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Forests 2014, 5(6), 1284-1303; doi:10.3390/f5061284

Forest Cover Database Updates Using Multi-Seasonal RapidEye Data—Storm Event Assessment in the Bavarian Forest National Park

Institute of Forest Management, Center of Life and Food Sciences Weihenstephan, Technische Universität München, Hans-Carl-von-Carlowitz-Platz 2, Freising 85354, Germany
Information Technologies Institute, Centre for Research and Technology Hellas, Building A, 6th km Harilaou-Thermi, P.O. Box 60361, Thessaloniki 57001, Greece
Author to whom correspondence should be addressed.
Received: 26 February 2014 / Revised: 19 May 2014 / Accepted: 29 May 2014 / Published: 11 June 2014
(This article belongs to the Special Issue Applications of Remote Sensing to Forestry)
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This study is a part of a research program that investigates the potential of RapidEye (RE) satellite data for timely updates of forest cover databases to reflect both regular management activities and sudden changes due to bark beetle and storms. Applied here in the Bavarian Forest National Park (BFNP) in southeastern Germany, this approach detected even small changes between two data takes, thus, facilitating documentation of regular management activities. In the case of a sudden event, forest cover databases also serve as a baseline for damage assessment. A storm event, which occurred on 13 July, 2011, provided the opportunity to assess the effectiveness of multi-seasonal RE data for rapid damage assessment. Images of sufficient quality (<20% cloud cover) acquired one day before the storm event were used as a baseline. Persistent cloud cover meant that the first “after event” image of sufficient quality was acquired six weeks later, on 22 August, 2011. Aerial images (AI) for the official damage assessment done by the BFNP administration were acquired on that same day. The RE analysis for damage assessment was completed two weeks after the post-event data take with an overall accuracy of 96% and a kappa coefficient of 0.86. In contrast, the official aerial image survey from the BFNP was first released in late November, eleven weeks later. Comparison of the results from the two analyses showed a difference in the detected amount of forest cover loss of only 3%. The estimated cost of the RE approach was four times less than that of the standard digital AI procedure employed by the BFNP. View Full-Text
Keywords: forest cover monitoring; multi-seasonal; RapidEye; aerial images; storm damage forest cover monitoring; multi-seasonal; RapidEye; aerial images; storm damage

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

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Elatawneh, A.; Wallner, A.; Manakos, I.; Schneider, T.; Knoke, T. Forest Cover Database Updates Using Multi-Seasonal RapidEye Data—Storm Event Assessment in the Bavarian Forest National Park. Forests 2014, 5, 1284-1303.

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