336 forest cover. Currently, NDVI is often used as an auxiliary input layer for other automated classification algorithms (e.g., Myeong et al. 2001). High-Resolution Image Classification. Classification of high- resolution images poses difficulties for algorithms because the size of an individual pixel is much smaller than the size of the object to be classified resulting in pixels of different spectral characteristics making up the same object. Tree canopies, for example, when viewed on submeter high-resolution imagery, are made up of pixels representing various levels of reflectance from the vegetated surfaces as well as pixels of shaded areas. This disparity of the constituents of urban forest cover makes it dif- ficult to classify tree canopies from their spectral signature alone. Several studies (Myeong et al. 2001; Zhang 2001; Irani and Galvin 2002) have used texture measures and, more recently, object-based methods (Walker and Briggs 2007; Zhou and Troy 2008) have been applied to high-resolution image classification of urban forest cover to overcome the difficulties presented by varying reflective properties among pixels representing one cat- egory. One of the primary difficulties in classifying urban forest cover is the differentiation between tree canopies and grass sur- faces. At an individual pixel level, tree canopies and grass sur- faces generally have a very similar spectral response. However, tree canopies generally contain areas of bright, higher reflectance values from vegetation surfaces and areas of darker, low reflec- tance values from the shadowy regions of the tree canopy. This effect creates a more coarse texture on the image of a tree canopy when compared with other vegetated surfaces such as grassy lawns or athletic fields, which generally have a smoother, finer texture. Algorithms designed to identify regions of coarse texture have been shown to be very useful in differentiating tree and shrub canopies from other vegetation surfaces (Myeong et al. 2001; Zhang 2001). Another method to handle highly variable pixel characteristics of urban forest canopy is through the use of object-based clas- sification. Images are segmented into clusters of similar pixels by considering any number of pixel-based metrics such as spec- tral or textural values. These segments, or objects, are then com- bined to form the final classification categories. Considering objects rather than individual pixels yield results that mimic human classification. Object-based image analysis has been used by Walker and Briggs (2007), O’Neil-Dunne (2007), and Galvin et al. (2007). Zhou and Troy (2008) classified urban land cover at the parcel level using object-oriented techniques. Object-based analyses can integrate many different types of input data. Air- borne LIDAR (laser ranging) data have been used to incorporate vegetation height information into the classification process (O’Neil-Dunne 2007). Subpixel Estimation. Traditionally, techniques using medium- resolution imagery have used “whole-pixel” classifications in which each pixel is designated as either “forested” or “not for- ested” with some threshold being used to determine the cutoff between the two classes. Because of the heterogeneous nature and number of mixed pixels in urban areas, whole-pixel classi- fications tend to misrepresent the amount and spatial distribution of urban forest cover. For subpixel urban forest cover, the goal is to estimate the percent tree canopy cover for each pixel as a real number between zero and 100. Zhu (1994) developed a ©2008 International Society of Arboriculture Walton et al.: Assessing Urban Forest Canopy Cover subpixel forest density map using a regression procedure from 1.1 km (0.66 mi) AVHRR multispectral imagery, which was later used to assess urban forest cover by Dwyer et al. (2000). The nonprofit group American Forests (2007) has used the ERDAS Imagine Subpixel Classifier (Applied Analysis, Inc., www.discover-aai.com/software/products/IMAGINE_Subpixel_ Classifier.htm) to map and assess change of urban forest canopy. Small (2001) and Small and Lu (2006) have used spectral mix- ture modeling to estimate subpixel vegetation abundance in the New York City region. Recently, with the subpixel tree canopy layer from the NLCD 2001 (Homer et al. 2007), urban forest cover and accuracy assessments are being conducted using this 30 m, nationwide resource by the USDA Forest Service, Syra- cuse, New York. The NLCD 2001 tree canopy estimate was generated using the Cubist (RuleQuest Research, www.rulequest. com) rule-based regression software (Huang et al. 2001). Subpixel urban forest cover maps generally represent the amount and spatial distribution of urban forest canopy cover better than whole-pixel classification techniques for medium- resolution imagery. Classification Accuracy Assessment. For all methods of digital image classification, an accuracy assessment of the map should be conducted to determine how accurately the map categories (e.g., tree cover) were classified. One relatively simple way to determine map classification accuracy is to randomly sample points throughout the classified area and ground-truth the clas- sification at those points either through site visits or aerial pho- tograph interpretation. The map classification at each point is then compared with the ground-truth estimate to determine map classification accuracy (Table 1). Typically, overall map accu- racies within the 80% to 90% accuracy range can be obtained, but accuracy varies among individual classification categories. As a result of the varying classification techniques and imagery products, understanding map classification accuracy is essential to understanding how useful the map will be for planning, man- agement, and analysis purposes. Assessing Urban Forest Canopy Change Many of these map products discussed here can be used as the basis of urban forest canopy change studies. These assessments can vividly demonstrate the effects of urbanization on the forest canopy resource and bolster public opinion with regard to local land development policies. Understanding how urban forest canopy cover is changing can be valuable information for the forest managers. Several groups have used imagery as the means to assess canopy cover change, including the nonprofit group American Forests (2007) in several U.S. cities and Poracsky and Lackner (2004) in Portland, Oregon. Two important factors in determining canopy cover change are 1) assessing change over a long enough time period for change to be evident; and 2) having a consistent methodology when developing each individual canopy map that is compared (Walton 2008). A consistent meth- odology will include using similar imagery and analysis methods when developing both products used in the comparison. Comparison Case Study To illustrate how different methods and imagery can lead to differing canopy cover estimates and accuracy, the results of several urban forest canopy cover assessments of Syracuse, New
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