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Forests 2017, 8(7), 251; doi:10.3390/f8070251

Using Intra-Annual Landsat Time Series for Attributing Forest Disturbance Agents in Central Europe

1
Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
2
Institute for Silviculture, Department of Forest- and Soil Sciences, University of Natural Resources and Life Sciences (BOKU) Vienna, Peter-Jordan-Str. 82, 1190 Vienna, Austria
3
Bavarian Forest National Park, Department for Conservation and Research, Freyunger Str. 2, D-94481 Grafenau, Germany
4
Chair of Wildlife Ecology and Management, Albert-Ludwigs-University Freiburg, Tennenbacher Str. 4, 79106 Freiburg, Germany
5
Integrative Research Institute on Transformations of Human-Environment Systems—IRI THESys, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
*
Author to whom correspondence should be addressed.
Received: 1 June 2017 / Revised: 5 July 2017 / Accepted: 12 July 2017 / Published: 14 July 2017
(This article belongs to the Special Issue Remote Sensing of Forest Disturbance)
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Abstract

The attribution of forest disturbances to disturbance agents is a critical challenge for remote sensing-based forest monitoring, promising important insights into drivers and impacts of forest disturbances. Previous studies have used spectral-temporal metrics derived from annual Landsat time series to identify disturbance agents. Here, we extend this approach to new predictors derived from intra-annual time series and test it at three sites in Central Europe, including managed and protected forests. The two newly tested predictors are: (1) intra-annual timing of disturbance events and (2) temporal proximity to windstorms based on prior knowledge. We estimated the intra-annual timing of disturbances using a breakpoint detection algorithm and all available Landsat observations between 1984 and 2016. Using spectral, temporal, and topography-related metrics, we then mapped four disturbance classes: windthrow, cleared windthrow, bark beetles, and other harvest. Disturbance agents were identified with overall accuracies of 76–86%. Temporal proximity to storm events was among the most important predictors, while intra-annual timing itself was less important. Moreover, elevation information was very effective for discriminating disturbance agents. Our results demonstrate the potential of incorporating dense, intra-annual Landsat time series information and prior knowledge of disturbance events for monitoring forest ecosystem change at the disturbance agent level. View Full-Text
Keywords: Landsat; time series; disturbance agent; attribution; intra-annual; Central Europe Landsat; time series; disturbance agent; attribution; intra-annual; Central Europe
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Oeser, J.; Pflugmacher, D.; Senf, C.; Heurich, M.; Hostert, P. Using Intra-Annual Landsat Time Series for Attributing Forest Disturbance Agents in Central Europe. Forests 2017, 8, 251.

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