Accessible light detection and ranging: estimating large tree density for habitat identification

Abstract

Abstract. Large trees are important to a wide variety of wildlife, including many species of conservation concern, such as the California spotted owl (Strix occidentalis occidentalis). Light detection and ranging (LiDAR) has been successfully utilized to identify the density of large-diameter trees, either by segmenting the LiDAR point cloud into individual trees, or by building regression models between variables extracted from the LiDAR point cloud and field data. Neither of these methods is easily accessible for most land managers due to the reliance on specialized software, and much available LiDAR data are being underutilized due to the steep learning curve required for advanced processing using these programs. This study derived a simple, yet effective method for estimating the density of large-stemmed trees from the LiDAR canopy height model, a standard raster product derived from the LiDAR point cloud that is often delivered with the LiDAR and is easy to process by personnel trained in geographic information systems (GIS). Ground plots needed to be large (1 ha) to build a robust model, but the spatial accuracy of plot center was less crucial to model accuracy. We also showed that predicted large tree density is positively linked to California spotted owl nest sites.

Estimating Ladder Fuels: A New Approach Combining Field Photography with LiDAR

“…This is especially important when plots are located in heterogeneous landscapes or near roads, and when plot sizes are small (so a small shift in plot center equates to a dramatically different area of the LiDAR point cloud). While some LiDAR-derived metrics are not as sensitive to shifts in plot center location [97], many likely are, and plot accuracy should be better accounted for in all cases.…”

Spotted owl foraging patterns following fuels treatments, Sierra Nevada, California

“…California spotted owl foraging habitat use has received less research attention (Keane ); available studies report foraging habitat as occurring close to nest trees, with more open canopies and greater structural and compositional heterogeneity compared with nesting habitat, including late seral forest, broadleaf forest, and post‐burn areas (Call et al , Irwin et al , Bond et al , Williams et al , Eyes et al ). Analyses of spotted owl foraging habitat use have previously been limited by coarse‐resolution habitat data that categorizes vegetation into broad categories based on tree species composition, size classes, and canopy‐cover classes, precluding opportunities to describe finer‐resolution habitat use patterns at the patch‐scale used by owls (Kramer et al ). However, recent availability of vegetation data from remotely sensed Light Detection and Ranging (LiDAR) provides high‐resolution imaging of forest structure and pattern, and holds promise for improved understanding of spotted owl habitat use (García‐Feced et al , Ackers et al , Kramer et al , North et al ).…”

“…Analyses of spotted owl foraging habitat use have previously been limited by coarse‐resolution habitat data that categorizes vegetation into broad categories based on tree species composition, size classes, and canopy‐cover classes, precluding opportunities to describe finer‐resolution habitat use patterns at the patch‐scale used by owls (Kramer et al ). However, recent availability of vegetation data from remotely sensed Light Detection and Ranging (LiDAR) provides high‐resolution imaging of forest structure and pattern, and holds promise for improved understanding of spotted owl habitat use (García‐Feced et al , Ackers et al , Kramer et al , North et al ).…”

Evaluating the uncertainty of Landsat-derived vegetation indices in quantifying forest fuel treatments using bi-temporal LiDAR data

“…This area of forest was dominated by small to mid-sized conifers with relatively high density (approximately 67% of canopy cover before the treatment) as a result of long-term fire suppression before treatments (Tempel et al, 2015). The fuel treatments were designed to reduce ladder fuels, or the forest fuels that provide vertical fuel continuity and can preheat unignited canopy fuels in a fire (Kramer et al, 2016(Kramer et al, , 2014Menning and Stephens, 2007). Thus, the treatments concentrated on mechanical thinning at selected locations (Collins et al, 2011b) and focused on low and mid-strata of canopy, with small to medium sized trees removed.…”

“…Laser pulses emitted by LiDAR sensor can penetrate through the forest canopy, and therefore, are less impacted by the shadowing or saturation effects (Ma et al, 2017a;Su et al, 2016a). Airborne LiDAR data combined with field measurements have been successfully applied to map tree height (Naesset, 1997;Naesset and Bjerknes, 2001), large tree density (Kramer et al, 2016), crown base height (Popescu and Zhao, 2008), canopy cover (Korhonen et al, 2011;Ma et al, 2017a), leaf area index (Korhonen and Morsdorf, 2014;Zheng and Moskal, 2009), fire-related forest stand structure metrics (Blanchard et al, 2011;Jakubowksi et al, 2013;Kelly et al, 2017;Kramer et al, 2014), and aboveground biomass (AGB) (Dalponte et al, 2018;Li et al, 2015;Luo et al, 2017;Su et al, 2016b;Tao et al, 2014;Zhao et al, 2012) from the individual tree to forest stand scale. LiDAR data have been increasingly used as an alternative or auxiliary data source in forest inventory (Korhonen et al, 2011;Wulder et al, 2012b).…”