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Tree Canopies and Urban Heat Island Effect Mitigation

TREE CANOPIES AND URBAN HEAT ISLAND EFFECT MITIGATION

The topic of this annotated bibliography is tree canopy mitigation of urban heat island (UHI) effects. The reason for this topic’s selection is that the author has both an interest in urban development studies dating back to a youthful encounter with the works of Jane Jacobs and because he currently holds a position in local government. Thus, this topic is one that is ‘close to home’ in terms of practical utility in shaping public policy, as well as in terms of personal interest. Another factor influencing this decision was the availability of peer-reviewed literature written at a level that would be, for the most part, accessible to the lay reader.
Street trees and other urban-dwelling plants have uses apart from any related to remote sensing, and having greenery interspersed throughout the concrete-and-asphalt arroyos of the modern urban-scape is something with aesthetic merit independent of any practical utility. That trees and other urban flora provide habitat for the small animals, birds and insects that dwell together with humanity in the hives we build for ourselves is yet another benefit. What interests the researcher, insofar as these readings are concerned, is the potential benefit to be obtained in mitigating the UHI effects that often accompany high-density city life. High-density situations are preferred among the ‘New Urbanist’ crowd who, like the author, take their cue from Jacobs in seeing economic, social and cultural benefits from concentration, as well as more-efficient land use, than can be found in the low-density urban sprawl created by ‘car culture’ and the concomitant ‘urban renewal’ movement of the 1950s and 1960s, which saw many old neighborhoods give way to Federally-funded freeway construction. The resulting ‘geography of nowhere’ (to borrow from the title of James Kunstler’s 1993 classic on the social ills of low-density urbanization) expressed itself in the form of suburbs, exurbs, ‘edge cities’ and a spatial displacement of human activity from the old urban cores outward even as the urbanization trend itself pulled more of humanity into its orbit, until the cities and megalopolises of the present became the norm for the human condition worldwide.
The UHI generated by these very large concentrations of human activity are not a new phenomenon; one of the works reviewed below mentions, in passing, that the phrase ‘heat island’ was first applied to city-generated heat by Howard in 1820. The spread of the Industrial Revolution to all inhabited quarters of the globe spread what was once an isolated phenomenon affecting only a handful of the largest Euro-American centers of manufacturing and commerce. Thus, the micro-climatic effects of fossil fuel-generated human action, in the form of manufacturing, motorized transportation, heating, ventilation and air conditioning (HVAC) and other energy-intensive activity is effectively akin to a slow-motion forest fire whose fuel is the primeval forest, swamp and jungle. There is some small irony in the notion that present-day trees may help us cope with the heat generated by burning the remains of their distant ancestors. (The impermeable concrete-and-asphalt surfaces that dominate at ground-level in cities also plays a significant part in generating UHI, but as those paved surfaces exist to facilitate human activity that involves the burning of fossil fuels, the point is still relevant in their regard.)
The research whose topic this paper has helped the author to refine his understanding, in lay terms, of the UHI effect and what tree canopies do to mitigate it. The articles also exposed him to a more technical view of the phenomena, data collection and analysis, considerations in modeling and research design, and to a more global view of the issue. In this latter regard, the material herein reviewed includes research from scientists in Nigeria, India, China, Australia, Canada and the United States, giving a global perspective that encompasses both newly-risen metropolises and older, first-wave industrial centers. It is his hope to apply this learning in ‘real-world’ situations as a local policy-maker in a small industrial city in mid-Michigan, and in other walks of life, as he has occasion to do so.

Decheng Zhou, Shuqing Zhao, Shuguang Liu, Liangxia Zhang, Chao Zhu
Surface urban heat island in China’s 32 major cities: Spatial patterns
and drivers. Remote Sensing of Environment, 152 (2014) 51-61.

This article summarized research pertaining to diurnal and seasonal variations in urban-suburban heat island intensity for 32 of China’s major cities using Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) ‘version 5’ data from 2003 and 2011. The authors examined two types of UHI: Surface and atmospheric. While the former are most experientially important to city dwellers, the latter has a greater impact on climate change at the macro level. The authors’ goals included understanding the phenomena so as to better equip other researchers and decision-makers to act to mitigate climate change as part of a broader strategy.

China was a good subject for this study as it offers extensive climate and topographic variety, from the subtropical, monsoon-influenced southeast to the arid highlands of the northwest and the cold continental, mountainous northeast whose border touches Siberian Russia. While some of China’s cities used in this study are of ancient origin, industrialization makes nearly all of them newcomers to the UHI phenomenon.

Land surface temperature (LST) information obtained via MODIS products, including the enhanced vegetation index (EVI), the bi-directional reflectance distance function (BRDF) and digital elevation model (DEM) data. For the purpose of this study, the above-referenced and other data used was analyzes with reference to the full spectrum of Chinese urban spatial patterning, referred to in this paper as the ‘Suburban Urban Heat Index’ or SUHI. Additional data used included meteorological records for each study city and region, and remotely-sensed night-time light signals, the latter being used as a proxy for anthropogenic heat release.

The comparison of SUHI to rural temperature data was done using ‘built-up intensity’ (BI) to differentiate between each 1 kilometer square pixel as to whether they were urban/suburban or rural in nature. Metropolitan boundaries were thus established for LST comparison purposes.

The researchers found a “significant and positive” relationship between night-time light signals and SUHI intensity (SUHII). There was a negative correlation between night-time SUHII and EVI during summer, a finding that supports the mitigating effect of vegetation on UHI. As the researchers expected, there was also a negative and strong correlation between daytime SUHII and EVI values; the ability of plants to mitigate heat effects through transpiration, which man-made surface materials generally cannot do, is the likeliest explanation for these findings. Negative heat island effect observed in northerly winter climates may be related to pollution, which peaks in winter due to coal burning for heat and in factor not corrected for in MODIS imagery used in this study.

Alberdo, or surface reflectivity (‘whiteness’), was expected to be significant as a SUHI driver in areas where extensive urban activity had taken place. The alberdo effect was expected to correlate with SUHI because greater heat storage during daytime is a known by-product of most surface building materials. This was supported by the researchers’ findings, as in wintertime alberdo being more pronounced in northern latitudes, where broad-leaf trees predominate, in contrast to more southerly study cities, which are dominated by evergreen vegetation.

A number of other factors also got examined in this study, such as climate and what effect ‘”hot-wet” soils has on SUHII versus “cold-dry,” the effects of topography and elevation and how all of the studies phenomena affect each other in combination. For the purpose of this study, however, the primary point of interest was the support given to the belief that tree canopies can mitigate UHI by preventing solar radiation from reaching the surface. Of secondary interest, this study was not the only one to mention increased violence as being a side-effect of UHI; cooler surface temperatures may lead to cooler heads and thus to less crime.

In summary, urban heat islands and vegetation positive correlation and summer versus winter variance and diurnal changes. Seasonal variations in canopy changes correlate with SUHII intensity. Evergreen canopy provides better all-year moderation of heat island phenomena, though broad-leaf trees offer a greater effect in summer.

Wei Huang, Yongnian Zeng & Songnian Li (2014): An analysis of urban expansion and its associated thermal characteristics using Landsat imagery, Geocarto International,
DOI:10.1080/10106049.2014.965756

These researchers had an interest in mapping land use-land cover (LULC) to monitor thermal phenomena in Changsha, China. Landsat TM/ETM+ imagery was used to assess Changsha’s urban growth and thermal characteristics associated with that activity. They focused on quantitative analysis and detection of urban build-up and density and their relationship to LST. They state that the extent of man-made impervious surface area correlates with urban intensity. Their interest was in going beyond comparing LULC by type with associated thermal characteristics and in looking at LST and how it correlates with impervious surface areas. The correlation between the normalized difference vegetation index (NDVI) and LST – a negative correlation in some of the writings reviewed by the researchers prior to embarking on their study – is worth noting, as it would support the idea that urban flora can help reduce the intensity of UHI.

The researchers proposed two new ways of looking at urban expansion; one involves approaching land use and density by way of an expansion metric calculus, while the other proposes an approach to analyzing data on urban thermal patterns and land use extent that builds on their land density/expansion calculus.

Landsat TM imagery from August 25, 1993, near the start of Changsha’s rapid urbanization and Landsat ETM+ imagery from September 24, 2001, both data sets being rectified to Transverse Mercator 1:5,000 topographic scale with a pixel size of 30 meters. Rectification to a RMSE of less than 0.5 pixels was achieved for all imagery, and EDRAS Imagine software was used to accomplish this.

The results of their study indicated that as Changsha expanded, replacing forest and field with impervious surfaces or bare soil, LST increased by an average of 4 degrees Celsius. The standard deviation of LST was relatively high in built-up areas compared to those still in a natural state. The researchers stated that their approach validates the use to LST change as a de facto proxy for urban expansion and density. (The lower standard deviation observed for
forest-land LST also supports the idea that tree/plant cover has a smoothing and mitigating effect on surface temperature.)

Ping Zhang, Marc L. Imhoff, Robert E. Wolfe & Lahouari Bounoua (2010) Characterizing urban
heat islands of global settlements using MODIS and nighttime lights products, Canadian
Journal of Remote Sensing: Journal canadien detection, 36:3, 185-196,
DOI:10.5589/m10-039

MODIS imagery and night-time lights were the study-focus of this UHI research. MODIS data collected annually from 2003 to 2005 was used to examine LST amplitude and UHI intensity for more than 3,000 urban settlements worldwide.

The researchers’ methodology centered on comparing Landsat ETM+ imagery and Nightlight data to define and describe impervious surface area (ISA) characteristics for use in defining which inhabited spaces meet the threshold definition of ‘urban’ at size (<10 kilometers) and density. The Landsat and Nightlight ISA products tracked well with each other, showing only scattered cases of significant variance when applied to urban settlements in the United States.
The Nightlight ISA data was also compared to MODIS land surface temperature (LST) information in various pairings of biomes and classes of ISAs. Shuttle Radar Mission Topography-30 (SMRTM-30) data was used to help control for elevation-related temperature variations. Biomes were grouped into four types – forest, grassland, arid and semi-arid/Mediterranean. Settlements in biomes such as alpine, tundra, rock and ice were excluded from the analysis. (This made sense to this writer, as these biomes represent extremes and would likely produce outlier results. One might think, for example, that Reykjavik, situated as it is in a biome characterized by volcanic rock and active processes of volcanism, would have a significantly different heat signature that other, similar Scandinavian cities, as the natural environment produces more heat spontaneously than human activity likely does, and thus ought to be a candidate for exclusion from this type of research.)

The researchers found that UHI intensity was negatively correlated with NDVI following comparison of urban and non-urban imagery while global UHI varied, depending on the season and city size, between 2.5 and 4.7 degrees. Variations in UHI intensity within this range correlated with settlement size, latitude and surrounding biome. As vegetation regulates the water balance in the atmosphere and on the surface, forest biomes have a larger temperature moderation impact than is the case with grasslands or biomes characterized be vegetative scarcity. This would support the idea that trees act as temperature regulators, although the research focus here was on UHI rather than on mitigation strategies.

Sharifi, E., & Lehmann, S. (2014). Comparative analysis of surface urban heat island effect in
central sydney. Journal of Sustainable Development, 7(3), 23-34. Retrieved from
http://ezproxy.emich.edu/login?url=http://search.proquest.com/docview/1535271630?accountid=10650

The surface layer UHI (sUHI) phenomenon in Sydney, Australia was the focus of this research effort, which drilled down to the precinct level to measure sUHI intensity and how streetscapes, horizontal surface (streets and rooftops) types affect it. The researchers define three layers of urban micro-climate affected by UHI: The surface layer, the canopy layer, which reaches to treetop and is also described as the human scale, and the boundary layer, which may extend to 1,500 meters above ground. They also consider urban geometry and how it affects sun and shadow, wind patterns, and the interplay of these with levels of heat storage.

sUHI levels in Sydney commonly reach 4 degrees Celsius above that found in the surrounding countryside. Given that Sydney is situated in a sub-tropical environment, daytime temperatures that would normally be regarded as warm become uncomfortable for outdoor human activity, driving people indoors, where they resort to air conditioning, which along with motorized transportation, is one of two identified major drivers of energy usage leading to sUHI. Thus, a vicious cycle exists: Warm temperatures make people more prone to stay indoors; as they do so, they make increased use of air conditioning; energy usage in support of air conditioning demand leads to sUHI; sUHI makes the urban outdoors warmer, driving more people to stay indoors more often, and so forth. Sydney also lies in a humid zone, which leads to greater night-time heat retention, experienced as warmer nights and mornings, than would be the case with a similar city at that latitude in an arid biome.

More-frequent heat waves in recent years, with the most recent including a record-setting temperature of 46 degrees Celsuis (approximately 115 degrees Fahrenheit) have led Sydney’s civic leaders to have sUHI studies using remote sensing to, for example, look at individual building thermal performance index (BTPI) measurements to determine envelope thermal behavior.

This research expanded the area of focus from individual buildings to the precinct level. Ariel thermal photography taken on February 4, 2009 (summertime in the southern hemisphere) by Digital mapping Australia with a resolution of 8 meters was examined in conjunction with Google Earth imagery from the same date to identify open spaces, greenery, spatial dimension and plot ratio.

Ten precincts were temperature-mapped, and while the variance from average was only 0.69 Celsius, there were over 2,000 individual data points used to calculate those precinct averages, and the variation among them on an individual basis was by as much as 5 degrees Celsius, from lowest to highest. These data points were taken from individual buildings and streets, so a very detailed picture of Sydney’s urban micro-climate emerges.

Residential density, something seen as desirable by the New Urbanist movement, has a direct correlation with sUHI, as evidenced in the literature the researchers reviewed, as well as in their own work: The hottest precincts are the ones with the greatest unit-to-acre density, over 100 per, in each of the precincts in question. The waste heat generated by such high concentrations is a factor in sUHI, but the researchers cautioned that some of these districts are magnets for day-time and/or night-time visitors, and that the large volume of commuters, visitors, shoppers, tourists, et cetera, render a direct correlation between per-acre residential unit density and sUHI more tenuous than might appear to be the case at first glance.

The researchers concluded that alberdo and other factors affecting sUHI intensity are less of a factor at rooftop level than at street-level, despite the greater amount of sunlight absorbed by rooftops as compared with surfaces at the bottom of street canyons. (Perhaps the greater air movement at above-canopy heights mitigates the heat storage activity of taller buildings, which are like small islands above the common height of surrounding structures.) As with other research, the conclusion here is that a positive correlation exists between open, hard-surface urban space and sUHI.

Greenscapes were examined using the Urban Greenery Plot Ratio (UGPR) for five Sydney precincts. The researchers found that there was, with considerable variation dependent on other factors, a relationship between surface temperatures in the studied precincts and UGPR. In high-0density precincts, urban greenery can have a greater positive effect on sUHI. The researchers recommend adding more greenery in high-density area while reducing hard-surface open space in other precincts, along with “a fine distribution of greenery’ to mitigate sUHI at the precinct level.

Gober, P., Brazel, A., Quay, R., Myint, S., Grossman-Clarke, S., Miller, A., & Rossi, S. (2010).
Using watered landscapes to manipulate urban heat island effects. American Planning
Association.Journal of the American Planning Association, 76(1), 109-121. Retrieved fromhttp://ezproxy.emich.edu/login?url=http://search.proquest.com/docview/229628748?accountid=10650

Using watered landscapes to cool Phoenix, AZ is the subject of the research summarized in the above-referenced article. The researchers used a Local-Scale Urban Meteorological Parameterization Scheme (LUMPS) to examine temperature and evaporation data in 10 Census tracts in Phoenix’s urban core. The focus was on what effect irrigated landscaping has on night-time temperatures.

Phoenix has experienced a summer night-time UHI effect of as much as 6 degrees Celsius, which would correlate with the projections of the most-pessimistic climate models. The research encompassed using city water-usage records as a proxy for outdoor landscape watering and examining Quickbird satellite imagery to validate the relative presence or absence of vegetation in the study tracts.

Like Sydney is the previous study, Phoenix has summer temperatures that are often incompatible with outdoor activity. This article states that summer temperatures normally remain above 100 degrees Fahrenheit until after 8:00 or 9:00 p.m.

Quickbird 4-band imagery was obtained and used to examine four parcel types – commercial, industrial, residential and open/undeveloped land. LUMPS data on water evaporation and city water records also factored into the analysis. A standard household indoor-use average of just over 5,000 gallons per month was used as a benchmark; all usage above this amount was assumed to be for outdoor watering. Ten percent of restaurant and hotel and other commercial water use was also assumed to be for landscaping, while 25% of car wash usage was assumed to have been consumed outdoors. LUMPS outputs closely correlated with City of Phoenix water usage records, validating its use to distinguish high water-use from low water-use tracts.

Three scenarios were run using the data available. One assumed water-use restrictions (a real possibility, and an actuality in neighboring California) with a resulting increase in un-watered open space, while another posited a more compact, dense city, and the remaining one posited a 20% increase in green-scape coverage.

The added green-scape scenario would produce “a true oasis in the desert,” with substantial reduction in night-time temperatures. The high-density scenario is a water-conservation model for a city where two-thirds of household water consumption is for outdoor use. (Outdoor water use rises with income, and one can easily imagine that wealthier households are better able to afford larger lots and to care for trees and lawns.)

A more compact urban-scape would result in less per-capita water use and would feature less open-space. This scenario produces a cooling effect by reducing flat, impervious surfaces even as it reduces the total amount of green-scape. The night-time cooling effect is less than in the first scenario but is still significant enough to be beneficial.

The final scenario, one where the city’s form does not change but where water usage is curtailed in response to prolonged drought or water shortage, produces a “desert city.” Residential areas would not experience much difference in night-time cooling, a finding consistent with research on outdoor garden watering reductions of up to 50% in Fresno, CA. Industrial and open/undeveloped areas would suffer the most from UHI if this were to come to pass.

The conclusions of this study were, in addition to what is stated above, that there is a threshold of diminishing marginal utility where additional green-scape (and the water use to support it) does not produce a corresponding reduction in night-time temperatures. Careful city planning can allow for the maximum amount of useful green-scape while also acting to curb excess planting and watering that would be a poor use of limited resources that have alternate uses. Another conclusion was that compactness is, in itself, not a major contributor to UHI when the additional density replaces flat, impervious surfaces or bare, undeveloped land, as the latter two surface types absorb daytime solar energy and release it at night. Managing open spaces of both varieties is this important from an urban planning perspective, and replacing parking lots with tall buildings may actually reduce UHI effects, irrespective of the presence or absence of greenery.

Weng, Qihao, lu, Dengsheng, Schubring, Jackelyn. Estimation of land surface temperature–
vegetation abundance relationship for urban heat island studies. Remote sensing of
environment, 89 (2004), 467-483.

This study looked at an alternative to the NDVI as an indicator of vegetative abundance when looking for a relationship to LST in examining UHI intensity. The researchers proposed instead to use vegetation fraction derived from a spectral mixture model. The imagery was derived from Landsat 7 ETM+ (Row/Path 31/21), and is of Indianapolis, IN on June 22, 2003. The imagery was un-mixed into three sub-types: Green vegetation, dry soil and shaded.

The researchers employed ‘linear spectral mixture analysis’ (LMSA), which “assumes that the spectrum measured by a sensor is a linear combination of the spectra of all components within the pixel” (p. 471). The results obtained by a constrained least-squares regression analysis represented temperature values that were referenced to a blackbody. These values were used to help construct a LULC map. The LULC characteristics, when compared with the temperature values previously mentioned, helped identify which LULC types were associated with localized UHI phenomena.

The researchers found a correlation between the percentage of land devoted to urban use and temperature homogeneity. Once urban or built-up use becomes the majority of a given area, temperatures become homogenized. They additionally found that temperature correlation coefficients were observable at 120 meter resolutions, which appeared to them to be the scale at which localized temperature variations exist at the operational scale. (This author takes that to mean that local temperature variations are measurable at this level, but perhaps not consistently and at significant levels at smaller-scale resolutions.)

The researchers concluded that vegetative abundance was one of the most important factors in controlling LST by “partitioning solar radiation into fluxes of sensible and latent heat and by limiting the proportion of vegetation and ground within the sensors instantaneous-field-of-view (IFOV)” (p. 480). They further concluded that there is a correlation between NDVI, LST and vegetative fraction imagery when these three are used to assess the thermal properties of an area under study. Finally, they noted that issues like sensor-to-target noise, atmospheric conditions, and both the scale and physical character of the terrain all contribute to the relative difficulty of remote sensing research into UHI phenomena.

Xiao-Ling Chen, Hong-Mei Zhao, Ping-Xiang Li, Zhi-Yong Yin. (2006.)
Remote sensing image-based analysis of the relationship between
urban heat island and land use/cover changes. Remote Sensing of Environment 104 (2006) 133–146

Urban Heat Island research on the rapidly-urbanizing Pearl River Delta (PRD) in southern China was the focus of Chen, et al. (This district includes Shenzhen, one of the 32 Chinese urban areas studied by Zhou, et al, in research previously reviewed in this paper.)

Quantitative measurements of surface temperature were attempted using Landsat-5 imagery from 1990, 1994, 1996 and 1998. These were used in conjunction with IKONOS 2000 imagery (4 meter spatial resolution) rectified to UTM and a 1:200,000-scale topographic map of Shenzhen to obtain LULC classification with a high degree of spatial resolution. (This region of China is rapidly urbanizing, with Shenzhen, for example, having grown from a little-known fishing village in 1978, at the start of China’s economic liberalization under Chairman Deng to a city of several million today. This makes the region ideal for examining the effects of rapid urbanization on LST over a fairly short time-frame.)

After image pre-processing the researchers looked for relationships between NDVI, ‘Normalized Difference Water Index’ (NDWI’ – a vegetation water-content measurement) and ‘Normalized Difference Built Index’ to see if correlations existed between study areas by LULC classification and the afore-mentioned indices.

The researchers found that their NDVI/NDWI/NDBI classification methods agreed 92 percent of the time with imagery analysis identification of LULC for the study areas. They also noted that there were seasonal influences on UHI intensity. Seasonal factors also affected on their data o when they were dealing with agricultural land (unsurprising when one considers that crops may not achieve full coverage until late spring and may be harvested by mid-autumn).

The islands of UHI present in 1990 have, over time, merged into one continuous phenomenon as urbanization has become the dominant land-use type in the study area. One surprising finding was that UHI intensity was greater in 1990 than in 1994 and 1996. This may, in the researchers’ estimation, be due to other land use changes that may have offset the expanding urbanization of that period, perhaps having to do with different crop types being cultivated.

With a considerable amount of water surface area in the PRD region, researchers were able to use temperature readings over these areas as a base-line against which to measure changes in the differences between those regions and the adjoining land over time. The researchers constructed a model depicting a theoretical baseline scenario without urbanization to use in calculating the contribution of each land-use type to the overall temperature, which they set at 23 degrees Celsius as a normalized average for the region.

Their findings were that built-up and bare/semi-bare land were the major contributors to the rise in UHI levels over the full time-frame of the study. Water-surface areas exercised a smaller overall share in determining regional temperatures over the life of the study. The researchers believe that, if the patterns they uncovered continue for a decade beyond 2000 (the concluding year of the study) that an additional 1 degree Celsius would be added to regional temperatures as a result of UHI effects.

Within the city of Shenzhen, the researchers found that paved areas had the highest recoded temperatures. Recreational areas, grassland (mainly found in residential areas) and industrial areas (where higher levels of impervious surfaces are often found) also recorded above-average temperatures. Water-surfaces and forest/park areas had the lowest temperatures, leading the researchers to conclude that UHI can be moderated by careful urban planning.

Additional research reviewed for this paper which did not directly deal with remote sensing but that are worth noting in passing include a study of biomass calculation (Nilanchal, Patel, Nilanchal and Majumdar, Arnab, 2001) that provides useful guidance on estimating the amount of tree crown present for both individual specimens and for areal analysis. The relevance of this work to the present study lies in adding some technical detail (the relative accuracy of sampling leaf debris on the ground to estimate crown size versus estimating it from ground observation) that may be of use in researching how much tree canopy provides optimal UHI mitigation in various urban settings. Knowing how to accurately estimate tree crown volume for existing plantings, when analyzed in conjunction with remote sensing imagery showing LST would aid in planning for the optimum number of trees per unit of analysis.

A study of heat mitigation in Enugu City, Nigeria (Enete, I.C,1 Alabi, M.O & Chukwudelunzu, V.U, Awka., Anyimgba, 2012) did not look at remote sensing data, but did provide information on LST readings taken in sun and shade. The authors also observed that there was less crime in cooler, shaded neighborhoods than in areas lacking tree cover, an important observation that has value for fields outside those specifically dealing with UHI from an environmental control standpoint. One interesting finding of their research was that shading parking areas led to reduced fuel evaporation from automobile gas tanks by about 2% on hot days, which had a positive impact on pollution/smog formation, as well as offering a reduction in fuel consumption.

Certain tree species performed better than others in providing usable shade that yielded significant relief for humans on a ‘comfort index.’ The unsurprising finding was that species with large, broadleaf crowns, such as the Catalpa, provided more shade value than did coconut or pine trees. The amount of benefit derived ranged widely, but one might say that the researchers literally validated the old saying that, ‘It’s 10 degrees cooler in the shade.’

Buildings incorporating ‘greenroofs’ as a UHI mitigation strategy was the subject of research (Jong Soo Lee, Jeong Tai Kim and Myung Gi Lee., 2013) that examined two strategies for accomplishing this. The researchers found that ‘greenroofs’ incorporating a thin bed of low-maintenance plants produced internal cooling benefits, while a more intensive variety that involves planting trees in a deep-bed system also produced cooling benefits. Vine-clad walls also offered some benefit, in terms of temperature relief.

In summary, ‘Urban Heat Islands’ (UHI) are an issue of growing importance in an era of rapid urbanization worldwide, and will continue to be of concern for the foreseeable future. Remote sensing of UHI phenomena is an important tool for measuring the extent of the problem, defining the boundaries of affected areas and for measuring UHI intensity where it exists in different seasons and at varying levels of urban density.

The research reviewed above dealt with several issues in UHI research, but the focus of this study was to ascertain what value trees has in mitigating UHI. A number of research articles reviewed supported the idea that trees can help to mitigate UHI by providing shade and absorbing solar radiation, which leads to less day-time heat retention and subsequent night-time radiation. Other factors come into play, such as the amount of pavement and other impermeable surfaces, the configuration of built-up structures and how they interact with wind, urban density, and relative humidity among them, but the significance of trees as counterbalance against the effects of UHI is well-documented.

Different species of trees provide varying amounts of shade, and thus relief of surface UHI effects. Trees that have large crowns and are broad-leaf are the best providers of shade. Other vegetation – wall vines and ‘greenroof’ plants – can also help reduce UHI. That vegetation can help regulate temperature should not be surprising, given that satellite imagery of thermal activity shows that bare ground shows greater diurnal variation than does grassland, and the latter performs less well in this regard than does woodland. The common sense notion that it is cooler in the shade, when writ large over modern urban-scape as a matter of policy, can lead to significant UHI mitigation, along with all of the secondary benefits that go with it: Greater human outdoor comfort, less crime and reduced fuel evaporation among them.

Master Bibliography

Chen, Xiao-Ling, Zhao, Hong-Mei, Li, Ping-Xiang, Ping-Xiang, Yin. (2006.)
Remote sensing image-based analysis of the relationship between
urban heat island and land use/cover changes. Remote Sensing of Environment 104 (2006) 133–146

Enete, I.C,1 Alabi, M.O & Chukwudelunzu, V.U, Awka., Anyimgba. (2012.) Tree canopy cover
variation effects on urban heat island in Enugu City, Nigeria. Developing Country Studies http://www.iiste.org ISSN 2224-607X (Paper) ISSN 2225-0565 (Online)
Vol 2, No.6, 2012

Gober, P., Brazel, A., Quay, R., Myint, S., Grossman-Clarke, S., Miller, A., & Rossi, S. (2010.)
Using watered landscapes to manipulate urban heat island effects. American Planning
Association.Journal of the American Planning Association, 76(1), 109-121. Retrieved fromhttp://ezproxy.emich.edu/login?url=http://search.proquest.com/docview/229628748?accountid=10650

Jong Soo Lee, Jeong Tai Kim and Myung Gi Lee. (2013.) Mitigation of urban heat island effect and greenroofs. Indoor and Built Environment 2014 23: 62 originally published online 29 January 2013

Nilanchal Patel, Nilanchal and Majumdar, Arnab (2001.) Comparative Assessment of the
Relationship of Satellite Data with the Above Ground Biomass of
Sal Trees [Shorea robusta] Determined from Phenologically Different Time Periods
Geo-spatial Information Science 14(3):177-183 Volume 14, Issue 3
DOI 10.1007/s11806-011-0492-1

Ping Zhang, Marc L. Imhoff, Robert E. Wolfe & Lahouari Bounoua (2010.) Characterizing urban heat islands of global settlements using MODIS and nighttime lights products, Canadian Journal of Remote Sensing: Journal canadien detection, 36:3, 185-196,
DOI:10.5589/m10-039

Sharifi, E., & Lehmann, S. (2014). Comparative analysis of surface urban heat island effect in
central sydney. Journal of Sustainable Development, 7(3), 23-34. Retrieved from
http://ezproxy.emich.edu/login?url=http://search.proquest.com/docview/1535271630?accountid=10650

Wei Huang, Yongnian Zeng & Songnian Li (2014): An analysis of urban expansion and its associated thermal characteristics using Landsat imagery, Geocarto International,
DOI:10.1080/10106049.2014.965756

Weng, Qihao, lu, Dengsheng, Schubring, Jackelyn. (2004.) Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote sensing of environment, 89 (2004), 467-483.

Zhou, Decheng, Zhao, Shuqing Liu, Shuguang, Zhang, Liangxia, Zhu, Chao. (2014.)
Surface urban heat island in China’s 32 major cities: Spatial patterns
and drivers. Remote Sensing of Environment, 152 (2014) 51-61.

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