Spatio-temporal behavior of brightness temperature in Tel-Aviv and its application to air temperature monitoring.

Ran Pelta, A Alexandra Chudnovsky, Joel Schwartz

Abstract read

In one paragraph

Article in Environmental pollution (Barking, Essex : 1987), 2016. 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

The trial behind it

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Ran PeltaTel-Aviv University, Enviro-Digital Laboratory, Department of Geography and Human Environment, Israel. Electronic address: ranpelta@gmail.com.

A Alexandra ChudnovskyTel-Aviv University, Enviro-Digital Laboratory, Department of Geography and Human Environment, Israel. Electronic address: achudnov@post.tau.ac.il.

Joel SchwartzHarvard T. H. Chan School of Public Health, Harvard University, Department of Environmental Health, Boston, MA, USA.

Funding

Translational Research Support CoreP30ES000002 · NIEHS · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI SUSAN A KORRICK · 1985 to 2026

$44.6M

NIEHS NIH HHS P30 ES000002

6 · The paper itself

Abstract

This study applies remote sensing technology to assess and examine the spatial and temporal Brightness Temperature (BT) profile in the city of Tel-Aviv, Israel over the last 30 years using Landsat imagery. The location of warmest and coldest zones are constant over the studied period. Distinct diurnal and temporal BT behavior divide the city into four different segments. As an example of future application, we applied mixed regression models with daily random slopes to correlate Landsat BT data with monitored air temperature (Tair) measurements using 14 images for 1989-2014. Our preliminary results show a good model performance with R(2) = 0.81. Furthermore, based on the model's results, we analyzed the spatial profile of Tair within the study domain for representative days.

Indexed as

CitiesTemperatureEnvironmental MonitoringIsraelModels, TheoreticalRegression AnalysisRemote Sensing TechnologyAir temperature (Tair)Brightness temperature (BT)LandsatLand surface temperature (LST)Thermal profileUrban heat island (UHI)Urban planning

Identifiers

PMID26499933

DOI10.1016/j.envpol.2015.09.007

PMCPMC4809040

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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.