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.
Highlights
In the last few decades, Europe was hit by a series of heavy storms, such as Vivian and Wiebke in 1990, Lothar in 1999, followed by Kyrill in 2007
We performed our study on two test sites in Bavaria, Germany, which presented both of the two damage patterns
Random Forest (RF) classification that demonstrated high accuracies, which were validated with independent reference data
Summary
In the last few decades, Europe was hit by a series of heavy storms, such as Vivian and Wiebke in 1990, Lothar in 1999, followed by Kyrill in 2007. It is expected that in the future the frequency of severe storms will further increase in Europe [1,2]. These storms damaged areas were often correlated with subsequent insect outbreaks, mainly European spruce bark beetle Ips typographus (L.). Between 1950 and 2000 the approximate average annual storm damage in Europe was 18.7 million m3 of wood, with most of the storm damage occurring in Central Europa and the Alps [1]. The average annual wood volume damage by bark beetles was 2.9 million m3 per year [1]
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