AN AUTOMATED OBJECT-BASED APPROACH TO DETECT SIREX ...

AN AUTOMATED OBJECT-BASED APPROACH TO DETECT SIREX-INFESTATION IN PINES Nishan Bhattarai, Graduate Student Lindi J. Quackenbush, Associate Professor Laura Calandra, Graduate Student Jungho Im, Assistant Professor Stephen A. Teale, Associate Professor [email protected], [email protected], [email protected], [email protected], [email protected]

ABSTRACT This paper explores an automated object-based method to characterize healthy and infested trees crowns in high resolution imagery. The objective was to utilize an automated binary class detection model and an active contour and hill climbing algorithm to investigate Sirex (Sirex noctilio) infestation in pines at the crown level. Healthy and Sirex-infested pines in two Sirex-infested plots were identified in the field and then 13 different spectral indices derived from 8-band pan-sharpened WorldView-2 imagery (0.5 m) were used for characterizing infestation in the pines at single pixel, 3×3 neighborhood, and crown levels. The classification accuracies from using the automated binary class detection model for single pixel and 3×3 neighborhoods were around 69-73% (kappa = 0.39-0.45), which slightly increased to around 72-75% (kappa = 0.45-0.5), when the sample tree locations represented by delineated tree crowns were used. The overall accuracy and kappa were further increased to 81% and 0.6, respectively, when assessment was made at tree crown level, using each delineated crown as an object. Three spectral indices: plant senescence reflectance index (PSRI), pigment specific simple ratio (PSSR), and structure insensitive pigment index (SIPI) were found to be most useful amongst the 13 indices in terms of accurately classifying healthy pines and Sirex-infested pines at mid-stage infestation. The study concluded that automated, object-based analysis can be used to investigate forest infestation at the individual crown level with good accuracies. KEYWORDS: active contour and hill climbing algorithm, automated binary class detection model, Sirex woodwasp, spectral indices, WorldView-2 imagery

INTRODUCTION Invasive species are a major challenge to the management of forests in the northeastern United States. Sirex woodwasp (Sirex noctilio) is one such invasive forest pest that is now infesting pines in the area. Sirex, native to Europe, Asia, and northern Africa has already caused extensive damage to pine forests in Australia, New Zealand, South America, and Africa (Ciesla, 2003). Hence, it is important to monitor potential spread of Sirex in northeastern forests. Traditional approaches for monitoring forest health, such as visual assessment from aerial and/or ground surveys, are important, but labor- and time-intensive, and are subject to human biases (Coops et al., 2003). Recent advances in remote sensing—in terms of spectral and spatial resolution—have increased the potential to provide more efficient processing than traditional approaches in terms of time, labor, and cost. Vegetation indices from satellite imagery with medium spatial and spectral resolution have been widely used in monitoring forest health (e.g., Joria et al., 1991; Luther et al., 1997; Royle and Lathrop, 1997). Commonly used indices include normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), utilizing the mid and near infrared bands (Gao, 1996; Royle and Lathrop, 2002; Wang et al., 2007). Other broadand narrow-band vegetation indices, derived from a combination of specific spectral bands, have been used for investigating vegetation health (Shafri et al., 2006; Zhao et al., 2005). While raster-based analysis often considers the relationship between adjacent pixels (Jensen, 2005; Darwish et al., 2003), all these studies used the most conventional method of image analysis, i.e., pixel by pixel analysis. However, the increasing availability of high resolution imagery has facilitated recent moves by researchers to focus on objects or segments as a defining unit of the imagery (Chubey et al., 2006). For high resolution imagery, pixel by pixel analysis may not be appropriate due to high variability amongst pixel values (Jensen, 2005). High spatial resolution satellite images have the potential for providing accurate information at the tree level using advanced techniques. For example, individual tree crowns can be detected and delineated using a number of algorithms such as watershed segmentation (Wang et al., 2004), region

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growing (Culvenor, 2002) and valley-following (Gougeon, 1995). However, these algorithms were developed for vertical imagery and produced lower accuracy (