Improved Prediction of Forest Variables Using Data Assimilation of Interferometric Synthetic Aperture Radar Data

Abstract

The statistical framework of data assimilation provides methods for utilizing new data for obtaining up-to-date forest data: existing forest data are forecasted and combined with each new remote sensing data set. This new paradigm for updating forest database, well known from other fields of study, will provide a framework for utilizing all available remote sensing data in proportion to their quality to improve prediction. It also solves the problem that not all remote sensing data sets provide information for the entire area of interest, since areas with no remote sensing data can be forecasted until new remote sensing data become available. In this study, extended Kalman filtering was used for assimilating data from 19 TanDEM-X InSAR images on 137 sample plots, each of 10-meter radius at a test site in southern Sweden over a period of 4 years. At almost all time points data assimilation resulted in predictions closer to the reference value than predictions based on data from that single time point. For the study variables Lorey's mean height, basal area, and stem volume, the median reduction in root mean square error was 0.4 m, 0.9 m(2)/ha, and 15.3 m(3)/ha (2, 3, and 6 percentage points), respectively.

Published in

Canadian Journal of Remote Sensing
2017, volume: 43, number: 4, pages: 374-383

SLU Authors

• Lindgren, Nils

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

• Persson, Henrik

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

• Nyström, Mattias

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

• Nyström, Kenneth

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

• Grafström, Anton

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

• Muszta, Anders

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

• Fransson, Johan

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

• Ståhl, Göran

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

• Olsson, Håkan

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

Associated SLU-program

Remningstorp

UKÄ Subject classification

Earth Observation
Forest Science

Publication identifier

• DOI: https://doi.org/10.1080/07038992.2017.1356220

Permanent link to this page (URI)

https://res.slu.se/id/publ/91938