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Title: Spring and autumn phenological variability across environmental gradients of Great Smoky Mountains National Park, USA

Author: Norman, Steven P.; Hargrove, William W.; Christie, William M.;

Date: 2017

Source: Remote Sensing

Publication Series: Scientific Journal (JRNL)

Description: Mountainous regions experience complex phenological behavior along climatic, vegetational and topographic gradients. In this paper, we use a MODIS time series of the Normalized Difference Vegetation Index (NDVI) to understand the causes of variations in spring and autumn timing from 2000 to 2015, for a landscape renowned for its biological diversity. By filtering for cover type, topography and disturbance history, we achieved an improved understanding of the effects of seasonal weather variation on land surface phenology (LSP). Elevational effects were greatest in spring and were more important than site moisture effects. The spring and autumn NDVI of deciduous forests were found to increase in response to antecedent warm temperatures, with evidence of possible cross-seasonal lag effects, including possible accelerated green-up after cold Januarys and early brown-down following warm springs. Areas that were disturbed by the hemlock woolly adelgid and a severe tornado showed a weaker sensitivity to cross-year temperature and precipitation variation, while low severity wildland fire had no discernable effect. Use of ancillary datasets to filter for disturbance and vegetation type improves our understanding of vegetation’s phenological responsiveness to climate dynamics across complex environmental gradients

Keywords: land surface phenology, MODIS, NDVI, PhenoCam, monitoring, landscape ecology, chilling degree days, growing degree days, legacy effects, biodiversity

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  • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.

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Norman, Steven P.;Hargrove, William W.; Christie, William M. 2017.Spring and autumn phenological variability across environmental gradients of Great Smoky Mountains National Park, USA. Remote Sensing. 9(5): 407-424. 18 p.  https://doi.org/10.3390/rs9050407.

 


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