Pest survey card on non‐European Monochamus spp.

Schenk, Martijn; Loomans, A.J.M.; Mart, Kinkar; Vos, Sybren

doi:10.2903/sp.efsa.2020.en-1781

Cited by 2 publications

(1 citation statement)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Together with the aforementioned drivers, the proximity to urban areas and roads seems to be another driving force influencing the number of detections in the grid. The presence of sawmills and wood depots was also an important factor, as recognized by previous studies [77]. Around all such facilities identified in the study region, a high density of declining trees (not exclusively caused by PWN infections; see the lower image of Figure 8, for an example) were found both in the field surveys and commercial satellite imagery.…”

Section: Spatial Analysissupporting

confidence: 63%

Detection of Tree Decline (Pinus pinaster Aiton) in European Forests Using Sentinel-2 Data

et al. 2022

View full text Add to dashboard Cite

Moderate-resolution satellite imagery is essential to detect conifer tree decline on a regional scale and address the threat caused by pinewood nematode (PWN), (Bursaphelenchus xylophilus. This is a quarantine organism responsible for pine wilt disease (PWD), which has caused substantial ecological and economic losses in the maritime pine (Pinus pinaster) forests of Portugal. This study describes the first instance of a pre-operational algorithm applied to Sentinel-2 imagery to detect PWD-compatible decline in maritime pine. The Random Forest model relied on a pre-wilting and an in-season image, calibrated with data from a 24-month long field campaign enhanced with Worldview-3 data and the analysis of biological samples (hyperspectral reflectance, pigment quantification in needles, and PWN identification). Independent validation results attested to the good performance of the model with an overall accuracy of 95%, particularly when decline affects more than 30% of the 100 m2 pixel of Sentinel-2. Spectral angle mapper applied to hyperspectral measurements suggested that PWN infection cannot be separated from other drivers of decline in the visible-near infrared domain. Our algorithm can be employed to detect regional decline trends and inform subsequent aerial and field surveys, to further investigate decline hotspots.

show abstract

Section: Spatial Analysissupporting

confidence: 63%

Detection of Tree Decline (Pinus pinaster Aiton) in European Forests Using Sentinel-2 Data

et al. 2022

View full text Add to dashboard Cite

show abstract

Pest survey card on Bursaphelenchus xylophilus

Schenk¹,

Loomans²,

Nijs³

et al. 2020

EFS3

Self Cite

View full text Add to dashboard Cite

This pest survey card was prepared in the context of the EFSA mandate on plant pest surveillance (M‐2017‐0137) at the request of the European Commission. The purpose of the document is to assist the Member States to plan annual survey activities of quarantine organisms using a statistically sound and risk‐based pest survey approach, in line with current international standards. The data requirements for such an activity include the pest distribution, its host range, its biology and risk factors, as well as available detection and identification methods. This document is part of a toolkit that consists of pest‐specific documents, such as the pest survey cards, and generic documents relevant for all pests to be surveyed, including the general survey guidelines and statistical software such as RiBESS+.

show abstract

Pest survey card on non‐European Monochamus spp.

Cited by 2 publications

References 37 publications

Detection of Tree Decline (Pinus pinaster Aiton) in European Forests Using Sentinel-2 Data

Detection of Tree Decline (Pinus pinaster Aiton) in European Forests Using Sentinel-2 Data

Pest survey card on Bursaphelenchus xylophilus

Contact Info

Product

Resources

About