Canadian Forest Service Publications
Fine‐Spatial Scale Predictions of Understory Species Using Climate and LiDAR‐Derived Terrain and Canopy Metrics. 2014. Nijland, W.; Coops, N.C.; Nielson, S.E.; Wulder, M.A.; Stenhouse, G. Journal of Applied Remote Sensing. 8(1). 16 p.
Issued by: Pacific Forestry Centre
Catalog ID: 35619
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Food and habitat resources are critical components wildlife management and conservation efforts. Grizzly bear (Ursus arctos) have diverse diets and habitat requirements particularly for understory plant species which are impacted by human developments and forest management activities. In this paper use Light Detection and Ranging (LiDAR) data to predict the occurrence of understorey plant species relevant to bear forage and compare our predictions to more conventional climate‐ and land cover‐based models. We use boosted regression trees to model each of the 14 understory species across 4435 km2 using occurrence (presence‐absence) data from 1,941 field plots. Three sets of models were fitted: climate‐only, climate and basic land and forest cover from Landsat 30m imagery, and third a climate and LiDAR‐derived model describing both the terrain and forest canopy. Resulting model accuracies varied widely among species. Overall, 8 of 14 species models were improved by including the LiDAR‐derived variables. For climate‐only models, mean annual precipitation and frost-free period were most important variables. With inclusion of LiDAR‐derived attributes, Depth to water table, terrain‐intercepted annual radiation, and elevation were most often selected. This suggests fine‐scale terrain conditions affect the distribution of the studied species more than canopy conditions.
Plain Language Summary
Understory vegetation (plant life that grows beneath the forest canopy) provides important food and habitat for Grizzly bears. Management agencies in Alberta are trying to balance the province’s economic development needs with the conservation needs of the grizzly bear. To achieve this they need to predict grizzly bear habitat relationships, including knowing what foods (plants) can be found where and when through the year. This requires understanding the horizontal distribution of the understory vegetation. Models that relate plant species presence/absence data to environmental variables (e.g. climate, soil, etc.) are commonly used to predict species abundance and occurrence. Land cover information (such as forest cover) derived from optical remote sensing imagery have been shown to improve these models. LiDAR (light detection and ranging) shows even more promise in estimating understory attributes. In this paper, scientists use LiDAR data to predict the occurrence of 14 understory plant species relevant to bear forage. The field data was collected in the Rocky Mountains and Foothill area in western Alberta. The researchers compare their LiDAR predictions to land cover-based models to determine if the LiDAR data improves their understanding of local distribution of bear foods. Three sets of models were compared: i) climate only; ii) climate and basic land and forest cover from Landsat imagery; and iii) a climate and LiDAR-derived model describing both the terrain and forest canopy. They found that plant distribution models developed with a combination of both broad-scale climate data and LiDAR-derived terrain and canopy information provided the best overall performance.