Evidence map›Paper›PMID 39738429›Full record

ArticleScientific reports2024

Mapping management intensity types in grasslands with synergistic use of Sentinel-1 and Sentinel-2 satellite images.

Maciej Bartold, Marcin Kluczek, Konrad Wróblewski, Katarzyna Dąbrowska-Zielińska, Piotr Goliński, Barbara Golińska

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Maciej BartoldInstitute of Geodesy and Cartography, Remote Sensing Centre, 27 Modzelewskiego St, 02-679, Warsaw, Poland. maciej.bartold@igik.edu.pl.
Marcin KluczekInstitute of Geodesy and Cartography, Remote Sensing Centre, 27 Modzelewskiego St, 02-679, Warsaw, Poland.
Konrad WróblewskiInstitute of Geodesy and Cartography, Remote Sensing Centre, 27 Modzelewskiego St, 02-679, Warsaw, Poland.
Katarzyna Dąbrowska-ZielińskaInstitute of Geodesy and Cartography, Remote Sensing Centre, 27 Modzelewskiego St, 02-679, Warsaw, Poland.
Piotr GolińskiDepartment of Grassland and Natural Landscape Sciences, Poznań University of Life Sciences, 11 Dojazd St, 60- 632, Poznań, Poland.
Barbara GolińskaDepartment of Grassland and Natural Landscape Sciences, Poznań University of Life Sciences, 11 Dojazd St, 60- 632, Poznań, Poland.

Funding

Polish-Norwegian Research Programme NOR/POLNOR/GrasSAT/0031/2019-00
6 · The paper itself

Abstract

Grasslands, being vital ecosystems with significant ecological and socio-economic importance, have been the subject of increasing attention due to their role in biodiversity conservation, carbon sequestration, and agricultural productivity. However, accurately classifying grassland management intensity, namely extensive and intensive practices, remains challenging, especially across large spatial extents. This research article presents a comprehensive investigation into the classification of grassland management intensity in two distinct regions of Poland, NUTS2 - namely Podlaskie (PL84) and Wielkopolskie (PL41), by integrating data from Sentinel-1 and Sentinel-2 satellite imagery. The study leverages the unique capabilities of Sentinel-1, a radar satellite, and Sentinel-2, an optical multispectral satellite, to overcome the limitations of using a single data source. Preprocessed Sentinel-1 and Sentinel-2 data were combined to extract spectral and textural features, providing valuable insights into grassland characteristics and patterns. Supervised classification using the Random Forest algorithm was used, and ground truth data from field surveys facilitated the creation of training samples. In Podlaskie, extensive grasslands achieved an overall accuracy (OA) of 84%, while intensive grasslands attained an OA of 83%. In Wielkopolskie, extensive grasslands exhibited an OA of 84%, while intensive grasslands achieved an OA of 83%. Additionally, the classification metrics, including user's accuracy (UA), F1 score, and producer's accuracy (PA), further highlighted the variations in classification accuracy. This comprehensive mapping of grassland management intensity using combined Sentinel-1 and Sentinel-2 data provides valuable insights for conservation agencies, agricultural stakeholders, and land managers. The study's findings contribute to sustainable land management and decision-making processes, facilitating the identification of ecologically valuable areas, optimizing agricultural productivity, and assessing the impacts of different management strategies. Furthermore, the research highlights the potential of Sentinel missions for grassland monitoring and emphasizes the importance of advanced remote sensing techniques for understanding and preserving these crucial ecosystems.

Indexed as

BackscatterEcosystem servicesExtensive and intensive managed grasslandMachine learningMultispectral bandsSatellite imagery

Identifiers

PMID39738429
PMCPMC11685408

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.