Building energy modelling and mapping using airborne LiDAR

Globally, buildings are responsible for more than 40% of energy demand and contribute more than 30% of CO₂ emissions. Various strategies and policies have been developed to reduce the negative of effects of energy use in the building sector, specifically targeting energy conservation and energy supply from renewable resources. As a basis for these strategies, decision-makers require estimates of existing energy demand. Traditionally, broad building sector energy estimates are derived using top-down modelling approaches that establish relations between energy use and variables such as income, fuel prices and gross domestic product. In contrast, individual building energy modelling has evolved sophisticated physically based simulations, populated by an abundance of variables related to building construction materials and components. However, for governments and decision-makers tasked with developing local strategies, techniques are needed to provide a detailed itemization of the building and environmental attributes that impact energy demand, as offered in building simulations, while maintaining the scalability to large areas provided in top-down models. Advances to geospatial technologies and datasets offer novel opportunities to satisfy these two conditions. Of particular interest is light detection and ranging (LiDAR), since it provides spatially contiguous measurements of urban form, otherwise unattainable across large areas. This dissertation presents a novel approach that integrates LiDAR data with building energy models to provide detailed and spatially contiguous estimates of energy demand in the residential building sector. LiDAR is used to augment building energy models by relating measured building form to internal energy components including envelope resistivity, fenestration and air leakage, and by assessing building envelope solar gains after accounting for local occlusions. Outcomes demonstrate that a LiDAR-based approach to building energy assessment is able to produce results that closely match those from manually informed building simulation software, thus offering a time and cost effective option for extensive and detailed analysis of energy demand. By presenting methods to decompose building energy demand into the site-specific components that influence energy end-use, this dissertation offers innovative opportunities to analyze and design spatially targeted building energy policies and strategies.Forestry, Faculty ofGraduat

Remote sensing applications for vegetation management in urban environments

Vegetation has been identified as an essential component of healthy urban environments, and the benefits of urban vegetation range widely from influences on the physical conditions of the city to the social well-being of the people who reside in these areas. As a result, ongoing research is important to understand the dynamic spatial components of urban vegetation to help urban planners and scholars manage this valuable resource. Advanced remote sensing technologies, such as high spatial resolution sensors and laser scanning devices, are useful tools for examining urban environments since they can capture detailed information regarding the material and structural composition of the urban surface. By providing a complete coverage of urban environments remote sensing technologies enable new possibilities to quantify the contributions of urban vegetation for a wealth of active processes in urban areas. The studies in this thesis examine several remote sensing devices to demonstrate the influence of urban vegetation on both physical and social aspects of urban environments. Three studies comprise the body of this work. They present new geographic techniques using remote sensing for: 1) the detailed classification of urban vegetation conditions; 2) quantifying the contribution of trees to solar radiation available for building rooftops; and 3) examining socioeconomic disparities related to urban green-space.Forestry, Faculty ofGraduat

Vegetation characteristics at the Vancouver EPiCC experimental sites

This report includes summary statistics and descriptions of the vegetation in the three EPiCC Vancouver experimental neighbourhoods, where flux towers were operated, namely the residential neighbourhoods "Vancouver-Oakridge", "Vancouver-Sunset" and the rural reference site Westham Island.Arts, Faculty ofGeography, Department ofUnreviewedFacult

Extracting Urban Vegetation Characteristics Using Spectral Mixture Analysis and Decision Tree Classifications

Urban vegetation cover is a critical component in urban systems modeling and recent advances in remote sensing technologies can provide detailed estimates of vegetation characteristics. In the present study we classify urban vegetation characteristics, including species and condition, using an approach based on spectral unmixing and statistically developed decision trees. This technique involves modeling the location and separability of vegetation characteristics within the spectral mixing space derived from high spatial resolution Quickbird imagery for the City of Vancouver, Canada. Abundance images, field based land cover observations and shadow estimates derived from a LiDAR (Light Detection and Ranging) surface model are applied to develop decision tree classifications to extract several urban vegetation characteristics. Our results indicate that along the vegetation-dark mixing line, tree and vegetated ground cover classes can be accurately separated (80% and 94% of variance explained respectively) and more detailed vegetation characteristics including manicured and mixed grasses and deciduous and evergreen trees can be extracted as second order hierarchical categories with variance explained ranging between 67% and 100%. Our results also suggest that the leaf-off condition of deciduous trees produce pixels with higher dark fractions resulting from branches and soils dominating the reflectance values. This research has important implications for understanding fine scale biophysical and social processes within urban environments

Characterizing Urban Surface Cover and Structure with Airborne Lidar Technology

Urban and landscape planners are becoming increasingly aware of the potential of light detection and ranging (lidar) technology to produce height and structural information over large geographic areas in both an economic and time-efficient fashion. In urban environments where the structural complexity is high, for example, lidar is seen as a critical and innovative dataset to improve the characterization of both vegetation and building attributes. Using a small-footprint, first- and last-return lidar dataset of Vancouver, Canada, we demonstrate the potential to derive a suite of attributes important for describing the interactions of the urban surface and atmosphere in weather forecasting, air pollution, and urban dispersion modelling. Two levels of attributes were defined. First, primary attributes such as building shape, size, and location and tree classification were calculated. Building extent and size were computed using an object-based approach based on connectivity and height rules. The classification of tree crown areas was derived from the location of last-return data, filtered to remove the incidence of last returns caused by the interaction of the lidar beam with building edges, and height rules. Validation showed that building areas derived from lidar compared well with aerial photography estimates (r2 = 0.96, p \u3c 0.001, n = 98). The percentage difference between estimates was equal to 16% (n = 83) when buildings were discriminated from the surrounding features. However, the percentage difference between estimates increased to 35% (n = 98) when commission errors were considered, as lidar often overestimated building areas due to closely spaced buildings (gaps less than 1–2 m) not being separated. Similarly, the height and area of lidar-extracted trees were highly correlated with field-based measurements (r2 = 0.84 and 0.76, respectively, p \u3c 0.001, n = 50). Once these primary attributes were derived, we demonstrate the extraction of a number of secondary attributes including building mean height, normalized building volume, building wall surface area, and interelement spacing. Of significance, this research has shown that lidar can provide spatially detailed estimates of urban structure and cover which characterize the aerodynamic and energetic properties of urban areas

Invasive shrub mapping in an urban environment from hyperspectral and LiDAR-derived attributes

Proactive management of invasive species in urban areas is critical to restricting their overall distribution. The objective of this work is to determine whether advanced remote sensing technologies can help to detect invasions effectively and efficiently in complex urban ecosystems such as parks. In Surrey, British Columbia, Canada, Himalayan blackberry (Rubus armeniacus) and English ivy (Hedera helix) are two invasive shrub species that can negatively affect native ecosystems in cities and managed urban parks. Random forest (RF) models were created to detect these two species using a combination of hyperspectral imagery, and light detection and ranging (LiDAR) data. LiDAR-derived predictor variables included irradiance models, canopy structural characteristics, and orographic variables. RF detection accuracy ranged from 77.8% to 87.8% for Himalayan blackberry and 81.9% to 82.1% for English ivy, with open areas classified more accurately than areas under canopy cover. English ivy was predicted to occur across a greater area than Himalayan blackberry both within parks and across the entire city. Both Himalayan blackberry and English ivy were mostly located in clusters according to a Local Moran’s I analysis. The occurrence of both species decreased as the distance from roads increased. This study shows the feasibility of producing highly accurate detection maps of plant invasions in urban environments using a fusion of remotely sensed data, as well as the ability to use these products to guide management decisions

Statistics on the built infrastructure at the Vancouver EPiCC experimental sites

This report documents statistics and descriptions of the built infrastructure and population in two study neighbourhoods that were part of the EPiCC network, namely the residential neighbourhoods of "Vancouver-Oakridge" and "Vancouver-Sunset". The report quantifies the built cover (plan area ratios) and 3D-structure (height, sky view factor) of the urban surface in the neighbourhoods as required for mesoscale urban parameterizations and ecosystem modeling. It also details a building typology approach and describes building characteristics (building and roof materials, heights, footprint, floor area, etc.) and the population density and distribution.Arts, Faculty ofGeography, Department ofUnreviewedFacult

A LiDAR-based urban metabolism approach to neighbourhood scale energy and carbon emissions modelling

[Research report published as hard copy (UBC)] A LIDAR-BASED URBAN METABOLISM APPROACH TO NEIGHBOURHOOD SCALE ENERGY AND CARBON EMISSIONS MODELLING prototypes a remote sensingbased means to neighbourhood-scale energy and carbon modelling. Building on a Vancouver case study neighbourhood for which remote sensing, atmospheric carbon flux, urban form, energy and emissions data have been compiled and aggregated, the project demonstrates a replicable neighbourhood-scale approach that illustrates: • Holistic, systems-based and context-sensitive approaches to urban energy and carbon emissions modelling. • Methods of deriving energy- and emissionsrelated urban form attributes (land use, building type, vegetation, for example) from remote sensing technologies. • Methods of integrating diverse emission and uptake processes (combustion, respiration, photosynthesis), on a range of scales and resolutions based on spatial and non-spatial data relevant to urban form, energy and emissions modelling. • Scalable, type-based methods of building energy modeling and scenario-building. • Benchmark comparisons of modelled estimates with directly measured energy consumption data and two years of directly measured carbon fluxes (emissions) on a research tower above the neighbourhood. 0.0.1 Key Model Results • Carbon imports: Based on project urban metabolism scope and methods, the study area imports approximately 6.69 kg C m⁻² year⁻¹ (or 1.04 t C cap⁻¹) in form of fuels, food and materials and uptakes 0.49 kg C m⁻² year⁻¹ from the atmosphere though photosynthesis of urban vegetation. • Carbon exports and sequestration: Sources within the study area emit 6.22 kg C m⁻² year⁻¹ (0.97 t C cap⁻¹) or 87% of the imports to the atmosphere, and 0.87 kg C m⁻² year⁻¹ (0.14 t C cap⁻¹) or 12% of the imports are exported laterally by waste. 1% of the imported carbon, or 0.09 kg C m⁻² year⁻¹ (0.01 t C cap⁻¹) is sequestered in urban soils and biomass. • Relevant emission processes: Out of all local emissions from the study area to the atmosphere, 2.47 kg C m⁻² year⁻¹ (40%) are originating from buildings, 2.93 kg C m⁻² year⁻¹ (47%) from transportation, 0.49 kg C m⁻² year⁻¹ (8%) from human respiration and 0.33 kg C m⁻² year⁻¹ (5%) from respiration of soils and vegetation. Emissions attributable to fuels, resource and food production, transport or transmission, and waste management outside the study neighborhood were not considered. • Fossil fuel emissions: Out of the local fossil fuel emissions in the study area, 46% originate from the building sector (natural gas), and 54% are attributable to transportation uses (gasoline, diesel). Out of the transportation emissions, 11% (0.31 kg C m⁻² year⁻¹) are attributable to carbon emitted on trips generated within the study area and 89% (2.62 kg C m⁻² year⁻¹) to carbon emitted on trips passing through the study area. • Renewable carbon cycling: Photosynthesis and human, soil and vegetation respiration take up / emit renewable carbon. These processes have potential to offset (take-up) carbon from other sources as well as generate (emit) carbon when carbon pools are disturbed, by urban land use change and (re-)development, for example. • Benchmark to direct emission measurements: Two years of measurements on a carbon flux tower in the centre of the study area allow a comparison of modelled results to directly measured carbon emissions. The modelled and measured emissions agreed very well i.e. 6.71 kg C m⁻² year⁻¹ were measured vs. 7.46 kg C m⁻² year⁻¹ modelled (refers to a subset of the study area weighted by the turbulent source are of the tower). The model is slightly overestimates actual emissions by 0.75 kg C m⁻² year⁻¹ (or 11%) which is mostly attributed to the lack of vehicle speed representation in the transportation model. 0.0.2 Key Findings on Project Methodology • Remote sensing: Remote sensing technologies such as LiDAR and multispectral satellite imagery have been demonstrated to be an effective means to generate, spatialize inputs and extract urban form and land cover data at fine scales (down to 1 m). These urban form attributes and data provide the inputs necessary to energy and emission modelling tasks in the building sector and to quantify vegetation emissions / uptake. • Building-type approach: Type-based modelling methods, data limitations aside, provide an effective means to scale building to neighbourhood energy modelling. These methods also facilitate definition of crucial morphological and performance attributes through which to filter remote sensing data and to scope potential mitigation strategies and scenarios. • Comparison of measured with modelled emissions: Direct carbon flux measurements on urban flux towers are demonstrated to be a method of validation of fine-scale emission inventories / models. Given the prototype nature of the approach and methods, close agreement between tower measurements and model results in this study is a successful and promising outcome. • Limitations: While promising, the urban metabolism approach demonstrated has also been necessarily limited in several ways. Only one metabolic aspect — mass balance of carbon, has been considered and measured. The spatial scale and complexity is modest — a 2km square ‘neighbourhood’ of moderate land use and urban form diversity. Out of study area carbon emissions generated in the production of food or consumer goods or the extent of local origin trips has not been considered. 0.0.3 Key Findings from Illustrative Scenarios • Material emissions reduction targets: Illustrative scenarios demonstrate that, on a per capita basis, local origin carbon emissions in the Sunset study area could meet British Columbia’s 2020 carbon reduction goal (33% below 2007 levels) with full adoption of current best practice space conditioning and vehicle fuel efficiency standards. However, progress toward greater emissions reductions beyond that goal require greater population and employment density in compact and mixed use, pedestrian- and transit-oriented patterns of urban form. Meeting British Columbia’s 2050 carbon reduction goal (80% below 2007 levels) would depend on full adoption of these best practice urban form strategies in combination with significant additional technological improvement in the energy efficiency of buildings, vehicles and infrastructure as well as significant human behaviour change toward less energy intensive lifestyles.Arts, Faculty ofGeography, Department ofReviewedFacultyResearcherGraduat