Mapping mangrove forests based on multi-tidal high-resolution satellite imagery

Remote Sensing

Volumn 10, Issue 9, 2018, Pages

  Xia, Qing ^a   Qin, Cheng Zhi ^a,b,c   Li, He ^a   Huang, Chong ^a   Su, Fen Zhen ^a  

^a INSTITUTE OF GEOGRAPHIC SCIENCES AND NATURAL RESOURCES RESEARCH   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^c NANJING NORMAL UNIVERSITY   (China)

Author keywords

High resolution satellite imagery; Mangrove forest mapping; Spectral signature; SVM classifier; Tide

Indexed keywords

CONSERVATION; ECOSYSTEMS; FORESTRY; PHOTOMAPPING; REMOTE SENSING; SUPPORT VECTOR MACHINES; TIDES;

GEOGRAPHICAL FEATURES; HIGH RESOLUTION IMAGERY; HIGH RESOLUTION SATELLITE IMAGERY; HIGH RESOLUTION SATELLITES; MANGROVE FOREST; REMOTE SENSING IMAGERY; SPECTRAL SIGNATURE; SVM CLASSIFIERS;

SATELLITE IMAGERY;

EID: 85053606429     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs10091343     Document Type: Article

References (82)

1. Giri, C.; Long, J.; Tieszen, L. Mapping and Monitoring Louisiana's Mangroves in the Aftermath of the 2010 Gulf of Mexico Oil Spill. J. Coast. Res. 2011, 277, 1059-1064
2. Zhang, X.; Treitz, P.; Chen, D.; Quan, C.; Shi, L.; Li, X. Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree based procedure. Int. J. Appl. Earth Obs. Geoinf. 2017, 62, 201-214
3. Jia, M.; Wang, Z.; Zhang, Y.; Ren, C.; Song, K. Landsat-based estimation of mangrove forest loss and restoration in Guangxi province, China, influenced by human and natural factors. IEEE J. Sel. Top. Appl. Earth Obs. 2015, 8, 311-323
4. Vo, Q.; Oppelt, N.; Leinenkugel, P.; Kuenzer, C. Remote sensing in mapping mangrove ecosystems-An object-based approach. Remote Sens. 2013, 5, 183-201
5. Kuenzer, C.; Bluemel, A.; Gebhardt, S.; Quoc, T.; Dech, S. Remote Sensing of Mangrove Ecosystems: A Review. Remote Sens. 2011, 3, 878-928
6. Walters, B.; Ronnback, P.; Kovacs, J.; Crona, B.; Hussain, S.; Badola, R.; Primavera, J.; Barbier, E.; Dahdouh-Guebas, F. Ethnobiology, socio-economics and management of mangrove forests: A review. Aquat. Bot. 2008, 89, 220-236
7. Abd-El Monsef, H.; Smith, S. A new approach for estimating mangrove canopy cover using Landsat 8 imagery. Comput. Electron. Agric. 2017, 135, 183-194
8. Jia, M.; Wang, Z.; Li, L.; Song, K.; Ren, C.; Liu, B.; Mao, D. Mapping China's mangroves based on an object-oriented classification of Landsat imagery. Wetlands 2014, 34, 277-283
9. Giri, C.; Zhu, Z.; Tieszen, L.; Singh, A.; Gillette, S.; Kelmelis, J. Mangrove forest distributions and dynamics (1975-2005) of the tsunami-affected region of Asia. J. Biogeogr. 2008, 35, 519-528
10. Lovelock, C.; Cahoon, D.; Friess, D.; Guntenspergen, G.; Krauss, K.; Reef, R.; Rogers, K.; Saunders, M.; Sidik, F.; Swales, A.; et al. The vulnerability of Indo-Pacific mangrove forests to sea-level rise. Nature 2015, 526, 559-563
11. Kirui, K.; Kairo, J.; Bosire, J.; Viergever, K.; Rudra, S.; Huxham, M.; Briers, R. Mapping of mangrove forest land cover change along the Kenya coastline using Landsat imagery. Ocean Coast. Manag. 2013, 83, 19-24
12. Long, J.; Giri, C. Mapping the Philippines' mangrove forests using Landsat imagery. Sensors 2011, 11, 2972-2981
13. Everitt, J.; Yang, C.; Sriharan, S.; Judd, F. Using High Resolution Satellite Imagery to Map Black Mangrove on the Texas Gulf Coast. J. Coast. Res. 2008, 246, 1582-1586
14. Wang, T.; Zhang, H.; Lin, H.; Fang, C. Textural-Spectral Feature-Based Species Classification of Mangroves in Mai Po Nature Reserve fromWorldview-3 Imagery. Remote Sens. 2016, 8, 24
15. Xun, L.; Wang, L. An object-based SVM method incorporating optimal segmentation scale estimation using Bhattacharyya Distance for mapping salt cedar (Tamarisk spp.) with QuickBird imagery. GISci. Remote Sens. 2015, 52, 257-273
16. Heenkenda, M.; Joyce, K.; Maier, S.; Bartolo, R. Mangrove species identification: ComparingWorldView-2 with aerial photographs. Remote Sens. 2014, 6, 6064-6088
17. Kux, H.; Souza, U. Object-based image analysis of WORLDVIEW-2 satellite data for the classification of mangrove areas in the city of São Luís, Maranhão State, Brazil. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2012, 4, 95-100
18. Malinverni, E.; Tassetti, A.; Mancini, A.; Zingaretti, P.; Frontoni, E.; Bernardini, A. Hybrid object-based approach for land use/land cover mapping using high spatial resolution imagery. Int. J. Geogr. Inf. Sci. 2011, 25, 1025-1043
19. Manson, F.; Loneragan, N.; Mcleod, I.; Kenyon, R. Assessing techniques for estimating the extent of mangroves: Topographic maps, aerial photographs and Landsat TM images. Mar. Freshw. Res. 2001, 52, 787-792
20. Heumann, B. Satellite remote sensing of mangrove forests: Recent advances and future opportunities. Prog. Phys. Geogr. 2011, 35, 87-108
21. Mitra, D.; Karmaker, S. Mangrove Classification in Sundarban using High Resolution Multi-Spectral Remote Sensing Data and GIS. Asian J. Environ. Disaster Manag. 2010, 2, 197
22. Neukermans, G.; Dahdouh-Guebas, F.; Kairo, J.; Koedam, N. Mangrove species and stand mapping in Gazi Bay (Kenya) using Quickbird satellite imagery. J. Spat. Sci. 2008, 53, 75-86
23. Green, E.; Clark, C.; Mumby, P.; Edwards, A.; Ellis, A. Remote sensing techniques for mangrove mapping. Int. J. Remote Sens. 1998, 19, 935-956
24. Giri, C.; Pengra, P.; Zhu, Z.; Singh, A.; Tieszen, L. Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000. Est. Coast. Shelf Sci. 2007, 73, 91-100
25. Everitt, J.; Judd, F.; Escobar, D.; Davis, M. Integration of remote sensing and spatial information technologies for mapping black mangrove on the Texas gulf coast. J. Coast. Res. 1996, 12, 64-69
26. Li, M.; Lee, S. Mangroves of China: A brief review. For. Ecol. Manag. 1997, 96, 241-259
27. Alongi, D. Present state and future of the world's mangrove forests. Environ. Conserv. 2002, 29, 331-349
28. Hernández Cornejo, R.; Koedam, N.; Ruiz Luna, A.; Troell, M.; Dahdouh-Guebas, F. Remote sensing and ethnobotanical assessment of the mangrove forest changes in the Navachiste-San Ignacio-Macapule lagoon complex, Sinaloa, Mexico. Ecol. Soc. 2005, 10, 16
29. Helmer, E.; Kennaway, T.; Pedreros, D. Land cover and forest formation distributions for St. Kitts, Nevis, St. Eustatius, Grenada and Barbados from decision tree classification of cloud-cleared satellite imagery. Caribb. J. Sci. 2008, 44, 175-198
30. Pal, M.; Mather, P. Support vector machines for classification in remote sensing. Int. J. Remote Sens. 2005, 26, 1007-1011
31. Yu, X.; Shao, H.; Liu, X.; Zhao, D. Applying Neural Network Classification to Obtain Mangrove Landscape Characteristics for Monitoring the Travel Environment Quality on the Beihai Coast of Guangxi, PR China. CLEAN-Soil Air Water 2010, 38, 289-295
32. Wang, L.; Silván-Cárdenas, J.; Sousa, W. Neural network classification of mangrove species from multi-seasonal Ikonos imagery. Photogram. Eng. Rem. Sens. 2008, 74, 921-927
33. Gao, J.; Chen, H.; Zhang, Y.; Zha, Y. Knowledge-based approaches to accurate mapping of mangroves from satellite data. Photogram. Eng. Remote Sens. 2004, 70, 1241-1248
34. Foody, G.; Mathur, A. The use of small training sets containing mixed pixels for accurate hard image classification: Training on mixed spectral responses for classification by a SVM. Remote Sens. Environ. 2006, 103, 179-189
35. Xin, H.; Zhang, L.;Wang, L. Evaluation of Morphological Texture Features for Mangrove Forest Mapping and Species Discrimination Using Multispectral IKONOS Imagery. IEEE Geosci. Remote Sens. 2009, 6, 393-397
36. Li, H.; Fu, H.; Han, Y.; Yang, J. Object-oriented classification of high-resolution remote sensing imagery based on an improved colour structure code and a support vector machine. Int. J. Remote Sens. 2010, 31, 1453-1470
37. Harken, J.; Sugumaran, R. Classification of Iowa Wetlands Using an Airborne Hyperspectral Image: A Comparison of the Spectral Angle Mapper Classifier and an Object-Oriented Approach. Can. J. Remote Sens. 2005, 31, 167-174
38. Kumar, L.; Sinha, P. Mapping Salt-Marsh Land-Cover Vegetation Using High-Spatial and Hyperspectral Satellite Data to Assist Wetland Inventory. GISci. Remote Sens. 2014, 51, 483-497
39. Kamal, M.; Phinn, S. Hyperspectral data for mangrove species mapping: A comparison of pixel-based and object-based approach. Remote Sens. 2011, 3, 2222-2242
40. Kanniah, K.;Wai, N.; Shin, A.; Rasib, A. Per-pixel and sub-pixel classifications of high-resolution satellite data for mangrove species mapping. Appl. GIS 2007, 3, 1-22
41. Wang, L.; Sousa, W.; Gong, P. Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. Int. J. Remote Sens. 2004, 25, 5655-5668
42. Genelett, D.; Gorte, B. A method for object-oriented land cover classification combining Landsat TM data and aerial photographs. Int. J. Remote Sens. 2003, 24, 1273-1286
43. Conchedda, G.; Durieux, L.; Mayaux, P. An object-based method for mapping and change analysis in mangrove ecosystems. ISPRS J. Photogram. Remote Sens. 2008, 63, 578-589
44. Flores De Santiago, F.; Kovacs, J.; Lafrance, P. An object-oriented classification method for mapping mangroves in Guinea, West Africa, using multipolarized ALOS PALSAR L-band data. Int. J. Remote Sens. 2013, 34, 563-586
45. Li, M.; Mao, L.; Shen, W.; Liu, S.; Wei, A. Change and fragmentation trends of Zhanjiang mangrove forests in southern China using multi-temporal Landsat imagery (1977-2010). Estuar. Coast. Shelf Sci. 2013, 130, 111-120
46. Rogers, K.; Lymburner, L.; Salum, R.; Brooke, B.;Woodroffe, C. Mapping of mangrove extent and zonation using high and low tide composites of Landsat data. Hydrobiologia 2017, 803, 49-68
47. Collins, D.; Avdis, A.; Allison, P.; Johnson, H.; Hill, J.; Piggott, M.; Hassan, M.; Damit, A. Tidal dynamics and mangrove carbon sequestration during the Oligo-Miocene in the South China Sea. Nat. Commun. 2017, 8, 15698
48. Li, S.; Tian, Q.; Yu, T.; Gu, X. The extraction of mangrove within intertidal zone based on multi-temporal HJ CCD images. In Proceedings of the 17th China Conference on Remote Sensing, Beijing, China, 27-31 August 2010
49. Satyanarayana, B.; Mohamad, K.; Idris, I.; Husain, M.; Dahdouh-Guebas, F. Assessment of mangrove vegetation based on remote sensing and ground-truth measurements at Tumpat, Kelantan Delta, East Coast of Peninsular Malaysia. Int. J. Remote Sens. 2011, 32, 1635-1650
50. Nardin, W.; Locatelli, S.; Pasquarella, V.; Rulli, M.; Woodcock, C.; Fagherazzi, S. Dynamics of a fringe mangrove forest detected by Landsat images in the Mekong River Delta, Vietnam. Earth Surf. Proc. Landf. 2016, 41, 2024-2037
51. Giri, S.; Mukhopadhyay, A.; Hazra, S.; Mukherjee, S.; Roy, D.; Ghosh, S.; Ghosh, T.; Mitra, D. A study on abundance and distribution of mangrove species in Indian Sundarban using remote sensing technique. J. Coast. Conserv. 2014, 18, 359-367
52. Huang, X.; Liu, X.; Zhang, L. A multichannel gray level co-occurrence matrix for multi/hyperspectral image texture representation. Remote Sens. 2014, 6, 8424-8445
53. Dian, Y.; Li, Z.; Pang, Y. Spectral and texture features combined for forest tree species classification with airborne hyperspectral imagery. J. Indian Soc. Remote Sens. 2015, 43, 101-107
54. Szantoi, Z.; Escobedo, F.; Abd-Elrahman, A.; Smith, S.; Pearlstine, L. Analyzing fine-scale wetland composition using high resolution imagery and texture features. Int. J. Appl. Earth Observ. 2013, 23, 204-212
55. National Marine Data and Information Service. Tide Tables, 1st ed.; Ocean Press: Beijing, China, 2016 and 2017; pp. 439-450, ISBN 978-7-5027-9129-2
56. Song, C.;Woodcock, C.; Seto, K.; Lenney, M.; Macomber, S. Classification and change detection using Landsat TM data: When and How to correct atmospheric effects? Remote Sens. Environ. 2001, 75, 230-244
57. Sun, W.; Chen, B.; Messinger, D. Nearest-neighbor diffusion-based pan-sharpening algorithm for spectral images. Opt. Eng. 2014, 53, 013107
58. Dorado-Munoz, L.; Messinger, D.; Bove, D. Integrating spatial and spectral information for enhancing spatial features in the Gough map of Great Britain. J. Cult. Herit. 2018. Available online: https://www.sciencedirect. com/science/article/pii/S1296207417307008 (accessed on 9 August 2018)
59. Zhao, J.; Huang, L.; Yang, H.; Zhang, D.; Wu, Z.; Guo, J. Fusion and assessment of high-resolution WorldView-3 satellite imagery using NNDiffuse and Brovey algorithms. In Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 10-15 July 2016
60. Chen, D.; Huang, J.; Jackson, T. Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near-and short-wave infrared bands. Remote Sens. Environ. 2005, 98, 225-236
61. Huang, J.; Chen, D.; Cosh, M. Sub-pixel reflectance unmixing in estimating vegetation water content and dry biomass of corn and soybeans cropland using normalized difference water index (NDWI) from satellites. Int. J. Remote Sens. 2009, 30, 2075-2104
62. Wilson, E.; Sader, S. Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sens. Environ. 2002, 80, 385-396
63. Onojeghuo, A.; Blackburn, G. Mapping reedbed habitats using texture-based classification of QuickBird imagery. Int. J. Remote Sens. 2011, 32, 8121-8138
64. Mhangara, P.; Odindi, J. Potential of texture-based classification in urban landscapes using multispectral aerial photos. S. Afr. J. Sci. 2013, 109, 1-8
65. Kim, M.; Warner, T.; Madden, M.; Atkinson, D. Multi-scale GEOBIA with very high spatial resolution digital aerial imagery: Scale, texture and image objects. Int. J. Remote Sens. 2011, 32, 2825-2850
66. Agüera, F.; Aguilar, F.; Aguilar, M. Using texture analysis to improve per-pixel classification of very high resolution images for mapping plastic greenhouses. ISPRS J. Photogram. Remote Sens. 2008, 63, 635-646
67. Pereira, F.; Kampel, M.; Cunha-Lignon, M. Mapping of mangrove forests on the southern coast of São Paulo, Brazil, using synthetic aperture radar data from ALOS/PALSAR. Remote Sens. Lett. 2012, 3, 567-576
68. Haralick, R.; Shanmugam, K.; Dinstein, I. Textural Features for Image Classification. IEEE Trans. Syst. Man Cybern. 1973, 3, 610-621
69. Tsai, F.; Chou, M. Texture augmented analysis of high resolution satellite imagery in detecting invasive plant species. J. Chin. Inst. Eng. 2006, 29, 581-592
70. Coulibaly, L.; Goïta, K. Evaluation of the potential of various spectral indices and textural features derived from satellite images for surficial deposits mapping. Int. J. Remote Sens. 2006, 27, 4567-4584
71. Shaban, M.; Dikshit, O. Improvement of classification in urban areas by the use of textural features: The case study of Lucknow city, Uttar Pradesh. Int. J. Remote Sens. 2001, 22, 565-593
72. Shafri, H.; Ramle, F. A Comparison of Support Vector Machine and Decision Tree Classifications Using Satellite Data of Langkawi Island. Inf. Tech. J. 2009, 8, 64-70
73. Yang, X. Parameterizing support vector machines for land cover classification. Photogram. Eng. Remote Sens. 2011, 77, 27-37
74. Wahidin, N.; Siregar, V.; Nababan, B.; Jaya, I.; Wouthuyzen, S. Object-based image analysis for coral reef benthic habitat mapping with several classification algorithms. Procedia Environ. Sci. 2015, 24, 222-227
75. Mallinis, G.; Koutsias, N.; Tsakiri-Strati, M.; Karteris, M. Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site. ISPRS J. Photogram. Remote Sens. 2008, 63, 237-250
76. Vapnik, V. The Nature of Statistical Learning Theory, 1st ed.; Springer: New York, NY, USA, 1995; Volume 37, pp. 34-35
77. Myint, S.; Giri, C.; Wang, L.; Zhu, Z.; Gillette, S. Identifying Mangrove Species and Their Surrounding Land Use and Land Cover Classes Using an Object-Oriented Approach with a Lacunarity Spatial Measure. GISci. Remote Sens. 2008, 45, 188-208
78. Kamal, M.; Phinn, S.; Johansen, K. Object-based approach for multi-scale mangrove composition mapping using multi-resolution image datasets. Remote Sens. 2015, 7, 4753-4783
79. Nascimento, W., Jr.; Souza-Filho, P.; Proisy, C.; Lucas, R. Mapping changes in the largest continuous Amazonian mangrove belt using object-based classification of multisensor satellite imagery. Estuar. Coast. Shelf Sci. 2013, 117, 83-93
80. Johansen, K.; Phinn, S.;Witte, C.; Philip, S.; Newton, L. Mapping banana plantations from object-oriented classification of SPOT-5 imagery. Photogram. Eng. Remote Sens. 2009, 75, 1069-1081
81. Myint, S.; Brazel, A.; Grossman-Clarke, S.;Weng, Q. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens. Environ. 2011, 115, 1145-1161
82. Akay, M. Support vector machines combined with feature selection for breast cancer diagnosis. Expert Syst. Appl. 2009, 16, 3240-3247