60 Hwang and Wiseman: Geospatial Applications for Urban Tree Canopy Assessment Advantages and Disadvantages of the PI Method We used i-Tree Canopy to examine the PI method because it is widely available to urban forestry practi- tioners. Proprietary remote sensing data and software are not required, and analysts can be quickly trained to use the application regardless of technical background. In addition, practitioners can assess UTC and land cover for a relatively large geographic area within a few hours. The current version of i-Tree Canopy (ver- sion 6.1) also allows users to load previous i-Tree Canopy projects to analyze and monitor UTC changes over time. These features afford access to UTC and land cover assessment that might otherwise not be possible for small localities or organizations with limited budgets and technical expertise. There are three notable limitations of i-Tree Canopy for urban forest planning and management. The first limitation is that the assessment does not provide detailed spatial information for where UTC exists in the study area. This lack of spatial information limits the utility of the tool for comparing tree canopy across the study area, for identifying spaces to plant new trees, and for prioritizing areas to protect existing tree can- opy. With that said, it might be possible to coerce useful information out of i-Tree Canopy for these purposes by carefully customizing the land cover classes at the beginning of an assessment. For example, a land cover class called “available planting space” might be cre- ated, and any sampling point that randomizes to a map location where there would be no impediments to tree planting (e.g., a road, building, or recreational field is not present) could then be classified as such. However, this approach would require considerable knowledge of land use in the study area and still would not clar- ify where these opportunities exist, only how prevalent these opportunities are, as a percentage of the total land area. A second limitation of i-Tree Canopy is the poten- tial error due to visual misinterpretation of land cover types viewed on the aerial photo. When using i-Tree Canopy, we had difficulties interpreting sample points located in certain areas: (1) shrub or tall grass, (2) heavy shadows, (3) edges between two different land cover types, and (4) near objects shifted by relief displacement (a geographic distortion present in ver- tical aerial photographs whereby tall structures appear to tilt and obscure smaller objects such as trees near them). Richardson and Moskal (2014) also reported ©2020 International Society of Arboriculture these imagery issues as potential sources of interpre- tation errors and pointed out that greater relief dis- placement would be a potential reason that i-Tree Canopy might overestimate tree canopy. Although human analysts currently have better acuity than computer algorithms for discerning the nuances of size, shape, shadow, and texture of tree versus non- tree vegetation (Campbell and Wynne 2011), consid- eration must be given to imagery misinterpretation when training analysts to use i-Tree Canopy. In addi- tion to human interpretation errors, Google Maps™ often causes misinterpretations due to the different image qualities across a study area. In i-Tree Canopy, Google Maps™ displays a mosaicked set of images with various data specifications, including different temporal (leaf-on and leaf-off images) and spatial resolutions (Taylor and Lovell 2012). Although urban and metropolitan areas tend to have greater temporal and spatial resolutions in Google Maps™ , small or rural localities may encounter outdated or lower-quality imagery in i-Tree Canopy, making an accurate UTC assessment more difficult. With time, imagery in those areas will likewise improve. A third limitation of i-Tree Canopy is that it offers only a simple random sampling scheme that esti- mates land cover across the whole study area. Simple random sampling might not be adequate when practi- tioners want to assess UTC in specific land uses (e.g., residential areas) or to compare UTC between two or more subregions (e.g., high- and low-density residen- tial areas) within the study area (Ott and Longnecker 2010). Since urban trees and potential planting spaces tend to occur predominantly on private residential parcels, being able to differentiate these areas is criti- cal for strategic tree planting and UTC conservation (Watkins et al. 2017). While i-Tree Canopy is not cur- rently configured to permit sampling based on strati- fication of land use, such an analysis could be coerced from the application by first creating geographic information system (GIS) shapefiles for each land use in an area of interest and then analyzing them sepa- rately in the application. Advantages and Disadvantages of the IC Method A major advantage of the IC method for UTC assess- ment is the acquisition of a wall-to-wall classification map of UTC and land cover. This detailed map can be
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