Data Assimilation in Forest Inventory: First Empirical Results

Forests

Anders Muszta¹  Kenneth Nyström¹  Jonas Bohlin¹  Johan E. S. Fransson¹  Mattias Nyström¹  Jörgen Wallerman¹  Göran Ståhl¹  Nils Lindgren¹  Håkan Olsson¹  Anton Grafström¹  Sarah Ehlers¹  Erik Willén² 

[1] Department of Forest Resource Management, Swedish University of Agricultural Sciences, 90183 Umeå, Sweden;Skogforsk, 75183 Uppsala, Sweden;

关键词: data assimilation;    extended Kalman filter;    forestry;    image matching;    photogrammetric point clouds;    digital aerial images;    forest inventory;   

DOI  :  10.3390/f6124384

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown