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Towards an Improved Large-Scale Gridded Population Dataset: A Pan-European Study on the Integration of 3D Settlement Data into Population Modelling

Palacios Lopez, Daniela und Esch, Thomas und MacManus, Kytt und Marconcini, Mattia und Sorichetta, Alessandro und Yetman, Gregorie und Zeidler, Julian und Dech, Stefan und Tatem, Andrew und Reinartz, Peter (2022) Towards an Improved Large-Scale Gridded Population Dataset: A Pan-European Study on the Integration of 3D Settlement Data into Population Modelling. Remote Sensing, 14 (325), Seiten 1-30. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/rs14020325. ISSN 2072-4292.

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Offizielle URL: https://www.mdpi.com/2072-4292/14/2/325/htm

Kurzfassung

Large-scale gridded population datasets available at the global or continental scale have become an important source of information in applications related to sustainable development. In recent years, the emergence of new population models has leveraged the inclusion of more accurate and spatially detailed proxy layers describing the built-up environment (e.g., built-area and building footprint datasets), enhancing the quality, accuracy and spatial resolution of existing products. However, due to the consistent lack of vertical and functional information on the built-up environment, large-scale gridded population datasets that rely on existing built-up land proxies still report large errors of under- and overestimation, especially in areas with predominantly high-rise buildings or industrial/commercial areas, respectively. This research investigates, for the first time, the potential contributions of the new World Settlement Footprint—3D (WSF3D) dataset in the field of large-scale population modelling. First, we combined a Random Forest classifier with spatial metrics derived from the WSF3D to predict the industrial versus non-industrial use of settlement pixels at the Pan-European scale. We then examined the effects of including volume and settlement use information into frameworks of dasymetric population modelling. We found that the proposed classification method can predict industrial and non-industrial areas with overall accuracies and a kappa-coefficient of ~84% and 0.68, respectively. Additionally, we found that both, integrating volume and settlement use information considerably increased the accuracy of population estimates between 10% and 30% over commonly employed models (e.g., based on a binary settlement mask as input), mainly by eliminating systematic large overestimations in industrial/commercial areas. While the proposed method shows strong promise for overcoming some of the main limitations in large-scale population modelling, future research should focus on improving the quality of the WFS3D dataset and the classification method alike, to avoid the false detection of built-up settlements and to reduce misclassification errors of industrial and high-rise buildings.

elib-URL des Eintrags:https://elib.dlr.de/185419/
Dokumentart:Zeitschriftenbeitrag
Zusätzliche Informationen:This work was supported by the EU-funded ACP-EU Natural Disaster Risk Reduction Program, managed by the Global Facility for Disaster Reduction and Recovery of the World Bank (contract nos. 7194331 and 7196541). This work was funded by the German Academic Exchange Service (DAAD) providing the research fellowship to Daniela Palacios Lopez No. 91687956.
Titel:Towards an Improved Large-Scale Gridded Population Dataset: A Pan-European Study on the Integration of 3D Settlement Data into Population Modelling
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Palacios Lopez, DanielaDaniela.PalaciosLopez (at) dlr.dehttps://orcid.org/0000-0001-6302-2491NICHT SPEZIFIZIERT
Esch, ThomasThomas.Esch (at) dlr.dehttps://orcid.org/0000-0002-5868-9045NICHT SPEZIFIZIERT
MacManus, KyttCIESINNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Marconcini, MattiaMattia.Marconcini (at) dlr.dehttps://orcid.org/0000-0002-5042-5176NICHT SPEZIFIZIERT
Sorichetta, AlessandroUniversity of Southhamptonhttps://orcid.org/0000-0002-3576-5826NICHT SPEZIFIZIERT
Yetman, GregorieCIESINNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Zeidler, JulianJulian.Zeidler (at) dlr.dehttps://orcid.org/0000-0001-9444-2296NICHT SPEZIFIZIERT
Dech, StefanStefan.Dech (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Tatem, AndrewUniversity of SouthhamptonNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Reinartz, Peterpeter.reinartz (at) dlr.dehttps://orcid.org/0000-0002-8122-1475NICHT SPEZIFIZIERT
Datum:11 Januar 2022
Erschienen in:Remote Sensing
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Ja
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:14
DOI:10.3390/rs14020325
Seitenbereich:Seiten 1-30
Verlag:Multidisciplinary Digital Publishing Institute (MDPI)
Name der Reihe:MPDI
ISSN:2072-4292
Status:veröffentlicht
Stichwörter:large-scale gridded population dataset; dasymetric modelling; accuracy assessment; World Settlement Footprint-3D; random forest classifier; spatial metrics; sustainable development
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 - Geowissenschaftl. Fernerkundungs- und GIS-Verfahren, R - Geoprodukte u. - Systeme, Services, R - Fernerkundung u. Geoforschung, R - Optische Fernerkundung
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Deutsches Fernerkundungsdatenzentrum > Dynamik der Landoberfläche
Deutsches Fernerkundungsdatenzentrum > Leitungsbereich DFD
Institut für Methodik der Fernerkundung > Photogrammetrie und Bildanalyse
Hinterlegt von: Palacios Lopez, Daniela
Hinterlegt am:16 Mai 2022 13:37
Letzte Änderung:19 Mai 2022 16:28

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