Abstract
Monitoring mangrove health and distribution requires reliable methods that can be undertaken rapidly and at a resolution that optimises costs and accuracy. The Landsat record has been used for this purpose, but its application has been limited by the capacity to provide accurate results that distinguish mangrove from adjoining communities. The Australian Geoscience Data Cube provides a framework for exploring the Landsat record from 1987 onwards, and as pre-processing has already been undertaken there are efficiencies gained using this resource. Using the Data Cube, we exploited the differential spectral signature of mangrove under high tide and low tide conditions at Darwin Harbour, Australia, a relatively stable mangrove ecosystem, using image composites that combined imagery corresponding to the highest 10% and lowest 10% of tides. By applying the automated RandomForest classification technique, we demonstrate the capacity to accurately determine the extent of mangrove zones. Classification identified five mangrove zones: (1) seaward margin dominated by Sonneratia alba, (2) Rhizophora zone dominated by Rhizophora stylosa, (3) tidal flat dominated by Ceriops tagal, (4) landward salt flat and (5) marginal hinterland. Image composites that included high and low tide images achieved the best outcomes with kappa co-efficient of 0.81 and overall accuracy of 82%.
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Notes
Sun synchronous sensors such as Landsat typically do not observe the full extent of the astronomical tidal range given that the highest and lowest astronomical tides typically occur at dusk and dawn rather than at 10:30 am which is the typical sun synchronous overpass time.
References
Asbridge, E., R. Lucas, C. Ticehurst & P. Bunting, 2016. Mangrove response to environmental change in Australia’s Gulf of Carpentaria. Ecology and Evolution 6: 3523–3539.
Aziz, A. A., S. Phinn & P. Dargusch, 2015. Investigating the decline of ecosystem services in a production mangrove forest using Landsat and object-based image analysis. Estuarine, Coastal and Shelf Science 164: 353–366.
BOM, 2016. Monthly Data Report – May 2016. Commonwealth of Australia, Canberra.
Brocklehurst, P. & B. Edmeades, 1996. Mangrove Survey of Darwin Harbour, Northern Territory, Australia. Technical Report
Brocklehurst, P. & B. Edmeades, 2003. Mangrove Survey of Bynoe Harbour, Northern Territory. Technical Report.
Crase, B., A. Liedloff, P. A. Vesk, M. A. Burgman & B. A. Wintle, 2013. Hydroperiod is the main driver of the spatial pattern of dominance in mangrove communities. Global Ecology and Biogeography 22: 806–817.
Díaz, B. M. & G. A. Blackburn, 2003. Remote sensing of mangrove biophysical properties: evidence from a laboratory simulation of the possible effects of background variation on spectral vegetation indices. International Journal of Remote Sensing 24: 53–73.
Egbert, G. D. & L. Erofeeva, 2010. OTIS – the OSU Tidal Inversion Software. In: Oregon State University. http://volkov.oce.orst.edu/tides/otis.html (Accessed 24 June 2016).
Eva, H. D., A. S. Belward, E. E. De Miranda, C. M. Di Bella, V. Gond, O. Huber, S. Jones, M. Sgrenzaroli & S. Fritz, 2004. A land cover map of South America. Global Change Biology 10: 731–744.
Farnsworth, E. J. & A. M. Ellison, 1997. The global conservation status of mangroves. Ambio, Stockholm.
Friess, D. A. & E. L. Webb, 2014. Variability in mangrove change estimates and implications for the assessment of ecosystem service provision. Global Ecology and Biogeography 23: 715–725.
Giri, C., B. Pengra, Z. Zhu, A. Singh & L. L. Tieszen, 2007. Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000. Estuarine, Coastal and Shelf Science 73: 91–100.
Giri, C., E. Ochieng, L. L. Tieszen, Z. Zhu, A. Singh, T. Loveland, J. Masek & N. Duke, 2011. Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecology and Biogeography 20: 154–159.
Giri, S., A. Mukhopadhyay, S. Hazra, S. Mukherjee, D. Roy, S. Ghosh, T. Ghosh & D. Mitra, 2014. A study on abundance and distribution of mangrove species in Indian Sundarban using remote sensing technique. Journal of Coastal Conservation 18: 359–367.
Gislason, P. O., J. A. Benediktsson & J. R. Sveinsson, 2006. Random forests for land cover classification. Pattern Recognition Letters 27: 294–300.
Guinea, M., 1987. Rapid Creek mangrove regeneration, thirteen years onward. In: A Workshop on Research and Management held in Darwin Australian National University, Darwin.
Ham, J., Y. Chen, M. M. Crawford & J. Ghosh, 2005. Investigation of the random forest framework for classification of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing 43: 492–501.
Hamilton, S. E. & D. Casey, 2016. Creation of a high spatio-temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC-21). Global Ecology and Biogeography 25: 729–738.
Heumann, B. W., 2011. Satellite remote sensing of mangrove forests: recent advances and future opportunities. Progress in Physical Geography 35: 87–108.
Jhonnerie, R., V. P. Siregar, B. Nababan, L. B. Prasetyo & S. Wouthuyzen, 2015. Random forest classification for mangrove land cover mapping using Landsat 5 TM and Alos Palsar imageries. Procedia Environmental Sciences 24: 215–221.
Kamal, M., S. Phinn & K. Johansen, 2015. Object-based approach for multi-scale mangrove composition mapping using multi-resolution image datasets. Remote Sensing 7: 4753–4783.
Kirui, K. B., J. G. Kairo, J. Bosire, K. M. Viergever, S. Rudra, M. Huxham & R. A. Briers, 2013. Mapping of mangrove forest land cover change along the Kenya coastline using Landsat imagery. Ocean & Coastal Management 83: 19–24.
Knudby, A., L. M. Nordlund, G. Palmqvist, K. Wikström, A. Koliji, R. Lindborg & M. Gullström, 2014. Using multiple Landsat scenes in an ensemble classifier reduces classification error in a stable nearshore environment. International Journal of Applied Earth Observation and Geoinformation 28: 90–101.
Kovacs, J. M., J. Wang & M. Blanco-Correa, 2001. Mapping disturbances in a mangrove forest using multi-date Landsat TM imagery. Environmental Management 27: 763–776.
Kuenzer, C., A. Bluemel, S. Gebhardt, T. V. Quoc & S. Dech, 2011. Remote sensing of mangrove ecosystems: a review. Remote Sensing 3: 878.
Leon, J. & C. D. Woodroffe, 2011. Improving the synoptic mapping of coral reef geomorphology using object-based image analysis. International Journal of Geographical Information Science 25: 949–969.
Lewis, A., L. Lymburner, M. B. J. Purss, B. Brooke, B. Evans, A. Ip, A. G. Dekker, J. R. Irons, S. Minchin, N. Mueller, S. Oliver, D. Roberts, B. Ryan, M. Thankappan, R. Woodcock & L. Wyborn, 2016. Rapid, high-resolution detection of environmental change over continental scales from satellite data – the earth observation Data Cube. International Journal of Digital Earth 9: 106–111.
Li, F., D. L. B. Jupp, M. Thankappan, L. Lymburner, N. Mueller, A. Lewis & A. Held, 2012. A physics-based atmospheric and BRDF correction for Landsat data over mountainous terrain. Remote Sensing of Environment 124: 756–770.
Li, M. S., L. Mao, W. Shen, S. Liu & A. Wei, 2013. Change and fragmentation trends of Zhanjiang mangrove forests in southern China using multi-temporal Landsat imagery (1977–2010). Estuarine, Coastal and Shelf Science 130: 111–120.
Liu, K., X. Li, X. Shi & S. Wang, 2008. Monitoring mangrove forest changes using remote sensing and GIS data with decision-tree learning. Wetlands 28: 336–346.
Long, J. B. & C. Giri, 2011. Mapping the Philippines’ mangrove forests using Landsat imagery. Sensors 11: 2972–2981.
Loveland, T. R. & J. L. Dwyer, 2012. Landsat: building a strong future. Remote Sensing of Environment 122: 22–29.
Luo, Y., M. Liao, J. Yan, C. Zhang & S. Shang, 2013. Development and demonstration of an artificial immune algorithm for mangrove mapping using landsat TM. IEEE Geoscience and Remote Sensing Letters 10: 751–755.
Manson, F. J., N. R. Loneragan, I. M. McLeod & R. A. Kenyon, 2001. Assessing techniques for estimating the extent of mangroves: topographic maps, aerial photographs and Landsat TM images. Marine and Freshwater Research 52: 787–792.
Mas, J. F., 2004. Mapping land use/cover in a tropical coastal area using satellite sensor data, GIS and artificial neural networks. Estuarine, Coastal and Shelf Science 59: 219–230.
Nascimento, W. R. J., P. W. M. Souza-Filho, C. Proisy, R. M. Lucas & A. Rosenqvist, 2013. Mapping changes in the largest continuous Amazonian mangrove belt using object-based classification of multisensor satellite imagery. Estuarine, Coastal and Shelf Science 117: 83–93.
Odum, W. E. & R. Johannes, 1975. The response of mangroves to man-induced environmental stress. Elsevier Oceanography Series 12: 52–62.
Pal, M., 2005. Random forest classifier for remote sensing classification. International Journal of Remote Sensing 26: 217–222.
Rogers, K., P. I. Boon, S. Branigan, N. C. Duke, C. D. Field, J. A. Fitzsimons, H. Kirkman, J. R. Mackenzie & N. Saintilan, 2016. The state of legislation and policy protecting Australia’s mangrove and salt marsh and their ecosystem services. Marine Policy 72: 139–155.
Saintilan, N., N. C. Wilson, K. Rogers, A. Rajkaran & K. W. Krauss, 2014. Mangrove expansion and salt marsh decline at mangrove poleward limits. Global Change Biology 20: 147–157.
Semeniuk, V., 1985. Mangrove environments of Port Darwin, Northern Territory: the physical framework and habitats. Journal of the Royal Society of Western Australia 67: 81–97.
Sixsmith, J., S. Oliver & L. Lymburner, A hybrid approach to automated landsat pixel quality. In: 2013 IEEE International Geoscience and Remote Sensing Symposium – IGARSS, 21–26 July 2013: 4146–4149.
Son, N. T., C. F. Chen, N. B. Chang, C. R. Chen, L. Y. Chang & B. X. Thanh, 2015. mangrove mapping and change detection in Ca Mau Peninsula, Vietnam, using Landsat data and object-based image analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8: 503–510.
Spalding, M. D., M. Kainuma & L. Collins, 2010. World Atlas of Mangroves. Earthscan, London/Washington DC.
Wightman, G. M., 1989. Mangroves of the Northern Territory. Northern Territory Botanical Bulletin 7: 1–130.
Williams, D., E. Wolanski & S. Spagnol, 2006. Hydrodynamics of Darwin Harbour. In Wolanski, E. (ed.), The Environment in Asia Pacific Harbours. Springer, Dordrecht: 461–476.
Woodroffe, C. D., 1995. Response of tide-dominated mangrove shorelines in Northern Australia to anticipated sea-level rise. Earth Surface Processes and Landforms 20: 65–85.
Woodroffe, C. & K. Bardsley, 1987. The distribution and productivity of mangroves in Creek H, Darwin Harbour. In Larson, H., M. Michie & J. Hanley (eds) Proceedings of a Workshop on Research and Management held in Darwin, 2–3 September 1987. North Australia Research Unit, Australian National University, Darwin: 81–121.
Acknowledgements
The authors thank the University of Wollongong Global Challenges Program for financial support. KR receives funding from the Australian Research Council (FT130100532). The authors also acknowledge the work undertaken by USGS and NASA to collect, collate, archive and distribute Landsat data. This paper is published with the permission of the CEO, Geoscience Australia.
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Guest editors: K. W. Krauss, I. C. Feller, D. A. Friess, R. R. Lewis III / Causes and Consequences of Mangrove Ecosystem Responses to an Ever-Changing Climate
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Rogers, K., Lymburner, L., Salum, R. et al. Mapping of mangrove extent and zonation using high and low tide composites of Landsat data. Hydrobiologia 803, 49–68 (2017). https://doi.org/10.1007/s10750-017-3257-5
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DOI: https://doi.org/10.1007/s10750-017-3257-5