Data assimilation in forest inventory: First empirical results

Forests

Volumn 6, Issue 12, 2015, Pages 4540-4557

  Nyström, Mattias ^a   Lindgren, Nils ^a   Wallerman, Jörgen ^a   Grafström, Anton ^a   Muszta, Anders ^a   Nyström, Kenneth ^a   Bohlin, Jonas ^a   Willén, Erik ^b   Fransson, Johan E S ^a   Ehlers, Sarah ^a   Olsson, Håkan ^a   Ståhl, Göran ^a  

^a SWEDISH UNIVERSITY OF AGRICULTURAL SCIENCES   (Sweden)

^b THE FORESTRY RESEARCH INSTITUTE OF SWEDEN   (Sweden)

Author keywords

Data assimilation; Digital aerial images; Extended Kalman filter; Forest inventory; Forestry; Image matching; Photogrammetric point clouds

Indexed keywords

ECOSYSTEMS; EXTENDED KALMAN FILTERS; FORESTRY; IMAGE MATCHING; KALMAN FILTERS; REMOTE SENSING;

AERIAL IMAGES; CANOPY HEIGHT MODELS; DATA ASSIMILATION; DATA ASSIMILATION TECHNIQUES; FOREST INVENTORY; FOREST INVENTORY DATA; POINT CLOUD; REMOTELY SENSED DATA;

FORECASTING;

BOREAL FOREST; DATA ASSIMILATION; EMPIRICAL ANALYSIS; FOREST CANOPY; FOREST INVENTORY; GROWTH MODELING; KALMAN FILTER; PHOTOGRAMMETRY; REMOTE SENSING; STAND DYNAMICS;

EID: 84953790547     PISSN: None     EISSN: 19994907     Source Type: Journal    
DOI: 10.3390/f6124384     Document Type: Article

References (32)

1. Wikström, P.; Edenius, L.; Elfving, B.; Eriksson, L.O.; Lämås, T.; Sonesson, J.; Öhman, K.; Wallerman, J.; Waller, C.; Klintebäck, F. The Heureka forestry decision support system: An overview. Math. Comput. For. Nat. Resour. Sci. 2011, 3, 87-94.
2. Baskent, E.Z.; Keles, S. Spatial forest planning: A review. Ecol. Model. 2005, 188, 145-173.
3. Næsset, E.; Gobakken, T.; Holmgren, J.; Hyyppä, H.; Hyyppä, J.; Maltamo, M.; Nilsson, M.; Olsson, H.; Persson, A.; Söderman, U. Laser scanning of forest resources: The Nordic experience. Scand. J. For. Res. 2004, 19, 482-499.
4. Leberl, F.; Irschara, A.; Pock, T.; Meixner, P.; Gruber, M.; Scholz, S.; Wiechert, A. Point Clouds: Lidar versus 3D Vision. Photogramm. Eng. Remote Sens. 2010, 76, 1123-1134.
5. Bohlin, J.; Wallerman, J.; Fransson, J.E.S. Forest variable estimation using photogrammetric matching of digital aerial images in combination with a high-resolution DEM. Scand. J. For. Res. 2012, 27, 692-699.
6. Gobakken, T.; Bollandsås, O.M.; Næsset, E. Comparing biophysical forest characteristics estimated from photogrammetric matching of aerial images and airborne laser scanning data. Scand. J. For. Res. 2015, 30, 73-86.
7. Pitt, D.G.; Woods, M.; Penner, M. A comparison of point clouds derived from stereo imagery and airborne laser scanning for the area-based estimation of forest inventory attributes in boreal Ontario. Can. J. Remote Sens. 2014, 40, 214-232.
8. Stepper, C.; Straub, C.; Pretzsch, H. Using semi-global matching point clouds to estimate growing stock at the plot and stand levels: Application for a broadleaf-dominated forest in central Europe. Can. J. For. Res. 2015, 45, 111-123.
9. Soja, M.J.; Persson, H.; Ulander, L.M.H. Estimation of forest height and canopy density from a single InSAR correlation coefficient. IEEE Geosci. Remote Sens. Lett. 2015, 12, 646-650.
10. Kugler, F.; Schulze, D.; Hajnsek, I.; Pretzsch, H.; Papathanassiou, K.P. TanDEM-X Pol-InSAR performance for forest height estimation. IEEE Geosci. Remote Sens. 2014, 52, 6404-6422.
11. Karjalainen, M.; Kankare, V.; Vastaranta, M.; Holopainen, M.; Hyyppä, J. Prediction of plot-level forest variables using TerraSAR-X stereo SAR data. Remote Sens. Environ. 2012, 117, 338-347.
12. Rabier, F. Overview of global data assimilation developments in numerical weather-prediction centres. Q. J. R. Meteorol. Soc. 2005, 131, 3215-3233.
13. Loew, A. Preface: Remote sensing data assimilation special issue. Remote Sens. Environ. 2008, 112, 1257.
14. Quaife, T.; Lewis, P.; de Kauwe, M.; Williams, M.; Law, B.E.; Disney, M.; Bowyer, P. Assimilating canopy reflectance data into an ecosystem model with an Ensemble Kalman Filter. Remote Sens. Environ. 2008, 112, 1347-1364.
15. Ehlers, S.; Grafström, A.; Nyström, K.; Olsson, H.; Ståhl, G. Data assimilation in stand-level forest inventories. Can. J. For. Res. 2013, 43, 1104-1113.
16. Dixon, B.L.; Howitt, R.E. Continuous forest inventory using a linear filter. For. Sci. 1979, 25, 675-689.
17. Czaplewski, R.L.; Alig, R.J.; Cost, N.D. Monitoring land/forest cover using the Kalman filter: A proposal. In Proceedings of the IUFRO Forest Growth Modeling and Prediction Conference, Minneapolis, MN, USA, 23-27 August 1987; pp. 1089-1096.
18. Czaplewski, R.L.; Thompson, M.T. Opportunities to improve monitoring of temporal trends with FIA panel data. In Proceedings of the Forest Inventory and Analysis (FIA) Symposium 2008, Park City, UT, USA, 21-23 October 2008; pp. 1-55.
19. Welch, G.; Bishop, G. An Introduction to the Kalman Filter; University of North Carolina: Chapel Hill, NC, USA, 2006.
20. Dowd, M. Bayesian statistical data assimilation for ecosystem models using Markov Chain Monte Carlo. J. Mar. Syst. 2007, 68, 439-456.
21. Hägglund, B.; Lundmark, J.E. Site index estimation by means of site properties. Scots pine and Norway spruce in Sweden. Stud. For. Suec. 1977, 138, 38.
22. Söderberg, U. Funktioner för Skogsindelning: Höjd, Formhöjd Och Barktjocklek för Enskilda Träd (Functions for Forest Management: Height, form Height and Bark Thickness of Individual Trees); Institutionen for skogstaxering: Umeå, Sweden, 1992.
23. Brandel, G. Volymfunktioner för Enskilda Träd: Tall, Gran Och Björk; Sveriges lantbruksuniversitet, Institutionen för skogsproduktion: Garpenberg, Sweden, 1990. (In Swedish)
24. Fridman, J.; Holm, S.; Nilsson, M.; Nilsson, P.; Ringvall, A.H.; Ståhl, G. Adapting National Forest Inventories to changing requirements-The case of the Swedish National Forest Inventory at the turn of the 20th century. Silva Fenn. 2014, 48, 1-29.
25. Hinz, A.; Dörstel, C.; Heier, H. DMC-The digital sensor technology of Z/I-Imaging. In Photogrammetric Week 01; Wichmann Verlag: Heidelberg, Germany, 2001; pp. 93-103.
26. Rothermel, M.; Wenzel, K.; Fritsch, D.; Haala, N. SURE-Photogrammetric surface reconstruction from imagery. In Proceedings of the LC3DWorkshop, Berlin, Germany, 4-5 December 2012; pp. 1-9.
27. Wenzel, K.; Rothermel, M.; Haala, N.; Fritsch, D. SURE-The ifp software for dense image matching. In Proceedings of the Photogrammetric Week 2013, Heidelberg, Germany, 4-6 September 2013; pp. 59-70.
28. Hirschmüller, H. Stereo processing by semiglobal matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell. 2008, 30, 328-341.
29. Lundgren, J.; Owemyr, P. Noggrannhetskontroll av Laserdata för ny Nationell Höjdmodell; Högskolan i Gävle: Gävle, Sweden, 2010.
30. Næsset, E. Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sens. Environ. 2002, 80, 88-99.
31. McGaughey, R.J. FUSION/LDV: Software for LiDAR Data Analysis and Visualization; FUSION Version 3.42; USDA Forest Service, Pacific Northwest Research Station, University of Washington: Seattle, WA, USA, 2014; p. 175.
32. Fahlvik, N.; Elfving, B.; Wikström, P. Evaluation of growth models used in the Swedish forest planning system Heureka. Silva Fenn. 2014, 48.