Classification of herbaceous vegetation using airborne hyperspectral imagery

Remote Sensing

Volumn 7, Issue 2, 2015, Pages 2046-2066

  Burai, Péter ^a   Deák, Balázs ^b   Valkó, Orsolya ^b   Tomor, Tamás ^a  

^a SZENT ISTVÁN UNIVERSITY   (Hungary)

^b MTA DE Biodiversity and Ecosystem Services Research Group   (Hungary)

Author keywords

Grassland; Habitat mapping; Hyperspectral; Maximum likelihood classifier; Minimum noise fraction; Nature conservation; Open landscape; Random forest; Support vector machine

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); CONSERVATION; DECISION TREES; FEATURE EXTRACTION; LEARNING ALGORITHMS; LEARNING SYSTEMS; MAXIMUM LIKELIHOOD; PIXELS; REMOTE SENSING; SAMPLING; SPECTROSCOPY; SUPPORT VECTOR MACHINES; VEGETATION;

GRASSLAND; HABITAT MAPPING; HYPERSPECTRAL; MAXIMUM LIKELIHOOD CLASSIFIERS; MINIMUM NOISE FRACTION; OPEN LANDSCAPE; RANDOM FORESTS;

IMAGE CLASSIFICATION;

EID: 84926284886     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs70202046     Document Type: Article

References (46)

1. Mace, G.M.; Norris, K.; Fitter, A.H. Biodiversity and ecosystem services: A multilayered relationship. Trends Ecol. Evol. 2012, 27, 19-26.
2. Lengyel, S.; Déri, E.; Varga, Z.; Horváth, R.; Tóthmérész, B.; Henry, P.Y.; Kobler, A.; Kutnar, L.; Babij, V.; Seliskar, A.; et al. Habitat monitoring in Europe: A description of current practices. Biodivers. Conserv. 2008, 17, 3327-3339.
3. Burai, P.; Lövei, G.; Lénárt, C.; Nagy, I.; Enyedi, P. Mapping aquatic vegetation of the Rakamaz-Tiszanagyfalui Nagy-Morotva using hyperspectral imagery. Acta Geogr. Debr. Landsc. Environ. Ser. 2010, 4, 1-10.
4. Kobayashi, T.; Tsend-Ayush, J.; Tateishi, R.A. New tree cover percentage map in Eurasia at 500 m resolution using MODIS data. Remote Sens. 2014, 6, 209-232.
5. Cole, B.; McMorrow, J.; Evans, M. Empirical modelling of vegetation abundance from airborne hyperspectral data for upland Peatland restoration monitoring. Remote Sens. 2014, 6, 716-739.
6. Alexander, C.; Bøcher, P.K.; Arge, L.; Svenning, J.C. Regional-scale mapping of tree cover, height and main phenological tree types using airborne laser scanning data. Remote Sens. Environ. 2014, 147, 156-172.
7. Zlinszky, A.; Schroiff, A.; Kania, A.; Deák, B.; Mücke, W.; Vári, Á.; Székely, B.; Pfeifer, N. Categorizing grassland vegetation with full-waveform airborne laser scanning: A feasibility study for detecting Natura 2000 habitat types. Remote Sens. 2014, 6, 8056-8087.
8. Beamish, D. Peat mapping associations of airborne radiometric survey data. Remote Sens. 2014, 6, 521-539.
9. Li, C.; Wang, J.; Hu, L.; Yu, L.; Clinton, N.; Huang, H.; Yang, J.; Gong, P.A. Circa 2010 thirty meter resolution forest map for China. Remote Sens. 2014, 6, 5325-5343.
10. Dalponte, M.; Bruzzone, L.; Gianelle, D. Fusion of hyperspectral and LiDAR remote sensing data for classification of complex forest areas. IEEE Trans. Geosci. Remote. Sens. 2008, 46, 1416-1427.
11. Thenkabail, P.S. Hyperspectral Remote Sensing of Vegetation; Taylor and Francis: New York, NY, USA, 2011; p. 781.
12. Adam, E.; Mutanga, O.; Rugege, D. Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: A review. Wetlands Ecol. Manag. 2010, 18, 281-296.
13. Plaza, A.; Benediktsson, J.A.; Boardman, J.W.; Brazile, J.; Bruzzone, L.; Camps-Valls, G.; Chanussot, J.; Fauvel, M.; Gamba, P.; Gualtieri, A.; et al. Recent advances in techniques for hyperspectral image processing. Remote Sens. Environ. 2009, 113, 110-122.
14. Vanden Borre, J.; Paelinckx, D.; Mücher, C.A.; Kooistra, L.; Haest, B.; de Blust, G.; Schmidt, A.M. Integrating remote sensing in Natura 2000 habitat monitoring: Prospects on the way forward. J. Nat. Cons. 2011, 19, 116-125.
15. Pu, R.; Bell, S. A protocol for improving mapping and assessing of seagrass abundance along the West Central Coast of Florida using Landsat TM and EO-1 ALI/Hyperion images. ISPRS J. Photogramm. Remote Sens. 2013, 83, 116-129.
16. Stratoulias, D.; Balzter, H.; Zlinszky, A.; Toth, V.R. Assessment of ecophysiology of lake shore reed vegetation based on chlorophyll fluorescence, field spectroscopy and hyperspectral airborne imagery. Remote Sens. Environ. 2015, 157, 72-84.
17. Huang, C.; Asner, G.P. Applications of remote sensing to alien invasive plant studies. Sensors 2009, 9, 4869-4889.
18. Mirik, M.; Ansley, R.J.; Steddom, K.; Jones, D.C.; Rush, C.M.; Michels, G.J., Jr.; Elliott, N.C. Remote distinction of a noxious weed (musk thistle: Carduus nutans) using airborne hyperspectral imagery and the Support Vector Machine Classifier. Remote Sens. 2013, 5, 612-630.
19. Green, A.A.; Berman, M.; Switzer, P.; Craig, M.D. A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Trans. Geosci. Remote Sens. 1988, 26, 65-74.
20. Landgrebe, D.A. Signal Theory Methods in Multispectral Remote Sensing; John Wiley & Sons: New York, NY, USA, 2003; p. 503.
21. Molnár, Z.; Bölöni, J.; Biró, M.; Horváth, F. Distribution of the Hungarian (semi-) natural habitats I. Marshes and grasslands. Acta Bot. Hung. 2008, 50, 59-105.
22. Eliáš, P.; Sopotlieva, D.; Díte, D.; Hájková, P.; Apostolova, I.; Senko, D.; Melecková, Z.; Hájek, M. Vegetation diversity of salt-rich grasslands in Southeast Europe. Appl. Veg. Sci. 2013, 16, 521-537.
23. Török, P.; Kapocsi, I.; Deák, B. Conservation and management of alkali grass-land biodiversity in Central-Europe. In Grasslands: Types, Biodiversity and Impacts; Zhang, W.J., Ed.; Nova Science Publishers Inc.: New York, NY, USA, 2012; pp. 109-118.
24. Borhidi, A.; Kevey, B.; Lendvai, G. Plant communities of Hungary; Akadémiai Kiadó: Budapest, Hungary, 2012; p. 544.
25. Kelemen, A.; Török, P.; Valkó, O.; Miglécz, T.; Tóthmérész, B. Mechanisms shaping plant biomass and species richness: Plant strategies and litter effect in alkali and loess grasslands. J. Veg. Sci. 2013, 24, 1195-1203.
26. Deák, B.; Valkó, O.; Alexander, C.; Mücke, W.; Kania, A.; Tamás, J.; Heilmeier, H. Fine-scale vertical position as an indicator of vegetation in alkali grasslands-Case study based on remotely sensed data. Flora- Morphol. Distribut. Funct. Ecol. Plants 2014, 209, 693-697.
27. Valkó, O.; Tóthmérész, B.; Kelemen, A.; Simon, E.; Miglécz, T.; Lukács, B.; Török, P. Environmental factors driving vegetation and seed bank diversity in alkali grasslands. Agric. Ecosyst. Environ. 2014, 182, 80-87.
28. Deák, B.; Valkó, O.; Török, P.; Tóthmérész, B. Solonetz meadow vegetation (Beckmannion eruciformis) in East-Hungary-An alliance driven by moisture and salinity. Tuexenia 2014, 34, 187-203.
29. Deák, B.; Valkó, O.; Tóthmérész, B.; Török, P. Alkali marshes of Central-Europe-Ecology, management and nature conservation. In Salt Marshes: Ecosystem, Vegetation and Restoration Strategies; Shao, B., Ed.; Nova Science Publishers Inc.: New York, NY, USA, 2014; pp. 1-11.
30. Pettorelli, N. The Normalised Difference Vegetation Index; Oxford University Press: Oxford, UK, 2013; p. 194.
31. Hurcom, S.J.; Harrison, A.R. The NDVI and spectral decomposition for semi-arid vegetation abundance estimation. Int. J. Remote Sens. 1998, 19, 3109-3126.
32. Rabe, A.; Jakimow, B.; Held, M.; van der Linden, S.; Hostert, P. EnMAP-Box. Version 2.0. 2014. Available online: www.enmap.org (accessed on 28 January 2015).
33. Heldens, W.; Heiden, U.; Esch, T.; Stein, E.; Muller, A. Can the future EnMAP mission contribute to urban applications? A literature survey. Remote Sens. 2011, 3, 1817-1846.
34. Mansour, K.; Mutanga, O.; Everson, T.; Adam, E. Discriminating indicator grass species for rangeland degradation assessment using hyperspectral data resampled to AISA Eagle resolution. ISPRS J. Photogramm. Remote Sens. 2012, 70, 56-65.
35. Burai, P.; Laposi, R.; Enyedi, P.; Schmotzer, A.; Kozma, B.V. Mapping invasive vegetation using AISA Eagle airborne hyperspectral imagery in the Mid-Ipoly-Valley. In Proceedings of the 3rd IEEE GRSS Workshop on Hyperspectral Image and Signal Processing-WHISPERS'2011, Lisboa, Portugal, 6-9 June 2011.
36. Maselli, F.; Conese, C.; Petkov, L.; Resti, R. Inclusion of prior probabilities derived from a nonparametric process into the maximum likelihood classifier. Photogramm. Eng. Remote Sens. 1992, 58, 201-207.
37. Richards, J.A. Remote Sensing Digital Image Analysis; Springer-Verlag: Berlin, Germany, 1999; p. 240.
38. Yang, C.; Everitt, J.H.; Fletcher, R.S.; Jensen, R.R.; Mausel, P.W. Evaluating AISA+ hyperspectral imagery for mapping black mangrove along the South Texas Gulf, Coast. Photogramm. Eng. Remote Sens. 2009, 75, 425-435.
39. Breiman, L. Random forests. Mach. Learn. 2001, 45, 5-32.
40. Lawrence, R.; Wood, S.; Sheley, R. Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest). Remote Sens. Environ. 2006, 100, 356-362.
41. Vapnick, V.N. Statistical Learning Theory; John Wiley and Sons Inc.: Hoboken, NJ, USA, 1988; p. 768.
42. Camps-Valls, G.; Bruzzone, L. Kernel-based methods for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2005, 43, 1351-1362.
43. Camps-Valls, G.; Gomez-Chova, L.; Calpe-Maravilla, J.; Martin-Guerrero, J.D.; Soria-Olivas, E.; Alonso-Chorda, L.; Moreno, J. Robust support vector method for hyperspectral data classification and knowledge discovery. IEEE Trans. Geosci. Remote Sens. 2004, 42, 1530-1542.
44. Congalton, R.G. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 1991, 37, 35-46.
45. Chan, J.C.-W.; Spanhove, T.; Ma, J.; Vanden Borre, J.; Paelinckx, D.; Canters, F. Natura 2000 habitat identification and conservation status assessment with superresolution enhanced hyperspectral (CHRIS/Proba) imagery. In Proceedings of GEOBIA 2010 geographic object-based image analysis, Ghent, Belgium, 29 June-2 July 2010.
46. Chopping, M.J.; Rango, A.; Ritchie, J.C. Improved semi-arid community type differentiation with the NOAA AVHRR via exploitation of the directional signal. IEEE Trans. Geosci. Remote Sens. 2002, 40, 1132-1149.