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

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Free keywords: forest management,forest carbon,synthetic aperture radar, Sentinel-1,Sentinel-2,TanDEM-X,ALOS-2 PALSAR-2,clustering, interferometry
 Abstract: 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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 Dates: 2022-09-282022
 Publication Status: Finally published
 Pages: -
 Publishing info: -
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 Rev. Type: -
 Identifiers: DOI: 10.1109/IGARSS46834.2022.9884618
GFZPOF: p4 T5 Future Landscapes
GFZPOFCCA: p4 CARF RemSens
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Title: IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Place of Event: Kuala Lumpur, Malaysia
Start-/End Date: 2022-07-17 - 2022-07-22

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Title: Proceedings
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 5777 - 5780 Identifier: -