Abstract
Increased frequencies and windspeeds of storms may cause disproportionately high increases in windthrow damage. Storm-felled trees provide a surplus of breeding material for bark beetles, often resulting in calamities in the subsequent years. Thus, the timely removal of fallen trees is regarded as a good management practice that requires strategic planning of salvage harvesting. Precise information on the number of stems and their location and orientation are needed for the efficient planning of strip roads and/or cable yarding lines. An accurate assessment of these data using conventional field-based methods is very difficult and time-consuming; remote sensing techniques may be a cost-efficient alternative. In this research, a methodology for the automatic detection of fallen stems from aerial RGB images is presented. The presented methodology was based on a line segment detection algorithm and proved to be robust regarding image quality. It was shown that the method can detect frequency, position, spatial distribution and orientation of fallen stems with high accuracy, while stem lengths were systematically underestimated. The methodology can be used for the optimized planning of salvage harvesting in the future and may thus help to reduce consequential bark beetle calamities after storm events.
Highlights
Academic Editor: Svein SolbergFor the period 1950–2000, 53% of timber loss in European forests was due to storm damage, in particular, the alpine zone of mountainous regions was heavily affected by storms [1].In Austria, storm damage accounts for a mean annual timber loss of 3.1 million m3, representing 12% of the total annual fellings [2].Increased frequencies and windspeeds of storms may cause disproportionately high increases in windthrow damage [3]
In 96% of all cases, severe storm damage occurred when soils were unfrozen and wet; during the observation period, the mean winter temperature in Switzerland increased by nearly 2 K, winter precipitation increased by nearly 50%, the maximum measured gust wind speed increased by 37% and the average number of strong gust wind speed events per winter half-year increased by a factor of 11.9 [4]
We present an approach that is based on the Forests 2022, 13, 90 line segment detection (LSD) algorithm by Grompone von Gioi et al [35], where the presented approach showed reasonably high consistency with the reference data with respect to the stem number, the distribution of azimuth angles and the spatial distribution of stems on the plots
Summary
Academic Editor: Svein SolbergFor the period 1950–2000, 53% of timber loss in European forests was due to storm damage, in particular, the alpine zone of mountainous regions was heavily affected by storms [1].In Austria, storm damage accounts for a mean annual timber loss of 3.1 million m3 , representing 12% of the total annual fellings [2].Increased frequencies and windspeeds of storms may cause disproportionately high increases in windthrow damage [3]. For the period 1950–2000, 53% of timber loss in European forests was due to storm damage, in particular, the alpine zone of mountainous regions was heavily affected by storms [1]. In Austria, storm damage accounts for a mean annual timber loss of 3.1 million m3 , representing 12% of the total annual fellings [2]. Increased frequencies and windspeeds of storms may cause disproportionately high increases in windthrow damage [3]. In 96% of all cases, severe storm damage occurred when soils were unfrozen and wet; during the observation period, the mean winter temperature in Switzerland increased by nearly 2 K, winter precipitation increased by nearly 50%, the maximum measured gust wind speed increased by 37% and the average number of strong gust wind speed events per winter half-year increased by a factor of 11.9 [4].
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