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Remote Sens. 2014, 6(4), 2782-2808; doi:10.3390/rs6042782

An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data

Biospheric Sciences Laboratory, Code 618, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Department of Forest Resources Management, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Royal Botanic Gardens, Kew, Richmond, Surrey TW9 3AE, UK
Max Planck Institute for Biogeochemistry, P.O. Box 10 01 64, D-07701 Jena, Germany
Departamento de Ciências e Engenharia do Ambiente, DCEA, Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, D-2829-516 Caparica, Portugal
Author to whom correspondence should be addressed.
Received: 10 September 2013 / Revised: 16 February 2014 / Accepted: 17 March 2014 / Published: 26 March 2014
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Forests contain a majority of the aboveground carbon (C) found in ecosystems, and understanding biomass lost from disturbance is essential to improve our C-cycle knowledge. Our study region in the Wisconsin and Minnesota Laurentian Forest had a strong decline in Normalized Difference Vegetation Index (NDVI) from 1982 to 2007, observed with the National Ocean and Atmospheric Administration’s (NOAA) series of Advanced Very High Resolution Radiometer (AVHRR). To understand the potential role of disturbances in the terrestrial C-cycle, we developed an algorithm to map forest disturbances from either harvest or insect outbreak for Landsat time-series stacks. We merged two image analysis approaches into one algorithm to monitor forest change that included: (1) multiple disturbance index thresholds to capture clear-cut harvest; and (2) a spectral trajectory-based image analysis with multiple confidence interval thresholds to map insect outbreak. We produced 20 maps and evaluated classification accuracy with air-photos and insect air-survey data to understand the performance of our algorithm. We achieved overall accuracies ranging from 65% to 75%, with an average accuracy of 72%. The producer’s and user’s accuracy ranged from a maximum of 32% to 70% for insect disturbance, 60% to 76% for insect mortality and 82% to 88% for harvested forest, which was the dominant disturbance agent. Forest disturbances accounted for 22% of total forested area (7349 km2). Our algorithm provides a basic approach to map disturbance history where large impacts to forest stands have occurred and highlights the limited spectral sensitivity of Landsat time-series to outbreaks of defoliating insects. We found that only harvest and insect mortality events can be mapped with adequate accuracy with a non-annual Landsat time-series. This limited our land cover understanding of NDVI decline drivers. We demonstrate that to capture more subtle disturbances with spectral trajectories, future observations must be temporally dense to distinguish between type and frequency in heterogeneous landscapes. View Full-Text
Keywords: Landsat; AVHRR; forest; disturbance; mortality; insect; harvest; US; classification; decision tree Landsat; AVHRR; forest; disturbance; mortality; insect; harvest; US; classification; decision tree

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Neigh, C.S.; Bolton, D.K.; Diabate, M.; Williams, J.J.; Carvalhais, N. An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data. Remote Sens. 2014, 6, 2782-2808.

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