Modeling the effective emissivity of the urban canopy using sky view factor

Yang, Jinxin; Wong, Man Sing; Menenti, Massimo; Nichol, Janet Elizabeth

doi:10.1016/j.isprsjprs.2015.04.006

Cited by 74 publications

(36 citation statements)

References 28 publications

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“…However, some challenging issues exist with UHI studies by remote sensing data. The retrieval of LSE in urban areas is still unresolved because of the variation of LSE with building materials, urban features and geometry, although various methods have been developed to retrieve LSE in urban areas, for example sub-pixel estimation [54], the classification-based method [55], the urban emissivity library [56] and estimating effective emissivity using the sky view factor [57]. These methods were able to solve the mentioned problem partially.…”

Section: Discussionmentioning

confidence: 99%

Seasonal Variations of the Surface Urban Heat Island in a Semi-Arid City

et al. 2016

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Abstract:The process of the surface urban heat island (SUHI) varies with latitude, climate, topography and meteorological conditions. This study investigated the seasonal variability of SUHI in the Tehran metropolitan area, Iran, with respect to selected surface biophysical variables. Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) was retrieved as nighttime LST data, while daytime LST was retrieved from Landsat 8 Thermal Infrared Sensor (TIRS) using the split-window algorithm. Both data covered the time period from September 2013 to September 2015. To assess SUHI intensity, we employed three SUHI indicators, i.e., the LST difference of urban-rural, that of urban-agriculture and that of urban-water. Physical and biophysical surface variables, including land use and land cover (LULC), elevation, impervious surface (IS), fractional vegetation cover (FVC) and albedo, were selected to estimate the relationship between LST seasonal variability and the surface properties. Results show that an inversion of the SUHI phenomenon (i.e., surface urban cool island) existed at daytime with the maximal value of urban-rural LST difference of´4 K in March; whereas the maximal value of SUHI at nighttime yielded 3.9 K in May. When using the indicators of urban-agriculture and urban-water LST differences, the maximal value of SUHI was found to be 8.2 K and 15.5 K, respectively. Both results were observed at daytime, suggesting the role of bare soils in the inversion of the SUHI phenomenon with the urban-rural indicator. Maximal correlation was observed in the relationship between night LST and elevation in spring (coefficient:´0.76), night LST and IS in spring (0.60), night LST and albedo in winter (´0.53) and day LST with fractional vegetation cover in summer (´0.41). The relationship between all surface properties with LST possessed large seasonal variations, and thus, using these relationships for SUHI modeling may not be effective. The only exception existed in the correlation between elevation and IS, which may be useful to simulate the SUHI at night. This study suggests that in semi-arid cities, such as Tehran, with the urban-rural indicator, a surface urban cool island may be observed in daytime while SUHI at nighttime; with other indicators, SUHI can be observed in both day and night. Thus, SUHI studies require the acquisition of remote sensing image data at both daytime and nighttime and careful selection of SUHI indicators.

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Section: Discussionmentioning

confidence: 99%

Seasonal Variations of the Surface Urban Heat Island in a Semi-Arid City

et al. 2016

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“…As demonstrated by previous studies, a variation of 1% in emissivity can produce a variation in temperature of about 0.3°C [9], which is generally higher than the one related to the SVF itself. Furthermore, if the effects of multiple reflections among the complex surfaces of an urban environment are considered, the effective emissivity may differ from the emissivity of the material, and a correlation between SVF and effective emissivity can be found [34]. Thus, emissivity is likely to be the major source of uncertainty in surface temperature mapping, especially considering the large variability of materials occurring in an urban environment and the variability of emissivity values even for the same material, due to aging and weathering [23,35].…”

Section: Discussionmentioning

confidence: 99%

Integration of Aerial Thermal Imagery, LiDAR Data and Ground Surveys for Surface Temperature Mapping in Urban Environments

Mandanici¹,

Conte²,

Girelli³

2016

Remote Sensing

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A single-band surface temperature retrieval method is proposed, aiming at achieving a better accuracy by exploiting the integration of aerial thermal images with LiDAR data and ground surveys. LiDAR data allow the generation of a high resolution digital surface model and a detailed modeling of the Sky-View Factor (SVF). Ground surveys of surface temperature and emissivity, instead, are used to estimate the atmospheric parameters involved in the model (through a bounded least square adjustment) and for a first assessment of the accuracy of the results. The RMS of the difference between the surface temperatures computed from the model and measured on the check sites ranges between 0.8°C and 1.0°C, depending on the algorithm used to calculate the SVF. Results are in general better than the ones obtained without considering SVF and prove the effectiveness of the integration of different data sources. The proposed approach has the advantage of avoiding the modeling of the atmosphere conditions, which is often difficult to achieve with the desired accuracy; on the other hand, it is highly dependent on the accuracy of the data measured on the ground.

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“…Sky obstruction can substantially reduce the amount of solar radiation reaching the ground [10], and hence SVF serves an important role in solar radiation modeling [11,12]. It was found that SVF was significantly correlated with surface emissivity in urban canyons [13]. Hence, the estimation accuracy of surface emissivity [14] and surface temperature [15] could be improved by is used for sky delineation.…”

Section: Introductionmentioning

confidence: 99%

Automatic Sky View Factor Estimation from Street View Photographs—A Big Data Approach

et al. 2017

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Hemispherical (fisheye) photography is a well-established approach for estimating the sky view factor (SVF). High-resolution urban models from LiDAR and oblique airborne photogrammetry can provide continuous SVF estimates over a large urban area, but such data are not always available and are difficult to acquire. Street view panoramas have become widely available in urban areas worldwide: Google Street View (GSV) maintains a global network of panoramas excluding China and several other countries; Baidu Street View (BSV) and Tencent Street View (TSV) focus their panorama acquisition efforts within China, and have covered hundreds of cities therein. In this paper, we approach this issue from a big data perspective by presenting and validating a method for automatic estimation of SVF from massive amounts of street view photographs. Comparisons were made with SVF estimates derived from two independent sources: a LiDAR-based Digital Surface Model (DSM) and an oblique airborne photogrammetry-based 3D city model (OAP3D), resulting in a correlation coefficient of 0.863 and 0.987, respectively. The comparisons demonstrated the capacity of the proposed method to provide reliable SVF estimates. Additionally, we present an application of the proposed method with about 12,000 GSV panoramas to characterize the spatial distribution of SVF over Manhattan Island in New York City. Although this is a proof-of-concept study, it has shown the potential of the proposed approach to assist urban climate and urban planning research. However, further development is needed before this approach can be finally delivered to the urban climate and urban planning communities for practical applications.

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Modeling the effective emissivity of the urban canopy using sky view factor

Cited by 74 publications

References 28 publications

Seasonal Variations of the Surface Urban Heat Island in a Semi-Arid City

Seasonal Variations of the Surface Urban Heat Island in a Semi-Arid City

Integration of Aerial Thermal Imagery, LiDAR Data and Ground Surveys for Surface Temperature Mapping in Urban Environments

Automatic Sky View Factor Estimation from Street View Photographs—A Big Data Approach

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