As remote-sensing technologies become more powerful, their use for biodiversity conservation purposes is worth exploring. One such technology is the Wet Areas Mapping Tool (WAM). WAM is a spatial representation of a relative moisture index, based on fine-scale topography derived from LIDAR data. To create WAM, LIDAR-based DEMs are used to predict the locations of flow channels; estimated moisture values are then determined based on the distance from these predicted channels and known bodies of water. WAM is currently used by forestry companies to improve operational practices (e.g., minimize the risk of rutting and soil compaction by guiding the locations of roads). This study explores extending the use of WAM to conservation applications, such as identifying areas of high biodiversity. Specifically, our objective was to assess if WAM can be used to predict diversity or composition of the understory vascular plant community. To do this, we conducted understory vegetation surveys across a moisture gradient based on WAM values. The surveys were done in three different boreal forest types, conifer-dominated, deciduous-dominated, and mixedwood forests, in the unharvested forest stands located within the Ecosystem Management Emulating Natural Disturbance (EMEND) experimental area, in northwestern Alberta.
Results/Conclusions
Preliminary analyses showed that the relationship between WAM values and diversity values (Simpson’s and Shannon’s Diversity numbers) vary among forest types. Of the three forest types, only conifer-dominated stands show WAM having a significant positive relationship with understory diversity. WAM provides a range of moisture estimates based on different flow-initiation thresholds, with higher thresholds providing more conservative wetness estimates. Our results show that in conifer-dominated stands, the higher threshold estimates were better at predicting diversity. Further analyses will explore the differences in community composition along WAM moisture gradients and the effect of forest structure variables on understory diversity. Some forest structure variables are also provided by the same LIDAR data used to create WAM. Thus, if forest structure proves to be better at predicting diversity, diversity can still be predicted with remote-sensing technology. As of now, the preliminary results suggest that remote-sensing technology is capable of predicting understory diversity, in some forest types.