The potential of terrestrial laser scanning for the estimation of understory biomass in coppice-with-standard systems

Biomass and Bioenergy

Volumn 47, Issue , 2012, Pages 20-25

  Seidel, Dominik ^a   Albert, Katja ^a   Fehrmann, Lutz ^a   Ammer, Christian ^a  

^a UNIVERSITY OF GÖTTINGEN   (Germany)

Author keywords

Allometry; Bioenergy; Biomass estimation; Coppice with standard; Diameter at breast height

Indexed keywords

BIOLOGY; CLIMATE CHANGE; ESTIMATION; FORESTRY; LASER APPLICATIONS; REGRESSION ANALYSIS; STEEL BEAMS AND GIRDERS; SURFACE ANALYSIS;

ALLOMETRIC REGRESSION MODELS; ALLOMETRY; BIO-ENERGY; BIOENERGY POTENTIAL; BIOMASS ESTIMATION; DIAMETER-AT-BREAST HEIGHTS; METHODICAL APPROACH; TERRESTRIAL LASER SCANNING; STANDARD SYSTEM;

BIOMASS;

ALLOMETRY; BIOENERGY; BIOFUEL; COPPICE; DIAMETER; UNDERSTORY; BIOMASS; CLIMATE CHANGE; ESTIMATION METHOD; FOREST ECOSYSTEM; NUMERICAL MODEL; STAND STRUCTURE;

LASERS; SCANNING;

EID: 84870673154     PISSN: 09619534     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.biombioe.2012.10.009     Document Type: Article

References (37)

1. Schlamadinger B., Marland G. The role of forest and bioenergy strategies in the global carbon cycle. Biomass Bioenergy 1996, 11:499-500.
2. Canadell J., Raupach M.R. Managing forests for climate change mitigation. Science 2008, 320:1456.
3. Lal R. Carbon sequestration. Phil Trans R Soc B 2008, 363:815-830.
4. Parikka M. Global biomass fuel resources. Biomass Bioenergy 2004, 27:613-620.
5. Nyland R.D. Silviculture concepts and applications 2006, McGraw-Hill Co, Inc., New York, 633 pp.
6. Utinek D. Conversions of coppices to a coppice-with-standards in Urban Forests of Moravský Krumlov. J For Sci 2004, 50(1):38-46.
7. Bruckman V.J., Yan S., Hochbichler E., Glatzel G. Carbon pools and temporal dynamics along a rotation period in Quercus dominated high forest and coppice with standards stands. For Ecol Manage 2011, 262(9):1853-1862.
8. Bundeswaldinventur 1992, Band I und II. Bundesministerium für Ernährung, Landwirtschaft und Forsten, Bonn.
9. Hochbichler E. Methods of oak silviculture in Austria. Ann For Sci 1993, 50:583-591.
10. Vallet P., Dhôte J.F., Le Moguédec G., Ravart M., Pignard G. Development of total aboveground volume equations for seven important forest tree species in France. For Ecol Manage 2006, 229:98-110.
11. Hytönen J., Lumme I., Törmälä T. Comparison of methods for estimating willow biomass. Biomass 1987, 14(1):39-49.
12. Verwijst T., Telenius B. Biomass estimation procedures in short rotation forestry. For Ecol Manage 1999, 121:137-146.
13. Brahim M.B., Gavaland A., Cabanettes A. Generalized allometric regression to estimate the biomass of populous in short rotation forestry. Scan J For Res 2010, 15(2):171-176.
14. Telenius B.F. Implications of vertical variation and within-stand variation in moisture content for biomass estimation of some willow and hybrid poplar clones. Scand J For Res 1997, 12:336-339.
15. Annighöfer P., Mölder I., Zerbe S., Kawaletz H., Terwei A., Ammer C. Biomass functions for the two alien species Prunus serotina Ehrh. and Robinia pseudoaccaia L. in floodplain forests of Northern Italy. Eur J For Res 2012, 131(5):1619-1635.
16. Zianis D., Muukkonen P., Mäkipää R., Mencuccini M. Silva Fennica Monographs, biomass and stem volume equations for tree species in Europe. Tampere, Finland 2005, Tammer -Paino Oy, Tampere, 63 Report No.: 4.
17. Wang C. Biomass allometric equations for 10 co-occurring tree species in Chinese temperate forests. For Ecol Manage 2006, 222:9-16.
18. Sawadogo L., Savadogo P., Tiveau D., Dayamba S.D., Zida D., Nouvellet Y., et al. Allometric prediction of above-ground biomass of eleven woody tree species in the Sudanian savanna-woodland of West Africa. J For Res 2010, 21(4):475-481.
19. Blujdea V.N.B., Pilli R., Dutca I., Ciuvat L., Abrudan I.V. Allometric biomass equations for young broadleaved trees in plantations in Romania. For Ecol Manage 2012, 264:172-184.
20. EUBIA- European Biomass Industry Association. Renewable energy House, Rue d'Arlon 63-65, B-1040 Brussels, Belgium. Available online at: Accessed on February 2, 2012. http://www.eubia.org/192.0.html.
21. Aschoff T., Spieker H. Algorithms for the automated detection of trees in laser scanner data. International archives of photogrammetry. Remote Sensing Spat Inf Sci 2004, 36:71-75.
22. Simonse M., Aschoff T., Spiecker H., Thies M. Automatic determination of forest inventory parameters using terrestrial laserscanning. Proceedings of the ScandLaser Scientific Workshop on Airborne laser scanning of forests; ScandLaser, 2003 2003, 251-257. Swedish University of Agricultural Sciences, Umeå, Sweden. J. Hyyppä, E. Næsset, E. Olsson, T. Granqvist Pahlén, H. Reese (Eds.).
23. Hopkinson C., Chasmer L., Young-Pow C., Treitz P. Assessing forest metrics with a ground-based scanning LIDAR. Can J For Res 2004, 34:573-583.
24. Maas H.G., Bienert A., Scheller S., Keane E. Automatic forest inventory parameter determination from terrestrial laser scanner data. Int J Remote Sens 2008, 29(5):1579-1593.
25. Tansey K., Selmes N., Anstee A., Tate N.J., Denniss A. Estimating tree and stand variables in a Corsican Pine woodland from terrestrial laser scanner data. J Rem Sens 2009, 30(19):5195-5209.
26. Pfeifer N., Gorte N., Winterhalder D. Automatic reconstruction of single trees from terrestrial laser scanner data. 20th ISPRS Congress; 2004 2004, 114-119. Turkey, Istanbul, ISPRS Archives. O. Altan (Ed.).
27. Seidel D., Leuschner C., Müller A., Krause B. Crown plasticity in mixed forests-Quantifying asymmetry as a measure of competition using terrestrial laser scanning. For Ecol Manage 2011, 261(11):2123-2132.
28. Van der Zande D., Hoet W., Jonckheere I., van Ardt J., Coppin P. Influence of measurement set-up of ground-based LiDAR for derivation of tree structure. Agri For Met 2006, 141:147-160.
29. Van der Zande D., Jonckheere I., Stuckens J., Verstraeten W.W., Coppin P. Sampling design of ground-based lidar measurements of forest canopy structure and its effect on shadowing. Can J Remote Sensing 2008, 34(6):526-538.
30. Király G., Brolly G. Modelling single trees from terrestrial laser scanning data in a forest reserve. Photogrammetric J Finland 2008, 21(1):37-50.
31. Litkey P., Liang X., Hyyppä J., Kukko A., Kaartinen H., Holopainen M. Single-scan TLS methods for forest parameter retrieval. SilviLaser 2008: 8th International Conference on LiDAR applications in forest assessment and inventory; 2008 2008, Bournemouth University, Edinburgh, Scotland. R.A. Hill, J. Rosette, J. Suarez (Eds.).
32. Seidel D., Fleck S., Leuschner C. Analyzing forest canopies with ground-based laser scanning: a comparison with hemispherical photography. Agri For Meteorol 2012, 154-155:1-8.
33. Gentle J.E. QR Factorization. Numerical linear algebra for applications in statistics 1998, Springer-Verlag, Berlin, 95-97.
34. Fehrmann L., Kleinn C. General considerations about the use of allometric equations for biomass estimation on the example of Norway spruce in central Europe. For Ecol Manage 2006, 236:412-421.
35. Ribeiro S., Fehrmann L., Soares C.P., Jacovine L.A., Kleinn C. Gaspar. Above- and belowground biomass in a Brazilian Cerrado. For Ecol Manage 2011, 262(3):491-499.
36. Fehrmann L. Alternative Methoden zur Biomasseschätzung auf Einzelbaumebene unter spezieller Berücksichtigung der k-nearest neighbour (k-nn) method. Dissertation, Chair of Forest Inventory and Remote Sensing, Göttingen, 2006.
37. Willmot C.J., Matsuura K. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim Res 2005, 30:79-82.