Next Article in Journal
Downscaling of MODIS One Kilometer Evapotranspiration Using Landsat-8 Data and Machine Learning Approaches
Next Article in Special Issue
Associations of Leaf Spectra with Genetic and Phylogenetic Variation in Oaks: Prospects for Remote Detection of Biodiversity
Previous Article in Journal / Special Issue
The Optimal Leaf Biochemical Selection for Mapping Species Diversity Based on Imaging Spectroscopy
Open AccessArticle

Integrated Analysis of Productivity and Biodiversity in a Southern Alberta Prairie

Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, AB T6G 2E3, Canada
Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
Department of Renewable Resources, University of Alberta, Edmonton, AB T6G 2H1, Canada
Institute of Agro-Environmental & Forest Biology (IBAF), National Research Council (CNR), Porano 05010, Italy
Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Department of Geography, University of California, Santa Barbara, CA 93106, USA
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Susan L. Ustin, Parth Sarathi Roy and Prasad S. Thenkabail
Remote Sens. 2016, 8(3), 214;
Received: 2 December 2015 / Revised: 20 February 2016 / Accepted: 29 February 2016 / Published: 8 March 2016
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
Grasslands play important roles in ecosystem production and support a large farming and grazing industry. An accurate and efficient way is needed to estimate grassland health and production for monitoring and adjusting management to get sustainable products and other ecosystem services. Previous studies of grasslands have shown varying relationships between productivity and biodiversity, with most showing either a positive or a hump-shaped relationship where productivity peaks at intermediate diversity. In this study, we used airborne imaging spectrometry combined with ground sampling and eddy covariance measurements to estimate the spatial pattern of production and biodiversity for two sites of contrasting productivity in a southern Alberta prairie ecosystem. Resulting patterns revealed that more diverse sites generally had greater productivity, supporting the hypothesis of a positive relationship between production and biodiversity for this site. We showed that the addition of evenness to richness (using the Shannon Index of dominant species instead of the number of dominant species alone) improved the correlation with optical diversity, an optically derived metric of biodiversity based on the coefficient of variation in spectral reflectance across space. Similarly, the Shannon Index was better correlated with productivity (estimated via NDVI (Normalized Difference Vegetation Index)) than the number of dominant species alone. Optical diversity provided a potent proxy for other more traditional biodiversity metrics (richness and Shannon index). Coupling field measurements and imaging spectrometry provides a method for assessing grassland productivity and biodiversity at a larger scale than can be sampled from the ground, and allows the integrated analysis of the productivity–biodiversity relationship over large areas. View Full-Text
Keywords: grassland; remote sensing; biomass; eddy covariance; biodiversity grassland; remote sensing; biomass; eddy covariance; biodiversity
Show Figures

Graphical abstract

MDPI and ACS Style

Wang, R.; Gamon, J.A.; Emmerton, C.A.; Li, H.; Nestola, E.; Pastorello, G.Z.; Menzer, O. Integrated Analysis of Productivity and Biodiversity in a Southern Alberta Prairie. Remote Sens. 2016, 8, 214.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop