Evidence map›Paper›PMID 36816231›Full record

ArticleHeliyon2023

National wetland mapping using remote-sensing-derived environmental variables, archive field data, and artificial intelligence.

Sébastien Rapinel, Léa Panhelleux, Guillaume Gayet, Rachel Vanacker, Blandine Lemercier, Bertrand Laroche, François Chambaud, Anis Guelmami, Laurence Hubert-Moy

Abstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
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

9 authors.

Sébastien RapinelLETG UMR 6554, University of Rennes - CNRS, place du recteur Henri le Moal, Rennes, 35000, France.
Léa PanhelleuxLETG UMR 6554, University of Rennes - CNRS, place du recteur Henri le Moal, Rennes, 35000, France.
Guillaume GayetPatriNat OFB-CNRS-MNHN, 57 rue Cuvier, Paris, 75231, France.
Rachel VanackerPatriNat OFB-CNRS-MNHN, 57 rue Cuvier, Paris, 75231, France.
Blandine LemercierSAS UMR 1069, Institut Agro Rennes-Angers - INRAE, 65 rue de Saint-Brieuc, Rennes, 35000, France.
Bertrand LarocheInfoSol-US 1106, INRAE, Orléans, 45075, France.
François ChambaudAgence de l'eau Rhône Méditerranée Corse, 2-4 allée de Lodz, Lyon, 69363, France.
Anis GuelmamiTour du Valat, Le Sambuc, Arles, 13200, France.
Laurence Hubert-MoyLETG UMR 6554, University of Rennes - CNRS, place du recteur Henri le Moal, Rennes, 35000, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

While wetland ecosystem services are widely recognized, the lack of fine-scale national inventories prevents successful implementation of conservation policies. Wetlands are difficult to map due to their complex fine-grained spatial pattern and fuzzy boundaries. However, the increasing amount of open high-spatial-resolution remote sensing data and accurately georeferenced field data archives, as well as progress in artificial intelligence (AI), provide opportunities for fine-scale national wetland mapping. The objective of this study was to map wetlands over mainland France (ca. 550,000 km

Indexed as

DatabasesDTMRandom forestRemote sensingSoilVegetation

Identifiers

PMID36816231
PMCPMC9929292

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.