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  Intercomparison of Earth Observation Data and Methods for Forest Mapping in the Context of Forest Carbon Monitoring

Antropov, O., Miettinen, J., Häme, T., Yrjö, R., Seitsonen, L., McRoberts, R. E., Santoro, M., Cartus, O., Malaga Duran, N., Herold, M., Pardini, M., Papathanassiou, K., Hajnsek, I. (2022): Intercomparison of Earth Observation Data and Methods for Forest Mapping in the Context of Forest Carbon Monitoring - Proceedings, IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (Kuala Lumpur, Malaysia 2022), 5777-5780.
https://doi.org/10.1109/IGARSS46834.2022.9884618

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Antropov, Oleg1, Autor
Miettinen, Jukka1, Autor
Häme, Tuomas1, Autor
Yrjö, Rauste1, Autor
Seitsonen, Lauri1, Autor
McRoberts, Ronald E1, Autor
Santoro, Maurizio1, Autor
Cartus, Oliver1, Autor
Malaga Duran, Natalia1, Autor
Herold, Martin2, Autor              
Pardini, Matteo1, Autor
Papathanassiou, Kostas1, Autor
Hajnsek, Irena1, Autor
Affiliations:
1External Organizations, ou_persistent22              
21.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum, ou_146028              

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Schlagwörter: forest management,forest carbon,synthetic aperture radar, Sentinel-1,Sentinel-2,TanDEM-X,ALOS-2 PALSAR-2,clustering, interferometry
 Zusammenfassung: ESA Forest Carbon Monitoring project (FCM) is developing Earth Observation based, user-centric approaches for forest carbon monitoring. Forest carbon accounting based on forest inventory requires precise and timely estimation of forest variables at various spatial levels accompanied by verifiable uncertainty information. In this paper, we present the algorithm trade-off and selection approach and preliminary results of the algorithm intercomparison exercise in the FCM project. The studies were performed over 7 European test sites located in Finland, Ireland, Romania, Spain and Switzerland, and one tropical forest site in Peru. EO datasets were represented by Sentinel-1, Sentinel-2, TanDEM-X and ALOS-2 PALSAR-2 imagery. Examined approaches include popular parametric and SAR/InSAR scattering physics based approaches, and nonparametric and machine learning approaches such as k-NN, random forests, support vector regression.

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 Datum: 2022-09-282022
 Publikationsstatus: Final veröffentlicht
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 Identifikatoren: DOI: 10.1109/IGARSS46834.2022.9884618
GFZPOF: p4 T5 Future Landscapes
GFZPOFCCA: p4 CARF RemSens
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Titel: IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Veranstaltungsort: Kuala Lumpur, Malaysia
Start-/Enddatum: 2022-07-17 - 2022-07-22

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Titel: Proceedings
Genre der Quelle: Konferenzband
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 5777 - 5780 Identifikator: -