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Monday, April 9 • 5:30pm - 7:00pm
POSTER: Mapping Floristic Gradients of Forest Composition Using Ordination and Landsat OLI Imagery in Southern Ohio’s Central Hardwoods

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AUTHORS: Bryce T. Adams*, Stephen N. Matthews – School of Environment and Natural Resources, The Ohio State University; Matthew P. Peters, Louis R. Iverson – Northern Research Station, U.S. Forest Service

ABSTRACT: Forests of eastern North America are incurring rapid species turnover as a result of recent changes to natural and anthropogenic disturbance processes and climate change. We employed an ordination-regression approach to mapping the current species composition of forest assemblages as floristic gradients in a 5,000-km2 area in southern Ohio’s Central Hardwoods Forest Region. Forest plot data (n = 699 plots; 99 species/genera) that comprehensively sampled the composition of both overstory and understory woody species across structurally- (open to closed canopy) and topographically-variable forest conditions were projected onto a 3D ordination solution using non-metric multidimensional scaling. Floristic gradients, via their ordination scores, were related to spectral reflectance provided by a multitemporal Landsat 8 OLI image using the regression-type Random Forest model. Approximately 53%, 42%, and 18% of the variation in the first, second, and third axes were captured by the Landsat spectra, respectively. The three predicted axes were merged to a RGB color composite for the final floristic gradient map, displaying the 3D compositional complexity across the landscape in terms of variation in color. The color of each pixel subsequently references to its unique position within the original 3D ordination space and, thus, a statistical approximation of its specific species composition. Our case study determined that this approach is a highly effective means to mapping forest cover types, and remains an attractive alternative to traditional classifications, utilizing discrete classes or clusters, because it is time-efficient, more realistic in that compositional turnover is represented in continuous fields rather than arbitrary breaks, and it overcomes the generalization problem inherent in categorizing groups a priori. Moving forward, our model will be a valuable tool in developing suitable management options on individual forest stands for restoration of desired species, adapting to a changing climate, and improving wildlife habitat quality in southern Ohio’s forest lands.

Monday April 9, 2018 5:30pm - 7:00pm CDT
Monroe Room

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