Data Sources

This section describes a number of available datasets for this study area that are relevant to this research. These were obtained from a number of sources including publicly available catalogues from the City of Toronto as well as data donated for research purposes from government entities and private companies. Detailing the available data serves to develop perspective for the decisions made when generating solar resource data in contrast with the ideal practices as described earlier in this chapter.

Topographic Data

Topographic data required to develop the DSMs for Solar Analyst are available from a number of sources for different parts of Toronto. These areas are naturally divided into two groups: the downtown area where a number of different data types form an overlapping but inconsistent coverage, and Black Creek in the northwest where LiDAR data are available for almost the entire neighbourhood. Due to the divide in data types, spatial discontinuity, and

varying data quality, the methodology for generating DSMs for both areas is different. For this reason, these areas will be discussed separately and will be identified as ‘Downtown’ and

‘Black Creek’.

The Black Creek data are a LiDAR point cloud in a standard LAS format that was recorded by an Optech aerial LiDAR system. The point cloud covers the majority of the Black Creek neighbourhood in the north west of Toronto. These data have an average point spacing of just over one metre and are classified as first, last, and two intermediate returns.

The Downtown data has three sources for building heights, each of which cover a different, but often overlapping area centred on the downtown core of Toronto. Building footprint data with single height values were developed by RMSI. This feature class begins downtown on the waterfront and covers a six-kilometre-wide swath that extends due north for approximately 12 kilometres or just past Eglinton Avenue. Additional building height data were made available from the City of Toronto Urban Design department in the form of an ESRI feature class that was converted from 3D CAD drawings. This feature class contains polygons with individual heights for each building component including multiple polygons for complex rooftops. The Urban Design dataset consists of buildings for the majority of the downtown area as well as some buildings along Yonge St. in North York. Finally, LiDAR point cloud data from Optech Inc. was made available for eight overlapping east-west swaths, covering approximately 35 square kilometres of the downtown area. To supplement these data sources when developing a DSM, an ESRI feature class of contour lines with a one metre interval was made available by the TRCA for the entirety of the City of Toronto.

Extents of these data sources are displayed in Figure 3.8.

  Figure 3.8: Data extents for each topographic source

Atmospheric Transmissivity Data

As discussed in Section 3.1.3, the broadband transmissivity values required by Solar Analyst can be calculated from average net irradiance for each month. These data are available from the University of Toronto at Mississauga’s (UTM) Department of Geography in the form of hourly measurements in millivolts that are converted to watts per square metre. These values were measured by a net radiometer and recorded to comma separated text files and are available online for dates between November 1999 and November 2012 (University of Toronto, 2012). In some cases there are missing data values for multiple days that have been annotated as null values. These need to be considered when averaging the same months from multiple years together, which is done to account for variations between years.

Land Cover Data

Land cover data that covers the City of Toronto is available in raster format from the city’s Open Data Project. Originally developed in 2009 as part of the Urban Tree Canopy Assessment, this raster is divided into eight classes: tree canopy, grass/shrub, bare earth, water, buildings, roads, over paved surfaces, and agriculture (City of Toronto, 2009b). This dataset was developed using a semi-automated process where 0.6 metre panchromatic imagery captured from the QuickBird satellite is classified using a combination of ERDAS Imagine and eCognition software. This process uses object-based image analysis to identify classes based on shape, size, length, texture, and other non-spectral properties. The results were then manually verified and edited by experts where necessary (Henry, 2012).

Surface Temperature Data

A project undertaken by the Earth Science Sector of Natural Resources Canada produced surface temperature maps of Toronto to assess the impacts of the urban heat-island effect.

Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) measure energy radiated from the surface in the thermal infrared spectrum between 10.4 and 12.5 µm.

Using a method that accounts for effects such as off-nadir measurements and atmospheric upwelling and downwelling, cell values for the imagery are converted from digital numbers to land surface temperature (LST) in degrees Celsius. The resulting products are images that are interpolated to 30 and 60m for TM and ETM+ respectively for six times between 1987 and 2011 (Maloley, 2009).

Landsat thermal data were first geometrically corrected using Geobase road network data as a reference. The eight bit digital numbers were converted to radiance values using gain and offset calibration values available at the United States Geological Survey (USGS) Landsat website. These were then converted to LST values using an inverse Planck function, tested to be accurate within plus or minus 2.0 °C (Schott and Volchok, 1985). This methodology did

not correct for atmospheric transmissivity and assumes each cell to have an emissivity value of 1.0 (Maloley, 2009).