A Photogrammetric Workflow for the Creation of a Forest Canopy Height Model from Small Unmanned Aerial System Imagery

Forests

Jonathan Lisein²  Marc Pierrot-Deseilligny¹  Stéphanie Bonnet² 

[1] Ecole Nationale des Sciences Géographiques, 6 et 8 Avenue Blaise Pascal Cité Descartes Champs-sur-Marne Marne la Vallée 77455, France; E-Mail:;Unit of Forest and Nature Management, University of Liège-Gembloux Agro-Bio Tech. 2, Passage des déportés, Gembloux 5030, Belgium; E-Mails:

关键词: canopy height;    forestry;    photogrammetry;    MICMAC;    Unmanned Aerial Systems;    UAS;    UAV;    forest inventory;    uneven-aged broadleaf stands;   

DOI  :  10.3390/f4040922

来源: mdpi

【 摘 要 】

The recent development of operational small unmanned aerial systems (UASs) opens the door for their extensive use in forest mapping, as both the spatial and temporal resolution of UAS imagery better suit local-scale investigation than traditional remote sensing tools. This article focuses on the use of combined photogrammetry and “Structure from Motion” approaches in order to model the forest canopy surface from low-altitude aerial images. An original workflow, using the open source and free photogrammetric toolbox, MICMAC (acronym for Multi Image Matches for Auto Correlation Methods), was set up to create a digital canopy surface model of deciduous stands. In combination with a co-registered light detection and ranging (LiDAR) digital terrain model, the elevation of vegetation was determined, and the resulting hybrid photo/LiDAR canopy height model was compared to data from a LiDAR canopy height model and from forest inventory data. Linear regressions predicting dominant height and individual height from plot metrics and crown metrics showed that the photogrammetric canopy height model was of good quality for deciduous stands. Although photogrammetric reconstruction significantly smooths the canopy surface, the use of this workflow has the potential to take full advantage of the flexible revisit period of drones in order to refresh the LiDAR canopy height model and to collect dense multitemporal canopy height series.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.