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Data assimilation : a prototype system to assimilate forest stand information

Nyström, Mattias (author)
Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för skoglig resurshushållning,Department of Forest Resource Management
Grafström, Anton (author)
Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för skoglig resurshushållning,Department of Forest Resource Management
Lindgren, Nils (author)
Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för skoglig resurshushållning,Department of Forest Resource Management
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Olsson, Håkan (author)
Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för skoglig resurshushållning,Department of Forest Resource Management
Ståhl, Göran (author)
Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för skoglig resurshushållning,Department of Forest Resource Management
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 (creator_code:org_t)
 
2016
English.
Series: Arbetsrapport / Sveriges lantbruksuniversitet, Institutionen för skoglig resurshushållning, 1401-1204
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  • The purpose of this report is to describe a data assimilation prototype program(Appendix A) developed to estimate forest stand data. The program was developed and tested on data col-lected on the forest estate Remningstorp in southern Sweden. Data assimilation can be used to sequentially combine remote sensing based estimates of forest variables with predictions from growth models. The assimilation routine implemented was the extended Kalman Filter. The program supports two different ways to assimilate the forest data: (1) pixel-wise and (2)stand-wise. In the pixel-wise way, raster cells are used as assimilation unit and can beaggregated to a stand for evaluation. In the stand-wise way, the whole stand is assimilatedas one unit. The two methods has pros and cons. The pixel-wise way is simple to use as nostand-delineation is needed, but might be subject to boundary effects and noise due to geo-metric errors. Using the developed code, it has been shown in three case studies that thecombination of time series of remote sensing data and forest growth functions provides bet-ter estimates of forest variables than only using forecasting, or only using the latest remotesensing data. This opens up for a new way to keep forest stand registers up to date.

Subject headings

LANTBRUKSVETENSKAPER  -- Lantbruksvetenskap, skogsbruk och fiske -- Skogsvetenskap (hsv//swe)
AGRICULTURAL SCIENCES  -- Agriculture, Forestry and Fisheries -- Forest Science (hsv//eng)

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