Improving broad scale forage mapping and habitat selection analyses with airborne laser scanning: the case of moose

Abstract

Abstract. Determining the spatial distribution of large herbivores is a key challenge in ecology and management. However, our ability to accurately predict this is often hampered by inadequate data on available forage and structural cover. Airborne laser scanning (ALS) can give direct and detailed measurements of vegetation structure. We assessed the effectiveness of ALS data to predict (1) the distribution of browse forage resources and (2) moose (Alces alces) habitat selection in southern Norway. Using ground reference data from 153 sampled forest stands, we predicted available browse biomass with predictor variables from ALS and/or forest inventory. Browse models based on both ALS and forest inventory variables performed better than either alone. Dominant tree species and development class of the forest stand remained important predictor variables and were not replaced by the ALS variables. The increased explanatory power from including ALS came from detection of canopy cover (negatively correlated with forage biomass) and understory density (positively correlated with forage biomass). Improved forage estimates resulted in improved predictive ability of moose resource selection functions (RSFs) at the landscape scale, but not at the home range scale. However, when also including ALS cover variables (understory cover density and canopy cover density) directly into the RSFs, we obtained the highest predictive ability, at both the landscape and home range scales. Generally, moose selected for high browse biomass, low amount of understory vegetation and for low or intermediate canopy cover depending on the time of day, season and scale of analyses. The auxiliary information on vegetation structure from ALS improved the prediction of browse moderately, but greatly improved the analysis of habitat selection, as it captured important functional gradients in the habitat apart from forage. We conclude that ALS is an effective and valuable tool for wildlife managers and ecologists to estimate the distribution of large herbivores.

The golden age of bio‐logging: how animal‐borne sensors are advancing the frontiers of ecology

“…Remote sensing data on vegetation structure, quality, and biomass can provide a solution for understanding the availability of resources to terrestrial herbivores. For instance, the MODIS satellite provides 250-m resolution data every eight days, allowing for inferences about the effects of vegetation phenology on animals (Bischof et al 2012), while 1-m resolution LiDAR imagery obtained from planes can characterize fine-scale vegetation structure such as canopy cover or understory biomass (Lone et al 2014). Pairing autonomous underwater vehicles (AUVs) or unmanned aerial vehicles (UAVs, or drones) with GPS tags is a promising emerging tool for measuring the prey available to both aquatic and terrestrial predators.…”

Predicting Selected Forest Stand Characteristics with Multispectral ALS Data

“…Since the late 1990s when the first studies on the application of ALS data in forestry and ecology appeared in the literature [3][4][5] numerous studies have been carried out using ALS data for a great variety of applications. In forestry, the main use is for the prediction of forest volume or other forest characteristics [6], while in the ecology community many studies are also related to animal habitat assessment [7][8][9]. ALS data are also widely used for the prediction of species diversity indices, like the Shannon and Simpson species diversity indices [10].…”

An efficient method to exploit LiDAR data in animal ecology

“…(), we calculated the understorey ( UStory ), midstory ( MStory ) and overstory vegetation ( OStory ). Each predictor described the proportion of returns within their height strata and captured information of the vegetation density (Lone, Loe, et al., ; Lone, van Beest, et al., ). Finally, we built the alternative models 6 and 7 using LiDAR‐PCs.…”

“…Light detection and ranging‐based metrics such as understorey density, canopy vertical distribution, canopy height and cover have been shown to be related to animal ecology across taxonomic groups (see table 1 in Davies & Asner, ; see table 1 in Simonson et al., ). Classification of LiDAR returns in height percentiles, fractional cover or forest density classes are an example of LiDAR‐derived metrics deployed in large herbivorous ecology (Lone, Loe, et al., ; Lone, van Beest, et al., ; Melin et al., ; Nijland, Nielsen, Coops, Wulder, & Stenhouse, ). Depending on the ecological question, researchers may decide to limit the use of LiDAR data describing vegetation within a certain height threshold, for example two metres above the ground for large herbivores, which is directly related to the feeding ecology of the target species (Ewald, Dupke, Heurich, Müller, & Reineking, ; Lone, van Beest, et al., ).…”

“…Classification of LiDAR returns in height percentiles, fractional cover or forest density classes are an example of LiDAR‐derived metrics deployed in large herbivorous ecology (Lone, Loe, et al., ; Lone, van Beest, et al., ; Melin et al., ; Nijland, Nielsen, Coops, Wulder, & Stenhouse, ). Depending on the ecological question, researchers may decide to limit the use of LiDAR data describing vegetation within a certain height threshold, for example two metres above the ground for large herbivores, which is directly related to the feeding ecology of the target species (Ewald, Dupke, Heurich, Müller, & Reineking, ; Lone, van Beest, et al., ). This approach, however, subsets LiDAR data based on what the researcher thinks an animal is looking for, rather than allowing the data to suggest a model based on where the animals have been recorded.…”