The search result changed since you submitted your search request. Documents might be displayed in a different sort order.
  • search hit 7 of 37
Back to Result List

Outbreak of Moroccan locust in Sardinia (Italy): a remote sensing perspective

Please always quote using this URN: urn:nbn:de:bvb:20-opus-297232
  • The Moroccan locust has been considered one of the most dangerous agricultural pests in the Mediterranean region. The economic importance of its outbreaks diminished during the second half of the 20th century due to a high degree of agricultural industrialization and other human-caused transformations of its habitat. Nevertheless, in Sardinia (Italy) from 2019 on, a growing invasion of this locust species is ongoing, being the worst in over three decades. Locust swarms destroyed crops and pasture lands of approximately 60,000 ha in 2022.The Moroccan locust has been considered one of the most dangerous agricultural pests in the Mediterranean region. The economic importance of its outbreaks diminished during the second half of the 20th century due to a high degree of agricultural industrialization and other human-caused transformations of its habitat. Nevertheless, in Sardinia (Italy) from 2019 on, a growing invasion of this locust species is ongoing, being the worst in over three decades. Locust swarms destroyed crops and pasture lands of approximately 60,000 ha in 2022. Drought, in combination with increasing uncultivated land, contributed to forming the perfect conditions for a Moroccan locust population upsurge. The specific aim of this paper is the quantification of land cover land use (LCLU) influence with regard to the recent locust outbreak in Sardinia using remote sensing data. In particular, the role of untilled, fallow, or abandoned land in the locust population upsurge is the focus of this case study. To address this objective, LCLU was derived from Sentinel-2A/B Multispectral Instrument (MSI) data between 2017 and 2021 using time-series composites and a random forest (RF) classification model. Coordinates of infested locations, altitude, and locust development stages were collected during field observation campaigns between March and July 2022 and used in this study to assess actual and previous land cover situation of these locations. Findings show that 43% of detected locust locations were found on untilled, fallow, or uncultivated land and another 23% within a radius of 100 m to such areas. Furthermore, oviposition and breeding sites are mostly found in sparse vegetation (97%). This study demonstrates that up-to-date remote sensing data and target-oriented analyses can provide valuable information to contribute to early warning systems and decision support and thus to minimize the risk concerning this agricultural pest. This is of particular interest for all agricultural pests that are strictly related to changing human activities within transformed habitats.show moreshow less

Download full text files

Export metadata

Additional Services

Share in Twitter Search Google Scholar Statistics
Metadaten
Author: Igor Klein, Arturo Cocco, Soner Uereyen, Roberto Mannu, Ignazio Floris, Natascha Oppelt, Claudia Kuenzer
URN:urn:nbn:de:bvb:20-opus-297232
Document Type:Journal article
Faculties:Philosophische Fakultät (Histor., philolog., Kultur- und geograph. Wissensch.) / Institut für Geographie und Geologie
Language:English
Parent Title (English):Remote Sensing
ISSN:2072-4292
Year of Completion:2022
Volume:14
Issue:23
Article Number:6050
Source:Remote Sensing (2022) 14:23, 6050. https://doi.org/10.3390/rs14236050
DOI:https://doi.org/10.3390/rs14236050
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 52 Astronomie / 526 Mathematische Geografie
6 Technik, Medizin, angewandte Wissenschaften / 63 Landwirtschaft / 630 Landwirtschaft und verwandte Bereiche
Tag:Dociostaurus maroccanus; Sentinel-2; abandoned land; agricultural pests; food security; locust outbreak; remote sensing
Release Date:2023/11/24
Date of first Publication:2022/11/29
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International