52 Hwang and Wiseman: Geospatial Applications for Urban Tree Canopy Assessment When assessing UTC using geospatial analysis of imagery, urban forestry practitioners can choose between either a photo interpretation (PI) method or a computerized image classification (IC) method. Pre- vious studies have shown that both methods provide reliable assessments of UTC and other land cover types (Walton et al. 2008; Richardson and Moskal 2014). However, there are distinct differences in their technical accessibility and their analytical outputs that must be carefully considered by urban forestry practitioners. In this paper, we aim to help practi- tioners understand the capabilities and limitations of these two UTC assessment methods by first review- ing their features, then describing geospatial tools that incorporate these methods, and finally examining their practical application through a case study of an urbanized college campus in the eastern United States. Use of geospatial analysis to perform UTC assess- ments has been rapidly advancing since the turn of the 21st century (Myeong et al. 2001). Compared to tree canopy measurements using conventional field- based techniques, which require considerable time and labor (McPherson et al. 2011), geospatial analy- sis provides rapid and efficient assessment of UTC and land cover across large land areas. Geospatial analysis of UTC involves detailed protocols that are technically complex and require specialized soft- ware, data, and skills (Hostetler et al. 2013). As such, small localities and nonprofit organizations may lack sufficient capabilities to assess UTC using geospatial analysis, limiting their ability to effectively monitor, plan, and manage their urban forests. The US Forest Service has sought to eliminate these barriers by creating two web-based UTC assess- ment tools for public use: i-Tree Canopy (http://can- opy.itreetools.org) and i-Tree Landscape (http:// landscape.itreetools.org). These two applications offer contrasting approaches to UTC assessment: i-Tree Canopy is a PI method, and i-Tree Landscape is an IC method. With i-Tree Canopy, a point sam- pling protocol is incorporated into a Google Maps™ interface, allowing users to view land cover across an area and to calculate the percentage of predetermined land cover types. With i-Tree Landscape, users are provided a mapping interface to query preclassified land cover maps obtained from the 2011 National Land Cover Database (NLCD). Although their assessment outputs are similar, there are fundamental differences in how these applications generate data ©2020 International Society of Arboriculture and how their assessments might be utilized for urban forest planning and management. Urban forestry practitioners must understand the differences between the PI and IC methods of UTC assessment so that they can choose the appropriate tool for their analysis needs. The PI method requires an analyst to visually examine randomly sampled points on an aerial or satellite photo and then classify the land cover for each point (Nowak et al. 1996). Although PI offers a faster and more technically straightforward UTC assessment than IC, there are limitations to PI data outputs. More specifically, PI can estimate the percentage of UTC and other land cover types in a particular area, but it cannot capture the distribution or connectivity of these cover types across the landscape. In addition, because PI uses sta- tistical sampling, the accuracy and certainty of UTC and land cover estimates from PI depends on the sam- ple size (number) of PI points evaluated in a study area. Any estimate derived using a sampling protocol has an associated margin of error (typically calcu- lated as a standard error), which indicates the degree of uncertainty associated with the estimate (Nowak et al. 1996; Nowak and Greenfield 2012). This uncer- tainty is often communicated with a confidence inter- val around the estimate, which is calculated using the standard error and shows the amount of deviation one might expect in the estimate upon repeated sampling with some degree of statistical confidence (usually 95% confidence). To generate an accurate UTC estimate and mini- mize the associated standard error, a sufficient point sample size is needed for a PI method such as i-Tree Canopy; the more sample points that are interpreted, the closer the estimate is to the “true” value and the lower the standard error of that estimate (Nowak et al. 1996; Nowak and Greenfield 2012). However, add- ing more interpretation points to the UTC assessment requires additional time and cost. Therefore, deter- mining an optimal sample size (not more or less than needed) can help urban forestry practitioners to use PI for UTC assessments in a rapid and efficient manner. Another potential source of error in PI is visual misinterpretation of the photos that are being point sampled by the analyst, resulting in erroneous classi- fication of the land cover. Interpretation errors may be caused by edges, shadows, and vegetation height (Parmehr et al. 2016). These errors can be minimized by providing adequate training to analysts and by
January 2020
| Title Name |
Pages |
Delete |
Url |
| Empty |
Search Text Block
Page #page_num
#doc_title
Hi $receivername|$receiveremail,
$sendername|$senderemail wrote these comments for you:
$message
$sendername|$senderemail would like for you to view the following digital edition.
Please click on the page below to be directed to the digital edition:
$thumbnail$pagenum
$link$pagenum
Your form submission was a success. You will be contacted by Washington Gas with follow-up information regarding your request.
This process might take longer please wait
