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Title: Long-Term Post-Disturbance Forest Recovery in the Greater Yellowstone Ecosystem Analyzed Using Landsat Time Series Stack

Forest recovery from past disturbance is an integral process of ecosystem carbon cycles, and remote sensing provides an effective tool for tracking forest disturbance and recovery over large areas. Although the disturbance products (tracking the conversion from forest to non-forest type) derived using the Landsat Time Series Stack-Vegetation Change Tracker (LTSS-VCT) algorithm have been validated extensively for mapping forest disturbances across the United States, the ability of this approach to characterize long-term post-disturbance recovery (the conversion from non-forest to forest) has yet to be assessed. Here in this study, the LTSS-VCT approach was applied to examine long-term (up to 24 years) post-disturbance forest spectral recovery following stand-clearing disturbances (fire and harvests) in the Greater Yellowstone Ecosystem (GYE). Using high spatial resolution images from Google Earth, we validated the detectable forest recovery status mapped by VCT by year 2011. Validation results show that the VCT was able to map long-term post-disturbance forest recovery with overall accuracy of ~80% for different disturbance types and forest types in the GYE. Harvested areas in the GYE have higher percentages of forest recovery than burned areas by year 2011, and National Forests land generally has higher recovery rates compared with National Parks. The results alsomore » indicate that forest recovery is highly related with forest type, elevation and environmental variables such as soil type. Findings from this study can provide valuable insights for ecosystem modeling that aim to predict future carbon dynamics by integrating fine scale forest recovery conditions in GYE, in the face of climate change. Lastly, with the availability of the VCT product nationwide, this approach can also be applied to examine long-term post-disturbance forest recovery in other study regions across the U.S.« less
Authors:
 [1] ;  [2] ;  [1] ;  [1] ;  [1] ;  [3] ;  [3] ;  [4]
  1. Univ. of Maryland, College Park, MD (United States). Dept. of Geographical Sciences
  2. Brookhaven National Lab. (BNL), Upton, NY (United States). Environmental and Climate Sciences Dept.
  3. Tsinghua Univ., Beijing (China). Ministry of Education Key Labor. for Earth System Modeling, Center for Earth System Science
  4. U.S. Geological Survey, Reston, VA (United States)
Publication Date:
Report Number(s):
BNL-113514-2017-JA
Journal ID: ISSN 2072-4292; R&D Project: 2016-BNL-EE630EECA-Budg; KP1701000
Grant/Contract Number:
SC0012704
Type:
Accepted Manuscript
Journal Name:
Remote Sensing
Additional Journal Information:
Journal Volume: 8; Journal Issue: 12; Journal ID: ISSN 2072-4292
Publisher:
MDPI
Research Org:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Wildland Fires; Timber Harvest; Detectable Forest Spectral Recovery; 1988 Yellowstone Fires
OSTI Identifier:
1344230