Evidence map›Paper›PMID 36643951›Full record

ArticleRemote sensing2022

Changes in Onset of Vegetation Growth on Svalbard, 2000-2020.

Stein Rune Karlsen, Arve Elvebakk, Hans Tømmervik, Santiago Belda, Laura Stendardi

Abstract read
In one paragraph

Article in Remote sensing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Increased CHGlobal change biology · 2026
    Article
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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

5 authors.

Stein Rune KarlsenNORCE Norwegian Research Centre AS, P.O. Box 6434, 9294 Tromsø, Norway.ORCID https://orcid.org/0000-0002-9456-6990
Arve ElvebakkNoregs Arktiske Universitetsmuseum, UiT-The Arctic University of Norway, 9037 Tromsø, Norway.
Hans TømmervikNorwegian Institute for Nature Research (NINA), FRAM-High North Research Centre for Climate and the Environment, Langnes, P.O. Box 6606, 9296 Tromsø, Norway.ORCID https://orcid.org/0000-0001-7273-1695
Santiago BeldaApplied Mathematics Department, University of Alicante, 03080 Alicante, Spain.ORCID https://orcid.org/0000-0003-3739-6056
Laura StendardiDepartment of Agriculture, Food, Environment, and Forestry (DAGRI), University of Florence, Piazzale Delle Cascine 18, 50144 Florence, Italy.ORCID https://orcid.org/0000-0002-9802-8962

Funding

European Research Council 755617
6 · The paper itself

Abstract

The global temperature is increasing, and this is affecting the vegetation phenology in many parts of the world. The most prominent changes occur at northern latitudes such as our study area, which is Svalbard, located between 76°30'N and 80°50'N. A cloud-free time series of MODIS-NDVI data was processed. The dataset was interpolated to daily data during the 2000-2020 period with a 231.65 m pixel resolution. The onset of vegetation growth was mapped with a NDVI threshold method which corresponds well with a recent Sentinel-2 NDVI-based mapping of the onset of vegetation growth, which was in turn validated by a network of in-situ phenological data from time lapse cameras. The results show that the years 2000 and 2008 were extreme in terms of the late onset of vegetation growth. The year 2020 had the earliest onset of vegetation growth on Svalbard during the 21-year study. Each year since 2013 had an earlier or equally early timing in terms of the onset of the growth season compared with the 2000-2020 average. A linear trend of 0.57 days per year resulted in an earlier onset of growth of 12 days on average for the entire archipelago of Svalbard in 2020 compared to 2000.

Indexed as

ArcticMODISNDVIonset of vegetation growthspatial scalesSvalbardtime seriestrend

Identifiers

PMID36643951
PMCPMC7614054

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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.