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Assimilating remote sensing data with forest growth models

Nyström, Mattias and Lindgren, Nils and Wallerman, Jörgen and Ehlers, Sarah and Grafström, Anton and Muszta, Anders and Nyström, Kenneth and Willén, Erik and Fransson, Johan E.S. and Bohlin, Jonas and Olsson, Håkan and Ståhl, Göran (2015). Assimilating remote sensing data with forest growth models. Stockholm, Sweden.

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Abstract

As we are entering an era of increased supply of remote sensing data, we believe that dataassimilation that combines growth forecasts of previous estimates with new observations of thecurrent state has a large potential for keeping forest stand registers up to date (Ehlers et al. 2013).The data assimilation will update a forest model e in an optimal way based on the uncertainties inthe forecast and the observations, each time new data becomes available. These forecasting andupdating steps can be repeated with new available observations to get improved estimations. In thisstudy we present the first practical results from data assimilation of mean tree height, basal area andgrowing stock. The remote sensing data used were canopy height models obtained from matching ofdigital aerial photos over the test site Remningstorp in Sweden. The photos were acquired 2003,2005, 2007, 2009, 2010 and 2012 and normalized with a DEM from airborne laser scanning.The procedure for the data assimilation was as follows: mean tree height, basal area and growingstock were predicted on 18 m × 18 m raster cells using the area based method. Ten meter radiussample plots were used as field calibration data. For each photo year, the field data were adjustedfor growth to have the same state year as each acquisition year of the photos. Growth models wereconstructed from National Forest Inventory plot data. Data assimilation could then be performed onraster cell level by initially start with the estimates from 2003 year´s photos. This prediction was thenforecasted to year 2005 by calculating the growth for the raster cell. This forecasted value is thenblended with the new remote sensing estimation collected 2005. The process was then repeated forthe following years where new measurements were available. In this study, extended Kalmanfiltering was used to blend the forecasted values with the new remote sensing measurements.Validation was done for 40 m radius field plots. Further, the results were also compared with twoalternative approaches: the first was to forecast the first remote sensing estimate to the endpointand the second was to use remote sensing data acquired at the endpoint only.The preliminary results for the eight forest stands show that the variances were lower when usingassimilation of new estimates and there were less fluctuation compared to only using remote sensingdata from the endpoint. However, the mean deviation from the measured value 2011 was lowerwhen only data from the endpoint were used. The assimilated values 2011 were consistently closerto the validation data compared to only forecasting the starting estimate from 2003 to 2011.

Authors/Creators:Nyström, Mattias and Lindgren, Nils and Wallerman, Jörgen and Ehlers, Sarah and Grafström, Anton and Muszta, Anders and Nyström, Kenneth and Willén, Erik and Fransson, Johan E.S. and Bohlin, Jonas and Olsson, Håkan and Ståhl, Göran
Title:Assimilating remote sensing data with forest growth models
Year of publishing :2015
Page range:39-40
Number of Pages:2
Place of Publication:Stockholm, Sweden
Publisher:European Association of Remote Sensing Laboratories
Language:English
Additional Information:Konferensabstrakt, ingår i The 35th EARSeL Symposium European Remote Sensing: Progress, Challenges and Opportunities : Symposium Programme & Abstract Book
Publication Type:Other
Article category:Scientific peer reviewed
Version:Published version
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 2 Engineering and Technology > 207 Environmental Engineering > Remote Sensing
Keywords:remote sensing
URN:NBN:urn:nbn:se:slu:epsilon-e-3492
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-e-3492
ID Code:13405
Faculty:S - Faculty of Forest Sciences
Department:(S) > Dept. of Forest Resource Management
(NL, NJ) > Dept. of Forest Resource Management
Deposited By: SLUpub Connector
Deposited On:16 Jun 2016 11:08
Metadata Last Modified:16 Jun 2016 11:08

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