Mapping biomass variables with a multi-source forest inventfory technique

Silva Fennica

Volumn 44, Issue 1, 2010, Pages 109-119

  Tuominen, Sakari ^a   Eerikäinen, Kalle ^a   Schibalski, Anett ^b   Haakana, Markus ^a   Lehtonen, Aleksi ^a  

^a FINNISH FOREST RESEARCH INSTITUTE   (Finland)

^b UNIVERSITY OF POTSDAM   (Germany)

Author keywords

Biomass maps; Biomass models; National forest inventory; Remote sensing

Indexed keywords

BIOENERGY POTENTIAL; BIOMASS MAP; BIOMASS MODELS; BIOMASS RESOURCES; CARBON STOCKS; CROSS VALIDATION; DIGITAL MAP; FIELD MEASUREMENT; FIELD PLOT; FINLAND; FOREST BIOMASS; GROWING STOCKS; MULTI-SOURCE FOREST INVENTORIES; MULTISOURCES; NATIONAL FOREST INVENTORIES; NATIONAL FOREST INVENTORY; REFERENCE DATA; SATELLITE IMAGES; THEMATIC MAPS;

ESTIMATION; FORESTRY; GLOBAL WARMING; GREENHOUSE GASES; IMAGE ANALYSIS; MAPS; REMOTE SENSING; SATELLITES;

BIOMASS;

ACCURACY ASSESSMENT; BIOENERGY; CARBON SEQUESTRATION; DIGITAL MAP; EMISSION INVENTORY; FOREST INVENTORY; GREENHOUSE GAS; IMAGE ANALYSIS; PHYTOMASS; SATELLITE IMAGERY; THEMATIC MAPPING;

ACCURACY; BIOMASS; DATA PROCESSING; ESTIMATION; FORESTRY; GREENHOUSE GASES; IMAGE ANALYSIS; REMOTE SENSING; SATELLITES;

FINLAND;

EID: 77953042883     PISSN: 00375330     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (31)

1. Ahti, T., Hämet-Ahti, L. & Jalas, J. 1968. Vegetation zones and their sections in northwestern Europe. Annales Botanici Fennici 5(3): 169-211.
2. Altman, N.S. 1992. An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician 46: 175-185.
3. Asner, G. 1998. Biophysical and biochemical sources of variability in canopy reflectance. Remote Sensing of Environment 64: 234-253.
4. Baccini, A., Friedl, M.A., Woodcock, C.E. & Warbington, R. 2004. Forest biomass estimation over regional scales using multisource data. Geophysical Research Letters 31(10): 1-4.
5. Chen, J. & Leblanc, S. 1997. A four-scale bidirectional reflectance model based on canopy architecture. IEEE Transactions on Geoscience and Remote Sensing, 35(5): 1316-1337.
6. Eerikäinen, K. 2009. A multivariate linear mixedeffects model for the generalization of sample tree heights and crown ratios in the Finnish National Forest Inventory. Forest Science 55(6): 480-493.
7. Franco-Lopez, H., Ek, A.R. & Bauer M.E. 2001. Estimation and mapping of forest density, volume and cover type using the k-nearest neighbors method. Remote Sensing of Environment 77: 251-274.
8. Henderson, C.R. 1975. Best linear unbiased estimation and prediction under a selection model. Biometrics 31(2):423-447.
9. IPCC. 2003. Good practice guidance for land use, landuse change and forestry. IPCC National Greenhouse Gas Inventories Programme. 295 p.
10. Karjalainen, T. & Kellomäki, S. 1996. Greenhouse gas inventory for land use changes and forestry in Finland based on international guidelines. Mitiga tion and Adaptation Strategies for Global Climate 1: 51-71.
11. Katila, M. & Tomppo, E. 2001. Selecting estimation parameters for the Finnish multi-source national forest inventory. Remote Sensing of Environment 76: 16-32.
12. Kilkki, P. & Päivinen, R. 1987. Reference sample plots to combine field measurements and satellite data in forest inventory. Department of Forest Mensuration and Management, University of Helsinki, Research Notes 19: 210-215.
13. Kobayashi, H., Suzuki, R. & Kobayashi, S. 2007. Reflectance seasonality and its relation to the canopy leaf area index in an eastern Siberian larch forest: Multi-satellite data and radiative transfer analysis. Remote Sensing of Environment 106: 238-252.
14. Laasasenaho, J. 1982. Taper curve and volume functions for pine, spruce and birch. Communicationes Instituti Forestalis Fenniae 108. 74 p.
15. Lang, M., Nilson, T., Kuusk, A., Kiviste, A. and Hordo, M. 2007. The performance of foliage mass and crown radius models in forming the input of a forest reflectance model: A test on forest growth sample plots and Landsat 7 ETM+ images. Remote Sensing of Environment 110: 445-457.
16. Lappi, J. 1986. Mixed linear models for analyzing and predicting change in stem form variation of Scots pine. Communicationes Instituti Forestalis Fenniae 134. 69 p.
17. Lappi, J. 1991. Calibration of height and volume equations with random parameters. Forest Science 37(3): 781-801.
18. Lehtonen, A., Mäkipää, R., Heikkinen, J., Sievänen, R. & Liski, J. 2004. Biomass expansion factors (BEF) for Scots pine, Norway spruce and birch according to stand age for boreal forests. Forest Ecology and Management 188: 211-224.
19. Muinonen, E. & Tokola T. 1990. An application of remote sensing for communal forest inventory. In: Proceedings from SNS/IUFRO workshop in Umeå 26-28 Feb. 1990 (pp. 35-42). Remote Sensing Laboratory, Swedish University of Agricultural Sciences, Umeå, Report 4.
20. Muukkonen, P. & Heiskanen, J. 2005. Estimating biomass for boreal forests using ASTER satellite data combined with standwise forest inventory data. Remote Sensing of Environment 99(4): 434-447.
21. Nilsson, M. 1997. Estimation of forest variables using satellite image data and airborne lidar. Doctoral thesis. Acta Universitatis Agriculturae Sueciae 17.
22. Petersson, H. & Ståhl, G. 2006. Functions for belowground biomass of Pinus sylvestris, Picea abies, Betula pendula and Betula pubescens in Sweden. Scandinavian Journal of Forest Research 21, Supplement 7: 84-93.
23. Rautiainen, M., Stenberg, P., Nilson, T. & Kuusk, A. 2004. The effect of crown shape on the reflectance of coniferous stands. Remote Sensing of Environment 89: 41-52.
24. Rautiainen, M., Stenberg, P., Nilson, T. & Lükk, T. 2009. Seasonal reflectance trends of hemiboreal birch forests. Remote Sensing of Environment 113: 805-815.
25. Repola, J., Ojansuu, R. & Kukkola, M. 2007. Biomass functions for Scots pine, Norway spruce and birch in Finland. Working Papers of the Finnish Forest Research Institute 53. ISBN 978-951-40-2046-9. 28 p. URL: http://www.metla.fi/julkaisut/workingpapers/2007/mwp053.htm.
26. Searle, S.R. 1971. Linear models. John Wiley & Sons, Inc., New York. 532 p.
27. Searle, S.R. 1987. Linear models for unbalanced data. John Wiley & Sons, Inc., New York. 536 p.
28. Tokola, T., Pitkänen, J., Partinen S. & Muinonen, E. 1996. Point accuracy of a non-parametric method in estimation of forest characteristics with different satellite materials. International Journal of Remote Sensing 17(12): 2333-2351.
29. Tomppo, E. 1990. Satellite image-based national forest inventory of Finland. The Photogrammetric Journal of Finland 12(1): 115-120.
30. Tomppo, E. 1993. Multi-source national forest inventory of Finland. In: Nyyssönen, A., Poso, S. & Rautala, J. (eds.). Proceedings of Ilvessalo Symposium on national forest inventories, 17-21 Aug. 1992, Finland. The Finnish Forest Research Institute Research Papers 444. p. 52-60.
31. Tomppo, E. 2006. The Finnish multi-source national forest inventory - small area estimation and map production. In: Kangas, A. & Maltamo, M. (eds.). Forest inventory - methodology and applications. Springer, Netherlands. p. 195-224.