Spectral heterogeneity predicts local-scale gamma and beta diversity of mesic grasslands

Authors: Polley, Herbert; Yang, Chenghai; WILSEY, BRIAN - Iowa State University; Fay, Philip

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/20/2019
Publication Date: 2/23/2019
Citation: Polley, H.W., Yang, C., Wilsey, B.J., Fay, P.A. 2019. Spectral heterogeneity predicts local-scale gamma and beta diversity of mesic grasslands. Remote Sensing. 11(4):458. https://doi.org/10.3390/rs11040458.
DOI: https://doi.org/10.3390/rs11040458

Interpretive Summary: Productivity and other ecosystem functions often are greater in grasslands in which plant species diversity, as influenced by both species numbers and relative abundances, is high than low. Field-based measurements of diversity are expensive, labor-intensive, and typically limited in spatial extent. Airborne remote sensing, by contrast, could provide rapid, cost-efficient assessments of diversity at the multiple spatial scales relevant to management and conservation. We tested the utility of using spatial differences in the remotely-sensed reflectance of sunlight from grassland canopies to model both spatial turnover in grassland species composition and abundances and total species diversity. We calculated diversity and spatial turnover in species from field measurements of the number and relative abundances of plant species across plots of two sizes (0.45 m2 and 35.2 m2) in Texas grasslands. Canopy reflectance in the visible through near infrared portion of sunlight was measured from an airborne platform. We found that statistical models of among-plot variation in reflectance explained 59%-85% of variance in total diversity and species change in space. Modeled diversity and turnover were more sensitive by a factor of 3 to a given level of among-plot variation in reflectance when analyzed across plots of small than larger size. As estimated from calibrated statistical models, spatial differences in species were greater, but total diversity was smaller for restored grassland on a lowland clay than upland silty clay soil. Our analyses indicate that both plant species diversity and spatial turnover in grassland species can be modeled by using spatial variation in reflected sunlight from vegetation. Diversity is linked to grassland productivity and other functions. Airborne remote sensing provides a promising alternative to field-based measurements to assess this critical predictor of grassland functioning.

Technical Abstract: Plant species diversity is an important metric of ecosystem functioning, but field assessments of diversity are constrained in number and spatial extent by labor and other expenses. We tested the utility of using spatial heterogeneity in the remotely-sensed reflectance spectrum of grassland canopies to model both spatial turnover in species composition and abundances (beta diversity) and species diversity at aggregate spatial scales (gamma diversity). Shannon indices of gamma and multiplicative beta diversity were calculated from field measurements of the number and relative abundances of plant species at each of two spatial grains (0.45 m2 and 35.2 m2) in mesic grasslands in central Texas, USA. Spectral signatures of reflected radiation at each grain were measured from ground-level or a UAV. Partial Least Squares Regression (PLSR) models explained 59%-85% of variance in gamma diversity and 68%-79% of variance in beta diversity using spatial heterogeneity in canopy optical properties. Modeled diversity was more sensitive by a factor of 3 to a given level of spectral heterogeneity when derived from data collected at the small than larger spatial grain. As estimated from calibrated PLSR models, beta diversity was greater, but gamma diversity was smaller for restored grassland on a lowland clay than upland silty clay soil. Both gamma and beta diversity of grassland can be modeled by using spatial heterogeneity in vegetation optical properties provided that the grain of reflectance measurements is conserved.