Short Communication. Using high resolution UAV imagery to estimate tree variables in Pinus pinea plantation in Portugal

Juan Guerra Hernandez, Eduardo Gonzalez-Ferreiro, Alexandre Sarmento, João Silva, Alexandra Nunes, Alexandra Cristina Correia, Luis Fontes, Margarida Tomé, Ramon Diaz-Varela


Aim of study: The study aims to analyse the potential use of low‑cost unmanned aerial vehicle (UAV) imagery for the estimation of Pinus pinea L. variables at the individual tree level (position, tree height and crown diameter).

Area of study: This study was conducted under the PINEA project focused on 16 ha of umbrella pine afforestation (Portugal) subjected to different treatments.

Material and methods: The workflow involved: a) image acquisition with consumer‑grade cameras on board an UAV; b) orthomosaic and digital surface model (DSM) generation using structure-from-motion (SfM) image reconstruction; and c) automatic individual tree segmentation by using a mixed pixel‑ and region‑based based algorithm.

Main results: The results of individual tree segmentation (position, height and crown diameter) were validated using field measurements from 3 inventory plots in the study area. All the trees of the plots were correctly detected. The RMSE values for the predicted heights and crown widths were 0.45 m and 0.63 m, respectively.

Research highlights: The results demonstrate that tree variables can be automatically extracted from high resolution imagery. We highlight the use of UAV systems as a fast, reliable and cost‑effective technique for small scale applications.

Keywords: Unmanned aerial systems (UAS); forest inventory; tree crown variables; 3D image modelling; canopy height model (CHM); object‑based image analysis (OBIA), structure‑from‑motion (SfM).






Unmanned aerial systems (UAS); forest inventory; tree crown variables; 3D image modelling; canopy height model (CHM); object‑based image analysis (OBIA), structure‑from‑motion (SfM)

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Bohlin J, Wallerman J, Fransson JE, 2012. Forest variable estimation using photogrammetric matching of digital aerial images in combination with a high resolution DEM. Scand J For Res 27: 692 699.

Calama R, Gordo FJ, Mutke S, Montero G, 2008. An empirical ecological-type model for predicting stone pine (Pinus pinea L.) cone production in the Northern Plateau (Spain). For Ecol Manag 255: 660 673.

Definiens AG, 2007. Definiens developer 7 reference book. Definiens AG, München, 195 pp.

Díaz Varela RA, de la Rosa R, León L, Zarco Tejada PJ, 2015. High resolution airborne UAV imagery to assess olive tree crown parameters using 3D photo reconstruction: Application in breeding trials. Remote Sens 7: 4213–4232.

Fritz A, Kattenborn T, Koch B, 2013. UAV based photogrammetric point clouds tree stem mapping in open stands in comparison to terrestrial laser scanner point clouds. Int. Arch. Photogramm. Remote Sens Spat Inf Sci 40: 141 146.

González Ferreiro E, Diéguez Aranda U, Barreiro Fernández L, Buján S, Barbosa M, Suárez JC, Miranda D, 2013. A mixed pixel and region based approach for using airborne laser scanning data for individual tree crown delineation in Pinus radiata D. Don plantations. Int J Remote Sens 34(21): 7671 7690.

Kattenborn T, Sperlich M, Bataua K, Koch B, 2014. Automatic single tree detection in plantations using UAV-based photogrammetric point clouds. Int Arch Photogramm Remote Sens Spat Inf Sci XL 3: 139 144.

Lisein J, Pierrot-Deseilligny M, Bonnet S, Lejeune P, 2013. A photogrammetric workflow for the creation of a forest canopy height model from small unmanned aerial system imagery. Forests 4: 922 944.

Nurminen K, Karjalainen M, Yu X, Hyyppä J, Honkavaara E, 2013. Performance of dense digital surface models based on image matching in the estimation of plot level forest variables. ISPRS J Photogramm Remote Sens 83: 104 115.

Moorthy I, Miller JR, Berni JAJ, Zarco Tejada P, Hu B, Chen J, 2011. Field characterization of olive (Olea europaea L.) tree crown architecture using terrestrial laser scanning data. Agric For Meteorol 151: 204 214.

Puliti S, Ørka HO, Gobakken T, Næsset E, 2015. Inventory of small forest areas using an unmanned aerial system. Remote Sens 7: 9632 9654.

Zarco Tejada PJ, Díaz Varela R, Angileri V, Loudjani P, 2014. Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. Eur J Agron 55: 89 99.

DOI: 10.5424/fs/2016252-08895