Assessing floristic composition with multispectral sensors-A comparison based: On monotemporal and multiseasonal field spectra

International Journal of Applied Earth Observation and Geoinformation

Volumn 21, Issue 1, 2012, Pages 218-229

  Feilhauer, Hannes ^a,b   Thonfeld, Frank ^b   Faude, Ulrike ^b,e   He, Kate S ^c   Rocchini, Duccio ^d   Schmidtlein, Sebastian ^b  

^a UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

^b UNIVERSITY OF BONN   (Germany)

^c MURRAY STATE UNIVERSITY   (United States)

^d RESEARCH AND INNOVATION CENTRE   (Italy)

^e EFTAS Fernerkundung   (Germany)

Author keywords

Conservation; Isometric feature mapping; Optical remote sensing; Partial Least Squares regression; Subclass information; Vegetation

Indexed keywords

FLORISTICS; LEAST SQUARES METHOD; MODELING; MULTISPECTRAL IMAGE; REMOTE SENSING; TEMPORAL ANALYSIS; VEGETATION MAPPING; VEGETATION TYPE;

EID: 84872779970     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2012.09.002     Document Type: Article

References (49)

1. Almeida-Neto, M., Lewinsohn, T.M., 2004. Small-scale autocorrelation and the interpretation of relationships between phenological parameters. Journal of Vegetation Science 15, 561-568.
2. Andrew, M.E., Ustin, S.L., 2008. The role of environmental context in mapping invasive plants with hyperspectral image data. Remote Sensing of Environment 112, 4301-4317.
3. Belluco, E., Camuffo, M., Ferrari, S., Modenese, L., Silvestri, S., Marani, A., Marani, M., 2006. Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing. Remote Sensing of Environment 105, 54-67.
4. Coops, N.C., Wulder, M.A., Iwanicka, D., 2009. Demonstration of a satellite-based index to monitor habitat at continental-scales. Ecological Indicators 9, 948-958.
5. Council of the European Communities, 1992. Council directive 92/43/EEC of 21 May 1992 on the conservation of natural habitats and of wild fauna and flora.
6. Dial, G., Bowen, H., Gerlach, F., Grodecki, J., Oleszczuk, R., 2003. IKONOS satellite, imagery, and products. Remote Sensing of Environment 88, 23-36.
7. Dorigo, W., Lucieer, A., Podobnikar, T., Carni, A., 2012. Mapping invasive Fallopia japonica by combined spectral spatial, and temporal analysis of digital orthophotos. International Journal of Applied Earth Observation and Geoinformation 19, 185-195.
8. Feilhauer, H., Faude, U., Schmidtlein, S., 2011. Combining Isomap ordination and imaging spectroscopy to map continuous floristic gradients in a heterogeneous landscape. Remote Sensing of Environment 115, 2513-2524.
9. Feilhauer, H., Schmidtlein, S., 2011. On variable relations between vegetation patterns and canopy reflectance. Ecological Informatics 6, 83-92.
10. Franke, J., Keuck, V., Siegert, F., 2012. Assessment of grassland use intensity by remote sensing to support conservation schemes. Journal for Nature Conservation 20, 125-134.
11. Förster, M., Frick, A., Walentowski, H., Kleinschmit, B., 2008. Approaches to utilising QuickBird data for the monitoring of NATURA 2000 habitats. Community Ecology 9, 155-168.
12. Ge, S., Everitt, J., Carruthers, R., Gong, P., Anderson, G., 2006. Hyperspectral characterization of canopy components and structure for phenological assessment of an invasive weed. Environmental Monitoring and Assessment 120, 109-126.
13. Ghioca-Robrecht, D.M., Johnston, C.M., Tulbure, M.G., 2008. Assessing the use of multiseason Quickbird imagery for mapping invasive species in a Lake Erie coastal marsh. Wetlands 28, 1028-1039.
14. Gleason, H.A., 1926. The individualistic concept of the plant association. Bulletin of the Torrey Botanical Club 53, 7-26.
15. Irons, J.R., Dwyer, J.L., Barsi, J.A., 2012. The next Landsat satellite: the Landsat Data Continuity Mission. Remote Sensing of Environment 122, 11-21.
16. Jackson, M.T., 1966. Effects of microclimate on spring flowering phenology. Ecology 47, 407-415.
17. Jacquemoud, S., Verhoef, W., Baret, F., Bacour, C., Zarco-Tejada, P.J., Asner, G.P., Franç ois, C., Ustin, S.L., 2009. PROSPECT + SAIL models: a review of use for vegetation characterization. Remote Sensing of Environment 113, S56-S66.
18. Kokaly, R.F., Rockwell, B.W., Haire, S.L., King, T.V.V., 2007. Characterization of postfire surface cover, soils, and burn severity at the Cerro Grande Fire, New Mexico, using hyperspectral and multispectral remote sensing. Remote Sensing of Environment 106, 305-325.
19. Kumar, L., Schmidt, K., Dury, S., Skidmore, A., 2001. Imaging spectrometry and vegetation science. In: van der Meer, F.D., de Jong, S.M. (Eds.), Imaging Spectroscopy. Basic Principles and Prospective Applications. Kluwer, Dordrecht, pp. 111-155.
20. Laba, M., Tsai, F., Ogurcak, D., Smith, S., Richmond, M.E., 2005. Field determination of optimal dates for the discrimination of invasive wetland plant species using derivate spectral analysis. Photogrammetric Engineering & Remote Sensing 71, 603-611.
21. Legendre, P., Anderson, M.J., 1999. Distance-based redundancy analysis: testing multispecies responses in multifactorial ecological experiments. Ecological Monographs 69, 1-24.
22. Legendre, P., Legendre, L., 1998. Numerical Ecology. Elsevier, Amsterdam.
23. Leemans, R., Zuidema, G., 1995. Evaluating changes in land cover and their importance for global change. Trends in Ecology & Evolution 10, 76-81.
24. Mahecha, M.D., Martinez, A., Lischeid, G., Beck, E., 2007. Nonlinear dimensionality reduction: alternative ordination approaches for extracting and visualizing biodiversity patterns in tropical montane forest vegetation data. Ecological Informatics 2, 138-149.
25. Markham, B.L., Storey, J.C., Williams, D.L., Irons, J.R., 2004. Landsat sensor performance: history and current status. IEEE Transactions on Geoscience and Remote Sensing 42, 2691-2694.
26. Martens, H., Martens, M., 2000. Modified Jack-knife estimation of parameter uncertainty in bilinear modelling by Partial Least Squares regression (PLSR). Food Quality and Preference 11, 5-16.
27. Martimort, P., Berger, M., Carnicero, B., Del Bello, U., Fernandez, V., Gascon, F., Silvestrin, P., Spoto, F., Sy, O., 2007. Sentinel-2 - the optical high-resolution mission for GMES operational services. ESA Bulletin 131, 18-23.
28. Melgani, F., Bruzzone, L., 2004. Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing 42, 1778-1790.
29. Mevik, B.H., Wehrens, R., Liland, K.H., 2011. pls: Partial Least Squares and Principal Component regression. http://mevik.net/work/software/pls.html (accessed 19.07.12).
30. Nagendra, H., Rocchini, D., 2008. High resolution satellite imagery for tropical biodiversity studies: the devil is in the detail. Biodiversity and Conservation 17, 3431-3442.
31. Neukum, G., the HRSC-Team,2001. The airborne HRSC-AX cameras: evaluation of the technical concept and presentation of application results after one year of operation. In: Photogramm. Week'01. Wichmann, pp. 117-130.
32. Oksanen, J., Blanchet, F.G., Kindt, R., Legendre, P., Minchin, P.R., O'Hara, R.B., Simpson, G.L., Solymos, P., Stevens, M.H.H., Wagner, H., 2011. vegan: Community Ecology package. http://cran.r-project.org,http://vegan.r-forge.r-project.org/ (accessed 19.07.12).
33. Price, J.C., 1994. How unique are spectral signatures? Remote Sensing of Environment 49, 181-186.
34. R Development Core Team, 2011. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, www.rproject. org (accessed 2012/07/19).
35. Richards, J.A., Jia, X., 2006. Remote Sensing Digital Image Analysis. Springer, Berlin/Heidelberg.
36. Sanchez-Hernandez, C., Boyd, D.S., Foody, G.M., 2007. Mapping specific habitats from remotely sensed imagery: support vector machine and support vector data description based classification of coastal saltmarsh habitats. Ecological Informatics 2, 83-88.
37. Savitzky, A., Golay, M.J.E., 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry 36, 1627-1639.
38. Schmidtlein, S., 2010. isopam: Isopam (Clustering). http://cran.r-project.org,http://www.geographie.uni-bonn.de/biogeo/resources/code/isopam (accessed 19.07.12).
39. Schmidtlein, S., Tichy, L., Feilhauer, H., Faude, U., 2010. A brute-force approach to vegetation classification. Journal of Vegetation Science 21, 1162-1171.
40. Schuster, C., Förster, M., Kleinschmit, B., 2012. Testing the red edge channel for improving land-use classifications based on high-resolution multi-spectral satellite data. International Journal of Remote Sensing 33, 5583-5599.
41. Spanhove, T., Vanden Borre, J., Delalieux, S., Haest, B., Paelinckx, D., 2012. Can remote sensing estimate fine-scale quality indicators of natural habitats? Ecological Indicators 18, 403-412.
42. Spurr, S.H., 1948. Aerial Photographs in Forestry. Ronald Press, New York.
43. Tenenbaum, J.B., de Silva, V., Langford, J.C., 2000. A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319-2323.
44. Thomas, V., Treitz, P., Jelinski, D., Miller, J., Lafleur, P., McCaughey, J.H., 2002. Image classification of a northern peatland complex using spectral and plant community data. Remote Sensing of Environment 84, 83-99.
45. Vanden Borre, J., Paelinckx, D., Mücher, C.A., Kooistra, L., Haest, B., De Blust, G., Schmidt, A.M., 2011. Integrating remote sensing in Natura 2000 habitat monitoring: prospects on the way forward. Journal for Nature Conservation 19, 116-125.
46. Vogelmann, J.E., Xian, G., Homer, C., Tolk, B., 2012. Monitoring gradual change using Landsat time series analyses: case studies in selected forest and rangeland ecosystems. Remote Sensing of Environment 122, 92-105.
47. Wold, S., Sjöström, M., Eriksson, L., 2001. PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 58, 109-130.
48. Xie, Y., Sha, Z., Yu, M., 2008. Remote sensing imagery in vegetation mapping: a review. Journal of Plant Ecology 1, 9-23.
49. Yamaguchi, Y., Kahle, A.B., Tsu, H., Kawakami, T., Pniel, M., 1998. Overview of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). IEEE Transactions on Geoscience and Remote Sensing 36, 1062-1071.