Phenology and Plant Functional Type Link Optical Properties of Vegetation Canopies to Patterns of Vertical Vegetation Complexity

Abstract

Vegetation vertical complexity influences biodiversity and ecosystem productivity. Rapid warming in the boreal region is altering patterns of vertical complexity. LiDAR sensors offer novel structural metrics for quantifying these changes, but their spatiotemporal limitations and their need for ecological context complicate their application and interpretation. Satellite variables can estimate LiDAR metrics, but retrievals of vegetation structure using optical reflectance can lack interpretability and accuracy. We compare vertical complexity from the airborne LiDAR Land Vegetation and Ice Sensor (LVIS) in boreal Canada and Alaska to plant functional type, optical, and phenological variables. We show that spring onset and green season length from satellite phenology algorithms are more strongly correlated with vegetation vertical complexity (R = 0.43–0.63) than optical reflectance (R = 0.03–0.43). Median annual temperature explained patterns of vegetation vertical complexity (R = 0.45), but only when paired with plant functional type data. Random forest models effectively learned patterns of vegetation vertical complexity using plant functional type and phenological variables, but the validation performance depended on the validation methodology (R2 = 0.50–0.80). In correlating satellite phenology, plant functional type, and vegetation vertical complexity, we propose new methods of retrieving vertical complexity with satellite data.

Plant phenotyping relevance

植生キャノピーの垂直複雑性という明示的な植物構造形質を、LiDAR・衛星フェノロジー・機械学習で推定し、検証する方法研究であり、測定手法が中心的です。

Code and data availability

The paper's phenotyping/structural analysis is built entirely on publicly archived datasets cited with DOIs/URLs in the text: NASA LVIS full-waveform LiDAR L1B/L2 (ABoVE and LVIS Facility versions) providing the vertical complexity measurements, NEON vegetation structure in situ plant trait data, the ABoVE Landsat land