Abstract
Climate change has increased the occurrence of heavy storms that cause damage to forests. After a storm, it is necessary to obtain knowledge about the injured trees quickly in order to detect and aid in collecting the fallen trees and estimate the total damage. The objective in this study was to develop an automatic method for storm damage detection based on comparisons of digital surface models (DSMs), where the after-storm DSM was derived by automatic image matching using high-altitude photogrammetric imagery. This DSM was compared to a before-storm DSM, which was computed using national airborne laser scanning (ALS) data. The developed method was tested using imagery collected in extreme illumination conditions after winter storms on 8 January 2012 in Finland. The image matching yielded a high-quality surface model of the forest areas, which were mainly coniferous and mixed forests. The entire set of major damage forest test areas was correctly classified using the method. Our results showed that airborne, high-altitude photogrammetry is a promising tool for automating the detection of forest storm damage. With modern photogrammetric cameras, large areas can be collected efficiently, and the imagery also provides visual, stereoscopic support for various forest storm damage management tasks. Developing methods that work in different seasons are becoming more important, due to the increase in the number of natural disasters.
Highlights
The risks of storms that cause damage in forests are increasing, due to climate change
ground control points (GCPs), the root mean square error (RMSE) at 26 checkpoints was less than 1.4 m in all of the coordinates
Data, the RMSE at the checkpoints was on the level of 1 m or less; the global shift values were similar to the case with 26 GCPs
Summary
The risks of storms that cause damage in forests are increasing, due to climate change. Detecting and delineating storm damage is a laborious and error-prone process; it is important to develop cost-efficient and highly automated methods. Forest damage detection methods are typically either based on comparisons of before and after storm data or on knowledge of how different scenes in the field are represented in the data. The former method needs compatible data taken before and after the storm. For the latter method, an extensive field reference is needed to model the system response for different field cases, which can be difficult to automate
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