Dunn, P.C.; Blesius, L. Modeling Insolation, Multi-Spectral Imagery and LiDAR Point-Cloud Metrics to Predict Plant Diversity in a Temperate Montane Forest. Geographies2021, 1, 79-103.
Dunn, P.C.; Blesius, L. Modeling Insolation, Multi-Spectral Imagery and LiDAR Point-Cloud Metrics to Predict Plant Diversity in a Temperate Montane Forest. Geographies 2021, 1, 79-103.
Dunn, P.C.; Blesius, L. Modeling Insolation, Multi-Spectral Imagery and LiDAR Point-Cloud Metrics to Predict Plant Diversity in a Temperate Montane Forest. Geographies2021, 1, 79-103.
Dunn, P.C.; Blesius, L. Modeling Insolation, Multi-Spectral Imagery and LiDAR Point-Cloud Metrics to Predict Plant Diversity in a Temperate Montane Forest. Geographies 2021, 1, 79-103.
Abstract
Incident solar radiation (insolation) passing through the forest canopy to the ground surface is either absorbed or scattered. This phenomenon, known as radiation attenuation, is measured using the extinction coefficient (K). The amount of radiation at the ground surface of a given site is effectively controlled by the canopy’s surface and structure, determining its suitability for plant species.Menhinick’s and Simpson biodiversity indexes were selected as spatially explicit response variables for the regression equation using canopy structure metrics as predictors. Independent variables include modeled area solar radiation, LiDAR derived canopy height, effective leaf area index data derived from multi-spectral imagery, and canopy strata metrics derived from LiDAR point-cloud data. The results support the hypothesis that, 1.) canopy surface and strata variability may be associated with understory species diversity due to habitat partitioning and radiation attenuation, and that, 2.) such a model can predict both this relationship and biodiversity clustering.The study data yielded significant correlations between predictor and response variables and was used to produce a multiple-linear model comprising canopy relief, texture of heights, and vegetation density to predict understory plant diversity. When analyzed for spatial autocorrelation, the predicted biodiversity data exhibited non-random spatial continuity.
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