Skip to main content
SLU publication database (SLUpub)

Conference abstract2015

Data assimilation in stand level forest inventory – first results

Lindgren, Nils; Nyström, Mattias; Wallerman, Jörgen; Ehlers, Sarah; Grafström, Anton; Muszta, Anders; Nyström, Kenneth; Willén, Erik; Fransson, Johan; Bohlin, Jonas; Olsson, Håkan; Ståhl, Göran
Mäkipää, Raisa (ed.); Kilponen, Tuire (ed.)


Data assimilation in stand-level forest inventory – first results 

Nils Lindgren 1 , Mattias Nyström1 , Jörgen Wallerman 1 , Sarah Ehlers 1 , Anton Grafström1 , Anders Muszta 1 , Kenneth Nyström1 , Erik Willen 2 , Johan Fransson 1 , Jonas Bohlin 1 , Håkan Olsson 1 , Göran Ståhl 1 
1Swedish University of Agricultural Sciences, Umeå, Sweden 
2Skogforsk, Uppsala, Sweden 
As we are entering an era of increased supply of remote sensing data, we believe that data assimilation has a large potential for keeping forest stand registers up to date (Ehlers et al. 2013). Data assimilation combines forecasts of previous estimates with new observations of the current state in an optimal way based on the uncertainties in the forecast and the observations. These forecasting and updating steps can be repeated with new available observations to get improved estimations. In the present study, we use canopy height models obtained from matching of digital aerial photos over the test site Remningstorp in Sweden, acquired 2003, 2005, 2007, 2009, 2010 and 2012 and normalized with a DEM from airborne laser scanning. Stem volume was estimated for each data acquisition and stand, using regression functions based on field reference data from sample plots. Forecasting was done with growth functions constructed from National Forest Inventory plots. The remote sensing estimates for each time point were assimilated with the forecasts of the previous estimates, using extended Kalman filtering. Validation was done on 40 m radius sample plots dominated by Norway spruce. Early results for three stands show that the variances were lower when using assimilation of new estimates and there was less fluctuation compared to repeated remote sensing estimates. The results for the assimilated data at year 2011 were also consistently closer to the validation data measured in 2011 compared to the remote sensing estimates from year 2011.

Published in

Natural resources and bioeconomy studies
2015, Volume: 29, pages: 37-37
Publisher: Natural Resources Institute Finland (Luke)


IBFRA 17th conference 2015