Detectability of Silvicultural Treatments in Time Series of Penetration Depth Corrected Tandem-X Phase Heights

Abstract

This study investigated the potential of utilizing time series of TanDEM-X phase heights, corrected for penetration depth, to detect silvicultural treatments in hemi-boreal forest. In total, 34 field plots with 40 m radius were used in conjunction with detailed forest management records to construct a reliable data set of treatments. The study area is situated in Remningstorp, a forest test site in southern Sweden. In the analysis, the temporal mean corrected phase heights were compared before and after a silvicultural treatment in order to quantify the effects of thinnings and clear-cuts on the phase height. As expected clear-cuts were highly distinguishable, but thinnings, while exhibiting a negative change in phase height on average, were not individually distinct from all untreated plots. Moreover, the results regarding the utility of applying penetration depth correction for the task were inconclusive. Overall, the results look very promising for using time series of phase height from TanDEM-X to map thinnings and clear-cuts, especially when several observations are available before and after the silvicultural treatment.

Keywords

TanDEM-X; forestry; change detection; time series; penetration depth; thinnings; clear-cuts

Published in

IEEE International Geoscience and Remote Sensing Symposium proceedings
2022, pages: 5909-5912
Title: IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Publisher: Institute of Electrical and Electronics Engineers

Conference

IEEE International Geoscience and Remote Sensing Symposium (IGARSS), JUL 17-22, 2022, Kuala Lumpur, MALAYSIA

SLU Authors

• Huuva, Ivan

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

• Persson, Henrik

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

• Wallerman, Jörgen

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

• Fransson, Johan

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

UKÄ Subject classification

Remote Sensing
Forest Science

Publication identifier

• DOI: https://doi.org/10.1109/IGARSS46834.2022.9884951
• eISBN: 978-1-6654-2792-0

Permanent link to this page (URI)

https://res.slu.se/id/publ/122100