Data assimilation techniques were used to estimate forest stand data in 2011 by sequentially combining remote sensing based estimates of forest variables with predictions from growth models. Estimates of stand data, based on canopy height models obtained from image matching of digital aerial images at six different time-points between 2003 and 2011, served as input 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˝37 1 N; long. 58˝28 1 E). The assimilation results were compared with two other methods used in practice for estimation of forest variables: the first was to use only the most recent estimate obtained 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 approach provided better results than the two alternative methods. Data assimilation of remote sensing time series 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 forest inventory data. The study constitutes a starting point for the development of a framework useful for sequentially utilizing all types of remote sensing data in order to provide precise and up-to-date estimates of forest stand parameters.
The effect of ongoing climate change on sub-arctic and alpine forests has led to increased interest in monitoring potential changes in the forest-tundra ecotone. In addition to climate change, insect damage, browsing pressure by herbivores such as moose and reindeer, as well as anthropogenic impacts will contribute to changes in the forest-tundra ecotone. These changes are difficult to monitor with manual methods because of the complex mosaic pattern of the ecotone. In this study, the possibility to predict maximum tree height, above ground tree biomass and canopy cover with airborne laser scanning (ALS) was therefore tested at a forest-tundra ecotone site near Abisko in northern Sweden (Lat. N 68°20', Long. E 19°01', 420-700 m a.s.l.). The forest in the area is dominated by mountain birch (Betula pubescens ssp. czerepanovii), which has highly irregular stem and canopy forms. Predictions from two different laser data acquisitions were compared. The first laser data set had 6.1 points m -2 and was obtained in 2008 with a TopEye MKII scanner carried by a helicopter flown at 500 m a.g.l. The second laser data set had 1.4 points m -2 and was obtained in 2010 with an Optech ALTM Gemini scanner carried by a fixed-wing aircraft flown at 1740 m a.g.l. Linear regression models were developed for the predictions using data from 73 sample plots with ten meter radius surveyed in 2009 and 2010. The relative RMSEs obtained for the TopEye and Optech data after leave-one-out cross-validation were, respectively, 8.8% and 9.5% for maximum tree height; 18.7% and 21.2% for above ground tree biomass; and, 16.8% and 18.7% for vertical canopy cover on plot level. The results were clearly improved by introducing a new procedure to compensate for unevenly distributed laser points. In conclusion, ALS has strong potential as a data source to map mountain birch biomass in the forest-tundra ecotone, even when using sparse point density ALS data.3
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