Ecologically-Based Metrics for Assessing Structure in Developing Area-Based, Enhanced Forest Inventories from LiDAR. Issue 1 (2nd January 2019) - Ayrey, Elias; Hayes, Daniel J.; Fraver, Shawn; Kershaw, John A.; Weiskittel, Aaron R.
Abstract: The authors developed a series of ecological metrics (EM) based on mechanistic principles for quantifying light detection and ranging (LiDAR) to develop forest inventories. These fall into 5 categories: canopy height, canopy complexity, individual tree attributes, crowding, and abiotic. The authors compared the effectiveness of the EMs with more traditional metrics (e.g., height percentiles) for modeling biomass, tree count, and species. They then examined each model's ability to transfer to different LiDAR datasets. They found that models based on the EMs performed similarly to those using traditional metrics on a single dataset, while facilitating transference to LiDAR of different density, seasonality, location, and type. Models based on the EMs resulted in an average of 15% less root mean squared error and 331% less bias when transferred, as opposed to traditional metrics. The authors also noted that different EMs were useful for predicting contrasting attributes. Those EMs that quantify height and size were important predictors of biomass. Those that quantify cover, individual tree tallies, shape, and canopy roughness were important predictors of tree count, while those that quantify canopy roughness and sensor parameters were important predictors of species. The authors conclude that the EMs can be useful predictors of forest attributes, and offer analysts better ecological reasoning for LiDAR-based inventories.