402 Shojanoori and Shafri: Review on the Use of Remote Sensing for Urban Forest Monitoring berg et al. 2009; Ma and Ju 2011; Pu and Landry 2012), SPOT (Kong and Nakagoshi 2005), RapidEye (Tigges et al. 2013), FormoSat-2 (Sun et al. 2007), and WorldView-1 and WorldView-2 (Immitzer et al. 2012; Latif et al. 2012; Nouri et al. 2014; Rapinel et al. 2014) have been increas- ingly used to detect and monitor urban forests. QuickBird and IKONOS are common high- resolution satellite imaging techniques in urban forest studies, and both have panchromatic and four multispectral bands (i.e., red, green, blue, and near infrared) with high spatial resolution (HSR) (Pan: 0.6 to MS: 2.44 m). As red and near- infrared bands are sensitive to vegetation and contain approximately 90% of the vegetation information, they can be used to detect vegetation (Li et al. 2010; Puissant et al. 2014). Nonetheless, more bands may be required to extract informa- tion on vegetation and trees from different land- cover types because of the complex environment of urban areas (Ouma and Tateishi 2008). As the resolution of the multispectral image is 2.4 m, objects smaller than 6 m present mixed pixels, whose spectral characteristics refer to mixed objects, such as roads and trees. To avoid this error, researchers have used different techniques. For instance, Hong et al. (2009) applied the grey-level co-occurrence matrix (GLCM) mask and hierarchical classification to improve the accuracy of the information, but these methods remain incapable of performing high-accuracy extraction in urban areas (Hong et al. 2009). Sev- eral high-resolution imaging techniques, such as IKONOS and QuickBird, are insufficient in clas- sifying urban vegetation into different species because of the limited number of spectral bands. The WorldView-2 satellite was launched in 2009 as an improved version of high-resolution satellites. This high-resolution imagery system employs eight spectral bands, including those sensitive to vegetation. These bands consist of four old bands, namely, blue, green, red, and near- infrared, and four new bands, namely, coastal (to detect chlorophyll content), yellow (to detect yel- lowness), red edge (to detect plant diseases and vegetation species), and near-infrared 2 (to study biomass) (Immitzer et al. 2012). Immitzer et al. (2012) demonstrated that WorldView-2 can pos- sibly detect urban forests because of its four new ©2016 International Society of Arboriculture bands (Pu 2009). Nevertheless, some misclas- sifications were observed in the classification of tree species because of spectral overlaps, complex structure of the area, and small tree crown, result- ing in mixed pixels. Airborne hyperspectral sensor is an excellent sensor that can be used to over- come spectral limitations. Ghiyamat and Shafri (2010) demonstrated that hyperspectral imagery provides adequate data to distinguish homog- enous and heterogeneous forest biodiversity, but urban areas present different environment char- acteristics and should be evaluated separately. Hyperspectral data present characteristics, such as narrow band, multi-channel, and continuous spectrum information, which can be used to detect urban vegetation (Hao et al. 2011). Several stud- ies on urban forests have been conducted using hyperspectral data (Wania and Weber 2007; Hao et al. 2011; Cho et al. 2012; Zhang and Qiu 2012; Adeline et al. 2013; Forzieri et al. 2013). Most researchers have demonstrated the effectiveness of hyperspectral data to accurately detect vegeta- tion and even tree species. Nevertheless, hyper- spectral data exhibit specific limitations, such as limited coverage, high volume, and high cost (Shafri et al. 2012), thereby compelling research- ers to use high-resolution satellite imageries. With technological developments in remote sensing, active sensors have been used to detect urban forests. Many traditional satellites can accurately detect tree species, but they can only delineate urban features and tree crowns in 2D by using reflected solar radiation. By contrast, active sensors, such as synthetic aperture radar (SAR) and light detection and ranging (LiDAR), can extract the shape of the tree crown and urban features in 3D even in the shadows and at night (Yao and Wei 2013; Zhou 2013; Maksymiuk et al. 2014). Therefore, the use of active sensors has improved the monitoring of urban forests. Although the benefits of RADAR sensors, such as SAR, to detect forests have been noted (Perko et al. 2010), related studies are mostly limited to large- scale natural forest classification and tree root eval- uation, whereas those on urban forests are rare and new. Maksymiuk et al. (2014) utilized SAR data with morphological attribute filters and detected single urban trees. Accordingly, further studies should be performed to evaluate the potential of SAR data to
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