| Autor: |
Dichou, Killian, Nickmilder, Charles, Tedde, Anthony, Franceschini, Sébastien, Brostaux, Yves, Dufrasne, Isabelle, Lessire, Françoise, Glesner, Noémie, Soyeurt, Hélène |
| Předmět: |
|
| Zdroj: |
Applied Sciences (2076-3417); Mar2024, Vol. 14 Issue 5, p1923, 14p |
| Abstrakt: |
Featured Application: The prospective identification of mowing events holds promise for application across diverse domains, including the assessment of arthropod biodiversity as an explanatory factor and the evaluation of general agricultural practices. An examination of their occurrences over time has the potential to enhance the efficient allocation of inherent territorial resources for animal feeding. The extensive identification of mowing events on a territory holds significant potential to help monitor shifts in biodiversity and contribute to assessing the impacts of drought events. Additionally, it provides valuable insights into farming practices and their consequential economic and ecological effects. To overcome challenges in obtaining reference grazing information directly from the field, this study introduces a novel methodology leveraging the compressed sward height (CSH) derived from Sentinel-1, Sentinel-2, and meteorological data, boasting an accuracy of 20 mm. Our central hypothesis posits that the mowing status of a parcel can be automatically discerned by analyzing the distribution and variation of its CSH values. Employing a two-step strategy, we first applied unsupervised algorithms, specifically k-means and isolation forest, and subsequently amalgamated the outcomes with a partial least squares analysis on an extensive dataset encompassing 194,657 pastures spanning the years 2018 to 2021. The culmination of our modeling efforts yielded a validation accuracy of 0.66, as ascertained from a focused dataset of 68 pastures. Depending on the studied year and with a threshold fixed at 0.50, 21% to 57% of all the parcels in the Wallonia dataset were tagged as mown by our model. This study introduces an innovative approach for the automated detection of mown parcels, showcasing its potential to monitor agricultural activities at scale. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
