Forest stand age classification using time series of photogrammetrically derived digital surface models

Scandinavian Journal of Forest Research

Volumn 31, Issue 2, 2016, Pages 194-205

  Vastaranta, Mikko ^a,b   Niemi, Mikko ^a,b   Wulder, Michael A ^c   White, Joanne C ^c   Nurminen, Kimmo ^b   Litkey, Paula ^b   Honkavaara, Eija ^b   Holopainen, Markus ^a,b   Hyyppä, Juha ^b  

^a UNIVERSITY OF HELSINKI   (Finland)

^b FINNISH GEOSPATIAL RESEARCH INSTITUTE   (Finland)

^c PACIFIC FORESTRY CENTRE   (Canada)

Author keywords

forest management; forestry; image matching; lidar; photogrammetry; time series

Indexed keywords

IMAGE MATCHING; OPTICAL RADAR; PHOTOGRAMMETRY; REMOTE SENSING; TEMPERATURE CONTROL; TIME SERIES;

AIRBORNE LASER SCANNING; CANOPY HEIGHT MODELS; CLASSIFICATION ACCURACY; DIGITAL SURFACE MODELS; DIGITAL TERRAIN MODEL; FOREST INVENTORY; FOREST MANAGERS; FOREST STRUCTURE;

FORESTRY;

EID: 84955662334     PISSN: 02827581     EISSN: 16511891     Source Type: Journal    
DOI: 10.1080/02827581.2015.1060256     Document Type: Article

References (40)

1. P.Axelsson. 2000. DEM generation from laser scanner data using adaptive TIN models. Int Arch Photogramm Remote Sens. 35:236–241.
2. J.B.Bradford, R.A.Birdsey, L.A.Joyce, M.G.Ryan. 2008. Tree age, disturbance history, and carbon stocks and fluxes in subalpine Rocky Mountain forests. Global Change Biol. 14:2882–2897. doi: 10.1111/j.1365-2486.2008.01686.x
3. A.Cajander. 1926. The theory of forest types. Acta For Fenn. 29:1–108.
4. H.Croft, J.Chen, T.Noland. 2014. Stand age effects on Boreal forest physiology using a long time-series of satellite data. Forest Ecol Manag. 328:202–208. doi: 10.1016/j.foreco.2014.05.023
5. L.I.Duncanson, K.O.Niemann, M.A.Wulder. 2010. Integration of GLAS and Landsat TM data for aboveground biomass estimation. Can J Remote Sens. 36:129–141. doi: 10.5589/m10-037
6. Forest Act. 1996. Metsälaki [Forest Act]. N: o.1093:12.12. Finnish.
7. T.Fujita, A.Itaya, M.Miura, T.Manabe, S.I.Yamamoto. 2003. Long-term canopy dynamics analysed by aerial photographs in a temperate old-growth evergreen broad-leaved forest. J Ecol. 91:686–693. doi: 10.1046/j.1365-2745.2003.00796.x
8. J.Heikkilä, E.Ärölä, S.Kilpiäinen 2011. Kaukokartoitusperusteisen metsien inventoinnin koealojen maastotyöohje [Field work guide for remote sensing based forest inventory]. Helsinki: Forestry Development Centre TAPIO, Finnish Forest Centre. Finnish.
9. H.Holmström, M.Nilsson, G.Ståhl 2010. Simultaneous estimations of forest parameters using aerial photograph interpreted data and the k nearest neighbour method. Scand J Forest Res. 16:67–78. doi: 10.1080/028275801300004424
10. M.Holopainen, A.Mäkinen, J.Rasinmäki, J.Hyyppä, H.Hyyppä, H.Kaartinen, R.Viitala, M.Vastaranta, A.Kangas. 2010a. Effect of tree-level airborne laser-scanning measurement accuracy on the timing and expected value of harvest decisions. Eur J Forest Res. 129:899–907. doi: 10.1007/s10342-009-0282-6
11. M.Holopainen, A.Mäkinen, J.Rasinmäki, K.Hyytiäinen, S.Bayazidi, I.Pietilä. 2010b. Comparison of various sources of uncertainty in stand-level net present value estimates. Forest Policy Econ. 12:377–386. doi: 10.1016/j.forpol.2010.02.009
12. M.Holopainen, M.Vastaranta, R.Haapanen, X.Yu, J.Hyyppä, H.Kaartinen, R.Viitala, H.Hyyppä. 2010c. Site-type estimation using airborne laser scanning and stand register data. Photogramm J Finland. 22:16–32.
13. M.Holopainen, M.Vastaranta, J.Hyyppä. 2014. Outlook for the next generation's precision forestry in Finland. Forests. 5:1682–1694. doi: 10.3390/f5071682
14. M.Holopainen, M.Vastaranta, J.Rasinmäki, J.Kalliovirta, A.Mäkinen, R.Haapanen, T.Melkas, X.Yu, J.Hyyppä. 2010d. Uncertainty in timber assortment estimates predicted from forest inventory data. Eur J Forest Res. 129:1131–1142. doi: 10.1007/s10342-010-0401-4
15. J.Hyyppä, H.Hyyppä, D.Leckie, F.Gougeon, X.Yu, M.Maltamo. 2008. Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests. Int J Remote Sens. 29:1339–1366. doi: 10.1080/01431160701736489
16. J.Hyyppä, H.Hyyppä, X.Yu, H.Kaartinen, A.Kukko, M.Holopainen 2009. Forest inventory using small-footprint airborne LiDAR. In: J.Shan, C.K.Toth, editor. Topographic laser ranging and scanning: principles and processing. Boca Raton, FL: CRC Press; p. 335–370.
17. J.Hyyppä, X.Yu, H.Hyyppä, M.Vastaranta, M.Holopainen, A.Kukko, H.Kaartinen, A.Jaakkola, M.Vaaja, J.Koskinen. 2012. Advances in forest inventory using airborne laser scanning. Remote Sens. 4:1190–1207. doi: 10.3390/rs4051190
18. A.Itaya, M.Miura, S.-I.Yamamoto. 2004. Canopy height changes of an old-growth evergreen broad-leaved forest analyzed with digital elevation models. Forest Ecol Manag. 194:403–411. doi: 10.1016/j.foreco.2004.03.003
19. J.Jalonen, I.Vanha-Majamaa. 2001. Immediate effects of four different felling methods on mature boreal spruce forest understorey vegetation in southern Finland. Forest Ecol Manag. 146:25–34. doi: 10.1016/S0378-1127(00)00446-1
20. J.Kalliovirta, T.Tokola. 2005. Functions for estimating stem diameter and tree age using tree height, crown width and existing stand database information. Silva Fenn. 39:227–248. doi: 10.14214/sf.386
21. R.E.Kennedy, P.A.Townsend, J.E.Gross, W.B.Cohen, P.Bolstad, Y.Wang, P.Adams. 2009. Remote sensing change detection tools for natural resource managers: understanding concepts and tradeoffs in the design of landscape monitoring projects. Remote Sens Environ. 113:1382–1396. doi: 10.1016/j.rse.2008.07.018
22. J.Koivuniemi, K.T.Korhonen. 2006. Inventory by compartments. In: A.Kangas, M.Maltamo. Forest inventory – methodology and applications. Dordrecht: Springer; p. 271–278.
23. I.Korpela. 2006. Geometrically accurate time series of archived aerial images and airborne lidar data in a forest environment. Silva Fenn. 40:109–126. doi: 10.14214/sf.355
24. K.Kraus. 1993. Photogrammetry, volume 1, fundamentals and standard processes, with contributions by Peter Waldhäusl. Bonn: Dümmlerbuch.
25. A.Lehtonen, R.Mäkipää, J.Heikkinen, R.Sievänen, J.Liski. 2004. Biomass expansion factors (BEFs) for Scots pine, Norway spruce and birch according to stand age for boreal forests. Forest Ecol Manag. 188:211–224. doi: 10.1016/j.foreco.2003.07.008
26. M.Maltamo, P.Packalèn, A.Suvanto, K.T.Korhonen, L.Mehtätalo, P.Hyvönen. 2009. Combining ALS and NFI training data for forest management planning: a case study in Kuortane, Western Finland. Eur J Forest Res. 128:305–317. doi: 10.1007/s10342-009-0266-6
27. E.Næsset, T.Gobakken, J.Holmgren, H.Hyyppä, J.Hyyppä, M.Maltamo, M.Nilsson, H.Olsson, Å.Persson, U.Söderman. 2004. Laser scanning of forest resources: the Nordic experience. Scand J Forest Res. 19:482–499. doi: 10.1080/02827580410019553
28. T.Nelson, M.A.Wulder, K.O.Niemann. 2001. Spatial resolution implications of digitising aerial photography for environmental applications. Imaging Sci J. 49:223–232.
29. K.O.Niemann. 1995. Remote sensing of fores stand age using airborne spectrometer data. Photogramm Eng Remote Sens. 61:1119–1127.
30. K.Nurminen, P.Litkey, E.Honkavaara, M.Vastaranta, M.Holopainen, P.Lyytikäinen-Saarenmaa, T.Kantola, M.Lyytikäinen. 2015. Automation aspects for the georeferencing of photogrammetric aerial image archives in forested scenes. Remote Sens. 7:1565–1593. doi: 10.3390/rs70201565
31. E.B.Racine, N.C.Coops, B.St-Onge, J.Begin. 2014. Estimating forest stand age from LiDAR-derived predictors and nearest neighbor imputation. Forest Sci. 60:128–136. doi: 10.5849/forsci.12-088
32. M.Vastaranta, M.A.Wulder, J.C.White, A.Pekkarinen, S.Tuominen, C.Ginzler, V.Kankare, M.Holopainen, J.Hyyppä, H.Hyyppä. 2013. Airborne laser scanning and digital stereo imagery measures of forest structure: comparative results and implications to forest mapping and inventory update. Can J Remote Sens. 39:382–395.
33. C.Véga, B.St-Onge. 2008. Height growth reconstruction of a boreal forest canopy over a period of 58 years using a combination of photogrammetric and lidar models. Remote Sens Environ. 112:1784–1794. doi: 10.1016/j.rse.2007.09.002
34. C.Véga, B.St-Onge. 2009. Mapping site index and age by linking a time series of canopy height models with growth curves. Forest Ecol Manag. 257:951–959. doi: 10.1016/j.foreco.2008.10.029
35. S.Walker. 2007. New features in SOCET SET®. In: Fritch D, editor. Proceedings of the Photogrammetric Week; 2007 Sept 3–7; Stuttgart: University of Stuttgart.
36. J.C.White, M.A.Wulder, A.Varhola, M.Vastaranta, N.C.Coops, B.D.Cook, D.Pitt, M.Woods. 2013a. A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach. Victoria: Natural Resources Canada, Canadian Forest Service, Canadian Wood Fibre Centre, Pacific Forestry Centre.
37. J.C.White, M.A.Wulder, M.Vastaranta, N.C.Coops, D.Pitt, M.Woods. 2013b. The utility of image-based point clouds for forest inventory: a comparison with airborne laser scanning. Forests. 4:518–536. doi: 10.3390/f4030518
38. C.Wu. 2013. Towards linear-time incremental structure from motion. In: Curless B, editor. Proceedings of International Conference on 3D Vision; 2013 Jun 29–July 1; Seattle. Washington, DC: University of Washington.
39. C.Wu, S.Agarwal, B.Curless, S.M.Seitz. 2011. Multicore bundle adjustment. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition; 2011 Jun 20–25; Colorado Springs, CO: Crowne Plaza.
40. M.A.Wulder, N.C.Coops, A.Hudak, F.Morsdorf, R.Nelson, G.Newnham, M.Vastaranta. 2013. Status and prospects for LiDAR remote sensing of forested ecosystems. Can J Remote Sens. 39:1–5. doi: 10.5589/m13-051