A spatially continuous look at topographic, edaphic, geologic, and snow controls on conifer stand structure
Abstract
Quantifying the drivers of fine-scale heterogeneity in the structure, composition, and function of subalpine forests remains a foundational problem for forest ecology. Extreme topographic complexity can cause local microclimate and soil-moisture regimes—and, consequently, forest demographic rates and physiognomy—to vary dramatically over small horizontal distances. Prior work on forest structural diversity in mountain domains has been limited because it has relied almost exclusively on plot and transect observations, which do not capture the full range of variability in stand structure or environmental gradients. We generated spatially continuous metrics of conifer stand structure in Colorado's East River watershed and evaluated those metrics' covariance with coextensive environmental factors. Deploying a novel open-source workflow to post-process and analyze full-waveform LiDAR data, we created a conifer tree crown map and gridded stand structure products based on a 2018 NEON Airborne Observation Platform acquisition. We compared LiDAR-derived estimates of (a) tree crown geospatial location and height, (b) total stem density, and (c) basal area against in situ measurements of trees >1 cm DBH taken in 17 conifer forest plots (N≅7500) between 2018 and 2021. Agreement exceeded 90% on all three measures. Generalized additive models estimated relationships between stand structure metrics and topographic characteristics, soil substrate, underlying geology, and annual peak snow accumulation. Stem density, quadratic mean diameter, and 90th percentile of height each had a significant, approximately quadratic relationship with elevation, with maxima between 2700 m and 2900 m. These same structure metrics increased linearly with more easterly aspects and with increasing topographic position. Stem density was higher on less consolidated geologic substrates. Adjusted R2 for the multivariate models ranged from 0.301 to 0.457. Beyond confronting a foundational question in forest ecology, our effort yielded important benchmark data for vegetation demographic models and a baseline for evaluating future forest change.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFM.B26B..04W