Evaluating the potential of worldview-2 data to classify tree species and different levels of ash mortality

Remote Sensing

Volumn 6, Issue 5, 2014, Pages 4515-4545

  Waser, Lars T ^a   Küchler, Meinrad ^a   Jütte, Kai ^b   Stampfer, Theresia ^b  

^a SWISS FEDERAL RESEARCH INSTITUTE WSL   (Switzerland)

^b Landesforst Mecklenburg Vorpommern   (Germany)

Author keywords

Ash dieback; Multispectral remote sensing; Object based image analysis; Remote sensing indices; Tree mortality; Tree species; Variable selection

Indexed keywords

PRINCIPAL COMPONENT ANALYSIS; REFORESTATION;

MULTISPECTRAL REMOTE SENSING; OBJECT BASED IMAGE ANALYSIS; REMOTE SENSING INDEX; TREE MORTALITY; TREE SPECIES; VARIABLE SELECTION;

REMOTE SENSING;

EID: 84901483801     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs6054515     Document Type: Article

References (83)

1. Allen, C.D.; Macalady, A.K.; Chenchouni, H.; Bachelet, D.; McDowell, N.; Vennetier, M.; Kitzberger, T.; Rigling, A.; Breshears, D.D.; Hogg, E.H.; et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 2010, 259, 660-684.
2. Jönsson, A.M.; Appelberg, G.; Harding, S.; Bärring, L. Spatio-temporal impact of climate change on the activity and voltinism of the spruce bark beetle Ips. typographus. Glob. Chang. Biol. 2009, 15, 486-499.
3. Gross, A.; Zaffarano, P.L.; Grünig, C.R. Reproductive mode and life cycle of the ash dieback pathogen Hymenoscyphus pseudoalbidus. Fungal Genet. Biol. 2012, 49, 977-986.
4. Meddens, A.J.H.; Hicke, J.A.; Vierling, L.A.; Hudak, A.T. Evaluating methods to detect bark beetle-caused tree mortality using single-date and multi-date Landsat imagery. Remote Sens. Environ. 2013, 132, 49-58.
5. Honkavaara, E.; Litkey, P.; Nurminen, K. Automatic storm damage detection in forests using high-altitude photogrammetric imagery. Remote Sens. 2013, 5, 1405-1424.
6. Eitel, J.U.H.; Vierling, L.A.; Litvak, M.E.; Long, D.S.; Schulthess, U.; Ager, A.A.; Krofcheck, D.J.; Stoscheck, L. Broadband, red-edge information from satellites improves early stress detection in a New Mexico conifer woodland. Remote Sens. Environ. 2011, 115, 3640-3646.
7. Coops, N.C.; Johnson, M.; Wulder, M.A.; White, J.C. Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation. Remote Sens. Environ. 2006, 103, 67-80.
8. Hicke, J.A.; Logan, J. Mapping white bark pine mortality caused by a mountain pine beetle outbreak with high spatial resolution satellite imagery. Int. J. Remote Sens. 2009, 30, 4427-4441.
9. Wulder, M.A.; White, J.C.; Coops, N.C.; Butson, C.R. Multi-temporal analysis of high spatial resolution imagery for disturbance monitoring. Remote Sens. Environ. 2008, 112, 2729-2740.
10. White, J.C.; Wulder, M.A.; Brooks, D.; Reich, R.; Wheate, R.D. Detection of red attack stage mountain pine beetle infestation with high spatial resolution satellite imagery. Remote Sens. Environ. 2005, 96, 340-351.
11. Kim, Y.; Yang, Z.; Cohen, W.B.; Lauver, C.L.; Vankat, J.L. Distinguishing between live and dead standing tree biomass on the North Rim of Grand Canyon National Park, USA using small-footprint lidar data. Remote Sens. Environ. 2009, 113, 2499-2510.
12. Vastaranta, M.; Kantola, T.; Lyytikäinen-Saarenmaa, P.; Holopainen, M.; Kankare, V.; Wulder, M. Area-based mapping of defoliation of scots pine stands using airborne scanning LiDAR. Remote Sens. 2013, 5, 1220-1234.
13. Coops, N.C.; Varhola, A.; Bater, C.W.; Teti, P.; Boon, S.; Goodwin, N.; Weiler, M. Assessing differences in tree and stand structure following beetle infestation using lidar data. Can. J. Remote Sens. 2009, 35, 497-508.
14. Frolking, S.; Palace, M.W.; Clark, D.B.; Chambers, J.Q.; Shugart, H.H.; Hurtt, G.C. Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure. J. Geophys. Res. 2009, 114, 1-27.
15. Pontius, J.; Martin, M.; Plourde, L.; Hallett, R. Ash decline assessment in emerald ash borer-infested regions: A test of tree-level, hyperspectral technologies. Remote Sens. Environ. 2008, 112, 2665-2676.
16. Fassnacht, F.E.; Latifi, H.; Ghosh, A.; Joshi, P.K.; Koch, B. Assessing the potential of hyperspectral imagery to map bark beetle-induced tree mortality. Remote Sens. Environ. 2014, 140, 533-548.
17. Garrity, S.R.; Allen, C.D.; Brumby, S.P.; Gangodagamage, C.; McDowell, N.G.; Cai, D.M. Quantifying tree mortality in a mixed species woodland using multitemporal high spatial resolution satellite imagery. Remote Sens. Environ. 2013, 129, 54-65.
18. Wulder, M.A.; Dymond, C.C.; White, J.C.; Leckie, D.G.; Carroll, A.L. Surveying mountain pine beetle damage of forests: A review of remote sensing opportunities. For. Ecol. Manag. 2006, 221, 27-41.
19. Skakun, R.S.; Wulder, M.A.; Franklin, S.E. Sensitivity of the thematic mapper enhanced wetness difference index to detect mountain pine beetle red-attack damage. Remote Sens. Environ. 2003, 86, 433-443.
20. Carleer, A.; Wolff, E. Exploition of very high resolution satellite data for tree species identification. Photogramm. Eng. Remote Sens. 2004, 70, 135-140.
21. Schlerf, M.; Atzberger, C.; Hill, J. Remote sensing of forest biophysical variables using HyMap imaging spectrometer data. Remote Sens. Environ. 2005, 95, 177-194.
22. Schlerf, M.; Atzberger, C.; Hill, J.; Buddenbaum, H.; Werner, W.; Schüler, G. Retrieval of chlorophyll and nitrogen in Norway spruce (Picea. abies L. Karst.) using imaging spectroscopy. Int. J. Appl. Earth Obs. 2010, 12, 17-26.
23. Chen, Q. Comparison of WorldView-2 and IKONOS-2 Imagery for Identifying Tree Species in the Habitat of an Endangered Bird Species in Hawaii 8-Band Research Challenge; Digital Globe: Longmont, CO, USA, 2011.
24. Stoffels, J.; Mader, S.; Hill, J.; Werner, W.; Ontrup, G. Satellite-based stand-wise forest cover type mapping using a spatially adaptive classification approach. Eur. J. For. Res. 2012, 131, 1071-1089.
25. 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. 2012, 4, 2661-2693.
26. 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. 2011, 115, 76-85.
27. Leckie, D.G.; Tinis, S.; Nelson, T.; Burnett, C.H.; Gougeon, F.A.; Cloney, E.; Paradine, D. Issues in species classification of trees in old growth conifer stands. Can. J. Remote Sens. 2005, 31, 175-190.
28. 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. 2009, 113, 1575-1586.
29. Heinzel, J.N.; Koch, B. Exploring full-waveform LiDAR parameters for tree species classification. Int. J. Appl. Earth Obs. Geoinf. 2011, 13, 152-160.
30. Marchisio, G.; Pacifici, F.; Padwick, C. On the Relative Predictive Value of the New Spectral Bands in the Worldwiew-2 Sensor. In Proceedings of 2010 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Honolulu, HI, USA, 25-30 July 2010; pp. 2723-2726.
31. Digital Globe. White Paper: The Benefits of the 8 Spectral Bands of WorldView-2; Digital Globe: Longmont, CO, USA, 2009.
32. Gwata, B. Developing high resolution clutter for wireless network propagation using WorldView-2 imagery. Proc. SPIE 2012, 8390, doi:10.1117/12.919091
33. Omar, H. Commercial Timber Tree Species Identification Using Multispectral WorldView-2 Data, 8-Band Research Challenge; Digital Globe: Longmont, CO, USA, 2011.
34. Pu, R.; Landry, S.A. Comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species. Remote. Sens. Environ. 2012, 124, 516-533.
35. Riggan, N.D., Jr.; Weih, R.C., Jr. A Comparison of Pixel-based versus object-based land use/land cover classification methodologies. J. Ark. Acad. Sci. 2009, 63, 145-152.
36. Whiteside, T.G.; Boggs, G.S.; Maier, S.W. Comparing object-based and pixel-based classifications for mapping savannas. Int. J. Appl. Earth Obs. 2011, 13, 884-893.
37. Ju, J.; Kolaczyk, E.D.; Gopal, S. Gaussian mixture discriminant analysis and sub-pixel land cover characterization in remote sensing. Remote Sens. Environ. 2003, 84, 550-560.
38. Guisan, A.; Edwards, T.E.; Hastie, T. Generalized linear and generalized additive models in studies of species distributions: Setting the scene. Ecol. Model. 2002, 157, 89-100.
39. Duda, R.O.; Hart, P.E.; Stork, D.G. Pattern Classification, 2nd ed.; John Wiley & Sons: New York, NY, USA, 2000.
40. Chavez, P.S., Jr.; Sides, S.C.; Anderson, A. Comparison of three different methods to merge multiresolution and multispectral data: Landsat TM and SPOT Panchromatic. Photogramm. Eng. Remote Sense. 1991, 57, 295-303.
41. Bakys, R.; Vasaitis, R.; Barklund, P.; Ihrmark, K.; Stenlid, J. Investigations concerning the role of Chalara fraxinea in declining Fraxinus excelsior. Plant Pathol. 2009, 58, 284-292.
42. Multiresolution Segmentation-An Optimization Approach for High Quality Multi-Scale Image Segmentation. Available online: http://www.isprs.org/proceedings/xxxviii/4-c7/pdf/Happ_143.pdf (accessed on 12 March 2014).
43. Benz, U.; Hofmann, P.; Willhauck, G.; Lingenfelder, I.; Heynen, M. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. Photogramm. 2004, 58, 239-258.
44. Gonzales, R.C.; Woods, R.E. Digital Image Processing, 2nd ed.; Prentice Hall: New York, NY, USA, 2000.
45. Schowengerdt, R.E. Remote Sensing: Models and Methods for Image Processing; Academic Press and Elsevier: Amsterdam, The Nederlands, 2006.
46. Yu, Q.; Gong, P.; Clinton, N.; Biging, G.; Kelly, M.; Schirokauer, D. Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogramm. Eng. Remote Sense. 2006, 72, 799-811.
47. Waser, L.T. Airborne Remote Sensing Data for Semi-Automated Extraction of Tree Area and Classification of Tree Species. Ph.D. Dissertation 20464, Swiss Federal Institute of Technology-ETH Zurich, 2012. Available online: http://e-collection.library.ethz.ch/view/eth:6087 (accessed on 12 March 2014).
48. Bannari, A.; Morin, D.; Huette, A.R.; Bonn, F.A. A review of vegetation indices. Remote. Sens. Environ. 1995, 13, 95-120.
49. Jackson, R.D.; Huete, A.R. Interpreting vegetation indices. Prev. Vet. Med. 1991, 11, 185-200.
50. Gitelson, A.A.; Kaufman, Y.; Merzlyak, M.N. Use of green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 1996, 58, 289-298.
51. Vogelmann, J.E.; Rock, B.N.; Moss, D.M. Red edge spectral measurements from sugar maple leaves. Int. J. Remote Sens. 1993, 14, 1563-1575.
52. Haboudane, D.; Miller, J.R.; Pattey, E.; Zarco-Tejada, P.; Strachan, I.B. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote. Sens. Environ. 2004, 90, 337-352.
53. ENVI, 2013. Available online: http://www.exelisvis.com/docs/VegetationIndices.html (accessed on 10 April 2014).
54. Index Database, 2013. Available online: http://www.indexdatabase.de/(accessed on 10 April 2014).
55. Cyperski, J. Untersuchung von Filtern zur Aufnahme von IR-Luftbildern für die Forstwirtschaft. Bachelor Thesis, Beuth Hochschule für Technik Berlin, Berlin, Germany, 10 April 2012; pp. 1-40.
56. Kaufman, Y.J.; Tanré, D. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Trans. Geosci. Remote Sens. 1992, 30, 261-270.
57. Filchev, L. An Assessment of European Spruce Bark Beetle Infestation Using WorldView-2 Satellite Data. In Proceedings of 1st European SCGIS Conference with International Participation -Best Practices: Application of GIS Technologies for Conservation of Natural and Cultural Heritage Sites" (SCGIS-Bulgaria, Sofia), Sofia, Bulgaria, 21-23 May 2012; pp. 9-16.
58. Hildebrandt, G. Fernerkundung und Luftbildmessung für Forstwirtschaft, Vegetationskartierung und Landschaftsökologie; Herbert Wichmann Verlag: Heidelberg, Germany, 1996.
59. Le Maire, G.; Francois, C.; Dufrene, E. Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sens. Environ. 2004, 89, 1-28.
60. Ahamed, T.; Tian, L.; Zhang, Y.; Ting, K.C. A review of remote sensing methods for biomass feedstock production. Biomass Bioenerg. 2011, 35, 2455-2469.
61. Viña, A.; Gitelson, A.A. New developments in the remote estimation of the fraction of absorbed photosynthetically active radiation in crops. Geophys. Res. Lett. 2005, 32, 1-4.
62. Sims, D.A.; Gamon, J.A. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens. Environ. 2002, 81, 337-354.
63. Salifu, T.; Agyare, W.A. Distinguishing land use types using surface albedo and normalized difference vegetation index derived from the SEBAL model for the Atankwidi and Afram subcatchments in Ghana. J. Eng. Appl. Sci. 2012, 7, 69-80.
64. Walz, U.; Hou, W. Charakterisierung der Landschaftsvielfalt mit RapidEye-Daten-Erste Ergebnisse und Erfahrungen. In RapidEye Science Archive (RESA)-Erste Ergebnisse, Proceedings of the 3; RESA Workshop: Erik Borg, Holger Daedelow (Hrsg.), 2011; pp. 75-85.
65. Chávez, R.; Clevers, J.G.P.W. Phase 2. In Object-Based Analysis of 8-Bands WorldView-2 Imagery for Assessing Health Condition of Desert Trees, 8-Band Research Challenge; Digital Globe: Longmont, CO, USA, 2011; pp. 1-8.
66. Latifi, H.; Nothdurft, A.; Koch, B. Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: Application of multiple optical/LiDAR-derived predictors. Forestry 2010, 83, 395-407.
67. Guisan, A.; Weiss, S.B.; Weiss, A.D. GLM versus CCA spatial modeling of plant species distribution. Plant Ecol. 2004, 143, 107-122.
68. Hosmer, D.W.; Lemeshow, S. Applied Logistic Regression, 2nd ed.; Wiley: New York, NY, USA, 2000.
69. Akaike, H. Information Theory as an Extension of the Maximum Likelihood Principle. In Proceedings of the 2nd International Symposium on Information Theory, Budapest, Hungary, 9-13 June 2013; Petrov, B.N., Csaki, F., Eds.; Akademiai Kiado: Budapest, Hungary, pp. 267-281.
70. R Statistics. 2013. Available online: http://cran.r-project.org/manuals.html (accessed on 10 April 2014).
71. VEGEDAZ. 2014. VEGADAZ Software Version 2014. Available online: http://www.wsl.ch/dienstleistungen/produkte/software/vegedaz/index_EN (accessed on 10 April 2014).
72. Fukunaga, K. Introduction to Statistical Pattern Recognition, 2nd ed.; Academic Press: New York, NY, USA, 1990.
73. Everitt, B.S.; Dunn, G. Applied Multivariate Data Analysis; Arnold Publisher: London, UK, 2001.
74. Swan, A.R.H.; Sandilands, M. Introduction to Geological Data Analysis; Blackwell Science: Oxford, UK, 1995.
75. Venables, W.N.; Ripley, B.D. Modern Applied Statistics with S, 4th ed.; Springer: New York, NY, USA, 2002.
76. Cohen, J. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 1960, 20, 37-46.
77. Richter, K.; Atzberger, C.; Hank, T.B.; Mauser, W. Derivation of biophysical variables from earth observation data: Validation and statistical measures. J. Appl. Remote Sens. 2012, 6, doi:10.1117/1.JRS.6.063557.
78. Shao, J. Linear model selection by cross-validation. J. Am. Stat. Assoc. 1993, 88, 486-494.
79. Efron, B. Estimating the error rate of a prediction rule: Improvement on cross-validation. J. Am. Statist. Assoc. 1983, 78, 316-331.
80. Hollander, M.; Wolfe, D.A. Nonparametric Statistical Methods, 2nd ed.; Wiley: New York, NY, USA, 1999.
81. 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 2011, 11, 1943-1958.
82. Meddens, A.J.H.; Hicke, J.A.; Vierling, L.A. Evaluating the potential of multispectral imagery to map multiple stages of tree mortality. Remote Sens. Environ. 2011, 115, 1632-1642.
83. Brauner, N.; Shacham, M. Role of range and precision of the independent variable in regression of data. J. Am. Inst. Chem. Eng. 1998, 44, 603-611.