Evidence map›Paper›PMID 31766418›Full record

ArticleSensors (Basel, Switzerland)2019

Calibration of an Autonomous Instrument for Monitoring Light Pollution from Drones.

Pietro Fiorentin, Carlo Bettanini, Damiano Bogoni

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
2.4field-weighted citation impact, top 11% of its field
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

1 citing paper in PubMed, 25 citations in OpenAlex.

  1. Article
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 at 1 institution in 1 country.

Pietro FiorentinDepartment of Industrial Engineering, University of Padova, 35100 Padova, Italy.ORCID 0000-0003-0583-4103
Carlo BettaniniDepartment of Industrial Engineering, University of Padova, 35100 Padova, Italy.
Damiano BogoniDepartment of Industrial Engineering, University of Padova, 35100 Padova, Italy.
University of Padua · IT

Funding

Department of Industrial Engineering, University of Padova TWINNING 2017
6 · The paper itself

Abstract

The paper presents the calibration activity on the imaging system of the MINLU instrument, an autonomous sensor suite designed for monitoring light pollution using commercial off-the-shelf components. The system is extremely compact and with an overall mass below 3 kg can be easily installed as a payload for drones or sounding balloons. Drones and air balloons can in fact play an important role in completing upward light emission measurement from satellites allowing an increased spatial and time resolution from convenient altitudes and positions. The proposed system can efficiently measure the luminous intensity and the spectral power density of on-ground emissions providing a useful tool to identify polluting sources and to quantify upward light flux. The metrological performance of the imaging system has been verified through an extensive laboratory test activity using referenced light sources: the overall uncertainty of the multi-luminance meter has been calculated to be 7% of the reading, while the multi-spectrometer has shown a full width at half maximum (FWHM) equal to 10 nm within the measuring range between 400 nm and 700 nm. When operating at an altitude of 200 m, the system can achieve a horizontal resolution at a ground level of 0.12 m with a wavelength resolution able to identify the different lamp technology of outdoor light sources, including light-emitting diode (LED) lights that are undetected by satellites.

Indexed as

artificial lightingimage luminance measuring deviceimaging systemlight pollutionphotometryremote sensingspectral analysisUAVurban analysis

Identifiers

PMID31766418
PMCPMC6928764
OpenAlexW2991454113

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.