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Monitoring selective logging in a pine dominated forest in Central Germany with repeated drone flights utilizing a low cost RTK quadcopter

Thiel, Christian and Müller, Marlin and Christian, Berger and Felix, Cremer and Dubois, Clémence and Hese, Sören and Baade, Jussi and Klan, Friederike and Pathe, Carsten (2020) Monitoring selective logging in a pine dominated forest in Central Germany with repeated drone flights utilizing a low cost RTK quadcopter. Remote Sensing, pp. 1-26. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/drones4020011. ISSN 2072-4292.

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Abstract

There is no doubt that unmanned aerial systems (UAS) will play an increasing role in Earth observation in the near future. The field of application is very broad and includes aspects of environmental monitoring, security, humanitarian aid, or engineering. In particular drones with camera systems are already widely used. The capability to compute ultra-high resolution orthomosaics and 3D point clouds from UAS imagery generates a wide interest in such systems in the science community but also in industry and agencies. In particular forestry sciences benefit from ultrahigh structural and spectral information as regular tree level-based monitoring becomes feasible. There is a great need for this kind of information as, for example, due to the spring and summer droughts in Europe in the years 2018/2019, large quantities of individual trees were damaged or even died. This study focuses on selective logging at the level of individual trees using repeated drone flights. Using the new generation of UAS, which allows for sub-decimeter-level positioning accuracies, a change detection approach based on bi-temporal UAS acquisitions was implemented. In comparison to conventional UAS, the effort of implementing repeated drone flights in the field was low because no ground control points needed to be surveyed. As shown in this study, the geometrical offset between the two collected datasets was below 10 cm across the site, which enabled a direct comparison of both datasets without the need for post-processing (e.g., image matching). For the detection of logged trees, we utilized the spectral and height difference between both acquisitions. For their delineation, an object-based approach was employed which was proven to be highly accurate (precision = 97.5%; recall = 91.6%). Due to the ease of use of such new generation, off-the-shelf consumer drones, their decreasing purchase costs, the quality of available workflows for data processing, and the convincing results presented here, UAS-based data can and should complement conventional forest inventory practices.

Item URL in elib:https://elib.dlr.de/139894/
Document Type:Article
Title:Monitoring selective logging in a pine dominated forest in Central Germany with repeated drone flights utilizing a low cost RTK quadcopter
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Thiel, ChristianChristian.Thiel (at) dlr.dehttps://orcid.org/0000-0001-5144-8145
Müller, MarlinDLR JenaUNSPECIFIED
Christian, BergerStadt JenaUNSPECIFIED
Felix, CremerDLR JenaUNSPECIFIED
Dubois, ClémenceFSU JenaUNSPECIFIED
Hese, SörenFSU JenaUNSPECIFIED
Baade, JussiFriedrich-Schiller-Universität JenaUNSPECIFIED
Klan, FriederikeFriederike.Klan (at) dlr.dehttps://orcid.org/0000-0002-1856-7334
Pathe, CarstenDLR JenaUNSPECIFIED
Date:9 April 2020
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.3390/drones4020011
Page Range:pp. 1-26
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2072-4292
Status:Published
Keywords:UAS, RTK quadcopter; structure from motion; repeated flights; change detection; forestry, selective logging, forest degradation
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:other
DLR - Research area:Raumfahrt
DLR - Program:R - no assignment
DLR - Research theme (Project):R - no assignment
Location: Jena
Institutes and Institutions:Institute of Data Science > Citizen Science
Deposited By: Thiel, Christian
Deposited On:07 Jan 2021 14:26
Last Modified:07 Jan 2021 14:26

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