HAL Id: hal-02734776
https://hal.inrae.fr/hal-02734776v2
Submitted on 2 Jun 2020
HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci- entific research documents, whether they are pub- lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.
Mapping the height of heterogeneous vegetation from UAV-borne visible images and DSM
Juliette Grimaldi, F. Helen, C. Pelletier, Vincent Bustillo, Thomas Houet
To cite this version:
Juliette Grimaldi, F. Helen, C. Pelletier, Vincent Bustillo, Thomas Houet. Mapping the height of het- erogeneous vegetation from UAV-borne visible images and DSM. 4th World Congress on Agroforestry, May 2019, Montpellier, France. 933 p., 2019, Book of abstracts. 4th World Congress on Agroforestry.
�hal-02734776v2�
575
Agroforestry 2019 - Poster L16 AF mapping
4th World Congress on Agroforestry
Strengthening links between science, society and policy 20-22 May 2019
Le Corum, Montpellier, France Book of Abstracts
L16.P.07 Mapping the height of heterogeneous vegetation from UAV-borne
visible images and DSM
Grimaldi J.1 (juliette.grimaldi@inra.fr), Helen F.2, Pelletier C.3, Bustillo V.4, Houet T.5
1INRA UMR System, Montpellier, France; 2Faculty of Engineering, Airbus, Toulouse, France; 3Faculty of Information Technology, Monash University, Melbourne, Australia; 4CESBIO, Toulouse, France;
5LETG-Rennes Costel -CNRS, Renes, France
Assessing vegetation structure is essential for studying light distribution and air flow regime within agroforestry plots. Canopy heights and foliage density notably stand as key variables and should be described at both the internal and at the landscape scale of the vegeta- tion. Based on this knowledge, a two-step generic methodology is proposed for describing vegetation structure of agroforestry plots using very high resolution stereoscopic RGB images acquired through UAV flights. It consists first in mapping the land cover and second in map- ping the height of vegetation canopy. Both steps were tested using images of three different agroforestry vineyards located in Southern-France. For the first step, using a Random Forest [1] supervised classification approach showed important confusion between grapevine and tree classes unless post-processing masks were applied. For the second step, the ‘filtered DSM’ method proposed by Zarco-Tejada et al. [2] was compared with a ‘DSM minus DTM’
approach.
For most of the tree species standing as individual trees at the study sites, this new method showed a higher overall accuracy for estimating their heights based on laser-meter measures of reference. In the particular case of very small leaved canopies, no conclusion could be drawn due to experimental bias. The ‘DSM minus DTM’ method also revealed several pro- mising methodological advantages: it preserves vegetation borderlines and it allows pixel at pixel applications.
RGB image and top canopy heights of the agroforestry vineyards at Domaine of Restinclières, France Keywords: UAV, Digital Surface Model, Digital terrain Model, supervised classification, top canopy height.
References:
1. Breiman L (2001) Machine learning 45:5–32
2. Zarco-Tejada PJ, Diaz-Varela R, Angileri V, Loudjani P (2014) European Journal of Agronomy 55:89–99
