Object-based habitat mapping using very high spatial resolution multispectral and hyperspectral imagery with LiDAR data

International Journal of Applied Earth Observation and Geoinformation

Volumn 59, Issue , 2017, Pages 79-91

  Onojeghuo, Alex Okiemute ^a   Onojeghuo, Ajoke Ruth ^a,b  

^a NNAMDI AZIKIWE UNIVERSITY   (Nigeria)

^b UNIVERSITY OF LEICESTER   (United Kingdom)

Author keywords

CHM; Eagle hyperspectral; Habitat mapping; Intensity; kNN; LiDAR; OBIA; QuickBird; Regression forest; SVM

Indexed keywords

ACCURACY ASSESSMENT; DIGITAL ELEVATION MODEL; DIGITAL TERRAIN MODEL; ECOLOGICAL MODELING; HABITAT STRUCTURE; IMAGE CLASSIFICATION; LIDAR; MACHINE LEARNING; MAPPING; QUICKBIRD; SATELLITE IMAGERY; SPATIAL RESOLUTION; SPECTRAL RESOLUTION;

CUMBRIA; ENGLAND; UNITED KINGDOM;

EID: 85048075963     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2017.03.007     Document Type: Article

References (58)

1. ASPRS, LAS Specification Version 1.4–R6. 2011 (Accessed 18 Febuaray, 2017) http://www.asprs.org/wp-content/uploads/2010/12/LAS_1-4_R6.pdf.
2. Adam, E., Mutanga, O., Rugege, D., Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetlands Ecol. Manage. 18 (2010), 281–296.
3. Agresti, A., An Introduction to Categorical Data Analysis. 1996, Wiley, New York, N.Y.
4. Anderson, J.E., Plourde, L.C., Martin, M.E., Braswell, B.H., Smith, M.-L., Dubayah, R.O., Hofton, M.A., Blair, J.B., Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest. Remote Sens. Environ. 112 (2008), 1856–1870.
5. Azimuth-Systems, AZGCORR: User's Manual. 2005, Azimuth Systems, UK.
6. Bannari, A., Morin, D., Bé Nié, G.B., Bonn, F.J., A Theoretical review of different mathematical models of geometric corrections applied to remote sensing images. Remote Sens. Rev. 13 (1995), 27–47.
7. Benz, U.C., 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. Remote Sens. 58 (2004), 239–258.
8. Bork, E.W., Su, J.G., Integrating LIDAR data and multispectral imagery for enhanced classification of rangeland vegetation: A meta analysis. Remote Sens. Environ. 111 (2007), 11–24.
9. Bradley, J.V., Distribution-Free Statistical Test. 1968, Prentice-Hall, Englewood Cliffs, New Jersy.
10. Breiman, L., Random forests. Mach. Learn. 45 (2001), 5–32.
11. Buddenbaum, H., Schlerf, M., Hill, J., Classification of coniferous tree species and age classes using hyperspectral data and geostatistical methods. Int. J. Remote Sens. 26 (2005), 5453–5465.
12. CBP, The Cumbria Biodiversity Action Plan: Habitat Action Plan. 2001.
13. Christian, B., Krishnayya, N., Classification of tropical trees growing in a sanctuary using Hyperion (EO-1) and SAM algorithm. Curr. Sci. 96 (2009), 1601–1607.
14. Cumbria Wildlife Trust, Cumbria BAP Species List Updated 2009. 2009 (Accessed 15 Febuary, 2017) http://www.cumbriawildlifetrust.org.uk/sites/default/files/cumbria%20bap%20species%20updated%20list%202009%20web.pdf.
15. 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 (2012), 258–270.
16. Digital Globe, QuickBird Imagery Products −Product Guide (Revision 4 7.1). 2006, DigitalGlobe Inc.
17. Dixon, B., Candade, N., Multispectral landuse classification using neural networks and support vector machines: one or the other, or both?. Int. J. Remote Sens. 29 (2008), 1185–1206.
18. Du, P., Xia, J., Zhang, W., Tan, K., Liu, Y., Liu, S., Multiple classifier system for remote sensing image classification: a review. Sensors 12 (2012), 4764–4792.
19. Foody, G.M., Mathur, A., A relative evaluation of multiclass image classification by support vector machines. IEEE Trans. Geosc. Remote Sens. 42 (2004), 1335–1343.
20. Foody, G., Boyd, D., Sanchez-Hernandez, C., Mapping a specific class with an ensemble of classifiers. Int. J. Remote Sens. 28 (2007), 1733–1746.
21. Geerling, G., Labrador-Garcia, M., Clevers, J., Ragas, A., Smits, A., Classification of floodplain vegetation by data fusion of spectral (CASI) and LiDAR data. Int. J. Remote Sens. 28 (2007), 4263–4284.
22. Ghimire, B., Rogan, J., Galiano, V.R., Panday, P., Neeti, N., An evaluation of bagging, boosting, and random forests for land-cover classification in Cape Cod Massachusetts, USA. GISci. Remote Sens. 49 (2012), 623–643.
23. Giacinto, G., Roli, F., Design of effective neural network ensembles for image classification purposes. Image Vision Comput. 19 (2001), 699–707.
24. Hill, R., Thomson, A., Mapping woodland species composition and structure using airborne spectral and LiDAR data. Int. J. Remote Sens. 26 (2005), 3763–3779.
25. 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 (2008), 1537–1552.
26. Huang, C., Davis, L., Townshend, J., An assessment of support vector machines for land cover classification. Int. J. Remote Sens. 23 (2002), 725–749.
27. Hughes, G., On the mean accuracy of statistical pattern recognizers. IEEE Trans. Inf. Theory 14 (1968), 55–63.
28. 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 (2012), 2661–2693.
29. JNCC, Handbook for Phase 1 Habitat Survey – A Technique for Environmental Audit. 2010, Joint Nature Conservation Committee.
30. Johnson, B., Xie, Z., Unsupervised image segmentation evaluation and refinement using a multi-scale approach. ISPRS J. Photogramm. Remote Sens., 66, 2011.
31. Jolliffe, I.T., Principal Component Analysis. 2002, Springer, New York.
32. 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 (2010), 2841–2852.
33. Karpouzli, E., Malthus, T., The empirical line method for the atmospheric correction of IKONOS imagery. Int. J. Remote Sens. 24 (2003), 1143–1150.
34. 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 (2010), 1141–1154.
35. Landis, J., Koch, G.G., The measurement of observer agreement for categorical data. Biometics 33 (1977), 159–174.
36. Mücher, C.A., Roupioz, L., Kramer, H., Bogers, M., Jongman, R.H., Lucas, R.M., Kosmidou, V., Petrou, Z., Manakos, I., Padoa-Schioppa, E., Synergy of airborne LiDAR and Worldview-2 satellite imagery for land cover and habitat mapping: a BIO_SOS-EODHaM case study for the Netherlands. Int. J. Appl. Earth Obs. Geoinf. 37 (2015), 48–55.
37. Moreno-Seco, F., Inesta, J.M., De León, P.J.P., Micó, L., Comparison of classifier fusion methods for classification in pattern recognition tasks. Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), 2006, Springer, 705–713.
38. Mountrakis, G., Im, J., Ogole, C., Support vector machines in remote sensing: a review. ISPRS J. Photogramm. Remote Sens. 66 (2011), 247–259.
39. Onojeghuo, A.O., Blackburn, G.A., Optimising the use of hyperspectral and LiDAR data for mapping reedbed habitats. Remote Sens. Environ. 115 (2011), 2025–2034.
40. Onojeghuo, A.O., Blackburn, G.A., Characterising reedbeds using lidar data: potential and limitations. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 6 (2013), 935–941.
41. Pal, M., Mather, P., Support vector machines for classification in remote sensing. Int. J. Remote Sens. 26 (2005), 1007–1011.
42. Peerbhay, K.Y., Mutanga, O., Ismail, R., Commercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu-Natal, South Africa. ISPRS J. Photogramm. Remote Sens. 79 (2013), 19–28.
43. Pohl, C., Van Genderen, J., Multisensor image fusion in remote sensing: concepts, methods and applications. Int. J. Remote Sens. 19 (1998), 823–854.
44. Pu, R., Gong, P., Tian, Y., Miao, X., Carruthers, R.I., Anderson, G.L., Invasive species change detection using artificial neural networks and CASI hyperspectral imagery. Environ. Monit. Assess. 140 (2008), 15–32.
45. Rapinel, S., Hubert-Moy, L., Clément, B., Combined use of LiDAR data and multispectral earth observation imagery for wetland habitat mapping. Int. J. Appl. Earth Observ. Geoinf. 37 (2015), 56–64.
46. Rodriguez-Galiano, V.F., Ghimire, B., Rogan, J., Chica-Olmo, M., Rigol-Sanchez, J.P., An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J. Photogramm. Remote Sens. 67 (2012), 93–104.
47. Solberg, A.H.S., Data fusion for remote sensing applications. Chen, C.H., (eds.) Signal and Image Processing for Remote Sensing, 2006, CRC Press, Boca Raton, 515–537.
48. Song, J.-H., Han, S.-H., Yu, K., Kim, Y.-I., Assessing the possibility of land-cover classification using lidar intensity data. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 34 (2002), 259–262.
49. Trimble, eCognition Developer 9.0 Reference Book. 2014.
50. Trimble, eCognition Developer 9.0 User Guide. 2014.
51. Vapnik, V.N., Vapnik, V., Statistical Learning Theory. 1998, Wiley New York.
52. Vepakomma, U., St-Onge, B., Kneeshaw, D., Spatially explicit characterization of boreal forest gap dynamics using multi-temporal lidar data. Remote Sens. Environ. 112 (2008), 2326–2340.
53. Vyas, D., Krishnayya, N., Manjunath, K., Ray, S., Panigrahy, S., Evaluation of classifiers for processing Hyperion (EO-1) data of tropical vegetation. Int. J. Appl. Earth Obs. Geoinf. 13 (2011), 228–235.
54. Zhang, X., Feng, X., Detecting urban vegetation from IKONOS data using an object-oriented approach. Int. Geosci. Remote Sens. Symp., 2005, p1475.
55. Zhang, C., Qiu, F., Mapping individual tree species in an urban forest using airborne lidar data and hyperspectral imagery. Photogramm. Eng. Remote Sens. 78 (2012), 1079–1087.
56. Zhang, C., Xie, Z., Data fusion and classifier ensemble techniques for vegetation mapping in the coastal Everglades. Geocarto Int. 29 (2014), 228–243.
57. Zhang, C., Combining hyperspectral and LiDAR data for vegetation mapping in the Florida Everglades. Photogramm. Engin. Remote Sens. 80 (2014), 733–743.
58. Zhang, C., Applying data fusion techniques for benthic habitat mapping and monitoring in a coral reef ecosystem. ISPRS J. Photogramm. Remote Sens. 104 (2015), 213–223.