Arboriculture & Urban Forestry 34(6): November 2008 be quite large and it may be time-consuming to change views from one point to another. Color infrared (CIR) imagery is useful in differentiating various types of vegetation because CIR can accentuate the subtle differences between types of trees, between grass and trees, and between vegetation and nonvegetative sur- faces. On all types of aerial and satellite images, trees overlap other ground surfaces such as buildings, roads, or parking lots (impervious surfaces) and bare soil, grass, or shrubs (permeable surfaces). Because the understory or ground cover cannot usually be seen, assessments of the full extent of surface features and canopy depth, useful for watershed analyses, for example, cannot be exactly determined from leaf-on aerial photographs or optical remotely sensed imagery. Types of Digital Imagery Several different types of digital aerial or satellite imagery have been used to assess urban forest canopy cover, including high- resolution digital aerial photographs, high-resolution satellite im- agery, medium-resolution satellite imagery, and low-resolution satellite imagery. High-resolution digital frame cameras have recently been used in place of traditional film aerial cameras (Myeong et al. 2001; Zhang 2001). The resulting imagery has the three portions of the spectrum (either red, green, and blue or near infrared [NIR], red, and green) separated into individual bands that can be used in further automated processing. These cameras generally produce images with submeter to meter spatial resolution, i.e., each im- age pixel represents an area on the ground 1 m × 1 m square or smaller. Emerge DSS (Applanix Corp., Richmond Hill, Ontario, Canada) and Leica ADS40 (Leica Geosystems AG, Heerbrugg, Switzerland) are examples of airborne sensor systems that pro- duce this type of imagery. High-resolution aerial and satellite imagery has been used to map urban forest cover in many cities, including Baltimore and Annapolis, Maryland, U.S. (Irani and Galvin 2002; Galvin et al. 2007); Syracuse, New York (Myeong et al. 2001); and New York City (www.oasisnyc.net/oasismap.htm). The most com- mon high-resolution satellite imagery, from the Quickbird and IKONOS satellites, consists of four multispectral bands (NIR, red, green, and blue) with spatial resolution of 2.4 m (7.9 ft) (Quickbird) or 4 m (13.2 ft) (IKONOS) coupled witha1m (3.3 ft) (IKONOS) or 0.6 m (2 ft) (Quickbird) spatial resolution pan- chromatic band. The multispectral bands can be combined with the higher resolution grayscale panchromatic band to produce “pansharpened” multispectral imagery at the higher resolution. These sensors have enough spectral coverage to allow the im- agery to be used successfully in automated classification algo- rithms. The spatial resolution is adequate to identify canopies of individual, open-grown trees. Medium-resolution satellite imagery, with spatial resolution of tens to a few hundred meters, lends itself to analysis of forest cover of entire cities or regional urbanized areas. This spatial resolution depicts areas that can have several land cover types in an individual pixel. For example, in a residential area, tree cano- pies, building roofs, lawns, and paved streets could be repre- sented within a single pixel. These “mixed pixels” are suited for specialized subpixel processing methods that are discussed sub- sequently. Enhanced Thematic Mapper (ETM) on the Landsat series of Earth imaging satellites is one of the most common examples of medium-resolution satellite imagery. Landsat’s 335 ETM captures six multispectral bands, three in the visible part of the spectrum and the remainder in the NIR and midinfrared portion of the spectrum. Because living vegetation is highly reflective in the NIR, these bands facilitate the differentiation of vegetated surfaces from other land cover. Landsat imagery has been used to map urban tree cover (e.g., Wang 1988; Iverson and Cook 2000). In recent classifications from the National Land Cover Database (NLCD) 2001 (Homer et al. 2007), a tree canopy layer is derived from Landsat ETM imagery and is being used to assess urban forest cover across the United States as part of the USDA Forest Service’s Resources Planning Act (RPA) program. Satellite imagery with a spatial resolution of several hundred meters and larger can be considered low resolution. Because of the complex mixing of land cover in developed areas and the large ground size of the pixels, low-resolution imagery is of limited usefulness for most urban forest cover mapping unless specialized subpixel analysis techniques are used. These tech- niques provide percent forest cover in each pixel and are de- scribed in the next section. Advanced Very-High Resolution Radiometer (AVHRR) imagery with 1.1 km (0.66 mi) spatial resolution has been used to assess urban forest cover in all the cities of the coterminous United States but had various limita- tions (Zhu 1994; Dwyer et al. 2000). The AVHRR sensor, pri- marily used for weather observations, collects data in one band in the green, one band in the reflected NIR, and three bands of the thermal parts of the spectrum so it is of marginal usefulness for general land cover mapping. The combination of low spatial and spectral resolutions of AVHRR produces highly variable results for urban forest cover mapping (Walton 2008). Classification of Digital Imagery Urban forest canopy cover can be mapped using the imagery described previously and one of many image classification tech- niques, including normalized difference vegetation index (NDVI), pixel-based, and object-oriented classification of high- resolution images and subpixel estimation of medium-resolution imagery. Normalized Difference Vegetation Index. The NDVI is a very simple method used to accentuate vegetation from imagery con- taining reflectance in the red and the NIR portions of the spec- trum. It is computed using the NIR and red (R) reflectance bands in the ratio as shown in Equation (1). The index produces high (brighter) values for pixels that contain more vegetation, but because it is a simple ratio, there can be other areas in an image such as sun glint from water surfaces that also produce high NDVI values. In addition, NDVI can be influenced by external factors (e.g., view angle, leaf orientation, and soil background; Campbell 2007) so that it does not produce a linear scale that is in direct proportion to the percentage of vegetation in the pixel. NDVI = NIR − R NIR + R (1) Early remote-sensing analysis of urban forests using multispec- tral imagery such as that by the nonprofit group American For- ests in the mid-1990s used the NDVI band ratio directly as a simple classification of urban forest cover. This approach is very limited because NDVI includes all vegetation and does not produce values that are linearly proportional to the amount of ©2008 International Society of Arboriculture
November 2008
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