Klein, Levente und Zhou, Wang und Albrecht, Conrad M. (2021) Quantification of Carbon Sequestration in Urban Forests. Tackling Climate Change with Machine Learning, 2021-07-23, virtual.
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Offizielle URL: https://www.climatechange.ai/papers/icml2021/46
Kurzfassung
Vegetation, trees in particular, sequester carbon by absorbing carbon dioxide from the atmosphere, however, the lack of efficient quantification methods of carbon stored in trees renders it difficult to track the process. Here we present an approach to estimate the carbon storage in trees based on fusing multispectral aerial imagery and LiDAR data to identify tree coverage, geometric shape, and tree species, which are crucial attributes in carbon storage quantification. We demonstrate that tree species information and their three-dimensional geometric shapes can be estimated from remote imagery in order to calculate the tree's biomass. Specifically, for Manhattan, New York City, we estimate a total of 52,000 tons of carbon sequestered in trees.
elib-URL des Eintrags: | https://elib.dlr.de/143821/ | ||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||||||
Titel: | Quantification of Carbon Sequestration in Urban Forests | ||||||||||||||||
Autoren: |
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Datum: | 2021 | ||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||
Open Access: | Ja | ||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||
In SCOPUS: | Nein | ||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||
Seitenbereich: | Seiten 1-5 | ||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||
Stichwörter: | carbon sequestration, vegetation management, machine learning, LiDAR, multi-spectral remote sensing, climate change | ||||||||||||||||
Veranstaltungstitel: | Tackling Climate Change with Machine Learning | ||||||||||||||||
Veranstaltungsort: | virtual | ||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||
Veranstaltungsdatum: | 23 Juli 2021 | ||||||||||||||||
Veranstalter : | International Conference on Machine Learning | ||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||
HGF - Programmthema: | Erdbeobachtung | ||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||
DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Künstliche Intelligenz | ||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||
Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > EO Data Science | ||||||||||||||||
Hinterlegt von: | Albrecht, Conrad M | ||||||||||||||||
Hinterlegt am: | 16 Sep 2021 12:11 | ||||||||||||||||
Letzte Änderung: | 24 Apr 2024 20:43 |
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