Mapping multi‐scale vascular plant richness in a forest landscape with integrated LiDARand hyperspectral remote‐sensing

Abstract The central role of floristic diversity in maintaining habitat integrity and ecosystem function has propelled efforts to map and monitor its distribution across forest landscapes. While biodiversity studies have traditionally relied largely on ground‐based observations, the immensity of the task of generating accurate, repeatable, and spatially‐continuous data on biodiversity patterns at large scales has stimulated the development of remote‐sensing methods for scaling up from field plot measurements. One such approach is through integrated Li DAR and hyperspectral remote‐sensing. However, despite their efficiencies in cost and effort, Li DAR ‐hyperspectral sensors are still highly constrained in structurally‐ and taxonomically‐heterogeneous forests ‐ especially when species’ cover is smaller than the image resolution, intertwined with neighboring taxa, or otherwise obscured by overlapping canopy strata. In light of these challenges, this study goes beyond the remote characterization of upper canopy diversity to instead model total vascular plant species richness in a continuous‐cover North Carolina Piedmont forest landscape. We focus on two related, but parallel, tasks. First, we demonstrate an application of predictive biodiversity mapping, using nonparametric models trained with spatially‐nested field plots and aerial Li DAR ‐hyperspectral data, to predict spatially‐explicit landscape patterns in floristic diversity across seven spatial scales between 0.01–900 m 2 . Second, we employ bivariate parametric models to test the significance of individual, remotely‐sensed predictors of plant richness to determine how parameter estimates vary with scale. Cross‐validated results indicate that predictive models were able to account for 15–70% of variance in plant richness, with Li DAR ‐derived estimates of topography and forest structural complexity, as well as spectral variance in hyperspectral imagery explaining the largest portion of variance in diversity levels. Importantly, bivariate tests provide evidence of scale‐dependence among predictors, such that remotely‐sensed variables significantly predict plant richness only at spatial scales that sufficiently subsume geolocational imprecision between remotely‐sensed and field data, and best align with stand components including plant size and density, as well as canopy gaps and understory growth patterns. Beyond their insights into the scale‐dependent patterns and drivers of plant diversity in Piedmont forests, these results highlight the potential of remotely‐sensible essential biodiversity variables for mapping and monitoring landscape floristic diversity from air‐ and space‐borne platforms.