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

Abstract

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.

Keywords

SAR; forest; volume; Sentinel; TanDEM-X

Published in

Title: IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium : proceedings
Publisher: Institute of Electrical and Electronics Engineers

Conference

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

SLU Authors

• Persson, Henrik

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

• Fransson, Johan

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

• Jonzen, Jonas

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

• Nilsson, Mats

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Forest Science
Remote Sensing

Publication identifier

• DOI: https://doi.org/10.1109/IGARSS39084.2020.9324055
• eISBN: 978-1-7281-6374-1

Permanent link to this page (URI)

https://res.slu.se/id/publ/110981