Thoreau Rory Tooke

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 < 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 < 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.

A geographical approach to identifying vegetation-related environmental equity in Canadian cities

The research in this paper addresses human — environment interactions in Canadian cities by examining the spatial distribution of vegetation in relation to various socioeconomic indicators. Specifically, intercity and intracity comparisons are evaluated using correlation analysis and geographically weighted regression (GWR). Vegetation abundance estimates derived from spectral mixture analysis of Landsat imagery are compared with Canadian census data for the cities of Montreal, Toronto, and Vancouver to quantify vegetation-related environmental equity in Canadas largest urban centres. Results exhibit strong and consistent correlations between median family income and vegetation fraction for Montreal (r = 0.473), Toronto (r = 0.467), and Vancouver (r = 0.456). Furthermore, examining the GWR results suggests that employing an adaptive bandwidth kernel technique with a manual selection of ten neighbours for each observation provides a greater range and higher median values for local regression estimates (Montreal: 0.69; Toronto: 0.74; Vancouver: 0.73) as compared with the Akaike information criterion-selection method. Finally, we discuss the potential application of the presented analysis techniques for urban planning and community-development initiatives, specifically associated with managing vegetation-related environmental equity at various scales. Possible applications of these techniques for urban planning purposes are discussed, and key methodological considerations for performing such an analysis are highlighted.

Classification of residential building architectural typologies using LiDAR

The availability of light detection and ranging (LiDAR) datasets over urban areas has significant potential to facilitate the automatic parameterization of sophisticated building energy models. In this paper we present an approach to building architectural typology classification using LiDAR data and decision tree regression. By integrating a suite of LiDAR derived building morphological characteristics with field training data we accurately classifed (84%, Kappa = 0.76) of the modelled residential building types. Furthermore, our analysis suggests that building characteristics related to height, volume and roof slope provide the most important predictor variables for classifying building typologies in the examined study area.

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, BC, 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.

Assessment of urban tree shade using fused LIDAR and high spatial resolution imagery

Advancements in high spatial resolution remote sensing technologies including multispectral satellite (Quickbird, IKONOS) and active airborne sensors (LIDAR - Light Detection and Ranging) are enabling detailed analysis of physical features across the urban environment. Often these datasets have been applied in isolation, however by fusing these technologies significant added benefit can be gained. Specifically, LIDAR data enables highly accurate extraction of three dimensional urban structures such as buildings, trees, and the underlying terrain; while multispectral data can provide accurate estimates of surface cover type. In this paper we present a technique to model and map seasonal solar radiation effects related to urban trees by integrating structural and spectral data. Results indicate that across the study area (The District of North Vancouver) trees reduce incoming potential solar radiation in summer by 4.38 MJm⁻²day⁻¹ (24%) and in winter by 0.28 MJm⁻²day⁻¹ (13%). In addition, solar radiation is decreased by 0.2 MJm⁻²day⁻¹ (11%) in winter when deciduous tree species are removed. Finally, solar radiation is summarized by urban land use and results suggest that radiation in developed regions is most affected by tree shading in single-family residential areas (3.5 MJm⁻²day⁻¹) and least affected in commercial areas (1.22 MJm⁻²day⁻¹).

A review of remote sensing for urban energy system management and planning

An urban energy system describes the integration of energy demand, supply and distribution in an urban environment. This review examines the role of remote sensing to each of these urban energy system components. Specifically, the relevant fundamental physical or statistical energy-modelling principals are considered in relation to remote sensing inputs. We suggest that active remote sensing technologies offer substantial opportunities for informing a wide range of urban energy assessments.

Introduction to Remote Sensing

Remote sensing is the science of gathering spatial information about the Earth’s surface (as well as the oceans and atmosphere) from a distance, using either handheld, aircraft, or satellite sensors. Such data are routinely used in landscape ecology to map, monitor, and manage landscapes. It is important to understand and fully appreciate the different types of electromagnetic radiation used to create geodata derived from remote sensing systems, the spectral and spatial properties of natural and manufactured materials, as well as the characteristics of airborne and satellite sensor systems. Understanding these fundamental aspects of remote sensing will assist landscape ecologists in understanding and distinguishing the diversity and heterogeneity of land cover types in their study regions and better assess how landscapes might have changed over time. This chapter will enable students to.