Forest inventory attribute estimation using airborne laser scanning, aerial stereo imagery, radargrammetry and interferometry-finnish experiences of the 3d techniques

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Volumn 2, Issue 3W4, 2015, Pages 63-69

  Holopainen, M ^a,b   Vastaranta, M ^a,b   Karjalainen, M ^c   Karila, K ^c   Kaasalainen, S ^c   Honkavaara, E ^c   Hyyppa J ^b,c  

^a UNIVERSITY OF HELSINKI   (Finland)

^b Centre of Excellence in Laser Scanning Research   (Finland)

^c FINNISH GEOSPATIAL RESEARCH INSTITUTE   (Finland)

Author keywords

Aerial stereo imagery; Airborne laser scanning; Area based approach; Forest inventory; Forest management planning; GIS; Random forest; Remote sensing; Sample plot; SAR interferometry; SAR radargrammetry

Indexed keywords

EID: 85051716279     PISSN: 21949042     EISSN: 21949050     Source Type: Conference Proceeding    
DOI: 10.5194/isprsannals-II-3-W4-63-2015     Document Type: Conference Paper

References (32)

1. Bohlin, J., Wallermandan, J. and Fransson, J.E.S. 2012. 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, doi:10.1080/02827581.2012.686625.
2. Haala, N., Hastedt, H., Wolf, K., Ressl, C. and Baltrusch, S. 2010. Digital photogrammetric camera evaluation Generation of digital elevation models. Photogramm Fernerkun. 2010, 2, 99-115.
3. Hirshmüller, H. 2008. Stereo processing by semi-global matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell. 2008, 30, 328-341, doi:10.1109/TPAMI.2007.1166.
4. Holopainen, M, Vastaranta, M and Hyyppä, J. 2014. Outlook for the Next Generation's Precision Forestry in Finland. Forests. 2014; 5(7):1682-1694.
5. Honkavaara, E., Litkey, P. and Nurminen, K. 2013. Automatic Storm Damage Detection in Forests Using High- Altitude Photogrammetric Imagery. Remote Sensing, Vol. 5, pp. 1405-1424.
6. Hudak, A., Crookston, N., Evans, J., Hall, D. and Falkowski, M. 2008. Nearest neighbor imputation of species-level, plotscale forest structure attributes from LiDAR data. Remote Sensing of Environment, Vol. 112, No. 5, pp. 2232-2245.
7. Hyyppä H. and Hyyppä, J. 1999. Comparing the accuracy of laser scanner with other optical remote sensing data sources for stand attribute retrieval. The Photogrammetric Journal of Finland 16(2): 5-15.
8. Hyyppä, J., Hyyppä, H., Inkinen, M., Engdahl, M., Linko, S., and Zhu, Y.-H. 2000. Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and Management 128(1):109-120.
9. Hyyppä, J., Yu, X., Hyyppä, H., Vastaranta, M., Holopainen, M., Kukko, A., Kaartinen, H., Jaakkola, A., Vaaja, M., Koskinen, J. and Alho, P. 2012. Advances in Forest Inventory Using Airborne Laser Scanning. Remote Sensing 2012, 4, 1190-1207; doi:10.3390/rs4051190.
10. Jakubowski, M.K., Guo, Q. and Kelly, M. 2013. Tradeoffs between lidar pulse density and forest measurement accuracy. Remote Sens. Environ. 2013, 130, 245-253, doi:10.1016/j.rse.2012.11.024.
11. Järnstedt, J., Pekkarinen, A., Tuominen, S., Ginzler, C., Holopainen, M. and Viitala, R. 2012. Forest variable estimation using a high-resolution digital surface model. ISPRS Journal of Photogrammetry and Remote Sensing, 74, pp. 78-84.
12. Kaasalainen, S., Holopainen, M., Karjalainen, M., Vastaranta, M., Kankare, V., Karila, K. and Osmanoglu, B. 2015. Combining Lidar and synthetic aperture radar to estimate forest biomass: status and prospects. Forests 2015, 6:252-270; doi:10.3390/f6010252.
13. Karila, K., Vastaranta, M., Karjalainen, M. and Kaasalainen, S. 2015. Tandem-X interferometry in the prediction of forest inventory attributes in managed boreal forests. Remote Sensing of Environment, in press.
14. Karjalainen, M., Kankare, V., Vastaranta, M., Holopainen, M. and Hyyppä, J. 2012. Prediction of plot-level forest variables using TerraSAR-X stereo SAR data. Remote Sensing of Environment, 117, 338-347.
15. Le Toan, T., Beaudoin, A., Riom, J. and Guyon, D. 1992. Relating forest biomass to SAR data. IEEE Transactions on Geoscience and Remote Sensing 1992, 30, 403-411.
16. Leberl, F., Irschara, A., Pock, T., Meixner, P., Gruber, M., Scholz, S. and Wiechert, A. 2010. Point clouds: LiDAR versus three-dimensional vision. Photogramm. Eng. Remote Sens. 2010, 76, 1123-1134.
17. Næsset, E., 1997a. Determination of mean tree height of forest stands using airborne laser scanner data. ISPRS Journal of Photogrammetry and Remote Sensing 52: 49-56.
18. Næsset, E., 1997b. Estimating timber volume of forest stands using airborne laser scanner data. Remote Sensing of Environment 61(2): 246-253.
19. Næsset, E., 2002. Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sensing of Environment 80: 88-99.
20. Nilsson, M. 1996. Estimation of tree heights and stand volume using airborne lidar system. Remote Sensing of Environment 56(1): 1-7.
21. Nurminen, K., Karjalainen, M., Yu, X., Hyyppä, J. and Honkavaara, E. 2013. Preformance of dense digital surface models based on image matching in the estimation of plot-level forest variables. ISPRS Journal of Photogrammetry and Remote Sensing, Vol 83, pp. 104-115.
22. Ouchi, K. 2013. Recent Trend and Advance of Synthetic Aperture Radar with Selected Topics. Remote Sensing 2013, 5, 716-807.
23. Praks, J., Antropov, O. and Hallikainen, M. 2012. LIDARAided SAR Interferometry Studies in Boreal Forest: Scattering Phase Center and Extinction Coefficient at X-and L-Band. IEEE Transactions on Geoscience and Remote Sensing 2012, 50, 3831-3843.
24. Raggam, H., Gutjahr, K., Perko, R. and Schardt, M. 2010. Assessment of the Stereo-Radargrammetric Mapping Potential of TerraSAR-X Multibeam Spotlight Data. IEEE Transactions on Geoscience and Remote Sensing 2010, 48, 971-977.
25. Treitz, P., Lim, K., Woods, M., Pitt, D., Nesbitt, D. and Etheridge, D. 2012. LiDAR sampling density for forest resource inventories in Ontario, Canada. Remote Sens. 2012, 4, 830-848, doi:10.3390/rs4040830.
26. Vastaranta, M., Wulder, M. A., White, J., Pekkarinen, A., Tuominen, S., Ginzler, C., Kankare, V., Holopainen, M., Hyyppä, J. and Hyyppä, H. 2013. Airborne laser scanning and digital stereo imagery measures of forest structure: Comparative results and implications to forest mapping and inventory update. Canadian Journal of Remote Sensing Vol. 39, No. 5, pp 382-395.
27. Vastaranta, M., Niemi, M., Karjalainen, M., Peuhkurinen, J., Kankare, V., Hyyppä, J. and Holopainen, M. 2014. Prediction of Forest Stand Attributes Using TerraSAR-X Stereo Imagery. Remote Sensing Vol. 6, No. 4, pp. 3227-3246.
28. Vastaranta, M., Holopainen, M., Karjalainen, M., Kankare, V., Hyyppä, J. and Kaasalainen, S. 2014. TerraSAR-X stereo radargrammetry and airborne scanning LiDAR height metrics in imputation of forest aboveground biomass and stem volume. IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 2, pp. 1197-1204, doi:10.1109/TGRS.2013.2248370
29. White, J.C., Wulder, M.A., Varhola, A., Vastaranta, M., Coops, N.C., Cook, B.D., Pitt, D. and Woods, M. 2013. A best practices guide for generating forest inventory attributes from airborne laser scanning data using the area-based approach. Information Report FI-X-10. Natural Resources Canada, Canadian Forest Service, Canadian Wood Fibre Centre, Pacific Forestry Centre, Victoria, BC. 50 p.
30. White, J.C., Wulder, M.A., Vastaranta, M., Coops, N.C., Pitt, D. and Woods, M. 2013. The utility of image-based point clouds for forest inventory: A comparison with airborne laser scanning. Forests, Vol., 4, pp. 518-536.
31. Wulder, M.A., Coops, N.C., Hudak, A.T., Morsdorf, F., Nelson, R., Newnham, G. and Vastaranta, M. 2013. Status and prospects for LiDAR remote sensing of forested ecosystems. Canadian Journal of Remote Sensing, Vol. 39, doi: 10.5589/m13-051.
32. Yu, X., Hyyppä, J., Holopainen, M. and Vastaranta, M. 2010. Comparison of Area-Based and Individual Tree-Based Methods for Predicting Plot-Level Forest Attributes. Remote Sensing 2(6): 1481-1495.