Evidence map›Paper›PMID 40999067›Full record

ArticleEnvironmental monitoring and assessment2025

Evaluating contaminated land and the environmental impact of oil spills in the Niger Delta region: a remote sensing-based approach.

Seyi Adewale Adebangbe, Deborah P Dixon, Brian Barrett

Abstract read
In one paragraph

Article in Environmental monitoring and assessment, 2025. 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.

Seyi Adewale AdebangbeSchool of Geographical & Earth Sciences, University of Glasgow, Glasgow, UK. Seyi.Adebangbe@glasgow.ac.uk.ORCID http://orcid.org/0009-0006-6562-0747
Deborah P DixonSchool of Geographical & Earth Sciences, University of Glasgow, Glasgow, UK.ORCID http://orcid.org/0000-0003-0846-6959
Brian BarrettSchool of Geographical & Earth Sciences, University of Glasgow, Glasgow, UK.ORCID http://orcid.org/0000-0002-4380-4020

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Niger Delta region of Nigeria is a major oil-producing area which experiences frequent oil spills that severely impacts the local environment and communities. Effective environmental monitoring and management remain inadequate in this area due to negligence, slow response times following oil spills, and difficulties regarding access and safety. This study investigates the pervasive issue of oil spills in the Niger Delta region, by employing a remote sensing approach, leveraging geospatial cloud computing and machine learning to evaluate vegetation health indices (SR, SR2, NDVI, EVI2, GRNDVI, GNDVI) derived from PlanetScope satellite data. These indices were analysed using Slow Moving Average regression, which revealed significant declines in vegetation health following oil spill events. The contaminated landcovers exhibit a Spearman's correlation coefficient (ρ) ranging from - 0.68 to - 0.82, P < 0.005 and P-values below 0.05 in most landcover categories, suggesting a clear and consistent downward trend in the indices' values, reflecting a decrease in vegetation health in contaminated areas between 2016 and 2023. A random forest classifier further quantified the extent of contaminated land cover, demonstrating the effectiveness of this method for monitoring environmental damage in this challenging terrain. Contaminated vegetation, wetland, farmland, and grassland cover approximately 4% (1180 ha) of the total Niger Delta area. This integrated approach will enable decision-makers, including government agencies and oil companies, to gain a deeper understanding of the environmental consequences of oil pollution and implement targeted mitigation and remediation strategies.

Indexed as

Environmental MonitoringPetroleum PollutionRemote Sensing TechnologyNigeriaCloud computingEnvironmental degradationMachine learningNiger DeltaOil pollutionVegetation health indices

Identifiers

PMID40999067
PMCPMC12464078

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.