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Modeling plant composition as community continua in a forest landscape with L i DAR and hyperspectral remote sensing
Author(s) -
Hakkenberg C. R.,
Peet R. K.,
Urban D. L.,
Song C.
Publication year - 2018
Publication title -
ecological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.864
H-Index - 213
eISSN - 1939-5582
pISSN - 1051-0761
DOI - 10.1002/eap.1638
Subject(s) - lidar , remote sensing , hyperspectral imaging , environmental science , vegetation (pathology) , vascular plant , plant community , context (archaeology) , geography , ecology , species richness , biology , medicine , pathology , archaeology
Abstract In light of the need to operationalize the mapping of forest composition at landscape scales, this study uses multi‐scale nested vegetation sampling in conjunction with Li DAR ‐hyperspectral remotely sensed data from the G‐Li HT airborne sensor to map vascular plant compositional turnover in a compositionally and structurally complex North Carolina Piedmont forest. Reflecting a shift in emphasis from remotely sensing individual crowns to detecting aggregate optical‐structural properties of forest stands, predictive maps reflect the composition of entire vascular plant communities, inclusive of those species smaller than the resolution of the remotely sensed imagery, intertwined with proximate taxa, or otherwise obscured from optical sensors by dense upper canopies. Stand‐scale vascular plant composition is modeled as community continua: where discrete community‐unit classes at different compositional resolutions provide interpretable context for continuous gradient maps that depict n ‐dimensional compositional complexity as a single, consistent RGB color combination. In total, derived remotely sensed predictors explain 71%, 54%, and 48% of the variation in the first three components of vascular plant composition, respectively. Among all remotely sensed environmental gradients, topography derived from Li DAR ground returns, forest structure estimated from Li DAR all returns, and morphological‐biochemical traits determined from hyperspectral imagery each significantly correspond to the three primary axes of floristic composition in the study site. Results confirm the complementarity of Li DAR and hyperspectral sensors for modeling the environmental gradients constraining landscape turnover in vascular plant composition and hold promise for predictive mapping applications spanning local land management to global ecosystem modeling.