Does urbanization increase diurnal land surface temperature variation? Evidence and implications

Chen, Yen‐Ching; Chiu, Hao-Wei; Su, Yuan; Wu, Yii-Chen; Cheng, Kit‐Yan

doi:10.1016/j.landurbplan.2016.06.014

Cited by 120 publications

(57 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…An understanding of LST is important for urban climatology, global environmental change and human-environment interactions [39,40] and the Intergovernmental Panel on Climate Change (IPCC) has pointed out the urgent need for the inclusion of long-term remote sensing-based LST data in global warming studies [37]. LST changes rapidly in space as well as in time [41,42] and it has been found that urbanization increases urban diurnal land surface temperature variation [43,44]; thus, an adequate characterization of LST spatial and temporal distribution requires measurements with detailed spatial and temporal resolution. Understanding the linkage between LST and urban surface characteristics is important for designing effective measures to mitigate the amplitude of SUHI [45].…”

mentioning

confidence: 99%

Quantifying the Trends in Land Surface Temperature and Surface Urban Heat Island Intensity in Mediterranean Cities in View of Smart Urbanization

2018

View full text Add to dashboard Cite

Land Surface Temperature (LST) is a key parameter for the estimation of urban fluxes as well as for the assessment of the presence and strength of the surface urban heat island (SUHI). In an urban environment, LST depends on the way the city has been planned and developed over time. To this end, the estimation of LST needs adequate spatial and temporal data at the urban scale, especially with respect to land cover/land use. The present study is divided in two parts: at first, satellite data from MODIS-Terra 8-day product (MOD11A2) were used for the analysis of an eighteen-year time series (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017) of the LST spatial and temporal distribution in five major cities of the Mediterranean during the summer months. LST trends were retrieved and assessed for their statistical significance. Secondly, LST values and trends for each city were examined in relation to land cover characteristics and patterns in order to define the contribution of urban development and planning on LST; this information is important for the drafting of smart urbanization policies and measures. Results revealed (a) positive LST trends in the urban areas especially during nighttime ranging from +0.412 • K in Marseille to +0.923 • K in Cairo and (b) the SUHI has intensified during the last eighteen years especially during daytime in European Mediterranean cities, such as Rome (+0.332 • K) and Barcelona (+0.307 • K).Surface urban heat island (SUHI), in particular, describes the land surface temperature (LST) differences between urban areas and their surroundings, and it is usually studied with the use of remote sensing data. The formation of SUHI can be mainly attributed to the increased absorption and trapping of solar radiation in urban areas associated with limited release of heat due to the low values of the thermal emission coefficients of manmade materials. Anthropogenic heat release from transport and the heating-cooling systems of the buildings further exacerbate the phenomenon [18][19][20]. Both UHI and SUHI can be detected throughout the year, but they are of particular public policy concern during the summer, because higher surface and air temperatures are associated with increases in electricity demand for air conditioning, air pollution, and heat stress-related mortality and illness [21][22][23][24][25]. Several SUHI studies have been performed in the Mediterranean area, most of them revealing that higher UHI intensities are found in the summer period [13,[26][27][28][29][30].Land surface temperature is a controlling factor for most of the physical, chemical and biological processes on the earth, and can be considered as a measure of climate change [31][32][33][34]. For the urban environment, LST is an important parameter for the monitoring of the energy exchange between the land surface and the atmosphere in terms of the sensible and latent heat fluxes [35][36][37][38] which are important when discussing the thermal effects of the cities on th...

show abstract

mentioning

confidence: 99%

Quantifying the Trends in Land Surface Temperature and Surface Urban Heat Island Intensity in Mediterranean Cities in View of Smart Urbanization

2018

View full text Add to dashboard Cite

show abstract

“…Urban areas are generally warmer than their rural surroundings, the so-called UHI (Lauwaet et al, 2016). The built-up environments contain large areas of impervious surfaces with increased thermal inertia (Chen et al, 2017), while also inhibiting evaporative cooling. This causes cities to experience higher air temperatures than rural areas, with night-time temperature differences up to 10 °C.…”

Section: Heat Waves and Urban Heat Islands (Uhi)mentioning

confidence: 99%

“…As stated by Chen et al (2017), atmospheric UHI are normally measured by in situ air temperature sensors. However, while the Surface UHI (SUHI) refers to the excess warmth of urban areas compared with their non-urbanized surroundings, it can be measured by Land Surface Temperature (LST) levels observed by thermal infrared remote sensing.…”

Section: Urban Heat Islands Delineationmentioning

confidence: 99%

“…The estimation of the thermal inertia of an earth surface object is dependent on two factors: the albedo and diurnal LST variation. Using SPOT and MODIS satellite images captured over a period of 17 years, Chen et al (2017) demonstrated that diurnal LST variation increases with urbanization. These kind of studies can help to define the measures that can reduce urban heat absorption such as urban parks, community green spaces, green roofs and cool or permeable pavements, in order to mitigate UHI effects.…”

Section: Urban Heat Islands Delineationmentioning

confidence: 99%

See 1 more Smart Citation

Exposure and Vulnerability Geospatial Analysis Using Earth Observation Data in the City of Liege, Belgium

Stéphenne

Beaumont

Hallot

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Risk situation can be mitigated by prevention measures, early warning tools and adequate monitoring of past experiences where Earth Observation and geospatial analysis have an adding value. This paper discusses the potential use of Earth Observation data and especially Land Cover / Land Use map in addressing within the three aspects of the risk assessment: danger, exposure and vulnerability. Evidences of the harmful effects of air pollution or heat waves are widely admitted and should increase in the context of global warming. Moreover, urban areas are generally warmer than rural surroundings, the so-called urban heat island. Combined with in-situ measurements, this paper presents models of city or local climate (air pollution and urban heat island), with a resolution of less than one kilometer, developed by integrating several sources of information including Earth Observation data and in particular Land Cover / Land Use. This assessment of the danger is then be related to a map of exposure and vulnerable people. Using dasymetric method to disaggregate statistical information on Land Cover / Land Use data, the SmartPop project analyzes in parallel the map of danger with the maps of people exposure A special focus on some categories at risk such as the elderly has been proposed by . Perspectives of the project includes the integration of a new Land Cover / Land Use map in the danger, exposure and vulnerability models and proposition of several aspects of risk assessment with the stakeholders of Wallonia.

show abstract

“…Remote sensing images have been widely used for earth surface monitoring [1][2][3][4][5][6][7][8], environmental change detection [9][10][11][12][13][14], and water resource management [15][16][17][18][19][20][21][22]. Many of these applications require land-use/land-cover (LULC) classifications derived from multispectral images.…”

Section: Introductionmentioning

confidence: 99%

Assessing Uncertainty in LULC Classification Accuracy by Using Bootstrap Resampling

Hsiao¹,

Cheng

2016

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Supervised land-use/land-cover (LULC) classifications are typically conducted using class assignment rules derived from a set of multiclass training samples. Consequently, classification accuracy varies with the training data set and is thus associated with uncertainty. In this study, we propose a bootstrap resampling and reclassification approach that can be applied for assessing not only the uncertainty in classification results of the bootstrap-training data sets, but also the classification uncertainty of individual pixels in the study area. Two measures of pixel-specific classification uncertainty, namely the maximum class probability and Shannon entropy, were derived from the class probability vector of individual pixels and used for the identification of unclassified pixels. Unclassified pixels that are identified using the traditional chi-square threshold technique represent outliers of individual LULC classes, but they are not necessarily associated with higher classification uncertainty. By contrast, unclassified pixels identified using the equal-likelihood technique are associated with higher classification uncertainty and they mostly occur on or near the borders of different land-cover.

show abstract

Does urbanization increase diurnal land surface temperature variation? Evidence and implications

Cited by 120 publications

References 32 publications

Quantifying the Trends in Land Surface Temperature and Surface Urban Heat Island Intensity in Mediterranean Cities in View of Smart Urbanization

Quantifying the Trends in Land Surface Temperature and Surface Urban Heat Island Intensity in Mediterranean Cities in View of Smart Urbanization

Exposure and Vulnerability Geospatial Analysis Using Earth Observation Data in the City of Liege, Belgium

Assessing Uncertainty in LULC Classification Accuracy by Using Bootstrap Resampling

Contact Info

Product

Resources

About