Für den Inhalt der Angaben zeichnet die Projektleitung verantwortlich.
Dieses von der Gebert Rüf Stiftung geförderte Projekt wird von folgenden weiteren Projektpartnern mitgetragen: Hochschule für Agrar-, Forst- und Lebensmittelwissenschaften HAFL; senseFly SA (drone manufacturer); Pix4D SA (processing software company), ecorisQ (developer of the single tree detection software).
Project no: GRS-047/17
Amount of funding: CHF 30'000
Duration: 03.2018 - 08.2019
Area of activity:
Pilotprojekte, 1998 - 2018
Building with wood is booming, but paradoxically Swiss forest owners and sawmills benefit only to a limited extent. 70% of the processed wood comes from abroad and Swiss forest owners, especially private individuals, are not currently doing much to ensure that more Swiss wood comes onto the market - the current timber price is too low for them. The abolition of the minimum euro exchange rate in January 2015 has aggravated the situation even further, because Swiss timber processors had to reduce their prices by an additional 15%. Political measures against the misery of the wood processors are currently being hotly debated. Parliament is discussing a revised forest law in which Swiss timber is to be given preference. However, only those who are efficient, innovative and rely on automation can survive on the market in the long term. With their prompt, cost-effective and yet very precise terrain and inventory data, drones offer the possibility of digitally recording the dynamic processes in the forest and thus enable the precise planning of timber harvesting, rejuvenation or other interventions. Single tree detection software uses drone data as the basis for estimating important tree parameters (tree position, height and diameter). This research project investigates how the workflow with drone data can be optimized and how single tree detection software can be used to increase the efficiency of forest management.
What is special about the project?
Drone images are commonly used today as optical support in the forestry sector. However, the potential of drone data and parameters that can be generated from single tree detection software is far from exhausted. The innovative and creative aspect of this project is to create a digital twin of the forest. This twin provides all important tree parameters for the researchers to model the forest, make estimations of interventions, plan and make predictions.
The overall objective of this project has been to contribute to increasing the efficiency of the forestry sector in Switzerland with a digital method based on drone imagery and single tree detection software. To compare the efficiency of our digital method, we needed to first thoroughly analyze the traditional terrestrial methods used to capture the most important tree parameters used for the management of forests. We therefore started by analyzing the efficiency the methods for capturing tree position (X and Y coordinates), breast-height diameter and tree height. The results from this analysis gave us a good benchmark to compare the efficiency of our digital method.
The optimization of the digital method included several steps in the workflow of deriving tree parameters from drone data. The first step focused on how to plan and conduct drone flights in difficult mountainous terrain. This included recommendations on optimal flight altitude, flight path overlaps, safety precautions, as well as numerous other tips on how to conduct efficient drone flights without having any detrimental effects on the quality of the aerial images. Subsequently, we concentrated on optimizing the generation of the normalized digital surface model, which is a miniature digital twin or model of the forest. Finally, we calibrated the single tree detection software for different types of forest stands.
The results of the comparison between the terrestrial and digital methods show that costs can indeed be cut by using the digital method, especially in large forest stands on steep terrain. The main deliverable of the project is a “best-practice” workflow for generating single tree parameters from drone imagery. The workflow is compiled as a self-explanatory script with screenshots and recommendations for forest practitioners who are interested in implementing the digital method themselves. During the project, the HAFL joined a partnership with an engineering firm through which drone-based services are now been offered.
We are convinced that drone-based services such as our method will be attracting more attention in the Swiss forestry scene, since costs need to be reduced to become more competitive on the market.
Persons involved in the project
, Projektleiter, wissenschaftlicher Mitarbeiter GIS, HAFL Prof. Dr. Luuk Dorren
, Qualitätsmanagement, Dozent für Naturgefahren und Geoinformationssysteme, HAFL
Peter Zürcher, BSc student in forest sciences, term paper “efficiency comparison of terrestrial measuring methods for recording single tree parameters”
HAFL assistants and students, support in the terrestrial measurement of the forest stands
Last update to this project presentation 07.08.2019