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Open AccessArticle

Monitoring Broadscale Vegetational Diversity and Change across North American Landscapes Using Land Surface Phenology

1
Eastern Forest Environmental Threat Assessment Center, USDA Forest Service, 200 W.T. Weaver Blvd., Asheville, NC 28804, USA
2
North Carolina Institute for Climate Studies, North Carolina State University, 155 Patton Ave., Asheville, NC 28801, USA
*
Author to whom correspondence should be addressed.
Forests 2020, 11(6), 606; https://doi.org/10.3390/f11060606
Received: 9 April 2020 / Revised: 19 May 2020 / Accepted: 25 May 2020 / Published: 27 May 2020
(This article belongs to the Special Issue Forest Biodiversity Conservation with Remote Sensing Techniques)
We describe a polar coordinate transformation of vegetation index profiles which permits a broad-scale comparison of location-specific phenological variability influenced by climate, topography, land use, and other factors. We apply statistical data reduction techniques to identify fundamental dimensions of phenological variability and to classify phenological types with intuitive ecological interpretation. Remote sensing-based land surface phenology can reveal ecologically meaningful vegetational diversity and dynamics across broad landscapes. Land surface phenology is inherently complex at regional to continental scales, varying with latitude, elevation, and multiple biophysical factors. Quantifying phenological change across ecological gradients at these scales is a potentially powerful way to monitor ecological development, disturbance, and diversity. Polar coordinate transformation was applied to Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series spanning 2000-2018 across North America. In a first step, 46 NDVI values per year were reduced to 11 intuitive annual metrics, such as the midpoint of the growing season and degree of seasonality, measured relative to location-specific annual phenological cycles. Second, factor analysis further reduced these metrics to fundamental phenology dimensions corresponding to annual timing, productivity, and seasonality. The factor analysis explained over 95% of the variability in the metrics and represented a more than ten-fold reduction in data volume from the original time series. In a final step, phenological classes (‘phenoclasses’) based on the statistical clustering of the factor data, were computed to describe the phenological state of each pixel during each year, which facilitated the tracking of year-to-year dynamics. Collectively the phenology metrics, factors, and phenoclasses provide a system for characterizing land surface phenology and for monitoring phenological change that is indicative of ecological gradients, development, disturbance, and other aspects of landscape-scale diversity and dynamics. View Full-Text
Keywords: biodiversity; ecological gradients; land surface phenology; landscape dynamics; phenological change; remote sensing biodiversity; ecological gradients; land surface phenology; landscape dynamics; phenological change; remote sensing
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Brooks, B.-G.J.; Lee, D.C.; Pomara, L.Y.; Hargrove, W.W. Monitoring Broadscale Vegetational Diversity and Change across North American Landscapes Using Land Surface Phenology. Forests 2020, 11, 606.

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