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Mapping Forest Disturbance Intensity in North and South Carolina Using Annual Landsat Observations and Field Inventory DataDisturbance and regrowth are vital processes in determining the roles of forest ecosystem in the carbon and biogeochemical cycles. Using time series observations, the vegetation change tracker (VCT) algorithm was designed to map the location, timing, and spectral magnitudes of forest disturbance events. While these spectral disturbance magnitudes are indicative of physical changes in tree cover or biomass, their quantitative relationships have yet to be established. This study focuses on estimating disturbance intensity as measured by percent basal area removal using spectral indices from the VCT algorithm over North and South Carolina. Repeat measurements on Forest Service Forest Inventory Analysis (FIA) ground plots, which provide changes in basal area between multiple dates at precise locations, are used for training and validation of the model. The overall R2 between predicted disturbance intensity and reference data is 0.66, and cross-validation prediction uncertainty is 14% in North Carolina. Possible causes of this uncertainty could be site heterogeneity and the temporal offset between ground measurements and satellite observations. Results show the area of stand clearing disturbances remains relatively stable around 1143 km2 yr−1 in North and South Carolina throughout the period of observations (1985–2015). The average amount of forest area affected by partial disturbance is much higher at 3287 km2 yr−1. The area of partial disturbances has strong inter-annual variability with a high value of 6000 km2 in 2007 and a low value of 1919 km2 in 2013.
Document ID
20210014062
Acquisition Source
Goddard Space Flight Center
Document Type
Reprint (Version printed in journal)
Authors
Xin Tao
(University at Buffalo, State University of New York Buffalo, New York, United States)
Chengquan Huang
(University of Maryland, College Park College Park, Maryland, United States)
Feng Zhao
(University of Maryland, College Park College Park, Maryland, United States)
Karen Schleeweis
(US Forest Service Washington D.C., District of Columbia, United States)
Jeffrey Masek
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Shunlin Liang
(University of Maryland, College Park College Park, Maryland, United States)
Date Acquired
April 20, 2021
Publication Date
November 28, 2018
Publication Information
Publication: Remote Sensing of Environment
Publisher: Elsevier
Volume: 221
Issue Publication Date: February 1, 2019
ISSN: 0034-4257
Subject Category
Earth Resources And Remote Sensing
Funding Number(s)
WBS: 509496.02.03.01.17.10
CONTRACT_GRANT: SPEC5732
CONTRACT_GRANT: GSFC - 614.0 GRANT
Distribution Limits
Public
Copyright
Portions of document may include copyright protected material.
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