Regional Variation in Forest Canopy Height and Implications for Koala (Phascolarctos cinereus) Habitat Mapping and Forest Management

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Previous research has shown that the Koala (Phascolarctos cinereus) prefers larger trees, potentially making this a key factor influencing koala habitat quality. Generally, tree height is considered at regional scales which may overlook variation at patch or local scales. In this study, we aimed to derive a set of parameters to assist in classifying koala habitat in terms of tree height, which can then be used as an overlay for existing habitat maps. To determine canopy height variation within a specific forest community across a broad area in eastern Australia, we used freely available Airborne Laser Scanning (ALS) data and adopted a straightforward approach by extracting maximum-height ALS returns within a total of 288 30 m × 30 m “virtual” ALS plots. Our findings show that while maximum tree heights generally fall within published regional-scale parameters (mean height 33.2 m), they vary significantly between subregions (mean height 28.8–39.0 m), within subregions (e.g., mean height 21.3–29.4 m), and at local scales, the tree heights vary in response to previous land-use (mean height 28.0–34.2 m). A canopy height dataset useful for habitat management needs to recognise and incorporate these variations. To examine how this information might be synthesised into a usable map, we used a wall-to-wall canopy height map derived from ALS to investigate spatial and nonspatial clustering techniques that capture canopy height variability at both intra-subregional (100s of hectares) and local (60 hectare) scales. We found that nonspatial K-medians clustering with three or four height classes is suited to intra-subregional extents because it allows for simultaneous assessment and comparison of multiple forest community polygons. Spatially constrained clustering algorithms are suited to individual polygons, and we recommend the use of the Redcap algorithm because it delineates contiguous height classes recognisable on a map. For habitat management, an overlay combining these height classification approaches as separate attributes would provide the greatest utility at a range of scales. In addition to koala habitat management, canopy height maps could also assist in managing other fauna; identifying forest disturbance, regenerating forest, and old-growth forest; and identifying errors in existing forest maps.