Estimation of the relationship between remotely sensed anthropogenic heat discharge and building energy use

https://doi.org/10.1016/j.isprsjprs.2011.10.007Get rights and content

Abstract

This paper examined the relationship between remotely sensed anthropogenic heat discharge and energy use from residential and commercial buildings across multiple scales in the city of Indianapolis, Indiana, USA. The anthropogenic heat discharge was estimated with a remote sensing-based surface energy balance model, which was parameterized using land cover, land surface temperature, albedo, and meteorological data. The building energy use was estimated using a GIS-based building energy simulation model in conjunction with Department of Energy/Energy Information Administration survey data, the Assessor’s parcel data, GIS floor areas data, and remote sensing-derived building height data. The spatial patterns of anthropogenic heat discharge and energy use from residential and commercial buildings were analyzed and compared. Quantitative relationships were evaluated across multiple scales from pixel aggregation to census block. The results indicate that anthropogenic heat discharge is consistent with building energy use in terms of the spatial pattern, and that building energy use accounts for a significant fraction of anthropogenic heat discharge. The research also implies that the relationship between anthropogenic heat discharge and building energy use is scale-dependent. The simultaneous estimation of anthropogenic heat discharge and building energy use via two independent methods improves the understanding of the surface energy balance in an urban landscape. The anthropogenic heat discharge derived from remote sensing and meteorological data may be able to serve as a spatial distribution proxy for spatially-resolved building energy use, and even for fossil-fuel CO2 emissions if additional factors are considered.

Introduction

The urban heat island (UHI) phenomenon is formed when higher atmospheric and surface temperatures in urbanized areas are observed over the surrounding rural areas (Voogt and Oke, 2003). Based on the surface energy balance theory, UHI is mainly caused by the combination of anthropogenic heat discharge due to energy consumption, increased impervious surface area, and decreased vegetation and water area (Kato and Yamaguchi, 2005). Human induced energy discharge has an important impact on the urban environment in terms of the surface energy balance. Quantification of each heat flux in the energy balance, especially the human induced anthropogenic heat discharge and its spatial pattern, is important to improve the understanding of human impacts on the urban environment, a key issue in global environmental change.

The methods used to estimate anthropogenic heat discharge can be grouped into three major categories: inventory approaches, micrometeorologically-based energy budget closure methods, and building energy modeling approaches (Sailor, 2011). For example, using a inventory approach Lee et al. estimated anthropogenic heat emissions in the Gyeong-In region of Korea in 2002 based on the energy consumption statistics data (Lee et al., 2009). Kato and Yamaguchi separated the contribution of anthropogenic heat discharge and heat radiation due to solar input to the sensible heat flux by using an energy balance method (Kato and Yamaguchi, 2005). Heiple and Sailor (2008) estimated the hourly energy consumption from residential and commercial buildings at 100 m spatial resolution in Houston, Texas using the building energy modeling method. Each of these methods has its strengths and limitations. Estimation of anthropogenic heat emissions using the inventory approach is based on the real energy consumption data at the spatial scale of utility service territories (Ichinose et al., 1999, Klysik, 1996, Lee et al., 2009, Sailor and Lu, 2004, Taha, 1997). It is difficult to quantify the spatial distribution of energy consumption at fine spatial and temporal scales. Spatial and temporal downscaling can only be achieved when additional data such as land use are employed. Energy balance method has been applied to estimate energy fluxes in urban areas from site and neighborhood scale to city scale (Belan et al., 2009, Masson et al., 2002, Oke, 1988, Oke et al., 1999, Pigeon et al., 2007, Pigeon et al., 2008). High spatial resolution anthropogenic heat discharge can be estimated based on micrometeorologically-based energy budget closure method by the combined use of remote sensing and meteorological data (Bastiaanssen et al., 1998a, Bastiaanssen et al., 1998b, French et al., 2005, Kato and Yamaguchi, 2005, Kato et al., 2008, Schmugge et al., 1998, van der Kwast et al., 2009). However, as the estimation of anthropogenic heat discharge is based on the residual of other components in the surface energy balance, each component introduces uncertainties and propagates errors toward the final estimated anthropogenic heat flux. By integrating the geospatial data and simulated temporal pattern of energy consumption for representative buildings, energy consumption in all buildings can be estimated using the building energy modeling method (Heiple and Sailor, 2008, Ichinose et al., 1999, Zhou and Gurney, 2010). However, the representative buildings may not be easily categorized and the use of representative buildings may introduce bias in the estimation of spatial and temporal patterns of energy use for some buildings as these buildings may have different behaviors of energy consumption in terms of magnitude and temporal pattern compared to the representative buildings.

Although anthropogenic heat discharge was studied using different methods in previous studies, there was limited research for cross-examination of the spatial pattern of anthropogenic heat discharge. Even though previous studies have provided insights into the human impacts on the urban environment directly or indirectly, simultaneous estimation of anthropogenic heat discharge using multiple methods will help to reduce the uncertainties in the study of urban heat fluxes and improve the understanding of the role of human activities in the surface energy balance and its contribution to UHI. This study aims to improve the understanding of the urban surface energy balance and clarify the spatial pattern of energy related human activities with the help of remote sensing data, technologies and building energy simulation in a highly urbanized area. Specifically, anthropogenic heat discharge and energy use from residential and commercial buildings were investigated using two different methods in the metropolitan Indianapolis, Indiana region. First, anthropogenic heat discharge was estimated using a surface energy balance method. Then energy use from residential and commercial buildings was calculated using a building energy modeling method. Finally, the relationship between remotely sensed anthropogenic heat discharge and building energy use was examined across multiple scales from pixel aggregations to census block groups.

Section snippets

Study area

In this study, Indianapolis/Marion County, Indiana was chosen as the experiment area (Fig. 1). Indianapolis is the capital of the State of Indiana, and was listed as the 14th largest city in the USA in 2008, with a population of 798,382 (US Census Bureau, 2009). The city has a temperate climate without pronounced wet or dry seasons, but has obvious seasonal changes. The total annual heating degree day value is 5521F, and the cooling degree day value is 1042F (National Weather Service, 2002).

Estimation of anthropogenic heat discharge

In this study, anthropogenic heat discharge was estimated based on a surface energy balance modeling method (Kato and Yamaguchi, 2005, Kato et al., 2008, Oke, 1988, Xu et al., 2008). The method is illustrated in the left panel of Fig. 2. The surface energy balance due to surface properties and anthropogenic heating in the near-surface in an urban area can be expressed as follows:Qf=H+LE+G-Rnwhere Qf is the anthropogenic heat discharge (W m−2), H is the sensible heat (W m−2), LE is the latent heat

Anthropogenic heat discharge

The anthropogenic heat discharge in the study area at noon time on June 16th, 2001 was estimated using the energy balance method. To reduce the uncertainties in the evaluation of anthropogenic heat discharge, especially in less developed areas, ISA data were used to exclude pixels with the ISA less than 25%. The result of anthropogenic heat discharge estimation is shown in Fig. 4. The result shows the spatial pattern and magnitude of anthropogenic heat discharge in the City of Indianapolis in a

Implications

There are still challenges to obtaining accurate estimation of each component of heat flux in the urban energy balance such as the ground heat flux. Accurate estimation of anthropogenic heat discharge will help to better understand the urban energy balance and separate the contribution of anthropogenic heat discharge from that of ground heat flux. By combining with energy use from other sources such as industrial buildings and transportation, the spatially-resolved energy use from residential

Conclusions

In this study, the anthropogenic heat discharge and energy use from residential and commercial buildings were estimated using two independent methods simultaneously in the City of Indianapolis in the summer time. The anthropogenic heat discharge was estimated using a remote sensing method, while the energy use from residential and commercial buildings was estimated with a GIS-based simulation method. The mean anthropogenic heat discharge is 32 W m−2 while the energy use from residential and

References (39)

  • Y. Shuai et al.

    An algorithm for the retrieval of 30-m snow-free albedo from Landsat surface reflectance and MODIS BRDF

    Remote Sensing of Environment

    (2011)
  • H. Taha

    Urban climates and heat islands: albedo, evapotranspiration, and anthropogenic heat

    Energy and Buildings

    (1997)
  • J.A. Voogt et al.

    Thermal remote sensing of urban climates

    Remote Sensing of Environment

    (2003)
  • W. Xu et al.

    Modelling of urban sensible heat flux at multiple spatial scales: a demonstration using airborne hyperspectral imagery of Shanghai and a temperature-emissivity separation approach

    Remote Sensing of Environment

    (2008)
  • R.G. Allen et al.

    Crop Evapotranspiration Guidelines for Computing Crop Water Requirements

    (1998)
  • B. Belan et al.

    Study of the anthropogenic component of urban heat balance

    Atmospheric and Oceanic Optics

    (2009)
  • Gillespie, A., Rokugawa, S., Hook, S., Matsunaga, T., Kahle, A., 1999. Temperature/emissivity separation algorithm...
  • C. Homer et al.

    Development of a 2001 National Land-Cover Database for the United States

    Photogrammetric Engineering and Remote Sensing

    (2004)
  • Y.J. Huang et al.

    481 Prototypical Commercial Buildings for 20 Urban Market Areas

    (1991)
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