Automatic Segment-Level Tree Species Recognition Using High Resolution Aerial Winter Imagery

References

• Baatz M., Schäpe M. (2000)—Multiresolution segmentation: An optimization approach for high quality multi-scale image segmentation. In: Strobl J., Blaschke T., Griesebner G. (Eds.), Angewandte Geographische Informations-Verarbeitung XII, Wichmann Verlag, Karlsruhe, pp. 12–23.
   Google Scholar
• Baatz M., Hoffmann C., Willhauck G. (2008)—Progressing from object-based to object- oriented image analysis. In: Blaschke T., Lang S. (Eds.), Object-based Image Analysis: Spatial Concepts for Knowledge-driven Remote Sensing Applications, Springer-Verlag, Berlin. doi: http://dx.doi.org/10.1007/978-3-540-77058-9_2.
   Google Scholar
• Betts R.A. (2000)—Offset of the potential carbon sink from boreal forestation by decreases in surface albedo. Nature, 408: 187–190. doi: http://dx.doi.org/10.1038/35041545.
   PubMed Web of Science ®Google Scholar
• Bonan G.B., Chapin F.S. III, Thompson S.L. (1995)—Boreal forest and tundra ecosystems as components of the climate system. Climatic Change, 29: 145–167. doi: http://dx.doi.org/10.1007/BF01094014.
   Web of Science ®Google Scholar
• Bonan G.B. (2008)—Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science, 320: 1444–1449. doi: http://dx.doi.org/10.1126/science.1155121.
   PubMed Web of Science ®Google Scholar
• Brandtberg T., Walter F. (1998)—Automatic delineation of individual tree crowns in high spatial resolution aerial images by multiple-scale analysis. Machine Vision and Applications, 11 (2): 64–73. doi: http://dx.doi.org/10.1007/s001380050091.
   Web of Science ®Google Scholar
• Brandtberg T. (1999)—Remote Sensing for Forestry Applications-A Historical Retrospect. Available online at: http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/BRANDTBERG/UK.html.
   Google Scholar
• Brandtberg T. (2002)—Individual tree-based species classification in high spatial resolution aerial images of forests using fuzzy sets. Fuzzy Sets and Systems, 132: 371–387. doi: http://dx.doi.org/10.1016/S0165-0114(02)00049-0.
   Web of Science ®Google Scholar
• Bright R.M., Astrup R., Strømman A.H. (2013)—Empirical models of monthly and annual albedo in managed boreal forests of interior Norway. Climatic Change, 120 (1): 183196. doi: http://dx.doi.org/10.1007/s10584-013-0789-1.
   Google Scholar
• Burnett C., Blaschke T. (2003)—A multi-scale segmentation/object relationship modeling methodology for landscape analysis. Ecological Modeling, 168: 233–249. doi: http://dx.doi.org/10.1016/S0304-3800(03)00139-X.
   Web of Science ®Google Scholar
• Culvenor D.S. (2002)—TIDA: An algorithm for delineation of tree crowns in high spatial resolution remotely sensed imagery. Computers & Geosciences, 28 (1): 33–44. doi: http://dx.doi.org/10.1016/S0098-3004(00)00110-2.
   Web of Science ®Google Scholar
• Dalponte M., Ørka H.O., Ene L.T., Gobakken T., Næsset E. (2014)—Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data. Remote Sensing of Environment, 140: 306–317. doi: http://dx.doi.org/10.1016/).rse.2013.09.006.
   Web of Science ®Google Scholar
• Definiens AG. (2007a)—Definiens Developer 7 user guide. Munich: Difiniens AG, p. 506.
   Google Scholar
• Definiens AG. (2007b)—Definiens Developer 7 Reference book. Munich: Definiens AG, p. 197.
   Google Scholar
• Erikson M. (2003a)—Segmentation of individual tree crowns in colour aerial photographs using region growing supported by fuzzy rules. Canadian Journal of Forest Research, 33 (8): 1557–1563. doi: http://dx.doi.org/10.1139/x03-062.
   Google Scholar
• Erikson M. (2003b)—Structure-preserving segmentation of individual tree crowns by Brownian motion. Proceedings of the 13^th Scandinavian conference on image analysis, June 29—July 2, 2003, Gothenburg, Sweden. Lecture Notes in Computer Science, 2749: 283–289, Berlin, Germany: Springer-Verlag. doi: http://dx.doi.org/10.1007/3-540-45103-X_39.
   Google Scholar
• Fang H., Wei S., Liang S. (2012)—Validation of MODIS and CYCLOPES LAI products using global field measurement data. Remote Sensing of Environment, 119: 43–54. doi: http://dx.doi.org/10.1016/j.rse.2011.12.006.
   Web of Science ®Google Scholar
• Gini R., Passoni, D., Pinto, L., Sona, G. (2014)—Use of Unmanned Aerial Systems for multispectral survey and tree classification: a test in a park area of northern Italy. European Journal of Remote Sensing, 47: 251–269. doi: http://dx.doi.org/10.5721/EuJRS20144716.
   Web of Science ®Google Scholar
• Gong P., Biging G.S., Lee S.M., Mei X., Sheng Y., Pu R., Xu B., Schwarz K.P., Mostafa M. (1999)—Photo-econometrics for forest inventory. Geographic Information Sciences, 5 (1): 9–14.
   Google Scholar
• Gougeon F.A., Leckie D.G., Paradine D., Scott I. (1999)—Individual tree crown species recognition: The Nahmint study. In D. Hill, D. Leckie (Eds.), Automated interpretation of high spatial resolution digital imagery for forestry, pp. 209–223.
   Google Scholar
• Haara A., Haarala M. (2002)—Tree species classification using semi-automatic delineation of trees on aerial images. Scandinavian Journal of Forest Research, 17: 556–565. doi: http://dx.doi.org/10.1080/02827580260417215.
   Web of Science ®Google Scholar
• Haralick R.M., Shanmungam K., Dinstein I. (1973)—Textural Features of Image Classification. IEEE Transactions on Systems, Man, and Cybernetics, 3 (6): 610–621. doi: http://dx.doi.org/10.1109/TSMC.1973.4309314.
   Web of Science ®Google Scholar
• Hirschmuller H. (2005)—Accurate and efficient stereo processing by semi-global matching and mutual information. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, 2: 807–814. doi: http://dx.doi.org/10.1109/CVPR.2005.56.
   Google Scholar
• Hyyppä J., Hyyppä H., Inkinen M., Engdahl M., Linko S., Zhu Y.-H. (1998)—Accuracy of different remote sensing data sources in the retrieval of forest stand attributes. Proceedings of the First International Conference on Geospatial Information in Agriculture and Forestry. Lake Buena Vista, FL, USA, 1–3 June 1998, 1: 370–377.
   Google Scholar
• Jing L., Hu B., Noland T., Li J. (2012)—An individual tree crown delineation method based on multi-scale segmentation of imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 70: 88–98. doi: http://dx.doi.org/10.1016/jisprsjprs.2012.04.003.
   Web of Science ®Google Scholar
• Ke Y., Quackenbush L.J. (2007)—Forest species classification and tree crown delineation using quickbird imagery. ASPRS 2007, Annual Conference, Tampa, Florida, May 7–11, 2007.
   Google Scholar
• Kim S.-R., Lee W.-K., Doo-Ahn Kwak D.-A., Biging G.S., Gong P., Jun-Hak Lee J.-H., Cho H.-K. (2011)—Forest cover classification by optimal segmentation of high resolution satellite imagery. Sensors, 11: 1943–1958. doi: http://dx.doi.org/10.3390/s110201943.
   Web of Science ®Google Scholar
• Korpela I. (2004)—Individual Tree Measurements by Means of Digital Aerial Photogrammetry. Silva Fennica Monographs, 3: 93 pp.
   Google Scholar
• Kuusinen N., Stenberg P., Korhonen L., Rautiainen M., Tomppo E. (2016)—Structural factors driving boreal forest albedo in Finland. Remote Sensing of Environment, 175: 43–51. doi: http://dx.doi.org/10.1016/j.rse.2015.12.035.
   Google Scholar
• Lamonaca A., Corona P., Barbati A. (2008)—Exploring forest structural complexity by multi-scale segmentation of VHR imagery. Remote Sensing of Environment, 112 (6): 2839–2849. doi: http://dx.doi.org/10.1016/j.rse.2008.01.017.
   Web of Science ®Google Scholar
• Landgrebe D. (1980)—The development of a spectral-spatial classifier for earth observational data. Pattern Recognition, 12: 165–175. doi: http://dx.doi.org/10.1016/0031-3203(80)90041-2.
   Web of Science ®Google Scholar
• Leckie D., Gougeon F., Hill D., Quinn R., Armstrong L., Shreenan R. (2003)—Combined high-density lidar and multispectral imagery for individual tree crown analysis. Canadian Journal of Remote Sensing, 295: 633–649. doi: http://dx.doi.org/10.5589/m03-024.
   Google Scholar
• Leckie D.G., Gougeon F.A., Tinis S., Nelson T., Burnett C.N., Paradine D. (2005)—Automated tree recognition in old growth conifer stands with high resolution digital imagery. Remote Sensing of Environment, 94: 311–326. doi: http://dx.doi.org/10.1016/j.rse.2004.10.011.
   Web of Science ®Google Scholar
• Lisein J., Michez A., Claessens H., Lejeune P. (2015)—Discrimination of Deciduous Tree Species from Time Series of Unmanned Aerial System Imagery. PLoS ONE, 10 (11): e0141006. doi: http://dx.doi.org/10.1371/journal.pone.0141006.
   Google Scholar
• Lukeš P., Rautiainen M., Manninen T., Stenberg P., Mottus M. (2014)—Geographical gradients in boreal forest albedo and structure in Finland. Remote Sensing of Environment, 152: 526–535. doi: http://dx.doi.org/10.1016/j.rse.2014.06.023.
   Google Scholar
• Maltamo M., Packalén P. (2014)—Species-specific management inventory in Finland. In: Maltamo M., Næsset E., Vauhkonen J. (Eds.), Forestry applications of airborne laser scanning-concepts and case studies, Managing Forest Ecosystems, 27: 241–252. doi: http://dx.doi.org/10.1007/978-94-017-8663-8_12.
   Google Scholar
• Manninen T., Stenberg P. (2009)—Simulation of the effect of snow covered forest floor on the total forest albedo. Agricultural and Forest Meteorology, 149 (2): 303–319. doi: http://dx.doi.org/10.1016/j.agrformet.2008.08.016.
   Google Scholar
• Manninen T., Korhonen L., Voipio P., Lahtinen P., Stenberg P. (2009)—Leaf area index (LAI) estimation of boreal forest using wide optics airborne winter photos. Remote Sensing, 4: 1380–1394. doi: http://dx.doi.org/10.3390/rs1041380.
   Google Scholar
• Manninen T., Korhonen L., Voipio P., Lahtinen P., Stenberg P. (2012)—Airborne estimation of boreal forest LAI in winter conditions: a test using summer and winter ground truth. IEEE Transactions on Geoscience and Remote Sensing, 50 (1): 68–74. doi: http://dx.doi.org/10.1109/TGRS.2011.2173939.
   Google Scholar
• Manninen T., Roujean J.-L. (2014)—Snortex, snow reflectance transition experiment. Finnish Meteorological Institute, Reports, 2014 (7) 68 p.
   Google Scholar
• Meyer P., Staenz K., Itten K.I. (1996)—Semi-automated procedures for tree species identification in high spatial resolution data from digitized colour infrared aerial photography. ISPRS Journal of Photogrammetry and Remote Sensing, 51: 5–16. doi: http://dx.doi.org/10.1016/0924-2716(96)00003-2.
   Web of Science ®Google Scholar
• Michez A., Piégay H., Lisein J., Claessens H., Lejeune P. (2016)—Mapping of riparian invasive species with supervised classification of Unmanned Aerial System (UAS) imagery. International Journal of Applied Earth Observation and Geoinformation, 44: 88–94. doi: http://dx.doi.org/10.1016/jjag.2015.06.014.
   Web of Science ®Google Scholar
• Ni W., Woodcock C.E. (2000)—Effect of canopy structure and the presence of snow on the albedo of boreal conifer forests. Journal of Geophysical Research Atmospheres, 105: 11879–11888. doi: http://dx.doi.org/10.1029/1999JD901158.
   Google Scholar
• Packalén P., Suvanto A., Maltamo M. (2009)—A two stage method to estimate species-specific growing stock. Photogrammetric Engineering and Remote Sensing, 75 (12): 1451–1460. doi: http://dx.doi.org/10.14358/PERS.75.12.1451.
   Google Scholar
• Pouliot D.A., King D. J., Bell F. W., Pitt D. G. (2002)—Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration. Remote Sensing of Environment, 82: 322–334. doi: http://dx.doi.org/10.1016/S0034-4257(02)00050-0.
   Web of Science ®Google Scholar
• Puliti S., Ørka H. O., Gobakken T., Næsset E. (2015)—Inventory of Small Forest Areas Using an Unmanned Aerial System. Remote Sensing, 7: 9632–9654. doi: http://dx.doi.org/10.3390/rs70809632.
   Web of Science ®Google Scholar
• Puttonen E., Litkey P., Hyyppä J. (2010)—Individual tree species classification by illuminated-shaded area separation. Remote Sensing, 2: 19–35. doi: http://dx.doi.org/10.3390/rs2010019.
   Web of Science ®Google Scholar
• R Core Team (2013)—R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available online at: http://www.R-project.org/.
   Google Scholar
• Roujean J.-L., Manninen T., Sukuvaara T., Peltoniemi J., Kaasalainen S., Hautecoeur O., Lahtinen P., Riihelä A., Siljamo N., Lötjönen M., Karjalainen T., Kontu A., Suokanerva H., Aulamo O., Lemmetyinen J., Suomalainen J., Hakala T., Kaartinen H., Thölix L., Meinander O., Karhu J. (2010)—SNORTEX: Remote sensing measurement of snowmelt in European boreal forest. iLEAPS Newslett, 9: 56–58.
   Google Scholar
• Singh M., Evans D., Tan B.S., Nin C. S. (2015)—Mapping and Characterizing Selected Canopy Tree Species at the Angkor World Heritage Site in Cambodia Using Aerial Data. PLoS ONE, 10(4): e0121558. doi: http://dx.doi.org/10.1371/journal.pone.0121558.
   Google Scholar
• Tuominen S., Pitkänen J., Balazs A., Korhonen K. T., Hyvöen 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: 1–20. doi: http://dx.doi.org/10.14214/sf.983.
   Google Scholar
• Vauhkonen J. Ørka H. O., Holmgren J., Dalponte M., Heinzel J., Koch B. (2014)—Tree species recognition based on airborne laser scanning and complementary data sources. In: Maltamo M., Næsset E., Vauhkonen J. (Eds.), Forestry applications of airborne laser scanning-concepts and case studies. Managing Forest Ecosystems, 27. doi: http://dx.doi.org/10.1007/978-94-017-8663-8_7.
   Google Scholar
• Venables W. N., Ripley B. D. (2002)—Modern Applied Statistics with S-PLUS. Statistics and Computing, Springer. doi: http://dx.doi.org/10.1007/978-0-387-21706-2.
   Google Scholar
• Villikka M., Packalén P., Maltamo M. (2012)—The suitability of leaf-off airborne laser scanner data in an area-based forest inventory of coniferous and deciduous trees. Silva Fennica, 46: 99–110. doi: http://dx.doi.org/10.14214/sf.68.
   Web of Science ®Google Scholar
• Voss M., Sugumaran R. (2008)—Seasonal effect on tree species classification in an urban environment using hyperspectral data, LiDAR, and an object-oriented approach. Sensors, 8: 3020–3036. doi: http://dx.doi.org/10.3390/s8053020.
   PubMed Web of Science ®Google Scholar
• Wang L. (2010)—A multi-scale approach for delineating individual tree crowns with very high resolution imagery. Photogrammetric Engineering and Remote Sensing, 76 (4): 371–378. doi: http://dx.doi.org/10.14358/PERS.76.4.371.
   Google Scholar
• Warner T. A., Lee J. Y., McGraw J.B. (1999)—Delineation and identification of individual trees in the eastern deciduous forest. Proceedings of the international forum: Automated interpretation of high spatial resolution digital imagery for forestry, Canadian Forest Service Pacific Forestry Centre Victoria, British Columbia, pp. 81–91.
   Google Scholar
• 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: 76–85. doi: http://dx.doi.org/10.1016/j.rse.2010.08.006.
   Web of Science ®Google Scholar
• 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: 518–536. doi: http://dx.doi.org/10.3390/f4030518.
   Web of Science ®Google Scholar