Bug Off! Woolly Adelgid Induced Hemlock Decline in the Great Smoky Mountains

Project Team: Great Smoky Mountains Ecological Forecasting Team
Team Location: University of Georgia, Athens, Georgia


Thirteen-year spatiotemporal analysis of eastern hemlock defoliation in Great Smoky Mountains National Park.

Thirteen-year spatiotemporal analysis of eastern hemlock defoliation in Great Smoky Mountains National Park.



Authors:
Jiaying He, Project Lead (University of Georgia)
Steven Weaver (University of Georgia)
Corbin Kling (University of Georgia)
Zennure Ucar (University of Georgia)


Mentors/Advisors:
Dr. Marguerite Madden (Department of Geography, University of Georgia)
Steve Padgett-Vasquez (Department of Geography, University of Georgia)


Past/Other Contributors:
Dr. Sergio Bernardes (Department of Geography, University of Georgia)
Auryn Baruch (University of Georgia)
Ning Chen (University of Georgia)


Abstract:


Evergreen eastern hemlock (Tsuga canadensis L.) trees play an ecologically vital role within Eastern deciduous forests by providing a unique habitat for many species of flora and fauna. The hemlocks in the Southern Appalachian Mountain region are currently facing an infestation of the non-native Hemlock Woolly Adelgid (HWA, Adelges tsugae), which feeds on and causes tree mortality. Discovered in Great Smoky Mountains National Park (GRSM) in 2002, the HWA have rapidly spread through this biodiverse forest due to a lack of native predators. This project was designed to map the spatiotemporal onset, spread, and extent of hemlock defoliation using data from NASA’s Earth Observing System (EOS). Additionally, the project compared the results to those of the U.S. Forest Service’s national forest disturbance monitoring system, ForWarn. Landsat 5 and Landsat 8 imagery were used to create yearly Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) datasets to evaluate health conditions of Eastern hemlocks in GRSM. Change detection methods, including image differencing and temporal segmentation algorithms, were used to identify spatiotemporal defoliation patterns. For the first time, trends in HWA forest decline in GRSM were created and compared to MODIS derived decline from ForWarn. The methodology and results from this project: 1) support forest management and insect control policies for the National Park Service, and 2) provide a reference data set for assessing forest damage in the Southern Appalachians.



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