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Proceedings Paper

Precision arboriculture: a new approach to tree risk management based on geomatics tools
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Paper Abstract

In green extensive context, RPAS (Remotely Piloted Aerial Systems) can provide information with a high geometric resolution. The photogrammetric survey shows the possibility of measuring morphometric parameters of forest stand or individual trees. The free accessibility to Copernicus Sentinel-2 (S2) data addresses to hypothesize scenarios where satellite spectral information and high geometric resolution of RPAS photogrammetric survey, jointly used, determine a deeper knowledge of tree characteristics. Study area is located within the “La Mandria” park (NW Italy). Survey was operated by a DJI-Phantom4 RPAS (GSD images = 5 cm). Image photogrammetric processing was achieved by AGISOFT Photoscan v1.2.4. The resulting point cloud was filtered and a raster DSM (Digital Surface Model) was generated with a GSD = 10 cm. The correspondent CHM (Canopy Height Model) was computed by difference using a DTM (Digital Terrain Model) available from the regional cartographic archive. An object-based approach (watershed segmentation) aimed at bordering tree crowns as vector polygons was run. Some tree stability parameters were obtained from CHM by zonal statistics for each crown that was also spectrally characterized (to explore its vigor) using a S2 image time series. The proposed method finds applications in the arboricultural field (ornamental context) for the survey of tree inventory data; the detected parameters can be used as input data for tree risk assessment/management models, especially in extensive contexts representing a new approach to single tree risk management based on innovative technologies and algorithms that can reduce costs of ground control/survey campaigns.

Paper Details

Date Published: 21 October 2019
PDF: 15 pages
Proc. SPIE 11149, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI, 111491G (21 October 2019);
Show Author Affiliations
S. De Petris, Univ. degli Studi di Torino (Italy)
R. Berretti, Univ. degli Studi di Torino (Italy)
F. Sarvia, Univ. degli Studi di Torino (Italy)
E. Borgogno-Mondino, Univ. degli Studi di Torino (Italy)


Published in SPIE Proceedings Vol. 11149:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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