A Multi-scale Segmentation Approach to Mapping Seagrass Habitats Using Airborne Digital Camera Imagery

The purpose of this study was to map the areal extent and density of submerged aquatic vegetation, principally the seagrasses, Zostera marina and Ruppia maritima, as part of ongoing monitoring for the Barnegat Bay, New Jersey National Estuary Program. We examine the utility of multiscale image segmentation/object-oriented image classification using the eCognition software to map seagrass across our 36,000 ha study area. The multi-scale image segmentation/ object oriented classification approach closely mirrored our conceptual model of the spatial structure of the seagrass habitats and successfully extracted the features of ecological interest. The agreement between the mapped results and the original field reference was 68 percent (Kappa 56.5 percent) for the four category map and 83 percent (Kappa 63.1 percent) for the presence/absence map; the agreement between the mapped results and the independent reference data was 71 percent (Kappa 43.0 percent) for a simple presence/absence map. While the aerial digital camera imagery employed in this study had the advantage of flexible acquisition, suitable image scale, fast processing return time, and comparatively low cost, it had inconsistent radiometric response from image to image. This inconsistency made it difficult to develop a rule-based classification that was universally applicable across the 14 individual image mosaics. However, within the individual scene mosaics, using the eCognition software in a “manual classification” mode provided a flexible and time effective approach to mapping seagrass habitats. Introduction Submerged aquatic vegetation (SAV) is a term used to describe a variety of estuarine and marine plants including seagrasses. Due to their important ecological role in many coastal ecosystems as well as their sensitivity to degraded water quality, seagrass has been widely adopted as an indicator of estuarine ecosystem health (Orth and Moore, 1983; Dennison et al. 1993; Short and Wyllie-Echevarria, 1996; Duarte, 1999). SAV has been adopted by the Barnegat Bay National Estuary Program as one of the key indicators of the environmental health of the Barnegat Bay system. Of special concern are the bay’s two principal species of seagrass, eelgrass (Zostera marina) and widgeon grass (Ruppia maritima). The bay’s seagrasses are an important element of the bay ecosystem, because they harness energy and nutrients that are consumed by other organisms. The A Multi-scale Segmentation Approach to Mapping Seagrass Habitats Using Airborne Digital Camera Imagery Richard G. Lathrop, Paul Montesano, and Scott Haag seagrass beds also provide a critical structural component in an otherwise barren sandy bottom, serving as essential habitat for a host of organisms from shellfish and crabs to fish and waterfowl. However, in recent years the bay’s seagrasses have suffered due to a host of problems including declining water quality, dredging, brown tides, macroalgal infestation, boat scarring, and disease. Due to the important role that seagrasses play in estuaries, there has been a considerable effort at developing sampling and mapping techniques to quantify the spatial distribution, biomass and health of seagrass communities, and monitor changes over time (Caloz and Collet, 1997; Lehmann and Lachavanne, 1997). Remote sensing approaches have seen increasing application to the mapping of seagrass beds due to their synoptic perspective and cost-effective mapping over large areas. Aerial photography (Zieman et al., 1989; Ferguson et al., 1993; Robbins, 1997; Kendrick et al., 2000; Kurz et al., 2000; Moore et al., 2000), airborne digital scanning systems (Clark et al., 1997; Mumby et al., 1997; Jaubert et al., 2003, Garono et al., 2004) as well as satellite-based remote sensing (e.g., Landsat Thematic Mapper) (Ackleson and Klemas, 1987; Ferguson and Korfmacher, 1997; Mumby et al., 1997; Ward et al., 1997) have all been shown to be effective in seagrass mapping and monitoring. In general, airborne imagery acquisition, rather than satellite imagery, finds wider application in seagrass monitoring as it provides high spatial resolution imagery, as well as greater flexibility in meeting often rigid temporal constraints for the optimal acquisition of imagery (i.e., sun angle, tide, wind, water, and clarity) (Dobson et al., 1995). More recently, there has been a move towards direct digital acquisition with digital framing cameras or scanning systems rather than analog film cameras. The most widely adopted approach for operational monitoring has been the visual interpretation and mapping from analog aerial photography (Zieman et al., 1989; Ferguson et al., 1993; Robbins, 1997; Kendrick et al., 2000; Kurz et al., 2000; Moore et al., 2000). This may or may not be followed up with digitizing of the maps for incorporation into a geographic information system (GIS). More recently, direct GIS capture through digital photogrammetric techniques or heads-up digitizing of digital rectified photography has seen wider application (Dobson et al., 1995). As an alternative to visual interpretation, per-pixel based multi-spectral classification approaches have been applied to airborne scanner and satellite imagery (Ackleson and Klemas, 1987; Ferguson and Korfmacher, 1997; PHOTOGRAMMETRIC ENGINEER ING & REMOTE SENS ING J u n e 2006 665 Center for Remote Sensing & Spatial Analysis, 14 College Farm Road, Rutgers University New Brunswick, NJ 08901-8551 ([email protected]). Photogrammetric Engineering & Remote Sensing Vol. 72, No. 6, June 2006, pp. 665–675. 0099-1112/06/7206–0665/$3.00/0 © 2006 American Society for Photogrammetry and Remote Sensing 04-131.qxd 5/13/06 12:42 PM Page 665