long-term post-disturbance forest recovery in the greater yellowstone ecosystem analyzed using landsat time series stack

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2016
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Abstract
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. 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 also 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. 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.
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zhao2016remotelong-term Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Feng R. Zhao;Ran Meng;Chengquan Huang;Maosheng Zhao;Feng A. Zhao;Peng Gong;Le Yu;Zhiliang Zhu
Journal Journal of pharmacological sciences
Year 2016
DOI
10.3390/rs8110898
URL
http://www.mdpi.com/2072-4292/8/11/898
Keywords Keywords not found

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