Hyperspectral UAV-Imagery and photogrammetric canopy height model in estimating forest stand variables

Silva Fennica

Volumn 51, Issue 5, 2017, Pages

  Tuominen, Sakari ^a   Balazs, Andras ^a   Honkavaara, Eija ^b   Pölönen, Ilkka ^c   Saari, Heikki ^d   Hakala, Teemu ^b   Viljanen, Niko ^b  

^a FINNISH FOREST RESEARCH INSTITUTE   (Finland)

^b FINNISH GEOSPATIAL RESEARCH INSTITUTE   (Finland)

^c UNIVERSITY OF JYVÄSKYLÄ   (Finland)

^d VTT TECHNICAL RESEARCH CENTRE OF FINLAND   (Finland)

Author keywords

Aerial imagery; Digital photogrammetry; Forest inventory; Hyperspectral imaging; Radiometric calibration; Stereo photogrammetric canopy modelling; UAVs

Indexed keywords

AERIAL PHOTOGRAPHY; CALIBRATION; ESTIMATION; HYPERSPECTRAL IMAGING; IMAGE ENHANCEMENT; IMAGE PROCESSING; NEAREST NEIGHBOR SEARCH; PHOTOGRAMMETRY; REMOTE SENSING; SPECTROSCOPY; STEREO IMAGE PROCESSING; UNMANNED AERIAL VEHICLES (UAV);

AERIAL IMAGERY; AIRBORNE LASER SCANNING; DIGITAL PHOTOGRAMMETRY; FOREST INVENTORY; HYPER-SPECTRAL IMAGERIES; HYPERSPECTRAL REFLECTANCE; K-NEAREST NEIGHBOURS; RADIOMETRIC CALIBRATIONS;

FORESTRY;

CALIBRATION; CLIMATE CONDITIONS; CONIFEROUS TREE; ECOLOGICAL MODELING; ESTIMATION METHOD; FOREST CANOPY; FOREST INVENTORY; IMAGING METHOD; PHOTOGRAMMETRY; RADIOMETER; REMOTE SENSING; SATELLITE DATA; SATELLITE IMAGERY; SATELLITE SENSOR; STAND STRUCTURE; UNMANNED VEHICLE;

FOREST MANAGEMENT; IMAGE ANALYSIS; INVENTORY CONTROL; SPECIES IDENTIFICATION; TREES;

LARIX; PICEA;

EID: 85037543520     PISSN: 00375330     EISSN: 22424075     Source Type: Journal    
DOI: 10.14214/sf.7721     Document Type: Article

References (58)

1. Aasen H., Burkart A., Bolten A., Bareth G. (2015). Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: from camera calibration to quality assurance. ISPRS Journal of Photogrammetry and Remote Sensing 108: 245-259. https://doi.org/10.1016/j.isprsjprs.2015.08.002.
2. Altman N.S. (1992). An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician 46(3): 175-185. https://doi.org/10.1080/00031305.1992.10475879.
3. Anderson K., Gaston K.J. (2013). Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Frontiers in Ecology and the Environment 11(3): 138-146. https://doi.org/10.1890/120150.
4. Axelsson P. (1999). Processing of laser scanner data - algorithms and applications. ISPRS Journal of Photogrammetry and Remote Sensing 54(2-3): 138-147. https://doi.org/10.1016/S0924-2716(99)00008-8.
5. Axelsson P. (2000). DEM generation from laser scanner data using adaptive TIN models. International Archives of Photogrammetry and Remote Sensing 33: 110-117.
6. Baltsavias E. (1999). A comparison between photogrammetry and laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing 54(2-3): 83-94. https://doi.org/10.1016/S0924-2716(99)00014-3.
7. Baltsavias E., Gruen A., Eisenbeiss H., Zhang L., Waser L.T. (2008). High-quality image matching and automated generation of 3D tree models. International Journal of Remote Sensing 29(5): 1243-1259. https://doi.org/10.1080/01431160701736513.
8. Beyer K., Goldstein J., Ramakrishnan R., Shaft U. (1999). When is “nearest neighbor” meaningful? In: Proceedings of the 7th International Conference on Database Theory (ICDT’99), January 10, 1999, Jerusalem, Israel. p. 217-235. https://doi.org/10.1007/3-540-49257-7_15.
9. Bohlin J., Wallerman J., Fransson J.E.S. (2012). Forest variable estimation using photogrammetric matching of digital aerial images in combination with a high-resolution DEM. Scandinavian Journal of Forest Research 27(7): 692-699. https://doi.org/10.1080/02827581.2012.686625.
10. Broadhurst D., Goodacre R., Jones A., Rowland J.J., Kell D.B. (1997). Genetic algorithms as a method for variable selection in multiple linear regression and partial least squares regression, with applications to pyrolysis mass spectrometry. Analytica Chimica Acta 348(1-3): 71-86. https://doi.org/10.1016/S0003-2670(97)00065-2.
11. Büttner A., Röser H.-P. (2014). Hyperspectral remote sensing with the UAS “Stuttgarter Adler” - system setup, calibration and first results. Photogrammetrie - Fernerkundung - Geoinformation 2014(4): 265-274. https://doi.org/10.1127/1432-8364/2014/0217.
12. Colomina I., Molina P. (2014). Unmanned aerial systems for photogrammetry and remote sensing: a review. ISPRS Journal of Photogrammetry and Remote Sensing 92: 79-97. https://doi.org/10.1016/j.isprsjprs.2014.02.013.
13. Garey M.R., Johnson D.S. (1979). Computers and intractability; a guide to the theory of np-completeness. W.H. Freeman & Co, New York, NY, USA.
14. Goetz A.F.H. (2009). Three decades of hyperspectral remote sensing of the Earth: a personal view. Remote Sensing of Environment 113(SUPPL. 1): S5-S16. https://doi.org/10.1016/j.rse.2007.12.014.
15. Gruen A., Li Z. (2002). Automatic DTM generation from Three-Line-Scanner (TLS) images. International Archives of Photogrammetry and Remote Sensing 34: 131-137.
16. Haala N., Hastedt H., Wolf K., Ressl C., Baltrusch S. (2010). Digital photogrammetric camera evaluation - generation of digital elevation models. Photogrammetrie - Fernerkundung - Geoinformation 2010(2): 99-115. https://doi.org/10.1127/1432-8364/2010/0043.
17. Hakala T., Honkavaara E., Saari H., Mäkynen J., Kaivosoja J., Pesonen L., Pölönen I. (2013). Spectral imaging from uavs under varying illumination conditions. In: ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, September 4, 2013, Paris. p. 189-194. https://doi.org/10.5194/isprsarchives-XL-1-W2-189-2013.
18. Haralick R.M. (1979). Statistical and structural approaches to texture. Proceedings of the IEEE 67(5): 786-804. https://doi.org/10.1109/PROC.1979.11328.
19. Haralick R.M., Shanmugam K., Dinstein I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics SMC-3(6): 610-621. https://doi.org/10.1109/TSMC.1973.4309314.
20. Hinneburg A., Aggarwal C.C., Keim D.A. (2000). What is the nearest neighbor in high dimensional spaces? In: Proceedings of the 26th International Conference on Very Large Data Bases, September 10, 2000, San Francisco, CA, USA. p. 506-515.
21. Honkavaara E., Saari H., Kaivosoja J., Pölönen I., Hakala T., Litkey P., Mäkynen J., Pesonen L. (2013). Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight uav spectral camera for precision agriculture. Remote Sensing 5(10): 5006-5039. https://doi.org/10.3390/rs5105006.
22. Hruska R., Mitchell J., Anderson M., Glenn N.F. (2012). Radiometric and geometric analysis of hyperspectral imagery acquired from an unmanned aerial vehicle. Remote Sensing 4(9): 2736-2752. https://doi.org/10.3390/rs4092736.
23. Hyyppä J., Pyysalo U., Hyyppä H., Samberg A. (2000). Elevation accuracy of laser scanning-derived digital terrain and target models in forest environment. In: Proceedings of EARSeL-SIG-Workshop, June 14, 2000, Dresden. p. 139-147.
24. Järnstedt J., Pekkarinen A., Tuominen S., Ginzler C., Holopainen M., Viitala R. (2012). Forest variable estimation using a high-resolution digital surface model. ISPRS Journal of Photogrammetry and Remote Sensing 74: 78-84. https://doi.org/10.1016/j.isprsjprs.2012.08.006.
25. 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. p. 210-215.
26. Koh L.P., Wich S.A. (2012). Dawn of drone ecology: low-cost autonomous aerial vehicles for conservation. Tropical Conservation Science 5(2): 121-132. https://doi.org/10.1177/194008291200500202.
27. Lillesand T.M., Kiefer R.W., Chipman J.W. (2004). Remote sensing and image interpretation (5th ed.). John Wiley & Sons Inc., New York, NY, USA. p. 763.
28. Lisein J., Pierrot-Deseilligny M., Bonnet S., Lejeune P. (2013). A photogrammetric workflow for the creation of a forest canopy height model from small unmanned aerial system imagery. Forests 4(4): 922-944. https://doi.org/10.3390/f4040922.
29. Lucieer A., Malenovský Z., Veness T., Wallace L. (2014). HyperUAS - imaging spectroscopy from a multirotor unmanned aircraft system. Journal of Field Robotics 31(4): 571-590. https://doi.org/10.1002/rob.21508.
30. Mäkynen J., Holmlund C., Saari H., Ojala K., Antila T. (2011). Unmanned aerial vehicle (UAV) operated megapixel spectral camera. Proceedings of SPIE 8186: 81860Y. https://doi.org/10.1117/12.897712.
31. Maltamo M., Malinen J., Packalén P., Suvanto A., Kangas J. (2006). Nonparametric estimation of stem volume using airborne laser scanning, aerial photography, and stand-register data. Canadian Journal of Forest Research 36(2): 426-436. https://doi.org/10.1139/x05-246.
32. Moser P., Vibrans A.C., McRoberts R.E., Næsset E., Gobakken T., Chirici G., Mura M., Marchetti M. (2017). Methods for variable selection in LiDAR-assisted forest inventories. Forestry 90(1): 112-124. https://doi.org/10.1093/forestry/cpw041.
33. Muinonen E., Tokola T. (1990). An application of remote sensing for communal forest inventory. In: Proceedings from SNS/IUFRO Workshop, 1990, Umeå. p. 35-42.
34. 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(1): 88-99. https://doi.org/10.1016/S0034-4257(01)00290-5.
35. Næsset E. (2004). Accuracy of forest inventory using airborne laser scanning: evaluating the first nordic full-scale operational project. Scandinavian Journal of Forest Research 19(6): 554-557. https://doi.org/10.1080/02827580410019544.
36. Nevalainen O., Honkavaara E., Tuominen S., Viljanen N., Hakala T., Yu X., Hyyppä J., Saari H., Pölönen I., Imai N., Tommaselli A. (2017). Individual tree detection and classification with UAV-based photogrammetric point clouds and hyperspectral imaging. Remote Sensing 9(3): 185. https://doi.org/10.3390/rs9030185.
37. Packalén P., Maltamo M. (2006). Predicting the plot volume by tree species using airborne laser scanning and aerial photographs. Forest Science 52(6): 611-622.
38. Packalén P., Maltamo M. (2007). The k-MSN method for the prediction of species-specific stand attributes using airborne laser scanning and aerial photographs. Remote Sensing of Environment 109(3): 328-341. https://doi.org/10.1016/j.rse.2007.01.005.
39. Packalén P., Maltamo M. (2008). Estimation of species-specific diameter distributions using airborne laser scanning and aerial photographs. Canadian Journal of Forest Research 38(7): 1750-1760. https://doi.org/10.1139/X08-037.
40. Puliti S., Ørka H.O., Gobakken T., Næsset E. (2015). Inventory of small forest areas using an unmanned aerial system. Remote Sensing 7(8): 9632-9654. https://doi.org/10.3390/rs70809632.
41. Pyysalo U. (2000). A method to create a three dimensional forest model from laser scanner data. Photogrammetric Journal of Finland 17(1): 34-42.
42. R Development Core Team. (2016). R: a language and environment for statistical computing. Vienna, Austria. https://www.r-project.org. [Cited 2 March 2017].
43. Saari H., Pellikka I., Pesonen L., Tuominen S., Heikkilä J., Holmlund C., Mäkynen J., Ojala K., Antila T. (2011). Unmanned Aerial Vehicle (UAV) operated spectral camera system for forest and agriculture applications. In: Proceedings of SPIE, October 6, 2011, Prague. p. 81740H. https://doi.org/10.1117/12.897585.
44. St-Onge B., Vega C., Fournier R.A., Hu Y. (2008). Mapping canopy height using a combination of digital stereo-photogrammetry and lidar. International Journal of Remote Sensing 29(11): 3343-3364. https://doi.org/10.1080/01431160701469040.
45. Stepper C., Straub C., Immitzer M., Pretzsch H. (2016). Using canopy heights from digital aerial photogrammetry to enable spatial transfer of forest attribute models: a case study in central Europe. Scandinavian Journal of Forest Research, 1-14. https://doi.org/10.1080/02827581.2016.1261935.
46. Suomalainen J., Anders N., Iqbal S., Roerink G., Franke J., Wenting P., Hünniger D., Bartholomeus H., Becker R., Kooistra L. (2014). A lightweight hyperspectral mapping system and photogrammetric processing chain for unmanned aerial vehicles. Remote Sensing 6 (11): 11013-11030. https://doi.org/10.3390/rs61111013.
47. Tomppo E. (1991). Satellite image-based national forest inventory of Finland. International Archives of Photogrammetry and Remote Sensing 28: 419-424.
48. Törmä M. (2000). Estimation of tree species proportions of forest stands using laser scanning. International Archives of Photogrammetry and Remote Sensing 33(Part B7): 1524-1531.
49. Tuominen S., Haapanen R. (2011). Comparison of grid-based and segment-based estimation of forest attributes using airborne laser scanning and digital aerial imagery. Remote Sensing 3(5): 945-961. https://doi.org/10.3390/rs3050945.
50. Tuominen S., Haapanen R. (2013). Estimation of forest biomass by means of genetic algorithm-based optimization of airborne laser scanning and digital aerial photograph features. Silva Fennica 47(1) article 902. https://doi.org/10.14214/sf.902.
51. Tuominen S., Pitkänen J., Balazs A., Korhonen K.T., Hyvönen P., Muinonen E. (2014). NFI plots as complementary reference data in forest inventory based on airborne laser scanning and aerial photography in Finland. Silva Fennica 48(2) article 983. https://doi.org/10.14214/sf.983.
52. Tuominen S., Balazs A., Saari H., Pölönen I., Sarkeala J., Viitala R. (2015). Unmanned aerial system imagery and photogrammetric canopy height data in area-based estimation of forest variables. Silva Fennica 49(5) article 1348. https://doi.org/10.14214/sf.1348.
53. Ullah S., Dees M., Datta P., Adler P., Koch B. (2017) Comparing airborne laser scanning, and image-based point clouds by semi-global matching and enhanced automatic terrain extraction to estimate forest timber volume. Forests 8(6): 215. https://doi.org/10.3390/f8060215.
54. Vauhkonen J., Tokola T., Packalén P., Maltamo M. (2009). Identification of scandinavian commercial species of individual trees from airborne laser scanning data using alpha shape metrics. Forest Science 55(1): 37-47.
55. Waser L.T., Ginzler C., Kuechler M., Baltsavias E., Hurni L. (2011). Semi-automatic classification of tree species in different forest ecosystems by spectral and geometric variables derived from Airborne Digital Sensor (ADS40) and RC30 data. Remote Sensing of Environment 115(1): 76-85. https://doi.org/10.1016/j.rse.2010.08.006.
56. White J.C., Wulder M.A., Vastaranta M., Coops N.C., Pitt D., Woods M. (2013). The utility of image-based point clouds for forest inventory: a comparison with airborne laser scanning. Forests 4(3): 518-536. https://doi.org/10.3390/f4030518.
57. Willighagen E., Ballings M. (2015). genalg: R based genetic algorithm. https://cran.r-project.org/web/packages/genalg/index.html. [Cited 2 March 2017].
58. Zarco-Tejada P.J., González-Dugo V., Berni J.A.J. (2012). Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sensing of Environment 117: 322-337. https://doi.org/10.1016/j.rse.2011.10.007.