The past decades have seen unprecedented global population growth and urbanization with over 50% of the Earth’s population living within cities (Small 2001; Weng 2014). Canada is at the leading edge of the curve, with 80% of Canadians now living in cities (Statistics Canada 2017). This places enormous pressures on the planning and management of urban regions to ensure their sustainability, with particular importance on natural urban environments. As a result, a comprehensive understanding of the urban environment is fundamental to ensure sustainable and adaptive urban ecosystems (Williams et al. 2018). The spatial-temporal distribution of vegetation within an urban environment is known as “greenspace”, and is a fundamental component of the urban environment. Greenspace has a critical role: it influences urban environmental conditions and energy exchange through the absorption of solar radiation and modulation of evapotranspiration, and plays an important role filtering urban water systems and reducing storm water runoff (Oke 1982; Nowak and Dwyer 2007). Studies have also indicated the significant social (Grahn and Stigsdotter 2003; Westphal 2003), economic (Tyrväinen et al. 2005), and aesthetic values (Tyrväinen et al. 2005; Jim and Chen 2006) associated with urban vegetation (Liu et al. 2017). For example, Kleinman and Geiger (2002) estimated that 100 trees absorb up to 5 tons of CO2 per year from the atmosphere and 450 kg of pollutants including ozone and particulates. Therefore, within an urban context, greenspaces are the primary means of maintaining intact natural ecosystems, capturing and storing carbon, and preserving biodiversity.
Traditionally, information about urban forest canopy has been obtained from field sampling, manual interpretation of aerial photography and, more recently, using technologies such as Google Street View (Liu et al. 2017; Li et al. 2015). In addition, many cities utilize inventory systems to collate tree location, species and condition information for street and park trees. However, these methods are expensive, labor-intensive, and time-consuming, and a lack of complete coverage (Alonzo et al. 2014). Remote sensing offers a unique and efficient approach for understanding and mapping urban landscapes providing synoptic views over large areas. Inclusion of remote sensing data provides spatial layers upon which relationships can be developed between urban greenspace and social issues such as access to parks and recreation areas and provides a platform for extrapolation and expanded assessment into broader contexts nationally and internationally (Sutton and Costanza 2002).
Classification of urban imagery at various spatial resolutions has been a major theme in urban landscape studies (i.e., Schneider 2012; Frolking et al. 2013; Castrence et al. 2014; Lin et al. 2014; Chen et al. 2015; Williams et al. 2018). Previous studies have applied fine spatial resolution imagery (e.g., Benz et al. 2004), hyperspectral data (e.g. Roberts et al. 1998; Heiden et al. 2007), and aerial photography (e.g., Hodgson et al. 2003) all of which offer a high degree of spatial or spectral detail and allow derivation of urban land cover information which in turn is important for inferring land-use, mapping ecosystem services, or modelling of more complex processes like air quality, hydrology, or carbon stocks and flows. Likewise, land cover and its change over time may also help with urban metabolism and ecological footprint studies (Kellett et al. 2013). With respect to mapping tree cover in urban environments optical data from very high spatial resolution satellites such as those of the Worldview and GeoEye series can provide imagery with a pixel size < 0.5 m and as a result have markedly increased the potential to map and classify tree species within complex urban environments (Novack et al. 2011; Richardson and Moskal 2014). In addition, new methods such as intelligent image segmentation and object-based classification techniques are also highly applicable for urban remote sensing applications (Myint et al. 2011).
Optical sensor-derived data, such as aerial photography and Landsat satellite imagery, however, are generally poor when characterizing the vertical structure of urban vegetation (Plowright et al. 2016). The dimensions and vertical architecture of trees reflect their productivity, age, overall health and vigor (Schomaker et al. 2007). A large, dense crown is an indicator of optimal tree growth, while less dense crowns can be indicative of poor health and stress (Zarnoch et al. 2004; Plowright et al. 2016). Although some vertical tree metrics can be estimated through indirect relationships with optical bands (Cohen and Spies 1992), additional three-dimensional data on tree condition is critically important.
Airborne laser scanning (ALS), also known as light detection and ranging (LiDAR), offers a means to directly measure the three-dimensional structure of vegetation. An ALS instrument emits pulses of light that are reflected off trees, ground surfaces, and other terrestrial features and can penetrate through gaps in the foliage, enabling ALS to directly measure the vertical aspects of tree crowns and forest canopies (Plowright et al. 2016; Coops et al. 2007). A key benefit of ALS is the capacity to reliably obtain high-precision, three-dimensional measurements of buildings and trees over broad spatial scales which, as a result, has attracted significant interest among urban and natural resource managers (Hudak et al. 2009; Williams et al. 2018). ALS has been shown to be highly accurate for estimating a range of vegetation parameters such as tree height, biomass, stand density, basal area, volume, and Leaf Area Index (LAI) (Liu et al. 2017; Riaño et al. 2004; Hudak et al. 2006; Næsset 2007; Edson and Wing 2011). Kim et al. (2009) and Kim et al. (2011) used intensity values and structure variables including standard deviations (SD) of heights, percentiles, and crown ratios derived from leaf-on and leaf-off data, for tree species differentiation. In urban environments Liu et al. (2017) evaluated the potential of ALS to map 15 common urban tree species using a Random Forest (RF) classifier in the City of Surrey, British Columbia, Canada. Results indicate an overall accuracy of 51.1%, 61.0% and 70.0% using hyperspectral, ALS and the combined data respectively. The overall accuracy for the two most important and iconic native coniferous species improved markedly from 78% up to 91% using the combined data. The results of this research highlight that variables derived from ALS data contributed more to the accurate prediction of species than hyperspectral features (Liu et al. 2017).
Large, mature trees are valued for a number of reasons by city dwellers and managers. Larger, older trees have consistently been shown to store more carbon (Stephenson et al. 2014), and support a diversity of bird taxa. The values are difficult, and in some cases impossible, to replicate with large numbers of smaller trees (Le Roux et al. 2015). This is because large older trees provide critical structural complexity that is beneficial to a variety of bird species, particularly habitat specialists that have co-evolved with mature forests (e.g., cavity nesters) (Lindenmayer and Laurance 2016). Older trees can also benefit surrounding trees by fostering a higher diversity of mycorrhizal fungi, which can facilitate nutrient transfer among trees of different age classes and species (Simard and Durall 2004; Twieg et al. 2007). For the general population as well these larger trees provide a range of ecosystem services, with large trees having high cultural and emotional value associated with them (Lindenmayer et al. 2014; Pearce et al. 2015). With large, old trees predicted to decline in urban landscapes (Le Roux et al. 2014) it is increasingly critical to identify, map and characterize (in terms of type, height and size) large trees over the city’s land base.
The City of Vancouver, British Columbia, Canada, developed an Urban Forest Strategy in 2014 with a specific target of planting 150,000 new trees by 2020 (City of Vancouver 2014). The plan includes policies and bylaws to protect existing trees, plant trees to increase urban forest canopy, and to manage a healthy, resilient urban forest for future generations of the city. Its goal is to plant 150,000 new trees between 2010 and 2020, and increase the urban forest canopy from 18% to 22% by 2050 (City of Vancouver 2014). Key to the strategy is to protect and maintain current trees, especially those which are mature and large. To detect, map and characterize these large trees we develop and apply an object-based approach for individual tree detection and segmentation designed to both determine tree locations (position of the stem) and to delineate the shape of the crowns. We then extract attributes of interest such as tree height and crown diameter. Subsequently, using a series of ALS metrics we examine the capacity of ALS data to predict if crowns are deciduous or coniferous. We compare the predictions with both existing databases of tree locations and new field data collections. In this paper we investigate the capacity of ALS data to individually detect, map and characterize large (taller than 15 m) trees within the City of Vancouver, recognising the additional cultural and ecological importance these trees have compared to the overall urban forest canopy.
Study area
Home to over 600,00 residents, the City of Vancouver, BC is the third largest city in Canada (Statistics Canada 2017). The city is bounded by the Coast Mountains and Burrard Inlet to the north and the Fraser River to the south, which flows into the Strait of Georgia in the west (Williams et al. 2018). Landuse and landcover includes densely built-up areas, extensive areas of lower-density single-family homes as well as varied greenspaces ranging from small parks less than 0.5 ha, to golf courses and the 405 ha Stanley Park (Vancouver Board of Parks and Recreation 2016). Most of Vancouver’s native forest vegetation was removed during early settlement and forest harvesting between 1860 and 1910. Remnant areas of temperate rain forest remain in Stanley Park and other large parks and is dominated by large evergreens: western hemlock (Tsuga heterophylla (Raf.) Sarg.), western red cedar (Thuja plicata Donn ex D.Don), and Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco). Exotic tree species are common as park and street trees with dominant species including maples and cherries (over 50% of all street trees), but also including ashes, lindens, oaks, magnolias, hornbeams, and beeches.
Recent estimates from ALS data indicate that the City of Vancouver has about 18% urban forest cover, with about 61% on public lands (streets and parks), and 39% on private lands (City of Vancouver 2018). Forest cover measurements indicate a minor decline in overall forest cover from 19% in 1995 to 18% in 2015. Most of the tree loss is associated with urban densification, including the loss of large, mature trees.
Data
The ALS data used in this study was acquired in February 2013 over the boundaries of the City of Vancouver. The discrete-return dataset was provided in 168 non-overlapping tiles in LAS format with a point density > 12 points·m− 2. The vertical and horizontal accuracies are 0.18 and 0.36 m (95% confidence interval), respectively. An example of a typical point cloud acquired over an urban area is shown in Fig. 1 and shows a profile of ALS returns.
To validate the tree detection, delineation, characterization and species determination, individual tree data within the city was compiled from three different sources. The first was a geodatabase which provides an extensive inventory of trees located in the public parks of the City of Vancouver. It has been collected by a combination of photo-interpretation and field visits. This dataset was used as a base layer providing the spatial coordinates for 22,211 trees. For a subset of 18,146 of these trees, the tree type (deciduous or coniferous) was specified. No height data is available in this existing database. A second dataset with tree height measurements for large significant trees obtained by laser rangefinder and species identification was available for Stanley Park and Kerrisdale area. To complement these datasets, an additional field campaign was completed in four city parks: Queen Elizabeth Park, Memorial West Park, Musqueam Park and Locarno Park. A Vertex ultrasound hypsometer was used to determine the height of identified crowns and the average of two tape measurements on perpendicular axes constituted the recorded value for crown diameter. The compiled dataset presented a total of 74 trees with height and type information, 51 of which with a crown diameter value.