Mapping of mangrove extent and zonation using high and low tide composites of Landsat data

Hydrobiologia

Volumn 803, Issue 1, 2017, Pages 49-68

  Rogers, Kerrylee ^a   Lymburner, Leo ^b   Salum, Rafaela ^a   Brooke, Brendan P ^b   Woodroffe, Colin D ^a  

^a UNIVERSITY OF WOLLONGONG   (Australia)

^b GEOSCIENCE AUSTRALIA   (Australia)

Author keywords

Data Cube; Image classification; Image composite; Landsat

Indexed keywords

ALGORITHM; ARRAY; IMAGE CLASSIFICATION; INTERTIDAL ENVIRONMENT; LANDSAT; MANGROVE; MAPPING; SATELLITE DATA; SPECTRAL ANALYSIS; TIDE; ZONATION;

AUSTRALIA; DARWIN HARBOUR; NORTHERN TERRITORY;

CERIOPS TAGAL; RHIZOPHORA; RHIZOPHORA STYLOSA; SONNERATIA ALBA;

EID: 85020702339     PISSN: 00188158     EISSN: 15735117     Source Type: Journal    
DOI: 10.1007/s10750-017-3257-5     Document Type: Article

References (48)

1. 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.
2. 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.
3. BOM, 2016. Monthly Data Report – May 2016. Commonwealth of Australia, Canberra.
4. Brocklehurst, P. & B. Edmeades, 1996. Mangrove Survey of Darwin Harbour, Northern Territory, Australia. Technical Report
5. Brocklehurst, P. & B. Edmeades, 2003. Mangrove Survey of Bynoe Harbour, Northern Territory. Technical Report.
6. 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.
7. 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.
8. 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).
9. 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.
10. Farnsworth, E. J. & A. M. Ellison, 1997. The global conservation status of mangroves. Ambio, Stockholm.
11. 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.
12. 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.
13. 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.
14. 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.
15. Gislason, P. O., J. A. Benediktsson & J. R. Sveinsson, 2006. Random forests for land cover classification. Pattern Recognition Letters 27: 294–300.
16. Guinea, M., 1987. Rapid Creek mangrove regeneration, thirteen years onward. In: A Workshop on Research and Management held in Darwin Australian National University, Darwin.
17. 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.
18. 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.
19. Heumann, B. W., 2011. Satellite remote sensing of mangrove forests: recent advances and future opportunities. Progress in Physical Geography 35: 87–108.
20. 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.
21. 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.
22. 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.
23. 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.
24. 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.
25. Kuenzer, C., A. Bluemel, S. Gebhardt, T. V. Quoc & S. Dech, 2011. Remote sensing of mangrove ecosystems: a review. Remote Sensing 3: 878.
26. 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.
27. 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.
28. 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.
29. 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.
30. 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.
31. Long, J. B. & C. Giri, 2011. Mapping the Philippines’ mangrove forests using Landsat imagery. Sensors 11: 2972–2981.
32. Loveland, T. R. & J. L. Dwyer, 2012. Landsat: building a strong future. Remote Sensing of Environment 122: 22–29.
33. 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.
34. 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.
35. 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.
36. 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.
37. Odum, W. E. & R. Johannes, 1975. The response of mangroves to man-induced environmental stress. Elsevier Oceanography Series 12: 52–62.
38. Pal, M., 2005. Random forest classifier for remote sensing classification. International Journal of Remote Sensing 26: 217–222.
39. 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.
40. 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.
41. 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.
42. 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.
43. 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.
44. Spalding, M. D., M. Kainuma & L. Collins, 2010. World Atlas of Mangroves. Earthscan, London/Washington DC.
45. Wightman, G. M., 1989. Mangroves of the Northern Territory. Northern Territory Botanical Bulletin 7: 1–130.
46. 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.
47. 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.
48. 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.