Hyperspectral Mapping Methods for Differentiating Mangrove Species along Karachi Coast

Authors: Sher Muhammad, Mirza Muhammad Waqar

Abstract:

It is necessary to monitor and identify mangroves types and spatial extent near coastal areas because it plays an important role in coastal ecosystem and environmental protection. This research aims at identifying and mapping mangroves types along Karachi coast ranging from 24.79⁰ to 24.85⁰ in latitude and 66.91⁰ to 66.97⁰ in longitude using hyperspectral remote sensing data and techniques. Image acquired during February, 2012 through Hyperion sensor have been used for this research. Image pre processing includes geometric and radiometric correction followed by Minimum Noise Fraction (MNF) and Pixel Purity Index (PPI). The output of MNF and PPI has been analyzed by visualizing it in n-dimensions for end member extraction. Well distributed clusters on the n-dimensional scatter plot have been selected with the region of interest (ROI) tool as end members. These end members have been used as an input for classification techniques applied to identify and map mangroves species including Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF) and Spectral Information Diversion (SID). Only two types of mangroves namely Avicennia Marina (White Mangroves) and Avicennia germinans (Black Mangroves) have been observed throughout the study area.

Keywords: Mangrove, Hyperspectral, SAM, SFF, SID.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1336969

References:

[1] M. Kamal and S. Phinn, "Hyperspectral data for mangrove species mapping: A comparison of pixel-based and object-based approach," Remote Sensing, vol. 3, pp. 2222-2242, 2011.
[2] C. Yang, et al., "Evaluating AISA+hyperspectral imagery for mapping black mangrove along the South Texas Gulf Coast," Photogrammetric Engineering and Remote Sensing, vol. 75, pp. 425-435, 2009.
[3] N. Marshall, "Mangrove conservation in relation to overall environmental considerations," in Ecology and Conservation of Southeast Asian Marine and Freshwater Environments including Wetlands, ed: Springer, pp. 303-309, 1994.
[4] C. Vaiphasa, Remote sensing techniques for mangrove mapping: Citeseer, 2006.
[5] E. P. Green, et al., "Remote sensing techniques for mangrove mapping," International Journal of Remote Sensing, vol. 19, pp. 935-956, 1998.
[6] M. Demuro and L. Chisholm, "Assessment of Hyperion for characterizing mangrove communities," in Proceedings of the International Conference the AVIRIS 2003 Workshop, pp. 18-23, 2003.
[7] A. Held, et al., "High resolution mapping of tropical mangrove ecosystems using hyperspectral and radar remote sensing," International Journal of Remote Sensing, vol. 24, pp. 2739-2759, 2003.
[8] J. Kairo, et al., "Application of remote sensing and GIS in the management of mangrove forests within and adjacent to Kiunga Marine Protected Area, Lamu, Kenya," Environment, Development and Sustainability, vol. 4, pp. 153-166, 2002.
[9] C. Kuenzer, et al., "Remote sensing of mangrove ecosystems: a review," Remote Sensing, vol. 3, pp. 878-928, 2011.
[10] E. Green, et al., "The assessment of mangrove areas using high resolution multispectral airborne imagery," Journal of Coastal Research, pp. 433-443, 1998.
[11] M. A. Folkman, et al., "EO-1/Hyperion hyperspectral imager design, development, characterization, and calibration," in Second International Asia-Pacific Symposium on Remote Sensing of the Atmosphere, Environment, and Space, pp. 40-51, 2001.
[12] J. B. Adams and A. R. Gillespie, Remote sensing of landscapes with spectral images: A physical modeling approach: Cambridge University Press, 2006.
[13] J. W. Boardman, "Analysis, understanding, and visualization of hyperspectral data as convex sets in n space," in SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, pp. 14-22, 1995.
[14] J. W. Boardman, et al., "Mapping target signatures via partial unmixing of AVIRIS data," 1995.