Using intra-annual Landsat time series for attributing forest disturbance agents in Central Europe

Julian Oeser, Dirk Pflugmacher, Cornelius Senf, Marco Heurich, Patrick Hostert

Research output: Contribution to journalArticlepeer-review

42 Scopus citations

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.

Original languageEnglish
Article number251
JournalForests
Volume8
Issue number7
DOIs
StatePublished - 14 Jul 2017
Externally publishedYes

Keywords

  • Attribution
  • Central Europe
  • Disturbance agent
  • Intra-annual
  • Landsat
  • Time series

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