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Forests 2017, 8(9), 306; doi:10.3390/f8090306

Early Stage Forest Windthrow Estimation Based on Unmanned Aircraft System Imagery

1
Department of Forest Management and Geodesy, Faculty of Forestry, Technical University in Zvolen, T. G. Masaryka 24, Zvolen 96053, Slovakia
2
Department of Forest Harvesting, Logistics and Ameliorations, Faculty of Forestry, Technical University in Zvolen, T. G. Masaryka 24, Zvolen 96053, Slovakia
3
Department of Geodesy and Geoinformation, TU Wien, Gusshausstrasse 27–29, Vienna 1040, Austria
4
Department of Surveying and Remote Sensing, Institute of Geomatics and Civil Engineering, Faculty of Forestry, University of Sopron, Sopron 9400, Hungary
*
Author to whom correspondence should be addressed.
Received: 6 June 2017 / Revised: 15 August 2017 / Accepted: 22 August 2017 / Published: 23 August 2017
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Abstract

Strong wind disturbances can affect large forested areas and often occur irregularly within a forest. Due to this, identifying damaged sites and estimating the extent of these losses are crucial for the harvesting management of salvage logging. Furthermore, the location should be surveyed as soon as possible after the disturbance to prevent the degradation of fallen trees. A fixed-wing type of unmanned aircraft system (UAS) with a compact digital camera was used in this study. The imagery was acquired on approximately 200 hectares where five large windthrow areas had occurred. The objective of the study was to determine the location of the windthrow areas using a semi-automatic approach based on the UAS imagery, and on the combination of UAS imagery with airborne laser scanning (ALS). The results were compared with reference data measured by global navigation satellite system (GNSS) devices. At the same time, windthrow areas were derived from Landsat imagery to investigate whether the UAS imagery would have significantly more accurate results. GNSS measurements and Landsat imagery are currently used in forestry on an operational level. The salvage logging was estimated for each forest stand based on the estimated areas and volume per hectare obtained from the forest management plan. The results from the UAS (25.09 ha) and the combined UAS/ALS (25.56 ha) methods were statistically similar to the reference GNSS measurements (25.39 ha). The result from Landsat, at 19.8 ha, was not statistically similar to the reference GNSS measurements or to the UAS and UAS/ALS methods. The estimate of salvage logging for the whole area, from UAS imagery and the forest management plan, overestimated the actual salvage logging measured by foresters by 4.93% (525 m3), when only the most represented tree species were considered. The UAS/ALS combination improved the preliminary results of determining windthrow areas which lead to decreased editing time for all operators. The UAS imagery shows potential for application to early-stage surveys of windthrow areas in forests. The advantages of this method are that it provides the ability to conduct flights immediately after the disturbance, the foresters do not need to walk within the affected areas which decreases the risk of injury, and allows flights to be conducted on cloudy days. The orthomosaic of the windthrow areas, as a by-product of data processing in combination with forest maps and forest road maps, can be used as a tool to plan salvage logging. View Full-Text
Keywords: unmanned aircraft system; windthrow; forest; photogrammetry; airborne laser scanning; Landsat; global navigation satellite system unmanned aircraft system; windthrow; forest; photogrammetry; airborne laser scanning; Landsat; global navigation satellite system
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Mokroš, M.; Výbošťok, J.; Merganič, J.; Hollaus, M.; Barton, I.; Koreň, M.; Tomaštík, J.; Čerňava, J. Early Stage Forest Windthrow Estimation Based on Unmanned Aircraft System Imagery. Forests 2017, 8, 306.

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