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Carbon Sequestration and Urban Heat Island Mitigation by Urban Forests

Klein, Levente J und Albrecht, Conrad M und Marianno, Fernando (2022) Carbon Sequestration and Urban Heat Island Mitigation by Urban Forests. 2022 ESA Living Planet Symposium, 2022-05-23 - 2022-05-27, Bonn, Germany.

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Kurzfassung

Nature-based carbon sequestration is one of the most straightforward ways to extract and to store carbon dioxide from the atmosphere. Urban forests hold the promise of optimized carbon storage and temperature reduction in cities. Remote sensing imagery can identify tree location and size, classify trees based on their species, and track tree health. Using multi- and hyperspectral overhead imagery, green vegetation can be separated from various land use types. Moreover, through further refinement of models by texture and contextual information, trees can get spatially separated from bushes and grass covered surfaces. While spectral-based tree identification can achieve accuracy of 90%, additional deep learning models using even noisy labeled data can further improve tree identification models. Once trees are identified in two-dimensional remote sensing images, allometric models allow to extract tree height and tree growth based on climate data, topography, and soil properties. The biomass of the trees is calculated for tree species using geometrical and phenological models. The carbon stored in trees can be quantified at individual tree level. Furthermore, the models allow to identify areas densely covered by trees to pinpoint bare land where further trees may be planted. Exploiting land surface temperature maps from satellite thermal measurements of, e.g., the Sentinel or Landsat missions, urban heat island can be mapped out at city scale. Urban heat islands may vary based on season and weather conditions; areas persistently warmer when compared to average city temperature background can be identified from time series of data. The correlation of local temperature, tree cover, and land perviousness helps to identify local climate zones. It also may refine and re-evaluate the definition of Local Climate Zones (LCZ). We employ the PAIRS geospatial information platform to demonstrate a scalable solution for tree delineation, carbon sequestration, and urban heat island identification for three global cities: Madrid, New York City, and Dallas, TX.

elib-URL des Eintrags:https://elib.dlr.de/186651/
Dokumentart:Konferenzbeitrag (Poster)
Titel:Carbon Sequestration and Urban Heat Island Mitigation by Urban Forests
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Klein, Levente Jkleinl (at) us.ibm.comNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Albrecht, Conrad MConrad.Albrecht (at) dlr.dehttps://orcid.org/0009-0009-2422-7289NICHT SPEZIFIZIERT
Marianno, Fernandofjmarian (at) us.ibm.comNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:26 Mai 2022
Referierte Publikation:Nein
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Status:veröffentlicht
Stichwörter:carbon sequestration, urban forests, machine learning, LiDAR, tree species identification, Big GeoData Processing
Veranstaltungstitel:2022 ESA Living Planet Symposium
Veranstaltungsort:Bonn, Germany
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:23 Mai 2022
Veranstaltungsende:27 Mai 2022
Veranstalter :European Space Agency
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:03 Jun 2022 10:45
Letzte Änderung:24 Apr 2024 20:48

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