Application of Haralick’s Texture Features for Rapid Detection of Windthrow Hotspots in Orthophotos

Abstract

Windthrow and storm damage are crucial issues in practical forestry. We propose a method for rapid detection of windthrow hotspots in airborne digital orthophotos. Therefore, we apply Haralick’s texture features on 50 × 50 m cells of the orthophotos and classify the cells with a random forest algorithm. We apply the classification results from a training data set on a validation set. The overall classification accuracy of the proposed method varies between 76% for fine distinction of the cells and 96% for a distinction level that tried to detect only severe damaged cells. The proposed method enables the rapid detection of windthrow hotspots in forests immediately after their occurrence in single-date data. It is not adequate for the determination of areas with only single fallen trees. Future research will investigate the possibilities and limitations when applying the method on other data sources (e.g., optical satellite data).

Deep learning based windthrow detection for winter storms

“…Remote sensing images based on satellite imagery, aerial photography, and Unmanned Aerial Vehicles (UAVs) have been previously used for windthrow detection. [4][5][6][7] One method for such detection is to use multi-temporal data where pre-and post-storm images are compared for recognizing windthrows. Another method is the use of a single post-storm image to identify fallen trees.…”