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Conference paper2020Peer reviewed

Combining TanDEM-X, Sentinel-2 and field data for prediction of species-wise stem volumes

Persson, Henrik; Fransson, Johan; Jonzén, Jonas; Nilsson, Mats


In this study, stem volume measured by the Swedish National Forest Inventory were modelled using the k nearest neighbor (kNN) algorithm, with k=1, 3, or 5 neighbors. As independent variables, the combination of two satellite sensors were used: the active radar sensor TanDEM-X and the passive optical sensor Sentinel-2. The results indicate that stem volume per species can be predicted relatively accurately, mainly due to the inclusion of Sentinel-2 data, while the total stem volume is largely predicted well due to inclusion of the TanDEM-X phase height. The prediction of total stem volume was, however, not significantly improved with the additional spectral information from Sentinel-2 about the tree species. The kNN method is somewhat limited in the highest range of volumes, since no extrapolation is supported. Thus, it is important to have a reference dataset representing the entire range of the population for a successful application. The main advantage of combining the two data sources is the convenient procedure of obtaining both the tree species classification and volumes (divided per species) in a single method. It is concluded, that when sufficient reference data are available, the kNN approach with a combination of radar and optical data provides additional information about the stem volumes (in terms of tree species), but without improving the prediction of the total stem volume accuracy.


SAR; forest; volume; Sentinel; TanDEM-X

Published in

Title: IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium : proceedings
eISBN: 978-1-7281-6374-1
Publisher: Institute of Electrical and Electronics Engineers


IGARSS 2020 Symposium, Remote Sensing for a Dynamic Earth, Virtual Symposium 26 sep-22 oct