Review of studies on tree species classification from remotely sensed data

Remote Sensing of Environment

Volumn 186, Issue , 2016, Pages 64-87

  Fassnacht, Fabian Ewald ^a   Latifi, Hooman ^b   Stereńczak, Krzysztof ^c   Modzelewska, Aneta ^c   Lefsky, Michael ^d   Waser, Lars T ^e   Straub, Christoph ^f   Ghosh, Aniruddha ^g  

^a KARLSRUHE INSTITUTE OF TECHNOLOGY   (Germany)

^b UNIVERSITY OF WÜRZBURG   (Germany)

^c FOREST RESEARCH INSTITUTE   (Poland)

^d Department of Environmental and Radiological Health Sciences   (United States)

^e SWISS FEDERAL RESEARCH INSTITUTE WSL   (Switzerland)

^f Bavarian State Institute of Forestry   (Germany)

^g UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Classification; Forestry; Mapping; Remote sensing; Scale; Tree species; Validation

Indexed keywords

DIGITAL STORAGE; FORESTRY; MAPPING; POPULATION STATISTICS; REMOTE SENSING;

CLASSIFICATION ACCURACY; REMOTE SENSING TECHNIQUES; REMOTE SENSING TECHNOLOGY; SCALE; SPECIES CLASSIFICATION; TREE SPECIES; TREE SPECIES COMPOSITION; VALIDATION;

CLASSIFICATION (OF INFORMATION);

ALGORITHM; BENCHMARKING; CLASSIFICATION; EXPERIMENTAL DESIGN; FORESTRY; LITERATURE REVIEW; MAPPING; MODEL VALIDATION; OPTIMIZATION; PLANTATION; REMOTE SENSING; SENSOR; SPECIES DIVERSITY; SPECIES INVENTORY; TREE;

EID: 84983070439     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2016.08.013     Document Type: Review

References (207)

1. Adelabu, S., Mutanga, O., Adam, E., Cho, M.A., Exploiting machine learning algorithms for tree species classification in a semiarid woodland using RapidEye image. J. Appl. Remote. Sens., 7(1), 2013 (073480–1–073480–13).
2. Alonzo, M., Bookhagen, B., Roberts, D.A., Urban tree species mapping using hyperspectral and lidar data fusion. Remote Sens. Environ. 148 (2014), 70–83.
3. Alonzo, M., Roth, K., Roberts, D., Identifying Santa Barbara's urban tree species from AVIRIS imagery using canonical discriminant analysis. Remote Sens. Lett. 4:5 (2013), 513–521.
4. Asner, G.P., Biophysical and biochemical sources of variability in canopy reflectance. Remote Sens. Environ. 64:3 (1998), 234–253.
5. Asner, G.P., Martin, R.E., Anderson, C.B., Knapp, D.E., Quantifying forest canopy traits: imaging spectroscopy versus field survey. Remote Sens. Environ. 158 (2015), 15–27.
6. Bauer, M.E., Burk, T.E., Ek, A.R., Coppin, P.R., Lime, S.D., Waish, T.A., Walters, D.K., Befort, W., Heinzen, D.F., Satellite inventory of Minnesota forest resources. Photogramm. Eng. Remote Sens. 60:3 (1994), 287–298.
7. Bergseng, E., Ørka, H., Næsset, E., Gobakken, T., Assessing forest inventory information obtained from different inventory approaches and remote sensing data sources. Ann. For. Sci. 72 (2015), 33–45.
8. Blackburn, G.A., Hyperspectral remote sensing of plant pigments. J. Exp. Bot. 58:4 (2007), 855–996.
9. Blackburn, G.A., Milton, E.J., Seasonal variations in the spectral reflectance of deciduous tree canopies. Int. J. Remote Sens. 16:4 (1995), 709–720.
10. Boerner, W.M., Mott, H., Lueneburg, E., Livingstone, E., Brisco, B., Brown, R.J., Paterson, S., Polarimetry in radar remote sensing: basic and applied concepts. Henderson, F.M., Lewis, A.J., (eds.) Principles and Applications of Imaging Radar, 1998, John Wiley & Sons, Ltd.
11. Boschetti, M., Boschetti, L., Oliveri, S., Casati, L., Canova, I., Tree species mapping with airborne hyper-spectral MIVIS data: The Ticino Park study case. Int. J. Remote Sens. 28:6 (2007), 1251–1261.
12. Brandtberg, T., Classifying individual tree species under leaf-off and leaf-on conditions using airborne LIDAR. ISPRS J. Photogramm. Remote Sens. 61 (2007), 325–340.
13. Brandtberg, T., Warner, T., High spatial-resolution remote sensing. Shao, G., Reynolds, K.M., (eds.) Computer Applications in Sustainable Forest Management: Including Perspectives on Collaboration and Integration, 2006, 19–41.
14. Carleer, A., Wolff, E., Exploitation of very high resolution satellite data for tree species identification. Photogramm. Eng. Remote Sens. 70:1 (2004), 135–140.
15. Castro-Esau, K., Sanchez-Azofeifa, G., Rivard, B., Wright, S., Quesada, M., Variability in leaf optical properties of Mesoamerican trees and the potential for species classification. Am. J. Bot. 93 (2006), 517–530.
16. Chambers, D., Périé, C., Casajus, N., de Blois, S., Challenges in modelling the abundance of 105 tree species in eastern North America using climate, edaphic, and topographic variables. For. Ecol. Manag. 291 (2013), 20–29.
17. Chance, C.M., Coops, N.C., Crosby, K., Aven, N., Spectral wavelength selection and detection of two invasive plant species in an urban area. Can. J. Remote. Sens. 41 (2016), 1–14.
18. Cho, M.A., Debba, P., Mathieu, R., Naidoo, L., van Aardt, J., Asner, G.P., Improving discrimination of savanna tree species through a multiple-endmember spectral angle mapper approach: canopy-level analysis. IEEE Trans. Geosci. Remote Sens. 48:11 (2010), 4133–4142.
19. Cho, M.A., Mathieu, G.P.R.A., Naidoo, L., van Aardt, J., Ramoelo, P.A., Debba, W.K., Main, R., et al. Mapping tree species composition in South African savannas using an integrated airborne spectral and LiDAR system. Remote Sens. Environ. 125 (2012), 214–226.
20. Chuine, I., Beaubien, E.G., Phenology is a major determinant of tree species range. Ecol. Lett. 4 (2001), 500–510.
21. Clark, M.L., Roberts, D.A., Species-level differences in hyperspectral metrics among tropical rainforest trees as determined by a tree-based classifier. Remote Sens. 4:6 (2012), 1820–1855.
22. Clark, M.L., Roberts, D.A., Clark, D.B., Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales. Remote Sens. Environ. 96:3–4 (2005), 375–398.
23. Coops, N.C., Hilker, T., Wulder, M.A., St-Onge, B., Newnham, G., Siggins, A., Trofymow, J.A., Estimating canopy structure of Doulgas -fir forest stands from discrete-return LiDAR. Trees-Struct. Funct. 21 (2007), 295–310.
24. Côté, J.-F., Widlowski, J.-L., Fournier, R.A., Verstraete, M.M., The structural and radiative consistency of three-dimensional tree reconstructions from terrestrial lidar. Remote Sens. Environ. 113:5 (2009), 1067–1081.
25. Culvenor, D.S., Coops, N., Preston, R., Tolhurst, K.G., A spatial clustering approach to automated tree crown delineation. Hill, D.A., Leckie, D.G., (eds.) International Forum On Automated High Resolution Digital Imagery For Forestry, 1999, 67–80 Victoria, B.C., Feburary 10–12, 1998.
26. Dalponte, M., Bruzzone, L., Gianelle, D., Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data. Remote Sens. Environ. 123:0 (2012), 258–270.
27. Dalponte, M., Ørka, H.O., Ene, L.T., Gobakken, T., Næsset, E., Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data. Remote Sens. Environ. 140 (2014), 306–317.
28. Dalponte, M., Ørka, H.O., Gobakken, T., Gianelle, D., Næsset, E., Tree species classification in boreal forests with hyperspectral data. IEEE Trans. Geosci. Remote Sens. 51:5 (2013), 2632–2645.
29. Datt, B., Recognition of Eucalyptus forest species using hyperspectral reflectance data. Proceedings of IEEE Geoscience and Remote Sensing Symposium, 2000, 4, 2000, 1405–1407.
30. Dickinson, C., Siqueira, P., Clewley, D., Lucas, R., Classification of forest composition using polarimetric decomposition in multiple landscapes. Remote Sens. Environ. 131 (2013), 206–214.
31. Dinuls, R., Erins, G., Lorencs, A., Mednieks, I., Sinica-Sinavskis, J., Tree species identification in mixed Baltic Forest using LiDAR and multispectral data. IEEE J. Sel. Top. in Appl. Earth Obs. Remote Sens. 5:2 (2012), 594–603.
32. Dobson, M.C., Pierce, L.E., Mcdonald, K., Sharik, T., Seasonal change in radar backscatter from mixed conifer and hardwood frorests in northern Michigan. Proceedings of: International Geoscience and Remote Sensing Symposium, 1991. IGARSS ‘91. Remote Sensing: Global Monitoring for Earth Management, 3, 1991, 1121–1124.
33. Dobson, M.C., Pierce, Ulaby, F., Knowledge-based land-cover classification using ERS-l/JERS-1 SAR composites. IEEE Trans. Geosci. Remote Sens. 34:1 (1996), 83–99.
34. Dobson, M.C., Ulaby, F.T., Pierce, L.E., Land-cover classification and estimation of terrain attributes using synthetic aperture radar. Remote Sens. Environ. 51 (1995), 199–214.
35. Dyk, A., Goodenough, D.G., Li, J.Y., Niemann, K.O., Guan, A., Chen, H., Duong, J., Multi-temporal, multi-angle evaluation with CHRIS of coastal forests. IGARSS’06, Proceedings of the IEEE International Conference on Geoscience and Remote Sensing Symposium, 2006. Denver, Colorado, USA, 2006.
36. Dymond, C.C., Mladenoff, D.J., Radeloff, V.C., Phenological differences in Tasseled Cap indices improve deciduous forest classification. Remote Sens. Environ. 80:3 (2002), 460–472.
37. Eid, T., Gobakken, T., Næsset, E., Comparing stand inventories for large areas based on photo interpretation and laser scanning by means of cost-plus-loss analyses. Scand. J. For. Res. 19 (2004), 512–523.
38. Einzmann, K., Ng, W.-T., Immitzer, M., Bachmann, M., Pinnel, N., Atzberger, C., Method analysis for collecting and processing in-situ hyperspectral needle reflectance data for monitoring Norway spruce. Photogrammetrie, Fernerkundung, Geoinformation 5:2014 (2014), 0423–0434.
39. Engler, R., Waser, L.T., Zimmermann, N.E., Schaub, M., Berdos, S., Ginzler, C., Psomas, A., Combining ensemble modeling and remote sensing for mapping individual tree species at high spatial resolution. For. Ecol. Manag. 310 (2013), 64–73.
40. Erikson, M., Species classification of individually segmented tree crowns in high-resolution aerial images using radiometric and morphologic image measures. Remote Sens. Environ. 91:3–4 (2004), 469–477.
41. Essery, R., Bunting, P., Hardy, J., Link, T., Marks, D., Melloh, R., Pomeroy, J., Rowlands, A., Rutter, N., Radiative transfer modelling of a coniferous canopy characterized by airborne remote sensing. J. Hydrometeorol. 9 (2008), 228–241.
42. European Environmental Agency, European Forest Types: Categories and Types for Sustainable Forest Management Reporting and Policy. (No. EEA Terchnical Report No 9/2006). 2007, EEA, Copenhagen Retrieved from http://www.env-edu.gr/Documents/European%20forest%20types.pdf.
43. Fassnacht, F.E., Koch, B., Review of forestry oriented multi-angular remote sensing techniques. Int. For. Rev. 14:3 (2012), 285–298.
44. Fassnacht, F.E., Neumann, C., Förster, M., Buddenbaum, H., Ghosh, A., Clasen, A., Joshi, P.K., Koch, B., Comparison of feature reduction algorithms for classifying tree species with hyperspectral data on three central European test sites. IEEE J. Sel. Top. in Appl. Earth Obs. Remote Sens. 7:6 (2014), 2547–2561.
45. Felbermeier, B., Hahn, A., Schneider, T., Study on user requirements sensing applications in forestry. Wagner, W., Székely, B., (eds.) ISPRS TC VII Symposium – 100 Years ISPRS, Vienna, Austria, July 5–7, 2010, IAPRS, Vol. XXXVIII, Part 7B, 2010.
46. Féret, J.-B., Asner, G.P., Spectroscopic classification of tropical forest species using radiative transfer modeling. Remote Sens. Environ. 115:9 (2011), 2415–2422.
47. Foody, G.M., Status of land cover classification accuracy assessment. Remote Sens. Environ. 80 (2002), 185–201.
48. Franklin, S.E., Hall, R.J., Moskal, L.M., Maudie, A.J., Lavigne, M.B., Incorporating texture into classification of forest species composition from airborne multispectral images. Int. J. Remote Sens. 21:1 (2000), 61–79.
49. Gao, B.C., Hoetz, A.F.H., Column atmospheric water-vapor and vegetation liquid water retrievals from airborne imaging spectrometer data. J. Geophys. Res.-Atmos. 95 (1990), 3549–3564.
50. Gärtner, P., Förster, M., Kleinschmit, B., The benefit of synthetically generated RapidEye and Landsat 8 data fusion time series for riparian forest disturbance monitoring. Remote Sens. Environ. 177 (2016), 237–247.
51. Gatziolis, D., Dynamic range-based intensity normalization for airborne, discrete return lidar data of forest canopies. Photogramm. Eng. Remote. Sens. 77:3 (2011), 251–259.
52. Ghiyamat, A., Shafri, H.Z.M., Amouzad, M.G., Shariff, A.R., Mansor, S., Hyperspectral discrimination of tree species with different classifications using single- and multiple-endmember. Int. J. Appl. Earth Obs. Geoinf. 23 (2013), 177–191.
53. Ghosh, A., Joshi, P.K., A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution WorldView 2 imagery. Int. J. Appl. Earth Obs. Geoinf. 26 (2014), 298–311.
54. Ghosh, A., Fassnacht, F.E., Joshi, P.K., Koch, B., A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales. Int. J. Appl. Earth Obs. Geoinf. 26 (2014), 49–63.
55. Gong, P., Pu, R., Yu, B., Conifer species recognition: an exploratory analysis of in situ hyperspectral data. Remote Sens. Environ. 62 (1997), 189–200.
56. Goodenough, D.G., Dyk, A., Niemann, K.O., Pearlman, J.S., Chen, H., Han, T., Murdoch, M., West, C., Processing Hyperion and ALI for forest classification. IEEE Trans. Geosci. Remote Sens. 41:6 (2003), 1321–1331.
57. Gougeon, F.A., Comparison of possible multispectral classification schemes for tree crowns individually delineated on high spatial resolution MEIS images. Can. J. Remote. Sens. 21 (1995), 1–9.
58. Grant, L., Diffuse and specular characteristics of leaf reflectance. Remote Sens. Environ. 22 (1987), 309–322.
59. Haara, A., Haarala, M., Tree species classification using semi-automatic delineation of trees on aerial images. Scand. J. For. Res. 17:6 (2002), 556–565.
60. Heikkinen, V., Tokola, T., Parkkinen, J., Korpela, I., Jääskeläinen, T., Simulated multispectral imagery for tree species classification using support vector machines. IEEE Trans. Geosci. Remote Sens. 48:3 (2010), 1355–2010.
61. Heinzel, J., Koch, B., Exploring full-waveform LiDAR parameters for tree species classification. Int. J. Appl. Earth Obs. Geoinf. 13:1 (2011), 152–160.
62. Heinzel, J., Koch, B., Investigating multiple data sources for tree species classification in temperate forest and use for single tree delineation. Int. J. Appl. Earth Obs. Geoinf. 18 (2012), 101–110.
63. Heller, R.C., Doverspike, G.E., Aldrich, R.C., Identification of tree species on large-scale panchromatic and color aerial photographs. Agriculture Handbook, No. 261, 1964, U.S. Department of agriculture, Forest Service 16 pages.
64. Hesketh, M., Sanchez-Azofeifa, G.A., The effect of seasonal spectral variation on species classification in the Panamanian tropical forest. Remote Sens. Environ. 118 (2012), 73–82.
65. Hill, R.A., Wilson, A.K., George, M., Hinsley, S.A., Mapping tree species in temperate deciduous woodland using time-series multi-spectral data. Appl. Veg. Sci. 13:1 (2010), 86–99.
66. Hoekman, D.H., Radar backscatter of forest stands. Int. J. Remote Sens. 6:2 (1985), 325–343.
67. Hoekman, D.H., Quiñones, M.J., Biophysical Forest type characterization in the Colombian Amazon by airborne polarimetric SAR. IEEE Trans. Geosci. Remote Sens. 40:6 (2002), 1288–1300.
68. Höfle, B., Hollaus, M., Lehner, H., Pfeifer, N., Wagner, W., Area-based parameterization of forest structure using full-waveform airborne laser scanning data. Hill, R.A., Rosette, J., Suárez, J., (eds.) Proceedings of SilviLaser 2008: 8th International Conference on LiDAR Applications in Forest Assessment and Inventory, 2008, Edinburgh, UK, 227–236.
69. Hollaus, M., Mücke, W., Höfle, B., Dorigo, W., Pfeifer, N., Wagner, W., Bauerhansl, C., et al. Tree species classification based on full-waveform airborne laser scanning data. Proceedings of Silvilaser 2009, 2009, College Station, Texas, USA, 1–9 (wl27www658.webland.ch).
70. Holloway, P.J., Structure and histochemistry of plant cuticular membranes: an overview. Cutler, D.F., Alvin, K.L., Price, C.E., (eds.) The plant cuticle, 1982, Academic Press, New York.
71. Holmgren, J., Persson, Å., Identifying species of individual trees using airborne laser scanner. Remote Sens. Environ. 90 (2004), 415–423.
72. Holmgren, J., Persson, Å., Söderman, U., Species identification of individual trees by combining high resolution LiDAR data with multi-spectral images. Int. J. Remote Sens. 29:5 (2008), 1537–1552.
73. Holopainen, M., Talvitie, M., Effect of data acqusistion accuracy on timing of stand harvests and expected net present value. Silva Fennica 40:3 (2006), 531–543.
74. Hovi, A., Korpela, I., Real and simulated waveform-recording LiDAR data in juvenile boreal forest vegetation. Remote Sens. Environ. 140 (2014), 665–678.
75. Hovi, A., Korhonen, L., Vauhkonen, J., Korpela, I., LiDAR waveform features for tree species classification and their sensitivity to tree- and acquisition related parameters. Remote Sens. Environ. 173 (2016), 224–237.
76. Hughes, G.F., On the mean accuracy of statistical pattern recognizers. IEEE Trans. Inf. Theory 14:1 (1968), 55–63.
77. Immitzer, M., Atzberger, C., Einzmann, K., Böck, S., Mattiuzzi, M., Wallner, A., Seitz, R., Pinnel, N., Müller, A., Frost, M., Fichten- und Kiefernkarte für Bayern. LWF aktuell (Bayerische Landesanstalt für Wald und Forstwirtschaft ) 106 (2015), 30–37 in German.
78. Immitzer, M., Atzberger, C., Koukal, T., Tree species classification with random Forest using very high spatial resolution 8-Band WorldView-2 satellite data. Remote Sens. 4:9 (2012), 2661–2693.
79. Immitzer, M., Atzberger, T.C., Koukal, Suitability of WorldView-2 data for tree species classification with special emphasis on the four new spectral bands. Photogrammetrie, Fernerkundung, Geoinformation 2012:5 (2012), 573–588.
80. Jacquemoud, S., Verhoef, W., Baret, F., Bacour, C., Zarco-Tejada, P.J., Asner, G.P., François, C., Ustin, S.L., PROSPECT + SAIL models: a review of use for vegetation characterization. Remote Sens. Environ. 113 (2009), 56–66.
81. Jakubowski, M.K., Li, W., Guo, Q., Kelly, M., Delineating individual trees from Lidar data: a comparison of vector- and raster-based segmentation approaches. Remote Sens. 5 (2013), 4163–4186.
82. Jansson, G., Angelstam, P., Threshold levels of habitat composition for the presence of long-tailed tit (Aegithalos audatus) in a boreal landscape. Landsc. Ecol. 14 (1999), 283–290.
83. Jensen, R.R., Hardin, P.J., Hardin, A.J., Classification of urban tree species using hyperspectral imagery. Geocarto International 27:5 (2012), 443–458.
84. Johansen, K., Phinn, S., Mapping structural parameters and species composition of riparian vegetation using IKONOS and Landsat ETM + data in Australian tropical savannahs. Photogramm. Eng. Remote. Sens. 72:1 (2006), 71–80.
85. Jones, T.G., Coops, N.C., Sharma, T., Assessing the utility of airborne hyperspectral and LiDAR data for species distribution mapping in the coastal Pacific Northwest, Canada. Remote Sens. Environ. 114:12 (2010), 2841–2852.
86. Junttila, S., Kaasalainen, S., Vastaranta, M., Hakala, T., Nevalainen, O., Holopainen, M., Investigating bi-temporal hyperspectral LiDAR measurements from declined trees- experiences from laboratory test. Remote Sens. 2015:7 (2015), 13863–13877.
87. Katoh, A., Moskal, L.M., Schiess, P., Swanson, M.E., Calhoun, D., Stuetzle, W., Capturing tree crown formation through implicit surface reconstruction using airborne lidar data. Remote Sens. Environ. 113:6 (2009), 1148–1162.
88. Kayitakire, F., Defourny, P., Forest type discrimination using multi-angle hyperspecral data. Proc. of the 2nd CHRIS/Proba Workshop, 2004, ESA/ESRIn, Frascatiy, Italy.
89. Ke, Y., Quackenbush, L.J., Im, J., Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification. Remote Sens. Environ. 114:6 (2010), 1141–1154.
90. Keller, M., Schimel, D.S., Hargrove, W., Hoffman, F.M., A continental strategy for the National Ecological Observatory Network. Front. Ecol. Environ. 6 (2008), 282–284.
91. Kennedy, C.E.J., Southwood, T.R.E., The number of species of insects associated with British trees: A re-analysis. J. Anim. Ecol. 53:2 (1984), 455–478.
92. Key, T., Warner, T.A., McGraw, J.B., Fajvan, M.A., A comparison of multispectral and multitemporal information in high spatial resolution imagery for classification of individual tree species in a temperate hardwood forest. Remote Sens. Environ. 75:1 (2001), 100–112.
93. Kim, S., Individual Tree Species Identification Using LIDAR-Derived Crown Structures and Intensity Data. Dissertation, 2007, University of Washington http://depts.washington.edu/rsgal/pubs/SooyoungKimPhDDissertation.pdf.
94. Kim, S.-R., Lee, W.-K., Kwak, D.-A., Biging, G.S., Gong, P., Lee, J.H., Cho, H.-K., Forest cover classification by optimal segmentation of high resolution satellite imagery. Sensors 11 (2011), 1943–1958.
95. Kim, S., McGaughey, R.J., Andersen, H.-E., Schreuder, G., Tree species differentiation using intensity data derived from leaf-on and leaf-off airborne laser scanner data. Remote Sens. Environ. 113:8 (2009), 1575–1586.
96. King, R.L., Ruffin, C., LaMastus, F.E., Shaw, D.R., The analysis of hyperspectral data using Savitzky-Golay filtering – practical issues 2. Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Hamburg, Germany, June 28–July 2, 2, 1999, 398–400.
97. Knowlton, D.J., Hoffer, R.M., Radar imagery for forest cover mapping. Proceedings of: 7th International Symposium on Machine Processing of Remotely Sensed Data with Special Emphasis on Range, Forest and Wetlands Assessment, 1981 Retrieved from http://docs.lib.purdue.edu/lars_symp/481.
98. Koch, B., Kattenborn, T., Straub, C., Vauhkonen, J., Segmentation of forests to tree objects. Maltamo, M., Naesset, E., Vauhkonen, J., (eds.) Forestry Applications of Airborne Laser Scanning, 2013, Springer.
99. Kokaly, R.F., Clark, R.N., Spectroscopic determination of leaf biochemistry using band-depths analysis of absorption features and stepwise linear regression. Remote Sens. Environ. 67:3 (1999), 267–287.
100. Korpela, I., Individual tree measurements by means of digital aerial photogrammetry. Silva Fennica, Monographs, 3, 2004, 2004 (93 pp).
101. Korpela, I.S., Tokola, T.E., Potential of aerial image-based monoscopic and multiview single-tree forest inventory: a simulation approach. For. Sci. 52:2 (2006), 136–147.
102. Korpela, I., Ørka, H.O., Hyyppä, J., Heikkinen, V., Tokola, T., Range and AGC normalization in airborne discrete-return LiDAR intensity data for forest canopies. ISPRS J. Photogramm. Remote Sens. 65 (2010), 369–379.
103. Korpela, I., Ørka, H.O., Maltamo, M., Tokola, T., Hyyppä, J., Tree species classification using airborne LiDAR—effects of stand and tree parameters, downsizing of training set, intensity normalization, and sensor type. Silva Fennica 44:2 (2010), 319–339.
104. Korpela, I., Heikkinen, V., Hokavaara, E., Rohrbach, F., Tokola, T., Variation and directional anisotropy of reflectance at the crown scale – implications for tree species classification in digital aerial images. Remote Sens. Environ. 115 (2011), 2062–2074.
105. Korpela, I., Hovi, A., Korhonen, L., Backscattering of individual lidar pulses from forest canopies explained by photogrammetrically derived vegetation structure. Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci. XL-1/W1 (2013), 171–176.
106. Korpela, I., Hovi, A., Morsdorf, F., Understory trees in airborne LiDAR data – selective mapping due to transmission losses and echo-triggering mechanisms. Remote Sens. Environ. 119 (2012), 92–104.
107. Korpela, I., Koskinen, M., Vasander, H., Holopainen, M., Minkkinen, K., Airborne small-footprint discrete-return LiDAR data in the assessment of boreal mire surface patterns, vegetation, and habitats. For. Ecol. Manag. 258 (2009), 1549–1566.
108. Korpela, I., Mehtätalo, L., Seppänen, A., Kangas, A., Tree species identification in aerial image data using directional reflectance signatures. Silva Fennica, 48(3), 2014, article id 1087.
109. Korpela, I., Tuomola, T., Tokola, T., Dahlin, B., Appraisal of seedling stand vegetation with airborne imagery and discrete-return LiDAR – an exploratory analysis. Silva Fennica 42:5 (2008), 753–772.
110. Kotchenova, S.Y., Shabanov, N.V., Knyazikhin, Y., Davis, A.B., Dubayah, R., Myneni, R.B., Modeling lidar waveforms with time-dependent stochastic radiative transfer theory for remote estimations of forest structure. J. Geophys. Res., 108(D15), 2003, 4484.
111. Kötz, B., Schaepman, M., Morsdorf, F., Bowyer, P., Itten, K., Allgöwer, B., Radiative transfer modeling within a heterogeneous canopy for estimation of forest fire fuel properties. Remote Sens. Environ. 92 (2004), 332–344.
112. Koukal, T., Atzberger, C., Schneider, W., Evaluation of semi-empirical BRDF models inverted against multi-angle data from a digital airborne frame camera for enhancing forest type classification. Remote Sens. Environ. 151 (2014), 27–43.
113. Kuhn, M., Johnson, K., Resampling Techniques. Chapter in: Applied Predictive Modeling, 2013, Springer, New York, NY, 69–73.
114. Kwok, R., Rignot, E., Way, J.B., Freeman, A., Holt, J., Polarization signatures of thawed and frozen forests of varying biomass. IEEE Trans. Geosci. Remote Sens. 32 (1994), 371–381.
115. Leckie, D.G., Ranson, K.J., Forestry Applications Using Imaging Radar. Henderson, F.M., Lewis, A.J., (eds.) Chapter in: Principles and Applications of Imaging Radar, Vol. 3, 1998, John Wiley & Sons, Ltd.
116. Leckie, D.G., Tinis, S., Nelson, T., Burnett, C., Gougeon, F.A., Cloney, E., Paradine, D., Issues in species classification of trees in old growth conifer stands. Can. J. Remote. Sens. 31:2 (2005), 175–190.
117. Lee, J.S., Grunes, M.R., Ainsworth, T.L., Du, L., Schuler, D.L., Cloude, S.R., Unsupervised classification using polarimetric decomposition and the complex Wishart classifier. IEEE Trans. Geosci. Remote Sens. 37:5 (1999), 2249–2258.
118. Lewis, A.J., Henderson, F.M., Holcomb, D.W., Radar fundamentals: the geoscience perspective. Henderson, F.M., Lewis, A.J., (eds.) Principles and Applications of Imaging Radar, 1998, John Wiley and Sons, Inc.
119. Liang, X., Hyppä, J., Matikainen, L., Deciduous-Coniferous tree classification using difference between first and last pulse laser signatures. IAPRS, Volume XXXVI, Part 3/W52, 2007, 253–257 Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.222.3625&rep=rep1&type=pdf.
120. Lu, D., Weng, Q., Survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens. 28 (2007), 823–870.
121. Maghsoudi, Y., Collins, M., Leckie, D.G., Polarimetric classification of Boreal forest using nonparametric feature selection and multiple classifiers. Int. J. Appl. Earth Obs. Geoinf. 19 (2012), 139–150.
122. Mallinis, G., Koutsias, N., Tsakiri-Strati, M., Karteris, M., Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site. ISPRS J. Photogramm. Remote Sens. 63:2 (2008), 237–250.
123. Marceau, D.J., Gratton, D.J., Fournier, R.A., Fortin, J.P., Remote sensing and the measurement of geographical entities in a forested environment. 2. The optimal spatial resolution. Remote Sens. Environ. 49 (1994), 105–117.
124. Martin, R., Asner, G., Sack, L., Genetic variation in leaf pigment, optical and photosynthetic function among diverse phenotypes of Metrosideros polymorpha grown in a common garden. Oecologia 151 (2007), 387–400.
125. Meyer, P., Staenz, K., Itten, K.I., Semi-automated procedures for tree species classification in high spatial resolution data from digitized colour infrared-aerial photography. ISPRS J. Photogramm. Remote Sens. 51 (1996), 5–16.
126. Mickelson, J.G., Civco, D.L., Silander, J.A., Delineating forest canopy species in the northeastern United States using multi-temporal TM imagery. Photogramm. Eng. Remote. Sens. 64:9 (1998), 891–904.
127. Moisen, G.G., Freeman, E.A., Blackard, J.A., Frescino, T.S., Zimmermann, N.E., Edwards, T.C. Jr., Predicting tree species presence and basal area in Utah: a comparison of stochastic gradient boosting, generalized additive models, and tree-based methods. Ecol. Model. 199:2 (2006), 176–187.
128. Moore, M.M., Bauer, M.E., Classification of forest vegetation in North-Central Minnesota using Landsat multispectral scanner and thematic mapper data. For. Sci. 36:2 (1990), 330–342.
129. Morain, S.A., Simonett, D.S., K-band radar in vegetation mapping. Photogramm. Eng. 33:7 (1967), 730–740.
130. Naidoo, L., Cho, M.A., Mathieu, R., Asner, G., Classification of savanna tree species, in the Greater Kruger National Park by integrating hyperspectral and LiDAR data in a random forest data mining environment. ISPRS J. Photogramm. Remote Sens. 69 (2012), 167–179.
131. Nicodemus, F.E., Richmond, J.C., Hsia, J.J., Ginsberg, I.W., Limperis, T., Geometrical considerations and nomenclature for reflectance. NBS Monogr., No. 160, 1977, National Bureau of Standards, U.S. Department of Commerce 52 p.
132. Ørka, H.O., Hauglin, M., Use of remote sensing for mapping of non-native conifer species. INA Fagrapport, 33, 2016 (76 pp).
133. Ørka, H.O., Dalponte, M., Gobakken, T., Næsset, E., Ene, L.T., Characterizing forest species composition using multiple remote sensing data sources and inventory approaches. Scand. J. For. Res. 28:7 (2013), 677–688.
134. Ørka, H.O., Næsset, E., Bollandsås, M., Classifying species of individual trees by intensity and structure features derived from airborne laser scanner data. Remote Sens. Environ. 113:6 (2009), 1163–1174.
135. Ortiz, S.M., Breidenbach, J., Knuth, R., Kändler, G., The influence of DEM quality on mapping accuracy of coniferous- and deciduous-dominated forest using TerraSAR-X images. Remote Sens. 4 (2012), 661–681.
136. Packalén, P., Suvanto, A., Maltamo, M., A two stage method to estimate species-specific growing stock. Photogramm. Eng. Remote Sens. 75:12 (2009), 1451–1460.
137. Pant, P., Heikkinen, V., Hovi, I.A., Korpela, Hauta-Kasari M., Tokola, T., Evaluation of simulated bands in airborne optical sensors for tree species identification. Remote Sens. Environ. 138:0 (2013), 27–37.
138. Pausas, J.G., Austin, M.P., Noble, I.R., A forest simulation model for predicting eucalypt dynamics and habitat quality for arboreal marsupials. Ecol. Appl. 7:3 (1997), 921–933.
139. Peerbhay, K.Y., Mutanga, O., Ismail, R., Investigating the capability of few strategically placed worldview-2 multispectral bands to discriminate Forest species in KwaZulu-Natal, South Africa. IEEE J. Sel. Top. in Appl. Earth Obs. Remote Sens. 7:1 (2014), 307–316.
140. Peña, M.A., Cruz, P., Roig, M., The effect of spectral and spatial degradation of hyperspectral imagery for the Sclerophyll tree species classification. Int. J. Remote Sens. 34:20 (2013), 7113–7130.
141. Pierce, L.E., Bergen, K.M., Dobson, M.C., Ulaby, F.T., Multitemporal land-cover classification using SIR-C/X-SAR imagery. Remote Sens. Environ. 64 (1998), 20–33.
142. Plourde, L.C., Ollinger, S.V., Smith, M.-L., Martin, M.E., Estimating species abundance in a northern temperate forest using spectral mixture analysis. Photogramm. Eng. Remote. Sens. 73:7 (2007), 829–840.
143. Pohl, C., Van Genderen, J., Review article multisensor image fusion in remote sensing: concepts, methods and applications. Int. J. Remote Sens. 19:5 (1998), 37–41.
144. Pope, K.O., Rey-Benayas, J.M., Paris, J.F., Radar remote sensing of forest and wetland ecosystems in the Central American tropics. Remote Sens. Environ. 48 (1994), 205–219.
145. Pu, R., Landry, S., A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species. Remote Sens. Environ. 124:0 (2012), 516–533.
146. Pu, R., Liu, D., Segmented canonical discriminant analysis of in situ hyperspectral data for identifying 13 urban tree species. Int. J. Remote Sens. 32:8 (2011), 2207–2226.
147. Pu, R., Landry, S., Zhang, J., Evaluation of atmospheric correction methods in identifying urban tree species with WorldView-2 imagery. IEEE Journal of selected topics in applied earth observation and remote sensing 8:5 (2015), 1886–1897.
148. Puttonen, E., Litkey, P., Hyyppä, J., Individual tree species classification by illuminated-shaded area separation. Remote Sens. 2 (2010), 19–35.
149. Ranson, K.J., Sun, G., Northern Forest classification using temporal Multifrequency and Multipolarimetric SAR images. Remote Sens. Environ. 47 (1994), 142–153.
150. Ranson, K.J., Saatchi, S., Sun, G., Boreal Forest ecosystem characterization with SIR-C/XSAR. IEEE Trans. Geosci. Remote Sens. 33:4 (1995), 867–876.
151. Ranson, K.J., Sun, G., Kharuk, V.I., Kovacs, K., Characterization of forests in western Sayani Mountains, Siberia from SIR-C SAR data. Remote Sens. Environ. 75 (2001), 188–200.
152. Reitberger, J., Krzystek, P., Stilla, U., Analysis of full waveform LIDAR data for the classification of deciduous and coniferous trees. Int. J. Remote Sens. 29:5 (2008), 1407–1431.
153. Riaño, D., Chuvieco, E., Condés, S., González-Matesanz, J., Ustin, S.L., Generation of crown bulk density for Pinus sylvestris L. From lidar. Remote Sens. Environ. 92 (2004), 345–352.
154. Ribeiro da Luz, B., Attenuated total reflectance spectroscopy of plant leaves: A tool for ecological and botanical studies. New Phytol. 172 (2006), 305–318.
155. Ribeiro da Luz, B., Crowley, J.K., Spectral reflectance and emissivity features of broad leaf plants: Prospects for remote sensing in the thermal infrared (8.0–14.0 μm). Remote Sens. Environ. 109 (2007), 393–405.
156. Ribeiro da Luz, B., Crowley, J.K., Identification of plant species by using high spatial and spectral resolution thermal infrared (8.0–13.5 μm) imagery. Remote Sens. Environ. 114 (2010), 404–413.
157. Rignot, E.J.M., Williams, C.L., Way, J., Viereck, L.A., Mapping of Forest types in Alaskan boreal forests using SAR imagery. IEEE Trans. Geosci. Remote Sens. 32:5 (1994), 1051–1059.
158. Roberts, D.A., Green, R.O., Adams, J.B., Temporal and spatial patterns in vegetation and atmospheric properties from AVIRIS. Remote Sens. Environ. 62 (1997), 223–240.
159. Roberts, D.A., Ustin, S.L., Ogunjemiyo, S., Greenberg, J., Dobrowski, S.Z., Chen, J., et al. Spectral and structural measures of northwest forest vegetation at leaf to landscape scale. Ecosystems 7 (2004), 545–562.
160. Rohde, W.G., Olson, C.E. Jr., Multispectral sensing of forest tree species. Photogramm. Eng. Remote. Sens. 38 (1972), 1209–1215.
161. Saatchi, S., Rignot, E., Classification of boreal Forest cover types using SAR images. Remote Sens. Environ. 60 (1997), 270–281.
162. Salisbury, J.W., Preliminary measurements of leaf spectral reflectance in the 8-14 μm region. Int. J. Remote Sens. 7:12 (1986), 1879–1886.
163. Salisbury, J.W., Milton, N.M., Thermal infrared (2.5- to 13.5-μm) directional hemispherical reflectance of leaves. Photogramm. Eng. Remote. Sens. 54:9 (1988), 1301–1304.
164. Sanchez-Azofeifa, G.A., Castro-Esau, K.L., Wright, S.J., Gamon, J., Kalacska, M., Rivard, B., Schnitzer, S.A., et al. Differences in leaf traits, leaf internal structure, and spectral reflectance between two communities of lianas and trees: Implications for remote sensing in tropical environments. Remote Sens. Environ. 113 (2009), 2076–2088.
165. Sandau, R., Roeser, H.-P., Valenzuela, A., Sandau, R., Roeser, H.-P., Valenzuela, A., (eds.) Small Satellite for Earth Observation, 2008, Springer.
166. Sarrazin, M.J.D., van Aardt, J.A.N., Asner, G.P., McGlinchy, J., Messinger, D.W., Wu, J., Fusing small-footprint waveform LiDAR and hyperspectral data for canopy-level species classification and herbaceous biomass modeling in savanna ecosystems. Can. J. Remote. Sens. 37:6 (2011), 653–665.
167. Sayn-Wittgenstein, L., Recognition of tree species on aerial photographs. Information Report FMR-X-118, 1978, Forest management Institute, Ottawa, Ontario.
168. Schaepman-Strub, G., Schaepman, M.E., Painter, T.H., Dangel, S., Martonchik, J.V., Reflectance quantities in optical remote sensing – Definitions and case studies. Remote Sens. Environ. 103 (2006), 27–42.
169. Schmidtlein, S., Zimmermann, P., Schüpferling, R., Weiß, C., Mapping the floristic continuum: Ordination space position estimated from imaging spectroscopy. J. Veg. Sci. 18 (2007), 131–140.
170. Schmitt, M., Shahzad, M., Zhu, X.X., Reconstruction of individual trees from multi-aspect TomoSAR data. Remote Sens. Environ. 165 (2015), 175–185.
171. Shang, X., Chisholm, L.A., Classification of Australian native Forest species using hyperspectral remote sensing and machine-learning classification algorithms. IEEE J. Sel. Top. in Appl. Earth Obs. Remote Sens. 7:6 (2014), 2481–2489.
172. Somers, B., Asner, G.P., Tree species mapping in tropical forests using multi-temporal imaging spectroscopy: wavelength adaptive spectral mixture analysis. Int. J. Appl. Earth Obs. Geoinf. 31 (2014), 57–66.
173. Song, C., Woodcock, C.E., Seto, K.C., Lenney, M.P., Macomber, S.A., Classification and change detection using Landsat TM data: when and how to correct atmospheric effects?. Remote Sens. Environ. 75 (2001), 230–244.
174. Stavrakoudis, D.G., Dragozi, E., Gitas, I.Z., Karydas, C.G., Decision fusion based on hyperspectral and multispectral satellite imagery for accurate forest species mapping. Remote Sens. 6 (2014), 6897–6928.
175. Stoffels, J., Sachtleber, T., Mader, S., Buddenbaum, H., Stern, O., Langshausen, J., Dietz, J., et al. Satellite-based derivation of high-resolution forest information layers for operational forest management. Forests 6 (2015), 1982–2013.
176. Suratno, A., Seielstad, C., Queen, L., Tree species identification in mixed coniferous forest using airborne laser scanning. ISPRS J. Photogramm. Remote Sens. 64:6 (2009), 683–693.
177. Thomlinson, J.R., Bolstad, P.V., Cohen, W.B., Coordinating methodologies for scaling landcover classifications from site-specific to global: steps toward validating global map products. Remote Sens. Environ. 70 (1999), 16–28.
178. Treuhaft, R.N., Asner, G.P., Law, B.E., Tuyl Van, S., Forest leaf area density profiles from the quantitative fusion of radar and hyperspectral data. J. Geophys. Res., 107(D21), 2002, 4568.
179. Trevett, J.W., Imaging Radar for Resources Surveys. 1986, Chapman and Hall, NY.
180. Ulaby, F.T., Sarabandi, K., McDonald, K., Whitt, M., Dobson, M.C., Michigan microwave canopy scattering model (MIMICS). Int. J. Remote Sens. 11:7 (1990), 1223–1253.
181. Ullah, S., Schlerf, M., Skidmore, A.K., Hecker, C., Identifying plant species using mid-wave infrared (2.5–6 μm) and thermal infrared (8–14 μm) emissivity spectra. Remote Sens. Environ. 118 (2012), 95–102.
182. Ustin, S., Gitelson, A.A., Jacquemoud, S.M., Asner, G.P., Gamon, J., Zarco-Tejada, Retrieval of foliar information about plant pigment systems from high resolution spectroscopy. Remote Sens. Environ. 113 (2009), 67–77.
183. Van Ewijk, K.Y., Randin, C.F., Treitz, P.M., Scott, N.A., Predicting fine-scale tree species abundance patterns using biotic variables derived from LiDAR and high spatial resolution imagery. Remote Sens. Environ. 150 (2014), 120–131.
184. Van Zyl, J., Unsupervised classification of scattering behavior using radar polarimetry data. IEEE Trans. Geosci. Remote Sens. 22:1 (1989), 36–45.
185. Vaughn, N.R., Moskal, L.M., Turnblom, E.C., Tree species detection accuracies using discrete point lidar and airborne waveform lidar. Remote Sens. 4:2 (2012), 377–403.
186. Vauhkonen, J., Hakala, T., Suomalainen, J., Kaasalainen, S., Nevalainen, O., Vastaranta, M., Holopainen, M., et al. Classification of spruce and pine trees using active hyperspectral LiDAR. IEEE Geosci. Remote Sens. Lett. 10:5 (2013), 1138–1141.
187. Vauhkonen, J., Ørka, H.O., Holmgren, J., Dalponte, M., Heinzel, J., Koch, B., Tree species recognition based on airborne laser scanning and complementary data sources. Forestry Applications of Airborne Laser Scanning, Managing Forest Ecosystems, 2014, Springer, Netherlands, 135–156.
188. Voss, M., Sugumaran, R., Seasonal effect on tree species classification in an urban environment using hyperspectral data, LiDAR, and an object-oriented approach. Sensors 8:5 (2008), 3020–3036.
189. Wagner, W., Hyyppä, J., Ullrich, A., Lehner, H., Briese, C., Kaasalainen, S., Radiometric calibration of full-waveform small-footprint airborne laser scanners. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 37 (2008), 163–168.
190. Wagner, W., Ullrich, A., Ducic, V., Melzer, T., Studnicka, N., Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner. ISPRS J. Photogramm. Remote Sens. 60:2 (2006), 100–112.
191. Walsh, S.J., Coniferous tree species mapping using LANDSAT data. Remote Sens. Environ. 9:1 (1980), 11–26.
192. Waser, L.T., Ginzler, C., Kuechler, M., Baltsavias, E., Hurni, L., 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 Sens. Environ. 115:1 (2011), 76–85.
193. Waser, L.T., Klonus, S., Ehlers, M., Küchler, M., Jung, A., Potential of Digital Sensors for Land Cover and Tree Species Classifications - A Case Study in the Framework of the DGPF-Project. J. Photogramm. Remote Sens. Geoinf. Process. 2 (2010), 141–156.
194. Waser, L.T., Küchler, M., Jütte, K., Stampfer, T., Evaluating the potential of WorldView-2 data to classify tree species and different levels of ash mortality. Remote Sens. 6:5 (2014), 4515–4545.
195. Wasser, L., Day, R., Chasmer, L., Taylor, A., Influence of vegetation structure on lidar-derived canopy height and fractional cover in forested riparian buffers during leaf-off and leaf-on conditions. PLoS One, 8(1), 2013, 10.1371/journal.pone.0054776.
196. Wichmann, V., Bremer, M., Lindenberger, J., Rutzinger, M., Georges, C., Petrini-Monteferri, F., Evaluating the potential of multispectral airborne lidar for topographic mapping and land cover classification. ISPRS Ann. Photogramm. Remote. Sens. Spat. Inf. Sci. II-3/W5 (2015), 113–119.
197. Wollersheim, M., Collins, M.J., Leckie, D., Estimating boreal forest species type with airborne polarimetric synthetic aperture radar. Int. J. Remote Sens. 32:9 (2011), 2481–2505.
198. Wolter, P.T., Mladenoff, D.J., Host, G.E., Crow, T.R., Improved forest classification in the Northern Lake States using multi-temporal Landsat imagery. Photogramm. Eng. Remote. Sens. 61:9 (1995), 1129–1143.
199. Yao, W., Krzystek, P., Heurich, M., Tree species classification and estimation of stem volume and DBH based on single tree extraction by exploiting airborne full-waveform LiDAR data. Remote Sens. Environ. 123 (2012), 368–380.
200. Youngentob, K.N., Roberts, D.A., Held, A.A., Dennison, P.E., Jia, X.P., Lindenmayer, D.B., Mapping two Eucalyptus subgenera using multiple endmember spectral mixture analysis and continuum-removed imaging spectrometry data. Remote Sens. Environ. 115:5 (2011), 1115–1128.
201. Yu, X., Litkey, P., Hyyppä, J., Holopainen, M., Vastaranta, M., Assessment of low density full-waveform airborne laser scanning for individual tree detection and tree species classification. For. Trees Livelihoods 5:5 (2014), 1011–1031.
202. Zhang, K., Hu, B., Individual urban tree species classification using very high spatial resolution airborne multi-spectral imagery using longitudinal profiles. Remote Sens. 4 (2012), 1741–1757.
203. Zhang, C., Qiu, F., Mapping individual tree species in an urban forest using airborne lidar data and hyperspectral imagery. Photogramm. Eng. Remote Sens. 78:10 (2012), 1079–1087.
204. Zhang, C., Xie, Z., Combining object-based texture measures with a neural network for vegetation mapping in the Everglades from hyperspectral imagery. Remote Sens. Environ. 124 (2012), 310–320.
205. Zimmermann, N.E., Edwards, T.C., Moisen, G.G., Frescino, T.S., Blackard, J.A., Remote sensing-based predictors improve distribution models or rare, early successional and broadleaf tree species in Utah. J. Appl. Ecol. 44 (2007), 1057–1067.
206. van Aardt, J.A.N., Wynne, R.H., Examining pine spectral separability using hyperspectral data from an airborne sensor: an extension of field-based results. Int. J. Remote Sens. 28:2 (2007), 431–436.
207. Puttonen, E., Soumalainen, J., Hakala, T., Räikkonen, E., Kaartinen, H., Kaasalainen, S., Litkey, P., Tree species classification from fused active hyperspectral reflectance and LIDAR measurements. For. Ecol. Manag. 260:10 (2010), 1843–1852.