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Forests 2017, 8(1), 21; doi:10.3390/f8010021

Windthrow Detection in European Forests with Very High-Resolution Optical Data

1
Institute of Surveying, Remote Sensing and Land Information (IVFL), University of Natural Resources and Life Sciences, Vienna (BOKU), Peter-Jordan-Strasse 82, 1190 Vienna, Austria
2
Bavarian State Institute of Forestry (LWF), Department of Information Technology, Hans-Carl-von-Carlowitz-Platz 1, 85354 Freising, Germany
3
German Aerospace Center (DLR), German Remote Sensing Data Center, Land Surface, 82234 Wessling, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Sean P. Healey and Warren B. Cohen
Received: 14 October 2016 / Revised: 16 December 2016 / Accepted: 31 December 2016 / Published: 6 January 2017
(This article belongs to the Special Issue Remote Sensing of Forest Disturbance)
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Abstract

With climate change, extreme storms are expected to occur more frequently. These storms can cause severe forest damage, provoking direct and indirect economic losses for forestry. To minimize economic losses, the windthrow areas need to be detected fast to prevent subsequent biotic damage, for example, related to beetle infestations. Remote sensing is an efficient tool with high potential to cost-efficiently map large storm affected regions. Storm Niklas hit South Germany in March 2015 and caused widespread forest cover loss. We present a two-step change detection approach applying commercial very high-resolution optical Earth Observation data to spot forest damage. First, an object-based bi-temporal change analysis is carried out to identify windthrow areas larger than 0.5 ha. For this purpose, a supervised Random Forest classifier is used, including a semi-automatic feature selection procedure; for image segmentation, the large-scale mean shift algorithm was chosen. Input features include spectral characteristics, texture, vegetation indices, layer combinations and spectral transformations. A hybrid-change detection approach at pixel-level subsequently identifies small groups of fallen trees, combining the most important features of the previous processing step with Spectral Angle Mapper and Multivariate Alteration Detection. The methodology was evaluated on two test sites in Bavaria with RapidEye data at 5 m pixel resolution. The results regarding windthrow areas larger than 0.5 ha were validated with reference data from field visits and acquired through orthophoto interpretation. For the two test sites, the novel object-based change detection approach identified over 90% of the windthrow areas (≥0.5 ha). The red edge channel was the most important for windthrow identification. Accuracy levels of the change detection at tree level could not be calculated, as it was not possible to collect field data for single trees, nor was it possible to perform an orthophoto validation. Nevertheless, the plausibility and applicability of the pixel-based approach is demonstrated on a second test site. View Full-Text
Keywords: windthrow; remote sensing; OBIA; Random Forests; hybrid change detection; large-scale mean shift windthrow; remote sensing; OBIA; Random Forests; hybrid change detection; large-scale mean shift
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Einzmann, K.; Immitzer, M.; Böck, S.; Bauer, O.; Schmitt, A.; Atzberger, C. Windthrow Detection in European Forests with Very High-Resolution Optical Data. Forests 2017, 8, 21.

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