Evidence map›Paper›PMID 41091749›Full record

ArticlePloS one2025

Land surface temperature evolution in rapidly urbanizing areas of Southeast Asia: Studies from Vietnam and Cambodia.

Bijeesh Kozhikkodan Veettil, Juliana Costi, Vanna Teck, Vikram Puri

Abstract read
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Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

4 authors.

Bijeesh Kozhikkodan VeettilLaboratory of Ecology and Environmental Management, Science and Technology Advanced Institute, Van Lang University, Ho Chi Minh City, Vietnam.
Juliana CostiInstituto de Oceanografia, Universiade Federal do Rio Grande, Rio Grande, Brazil.
Vanna TeckFaculty of Science and Technology, Svay Rieng University, Svay Rieng City, Cambodia.ORCID https://orcid.org/0000-0002-8191-1580
Vikram PuriSchool of Computer Science, Duy Tan University, Da Nang, Vietnam.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Land surface temperature (LST) is one of the crucial variables in urban microclimate studies. Satellite-based thermal data and vegetation indices, like the normalized difference vegetation index (NDVI), help to understand changes in LST and the development of urban heat islands (UHI). We analyzed the variations in LST and vegetation coverage in two rapidly urbanizing provinces, located in southern Vietnam and Cambodia, respectively, over the 10 years from 2013 to 2025. Additionally, complementary ERA5 Interim air temperature data were also utilized. The satellite and in situ data analysis have been used to understand the impacts of urbanization on LSTs. Spatiotemporal changes in NDVI showed rapid urbanization in the eastern region of Battambang city (39.2 km2 to 47.8 km2) and throughout the southern areas of Binh Duong Province (387 km2 to 464.3 km2). Time-series analysis indicated a consistent increase in LST in both study sites. There has been a notable increase in minimum LST since 2017 in the entire city of Battambang, whereas the central area of Battambang has become consistently warmer after 2020. The minimum estimated LST in Battambang varied between 16.1 °C and 28.58 °C (and increased 0.35 °C per year), whereas the maximum LST varied between 29.2 °C to 40.23 °C (and increased 0.36 °C per year). The LST in southern Binh Duong increased gradually during the study period, primarily due to rapid urbanization and vegetation loss. The minimum estimated LST in Binh Duong varied between 13.2 °C to 24.73 °C (and increased 0.26 °C per year), whereas the maximum LST varied between 34.6 °C to 41.3 °C (and increased 0.024 °C per year). The outcome of this study holds considerable importance, as the phenomenon of UHI formation has been documented in rapidly expanding cities and impervious surfaces globally, especially in Southeast Asia.

Indexed as

TemperatureUrbanizationCambodiaCitiesClimate ChangeVietnam

Identifiers

PMID41091749
PMCPMC12527137

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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.