Evidence map›Paper›PMID 41704499›Full record

ArticleData in brief2026

PhenoStras2022: A 2022 smartphone-based image and field phenology dataset for monitoring urban trees in Strasbourg, France.

Clément Bressant, Pierre-Alexis Herrault, Anne Puissant

Abstract read
In one paragraph

Article in Data in brief, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Clément BressantLIVE UMR 7362 CNRS, University of Strasbourg, Strasbourg, France.
Pierre-Alexis HerraultLIVE UMR 7362 CNRS, University of Strasbourg, Strasbourg, France.
Anne PuissantLIVE UMR 7362 CNRS, University of Strasbourg, Strasbourg, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Urban trees provide critical ecosystem services but remain vulnerable to climate change and urban environmental stresses. To improve understanding of their phenological dynamics and support reproducible urban vegetation monitoring, PhenoStras2022 is introduced, a dataset based on non-destructive, low-cost (using a portable smartphone-based setup) ground-based observations. Data were collected throughout a complete phenological cycle in 2022 across 40 green sites in Strasbourg, France. Two types of photographic acquisitions were conducted: Digital Hemispherical Photography (DHP) and Front-View Photography (FVP), complemented by field-based phenological observations following a simplified BBCH scale. The dataset is organized to enable a range of applications, including remote sensing products validation and urban climate studies. PhenoStras2022 addresses the current lack of ground-based urban phenology data and provides a robust foundation for analysing seasonal patterns and the resilience of urban trees. It also promotes participatory science by offering an accessible, replicable acquisition protocol, contributing to the strengthening of phenological monitoring networks in the context of a changing climate.

Indexed as

Digital hemispherical photographyFront-view photography, BBCH scale, Low-costIn situ

Identifiers

PMID41704499
PMCPMC12907632

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.