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Geospatial Analysis of the Effects of Canopy Insolation Partitioning on Biodiversity in a Temperate Montane Forest
Incident solar radiation (insolation) passing through the forest canopy to the ground surface is either absorbed or scattered. This phenomenon is known as attenuation and is measured in forest ecology using the extinction coefficient (K). The effect of K on understory photosynthetically available radiation (PAR) and microclimate may be associated with plant species diversity, as distinct species and communities have unique habitat requirements. The objective of this study is to model insolation and canopy structure to observe effects of predictors representative of K on understory plant biodiversity using remotely sensed and botanical field data. We used two taxonomic diversity indexes (Menhinick’s and Simpson) to describe the surveyed plant community in a natural temperate montane forest, modeling the index values at the plot level as response variables. Independent variables included localized area incident solar radiation estimated using a solar model, LiDAR derived canopy height model, effective leaf area index (LAI) estimates derived from multi-spectral imagery and canopy strata metrics derived from LiDAR point cloud data. Considering the impact of atmospheric components above the canopy layer and an assumption that incident short- wave solar radiation to Earth’s vegetated surfaces is primarily absorbed in the canopy layer, we used a multiple linear model to predict canopy metrics controlling the sub- canopy surface radiant flux to develop the hypothesis that 1.) canopy structural variability is associated with the biodiversity of stand plant species through habitat partitioning and, 2.) a prediction model can be developed to validate this relationship spatially. The available data indicated many and varied correlations between predictor and response variables as well as a statistically valid linear model comprising the canopy relief, the texture, and vegetation density with understory plant diversity. When analyzed for spatial autocorrelation, the predicted biodiversity data exhibited non-random spatial continuity.