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dc.contributor.authorLind, Leevi
dc.contributor.authorCerra, Daniele
dc.contributor.authorPato, Miguel
dc.contributor.authorPölönen, Ilkka
dc.date.accessioned2024-09-24T09:53:58Z
dc.date.available2024-09-24T09:53:58Z
dc.date.issued2024
dc.identifier.citationLind, L., Cerra, D., Pato, M., & Pölönen, I. (2024). Analyzing Artificial Nighttime Lighting Using Hyperspectral Data from ENMAP. In <i>IGARSS 2024 : 2024 IEEE International Geoscience and Remote Sensing Symposium</i> (pp. 8069-8073). IEEE. IEEE International Geoscience and Remote Sensing Symposium proceedings. <a href="https://doi.org/10.1109/igarss53475.2024.10641253" target="_blank">https://doi.org/10.1109/igarss53475.2024.10641253</a>
dc.identifier.otherCONVID_242635177
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/97184
dc.description.abstractOver the years, space-based remote sensing of nighttime light has mostly utilized panchromatic or multispectral sensors. The hyperspectral mission EnMAP, primarily intended for daytime observations, can also produce hyperspectral data of nighttime lighting. EnMAP data from the Las Vegas Strip was analyzed by detecting locations of certain lighting types using matched filtering and detection of sharp emission spikes at known wavelengths. Additionally, images from different nights were compared to determine how changes in observation geometry affect the observed spectra. The results indicate that corrections for geometric effects would be necessary to produce robust time-series data. The EnMAP data were also used to approximate two in-dices related to the efficiency and spectral quality of the light, the luminous efficiency of radiation (LER) and the spectral G index. Future developments will include ana-lyzing scenes from other cities using similar approaches. Program code used in this work is available at https://github.com/silmae/EnMAP_nightlights.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartofIGARSS 2024 : 2024 IEEE International Geoscience and Remote Sensing Symposium
dc.relation.ispartofseriesIEEE International Geoscience and Remote Sensing Symposium proceedings
dc.rightsIn Copyright
dc.subject.othergeometry
dc.subject.otherstrips
dc.subject.othercodes
dc.subject.otherfiltering
dc.subject.otherurban areas
dc.subject.otherlighting
dc.subject.othersensors
dc.titleAnalyzing Artificial Nighttime Lighting Using Hyperspectral Data from ENMAP
dc.typeconference paper
dc.identifier.urnURN:NBN:fi:jyu-202409246062
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.relation.isbn979-8-3503-6033-2
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatuspeerReviewed
dc.format.pagerange8069-8073
dc.relation.issn2153-6996
dc.type.versionacceptedVersion
dc.rights.copyright© IEEE
dc.rights.accesslevelembargoedAccessfi
dc.type.publicationconferenceObject
dc.relation.conferenceIEEE International Geoscience and Remote Sensing Symposium
dc.relation.grantnumber335615
dc.subject.ysokoodit
dc.subject.ysogeometria
dc.subject.ysokaupunkiseudut
dc.subject.ysovalaistus
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p9344
jyx.subject.urihttp://www.yso.fi/onto/yso/p8708
jyx.subject.urihttp://www.yso.fi/onto/yso/p6390
jyx.subject.urihttp://www.yso.fi/onto/yso/p9809
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1109/igarss53475.2024.10641253
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramOthers, AoFen
jyx.fundingprogramMuut, SAfi
jyx.fundinginformationFunded by Academy of Finland Smart-HSI project (grant number 335615).
dc.type.okmA4


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