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Open AccessArticle

Data Assimilation in Forest Inventory: First Empirical Results

1
Department of Forest Resource Management, Swedish University of Agricultural Sciences, 90183 Umeå, Sweden
2
Skogforsk, 75183 Uppsala, Sweden
*
Author to whom correspondence should be addressed.
Academic Editor: Joanne C. White
Forests 2015, 6(12), 4540-4557; https://doi.org/10.3390/f6124384
Received: 15 September 2015 / Revised: 1 December 2015 / Accepted: 4 December 2015 / Published: 11 December 2015
(This article belongs to the Special Issue Image-Based Point Clouds for Forest Inventory Applications)
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′ N; long. 58°28′ 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. View Full-Text
Keywords: data assimilation; extended Kalman filter; forestry; image matching; photogrammetric point clouds; digital aerial images; forest inventory data assimilation; extended Kalman filter; forestry; image matching; photogrammetric point clouds; digital aerial images; forest inventory
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MDPI and ACS Style

Nyström, M.; Lindgren, N.; Wallerman, J.; Grafström, A.; Muszta, A.; Nyström, K.; Bohlin, J.; Willén, E.; Fransson, J.E.S.; Ehlers, S.; Olsson, H.; Ståhl, G. Data Assimilation in Forest Inventory: First Empirical Results. Forests 2015, 6, 4540-4557. https://doi.org/10.3390/f6124384

AMA Style

Nyström M, Lindgren N, Wallerman J, Grafström A, Muszta A, Nyström K, Bohlin J, Willén E, Fransson JES, Ehlers S, Olsson H, Ståhl G. Data Assimilation in Forest Inventory: First Empirical Results. Forests. 2015; 6(12):4540-4557. https://doi.org/10.3390/f6124384

Chicago/Turabian Style

Nyström, Mattias; Lindgren, Nils; Wallerman, Jörgen; Grafström, Anton; Muszta, Anders; Nyström, Kenneth; Bohlin, Jonas; Willén, Erik; Fransson, Johan E.S.; Ehlers, Sarah; Olsson, Håkan; Ståhl, Göran. 2015. "Data Assimilation in Forest Inventory: First Empirical Results" Forests 6, no. 12: 4540-4557. https://doi.org/10.3390/f6124384

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