dc.contributor.author | Lind, Leevi | |
dc.contributor.author | Cerra, Daniele | |
dc.contributor.author | Pato, Miguel | |
dc.contributor.author | Pölönen, Ilkka | |
dc.date.accessioned | 2024-09-24T09:53:58Z | |
dc.date.available | 2024-09-24T09:53:58Z | |
dc.date.issued | 2024 | |
dc.identifier.citation | Lind, 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.other | CONVID_242635177 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/97184 | |
dc.description.abstract | Over 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.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | IEEE | |
dc.relation.ispartof | IGARSS 2024 : 2024 IEEE International Geoscience and Remote Sensing Symposium | |
dc.relation.ispartofseries | IEEE International Geoscience and Remote Sensing Symposium proceedings | |
dc.rights | In Copyright | |
dc.subject.other | geometry | |
dc.subject.other | strips | |
dc.subject.other | codes | |
dc.subject.other | filtering | |
dc.subject.other | urban areas | |
dc.subject.other | lighting | |
dc.subject.other | sensors | |
dc.title | Analyzing Artificial Nighttime Lighting Using Hyperspectral Data from ENMAP | |
dc.type | conference paper | |
dc.identifier.urn | URN:NBN:fi:jyu-202409246062 | |
dc.contributor.laitos | Informaatioteknologian tiedekunta | fi |
dc.contributor.laitos | Faculty of Information Technology | en |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | |
dc.relation.isbn | 979-8-3503-6033-2 | |
dc.type.coar | http://purl.org/coar/resource_type/c_5794 | |
dc.description.reviewstatus | peerReviewed | |
dc.format.pagerange | 8069-8073 | |
dc.relation.issn | 2153-6996 | |
dc.type.version | acceptedVersion | |
dc.rights.copyright | © IEEE | |
dc.rights.accesslevel | embargoedAccess | fi |
dc.type.publication | conferenceObject | |
dc.relation.conference | IEEE International Geoscience and Remote Sensing Symposium | |
dc.relation.grantnumber | 335615 | |
dc.subject.yso | koodit | |
dc.subject.yso | geometria | |
dc.subject.yso | kaupunkiseudut | |
dc.subject.yso | valaistus | |
dc.format.content | fulltext | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p9344 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p8708 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p6390 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p9809 | |
dc.rights.url | http://rightsstatements.org/page/InC/1.0/?language=en | |
dc.relation.doi | 10.1109/igarss53475.2024.10641253 | |
dc.relation.funder | Research Council of Finland | en |
dc.relation.funder | Suomen Akatemia | fi |
jyx.fundingprogram | Others, AoF | en |
jyx.fundingprogram | Muut, SA | fi |
jyx.fundinginformation | Funded by Academy of Finland Smart-HSI project (grant number 335615). | |
dc.type.okm | A4 | |