Abstract
The number of severe storm events has increased in recent decades due to climate change. These storms are one of the main causes for timber loss in European forests and damaged areas are prone to further degradation by, for example, bark beetle infestations. Usually, manual mapping of damaged areas based on aerial photographs is conducted by forest departments. This is very time-consuming and therefore automatic detection of windthrows based on active and passive remote sensing data is an ongoing research topic. In this study we evaluated state-of-the-art Convolutional Neural Networks (CNNs) in combination with Geographic Information Systems (GIS) for calamity assessment. The study area is in in the northern part of Hesse (Germany) and was covered by twelve Sentinel-2 scenes from 2018. Labels of damaged areas from the Friedericke storm (18 January 2018) were provided by HessenForst. We conducted several experiments based on a custom U-Net setup to derive the optimal architecture and input data as well as to assess the transferability of the model. Results highlight the possibility to detect damaged forest areas using Sentinel-2 data. Using a binary classification, accuracies of more than 92% were achieved with an Intersection over Union (IoU) score of 46.6%. The proposed workflow was integrated into ArcGIS and is suitable for fast detection of damaged areas directly after a storm and for disaster management but is limited by the deca-meter spatial resolution of the Sentinel-2 data.
Highlights
IntroductionThey caused severe infrastructure damage in many European countries and had a heavy impact on forests
There has been a clearly increasing trend of storm events during the previous decades [1,2,3,4].The most notable storms in Europe were Vivian and Wiebke in 1990, Lothar in 1999 and Kyrill in2007 [3]
We see the best performance at a rate of 0.001 which is in accordance with other studies in deep learning
Summary
They caused severe infrastructure damage in many European countries and had a heavy impact on forests. Winter storms are considered as the main source of damage (50%) within all unplanned logging [5]. Scientists assume that by the end of the century the damage caused by storms in forests will double or even quadruple [1], mostly due to climate change [3,6]. A common indirect effect of storms are bark beetle infestations that spread around the damaged areas. The direct correlation between bark beetle reproduction and storm damage was identified by Forster and Meier [7]. The authors show that storms are the second most important cause for the reproduction of bark beetle populations, especially the European spruce bark beetle (Ips typographus L.)
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