Open access publications in the SLU publication database

Abstract

Data assimilation techniques were used to estimate forest stand data in 2011 bysequentially combining remote sensing based estimates of forest variables with predictions fromgrowth models. Estimates of stand data, based on canopy height models obtained from imagematching of digital aerial images at six different time-points between 2003 and 2011, served asinput to the data assimilation. The assimilation routines were built on the extended Kalman filter.The study was conducted in hemi-boreal forest at the Remningstorp test site in southern Sweden(lat. 13˝371 N; long. 58˝281 E). The assimilation results were compared with two other methodsused in practice for estimation of forest variables: the first was to use only the most recent estimateobtained from remotely sensed data (2011) and the second was to forecast the first estimate (2003)to the endpoint (2011). All three approaches were validated using nine 40 m radius validation plots,which were carefully measured in the field. The results showed that the data assimilation approachprovided better results than the two alternative methods. Data assimilation of remote sensing timeseries has been used previously for calibrating forest ecosystem models, but, to our knowledge,this is the first study with real data where data assimilation has been used for estimating forestinventory data. The study constitutes a starting point for the development of a framework usefulfor sequentially utilizing all types of remote sensing data in order to provide precise and up-to-dateestimates of forest stand parameters.