Automatic Storm Damage Detection in Forests Using High‑Altitude Photogrammetric Imagery

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.

A Novel Approach for Coarse-to-Fine Windthrown Tree Extraction Based on Unmanned Aerial Vehicle Images

“…Although areas with concentrated trees were identified, due to the effects of resolution and obstruction, it was difficult to achieve extraction of individual trees via optical remote sensing images and standard aerial photographs. LiDAR, with its ability to penetrate vegetation and forest canopies, proved to be an effective tool for extraction of individual fallen trees, thereby overcoming the problem of obstruction [9][10][11][12][13]. The focus of current research is the identification and extraction of individual fallen trees using airborne laser scanning.…”

Countrywide Stereo-Image Matching for Updating Digital Surface Models in the Framework of the Swiss National Forest Inventory

“…The acquisition of aerial stereo imagery has a long tradition in many countries and such images are used to update topographic maps and for orthophoto production. There are a variety of studies based on digital aerial images for canopy height generation which have been mainly carried out in Canada [12,13], Germany [14][15][16], Sweden [17] and Finland [18][19][20][21].…”

Cicada (<i>Magicicada</i>) Tree Damage Detection Based on UAV Spectral and 3D Data

“…Specifically, the inclusion of DTM and DSM to create a CHM proved to be critical for the classification of the cicada damage. A similar approach was earlier applied using combinations of high-resolution imagery and Lidar and was found to be successful in many applications [15,[45][46][47]. We believe that the UAV data can be used in many of these situations, being a source of 3D and spectral information from the same source, reducing costs and time of acquisition.…”

“…There are at least three major strategies for using remote sensing to assess forest damage: early damage detection, extent mapping, and damage quantification [6]. In forest health, most studies have used remote sensing techniques to map forest conditions at a regional or stand level [9][10][11][12][13][14][15]. Individual tree damage is often investigated for disturbance across stand level extents [16][17][18].…”