Dr. Filippo Sarvia Profile

Dr. Filippo Sarvia

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Proceedings Article | 21 October 2019 Paper

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

S. De Petris, R. Berretti, F. Sarvia, E. Borgogno-Mondino

Proceedings Volume 11149, 111491G (2019) https://doi.org/10.1117/12.2532778

KEYWORDS: Vegetation, Decision support systems, Control systems, Roads, Data modeling, Geomatics, Image segmentation, Photogrammetry, Failure analysis

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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.

Proceedings Article | 21 October 2019 Paper

Remotely sensed data to support insurance strategies in agriculture

F. Sarvia, S. De Petris, E. Borgogno-Mondino

Proceedings Volume 11149, 111491H (2019) https://doi.org/10.1117/12.2533117

KEYWORDS: Agriculture, Climatology, MODIS, Satellites, Climate change, Calibration, Remote sensing, Near infrared, Vegetation, Natural disasters

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Climate variability is one of the greatest risks for farmers. The ongoing increase of natural calamities suggests that insurance strategies have to be more dynamic than previously. In this work a remote sensing-based service prototype is presented aimed at supporting insurance companies by defining an operative tool to objectively calibrate insurance annual fares, tending to cost reduction able to attract more potential customers. Methodology was applied to an agriculture devoted area located in the Vercelli province (Piemonte - NW Italy). COPERNICUS Sentinel-2 Level 2A image time series were used for this purpose jointly with MODIS data. High resolution Sentinel-2 data (GSD = 10 m) were used to map local spatial differences of crop performance, aimed at locally tuning insurance risk and fares around the average one estimated with reference to MODIS data on a longer period. The agricultural seasons 2018 were considered for this purpose. Although the work with MODIS data was carried out by authors in previous works, their integration with S2 data proved to locally tune at single field and crop type level the agronomic performances of insured areas.

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