Discrimination of Mangrove Ecosystem Objects on the Visible Spectrum Using Spectroradiometer HR-1024

Amal Arfan(1*)

(1) Jurusan Geografi, FMIPA Universitas Negeri Makassar
(*) Corresponding Author

Abstract

The study was conducted to determine whether the vegetation in the mangrove ecosystem, can be contrasted with another objectt, using Spectroradiometer HR-1024. The data used is data visible spectrum(400-700 nm)  which resulted in 204 bands. The analysis used is the integrated analysis with three levels. First, using ANOVA to determine significant differences in spectral reflectance between vegetation with water, wet soil and dry soil. Second, using Step wise Canonical Discriminant Analysis to identify the most sensitive band for discrimination reflection spectrum. This analysis which resulted in six bands are considered practical to distinguish vegetation with another object namely  401.5 nm, 416.9 nm, 508.2 nm, 599.3 nm, 660.3nm and 689.2 nm. Third using the Jeffries-Matusita separability index which resulted in the separation index of mangrove vegetation, water, wet soil and dry soil is 1.414.

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References

Adam, E., & Muntanga, O. 2009. Spectral discrimination of papyrus vegetation (Cyperuspapyrus L.) in swamp wetlands using field spectrometry. ISPRS Journal Photogrammetry and Remote Sensing ,64 (2009), 612-620.

Ajithkumar, T.T., Thangaradjou, T., & Kannan, L. 2008.Spectral reflectance properties of mangrove species of the Muthupettai mangrove environment, Tamil Nadu.Journal of Environmental Biology, 29(5), 785-788.

Al, C,. Liao NF., Tian LX., Shi YX., & Li, B.J. 2010. Identification of cucumber disease using hyperspectral imaging and discriminate analysis. Guang Pu Xue Yu Guang Pu Fen X, 30(5), 1357-61.

Alongi, D.M. 2002. Present state and future of the world’s mangrove forests. Environ. Conserv, 29, 331-349.

Aschbacher, J., Tiangco, P., Giri, C.P., Ofren, R.S., Paudyal, D.R., & Ang, Y.K. 1995. Comparison of different sensors and analysis techniques for tropical mangrove forest mapping.In: Proceedings of the International Conference IGARSS, 2109-2111.

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 (1), 54-67.

Berlanga-Robles, C.A., & Ruiz-Luna, A. 2002. Land use mapping and change detection in the coastal zone of northwest Mexico using remote sensing techniques. Journal of Coastal Research, 18, 514-522.

Brown, K. 2004. Increasing classification accuracy of coastal habitats using integrated airborne remote sensing. EAR SeL Proceedings, 3 (1), 34-42.

Chun, B. B., Jafri, M.Z.M., & San, L.H. 2011. Reflectance Characteristic of Certain MangroveSpecies at Matang Mangrove Forest Reserve,Malaysia. Proceeding of the 2011 IEEE International Conference on Space Science and Communication (IconSpace) 12-13 July 2011, Penang, Malaysia.

Danoedoro, P. 2012. Pengantar Penginderaan Jauh Digital.Yogyakarta.

Enrica, B., Monica, C., Sergio, F., Lorenza, M., Sonia, S., Alessandro, M., & Marco, M. 2006. Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing. Remote Sensing of Environment, 105 (1), 54-67.

ERDAS Field Guide.2005. Leica Ecosystems Geospatial Imaging.LLC.

Gao, J. 1999. A comparative study on spatial and spectral resolutions of satellite data in mapping mangrove forests.International Journal of Remote Sensing , 2823-2833.

Green, E.P., Clark, C.D., & Edwards, A.J. 2000.Image classification and habitat mapping. In: Remote Sensing Handbook for Tropical Coastal Management. UNESCO, Paris, 141-154.

Hogart, P.J. 1999. The Biology of Mangroves. Oxford University Press, New York

Howari, F. M., Jordan, B. R., Bouhounce, N., & Wyllie-Echeverria, S. 2009. Field and remote-sensing assessment of mangrove forests and seagrass beds in the Northwestern Part of the United Arab Emirates.Journal of Coastal Research, 25(1), 48-56.

Ismail, R., Mutanga, O., & Ahmed, F. 2007. Discriminating Sirex noctilio attack in pine forest plantations in South Africa using high spectral resolution data. In: Kalacska, M., Sanchez Azofeifa, A. (Eds.), Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests. Taylor and Francis, CRC Press, Routledge, USA, 350.

Kamaruzaman, J., & Kasawani, I. 2007. Imaging spectrometry on mangrove species identification and mapping in Malaysia. Wseas Transctions on Biology and Biomedicine, 8 (4) , 118-126.

Klecka, W. R. 1980. Discriminant analysis. Beverly Hills, CA: Sage.

Lillesand, T.M., & Kiefer, R.W. 1970. Remote Sensing and Image Interpretation.Wiley, Chichester.

Liu, K., & Li, X. 2008. Monitoring mangrove forest changes using remote sensing and GIS data with decision-tree learning wetlands. The Society of Wetland Scientists, 28(2), 336–346.

Manson, F.J., Loneragan, N.R., & Phinn, S.R. 2005. Spatial and temporal variation in distribution of mangroves in Moreton Bay, subtropical Australia: a comparison of pattern metrics and change detection analysesbased on aerial photographs. Estuarine, Coastal and Shelf Science, 57, 653-666.

Murray, M. R., Zisman, S. A., Furley, P. A., Munro, D. M., Gibson, J., & Ratter, J.2003.The mangroves of Belize Part 1.Distribution, composition and classification.Forest Ecology and Management, 174(1-3), 265–279.

Nagelkerken, I., Blaber, S. J. M., Bouillon, S., Green, P., Haywood, M., & Kirton, L. G. 2008. The habitat function of mangroves for terrestrial and marine fauna: A review. Aquatic Botany,89, 155–185.

Pengra, B.W., Johnston, C.A., & Loveland, T.R. 2007. Mapping an invasive plant, Phragmites australis, in coastal wetlands using the EO-1 Hyperion hyperspectral sensor. Remote Sensing of Environment,108 (1), 74-81.

Ramsey, E.W., & Jensen, J.R. 1996. Remote sensing of mangrove wetlands: relating canopy spectra to site-specific data. Photogrammetric Engineering and Remote Sensing,62, 939-948.

Richards, J.A. 1993. Remote Sensing Digital Image Analysis: An Introduction. Springer-Verlag, Berlin.

Rosso, P.H., Ustin, S.L., & Hastings, A. 2005.Mapping marshland vegetation of San Francisco Bay, California, using hyperspectral data.International Journal of Remote Sensing, 26 (23), 5169-5191.

Schmidt, K.S., & Skidmore, A.K. 2003. Spectral discrimination of vegetation types in a coastal wetland. Remote Sensing of Environment , 85 (1), 92-108.

Sheridan, P., & Hays, C. 2003. Are mangroves nursery habitat for transient fishes and decapods Wetlands, 23(2), 449–458.

Spalding, M.D. 1998. Patterns of biodiversity in coral reefs and mangroves: global and local scales.Ph.D.Thesis. University of Cambridge, U.K.

Sulong, I., Mohd-Lokman, H., Mohd-Tarmizi, K., & Ismail, A. 2002. Mangrove mapping using Landsat imagery and aerial photographs: Kemaman District, Terengganu. Malaysia Environment, Development and Sustainability, 4, 135-152.

Thenkabail, P.S., Eden A., Enclona, E.A., Mark, S., Ashton, M.S., & Van Der Meer, B. 2004. Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications.Remote Sensing of Environment,91, 345 – 376.

Vaiphasa, C., Ongsomwang, S., Vaiphasa, T., Skidmore, A.K. 2005. Tropical mangrove species discrimination using hyperspectral data: A laboratory study. Estuarine, Coastal, and Shelf Science, 65 (1-2), 371-379.

Van Aardt, J.A.N., & R.H. Wynne. 2007. Examining pine separability using hyperspectral data from an airborne sensor: An extension of field-based results. International Journal of Remote Sensing, 28, 431-436.

Walters, B.B., Ronnback, P., Kovacs, J.M., Crona, B., Hussain, S.A., Badola, R., Primavera, J.H., Barbier, E., & Dahdouh-Guebas, F. 2008. Ethnobiology, socio-economics and management of mangrove forests: A review. Aquat. Bot, 89, 220-236.

Zhao, G., & Maclean, A.L. 2000. A comparison of Canonical Discriminant Analysis and Principal Compenent Analysis for Spectral Transformation. Photogrammetric Engeneering & Remote Sensing, 66, 841 – 847.

DOI: 10.23917/forgeo.v29i1.794

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