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Conference Paper

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

Authors

Antropov,  Oleg
External Organizations;

Miettinen,  Jukka
External Organizations;

Häme,  Tuomas
External Organizations;

Yrjö,  Rauste
External Organizations;

Seitsonen,  Lauri
External Organizations;

McRoberts,  Ronald E
External Organizations;

Santoro,  Maurizio
External Organizations;

Cartus,  Oliver
External Organizations;

Malaga Duran,  Natalia
External Organizations;

/persons/resource/herold

Herold,  Martin
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Pardini,  Matteo
External Organizations;

Papathanassiou,  Kostas
External Organizations;

Hajnsek,  Irena
External Organizations;

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Citation

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


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5015270
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.