Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 3 August 2021 doi:10.20944/preprints202108.0078.v1 Article Modeling Insolation, Multi-spectral Imagery and LiDAR Point-cloud Metrics to Predict Plant Diversity in a Temperate Montane Forest Paul Dunn 1,* and Leonhard Blesius 2 1 Department of Geography and Environment, San Francisco State University;
[email protected] 2 Department of Geography and Environment, San Francisco State University;
[email protected] * Author to whom correspondence should be addressed. Abstract: Incident solar radiation (insolation) passing through the forest canopy to the ground sur- face 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 var- iables for the regression equation using canopy structure metrics as predictors. Independent varia- bles 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 veg- etation density to predict understory plant diversity.