that urban Las Vegas exhibits a cooling effect whereas Tampa Bay exhibits a more typical urban heating effect. However, in both cities, areas with higher percentages of impervious surfaces were usually associated with higher temperatures. Remotely derived vegetation variables such as NDVI are frequently highly correlated with urban temperature (Chen et al., 2006; Yuan and Bauer, 2007). A CAse study In terre HAute, IndIAnA | 49 The Journal of Terrestrial Observation | Volume 2 Number 2 (Spring 2010) Chen et al. (2006) also found correlations between urban temperature and other indices, including the Normalized Difference Water Index (NDWI), Normalized Difference Bareness Index (NDBaI), and Normalized Difference Build-up Index (NDBI). Weng et al. (2004) generated fractional vegetation data from a Landsat 7 ETM+ image covering Indianapolis, Indiana. They found a stronger relationship between land surface temperature and unmixed vegetation fraction than existed between land surface temperature and NDVI. Other studies have examined the specific impact of vegetation variables on urban temperature. Hardin and Jensen (2007) found that a biophysical measure, Leaf Area Index, accounts for much of the variation in urban surface kinetic temperature. This supports the findings of Weng and Yang (2004), i.e., while urban expansion increases urban temperature, those increases can be mitigated by strategically planting vegetation. Chen et al. (2006) observed that rapidly urbanized areas are more prone to the increases in temperature than areas of slow development. This may be the result of the initial vegetation clearing and subsequent planting of immature grass, shrubs, and trees. Kottmeier et al. (2007) examined the urban microclimate and the role impervious and vegetated surfaces have in contributing to surface temperature. They concluded that the cooling effect from shading (coming primarily from trees, high-growth vegetation, and buildings) may outweigh the heating effect of rooftops. Katpatal et al. (2008) found a strong relationship between a Landsat TM derived land cover dataset and urban meteorological data. They also suggested that remote sensing data can be used to accurately depict the relationship between land use/cover and urban temperature. Peña (2007) used Landsat Enhanced Thematic Mapper Plus (ETM+) to investigate the correlation of urban surface temperature and urban land cover to explain the formation of an urban “heat sink” in Santiago City, Chile. Xiao et al., (2007) found that the arrangement of impervious surfaces was highly correlated with land surface temperature in Beijing, China. Finally, Liu and Weng (2009) examined the relationship between urban temperature and land surface patterns at multiple spatial scales in Indianapolis, Indiana. They found that 90 meters was the ideal spatial scale to investigate the covariation between land cover and land surface temperature.