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Remote Sensing of Mangrove Forests: Current Techniques and Existing Databases

  • Stuart E. Hamilton
  • Gustavo A. Castellanos-Galindo
  • Marco Millones-Mayer
  • Mara Chen
Chapter
Part of the Coastal Research Library book series (COASTALRL, volume 25)

Abstract

This chapter examines the major global remotely sensed mangrove databases that have become accessible since the year 2000. By doing so, we summarize the significant methodological differences between each product and provide a best estimate of post-2000 mangrove cover at the global level. We then review remotely sensed mangrove area findings in-depth for all nations in the Western Hemisphere and Oceania with mangrove holdings in the top 20 nations globally and then summarize the findings for all nations in the Western Hemisphere and Oceania with mangrove holding in the top fifty nations globally. In addition to reporting the mangrove area quantified by each national remotely sensed database, we assess the temporal domain, the spatial resolution, the instruments used, the techniques applied, the validation performed, and the error statistics reported for each national mangrove estimate. We then compare the national remotely sensed mangrove area estimates provided with each other and with the global estimates for each nation and then arrive at a post-2000 best estimate of mangrove cover for each country. Next, we review the common remote sensing techniques and instruments used to map and monitor mangrove forests throughout this chapter. Finally, we assess the future requirements of the mangrove community considering what the remote sensing community can realistically deliver. We find that national remote sensing estimates of mangrove forest area align well with the global remotely sensed measures of mangrove forest area and can, in general, be used with confidence to manage and monitor mangrove forests.

Keywords

Remote sensing Mangrove forests Mangrove forest area Landsat Mangrove 

References

  1. Acosta-Velázquez J, Rodríguez-Zuñiga T, Díaz-Gallegos JR, Cerdeira-Estrada S, Troche-Souza C, Cruz I, Ressl R, Jiménez R (2009) Assessing a nationwide spatial distribution of mangrove forest for Mexico: an analysis with high resolution images. In: 33rd international symposium on remote sensing of environment. Stressa, pp 1–4Google Scholar
  2. Acosta-Velázquez J, Rodríguez-Zúñiga M, Cerdeira-Estrada S, Cruz I, Ressl R, Ascensión M (2007) Los manglares de México: estado actual y establecimiento de un programa de monitoreo a largo plazo: 1a etapa, Informe del proyecto DQ056. CONABIO, Mexico CityGoogle Scholar
  3. Bhattarai B, Giri C (2011) Assessment of mangrove forests in the Pacific region using Landsat imagery. J Appl Remote Sens 5(1):053509-053509-053511CrossRefGoogle Scholar
  4. Binh TNKD, Vromant N, Hung N, Hens L, Boon EK (2005) Land cover changes between 1968 and 2003 in Cai Nuoc, Ca Mau Peninsula, Vietnam. Environ Dev Sustain 7(4):519–536.  https://doi.org/10.1007/s10668-004-6001-zCrossRefGoogle Scholar
  5. Blankespoor B, Dasgupta S, Lange G-M (2016) Mangroves as a protection from storm surges in a changing climate. Ambio 1–14Google Scholar
  6. Bontemps S, Defourny P, Bogaert EV, Arino O, Kalogirou V, Perez JR (2011a) GLOBCOVER 2009-products description and validation reportGoogle Scholar
  7. Bontemps S, Defourny P, Bogaert EV, Arino O, Kalogirou V, Perez JR (2011b) GLOBCOVER 2009: products description and validation report, vol 2.2. European Space Agency, LouvainGoogle Scholar
  8. Calvo MC (2015) Area of forests susceptible to forest management in Costa Rica and estimation of its productive potential. Fondo Nacional de Financiamiento Forestal Forest Monitoring System for REDD+ Costa Rica, Costa RicaGoogle Scholar
  9. Carrasquel G (2013) Venezuelan coastal mangroves are shrinking. Environmental, Ecologists and Conservation News from the Americas. 06/23/2013Google Scholar
  10. Chang S, Green A, Kelley E (2015) A preliminary assessment of the blue carbon capacity of Belizean mangroves with ecological, economic, and policy perspectives. Duke University, DurhamGoogle Scholar
  11. Chauvaud S, Bouchon C, Maniere R (1998) Remote sensing techniques adapted to high resolution mapping of tropical coastal marine ecosystems (coral reefs, seagrass beds and mangrove). Int J Remote Sens 19(18):3625–3639CrossRefGoogle Scholar
  12. Chavez PS (1996) Image-based atmospheric corrections-revisited and improved. Photogramm Eng Remote Sens 62(9):1025–1035Google Scholar
  13. Cherrington EA, Hernandez BE, Trejos NA, Smith OA, Anderson ER, Flores AI, Garcia BC (2010) Identification of threatened and resilient mangroves in the Belize barrier reef system, 1st edn. Water Center for the Humid Tropics of Latin America and the Caribbean (CATHALAC), Panama CityGoogle Scholar
  14. CLIRSEN (2007) Actualizacion Del Estudio Multitemporal de Manglares, Camaroneras Y Areas Salinas En Las Costa Continental Ecuatoriana Al Ano 2006, vol 1. Centro De Levantamientos Integrados De Recursos Naturales Por Sensores Remotos, QuitoGoogle Scholar
  15. Corcoran E, Ravilious C, Skuja M (2007) Mangroves of Western and Central Africa, vol 26. Biodiversity related projects in Africa, vol 26. UNEP/Earthprint, CambridgeGoogle Scholar
  16. Dahdouh-Guebas F, Van Pottelbergh I, Kairo JG, Cannicci S, Koedam N (2004) Human-impacted mangroves in Gazi (Kenya): predicting future vegetation based on retrospective remote sensing, social surveys, and tree distribution. Mar Ecol Prog Ser 272:77–92CrossRefGoogle Scholar
  17. Demuro M, Chisholm L (2003) Assessment of Hyperion for characterizing mangrove communities. In: Proceedings of the 12th JPL AVIRIS airborne earth science workshop, Pasadena, CA, USAGoogle Scholar
  18. Duarte E, Díaz OO, Maradiaga I, Casco F, Fuentes D, Jiménez A, Emanuelli P, Milla F (2014) Mapa Forestal y de Cobertura de la Tierra de Honduras: Análisis de Cifras Nacionales. Integrando Esfuerzos Para Un Buen Manejo De Los Bosques. Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ), Bonn.  https://doi.org/10.13140/RG.2.1.1553.3046CrossRefGoogle Scholar
  19. Everitt J, Escobar D, Judd F (1991) Evaluation of airborne video imagery for distinguishing black mangrove (Avicennia germinans) on the lower Texas Gulf Coast. J Coast Res 7:1169–1173Google Scholar
  20. Florida Water Management Districts (2012) Mangroves Florida. Fish and Wildlife Research Institute (FWRI), St. PetersburgGoogle Scholar
  21. Gallo M (2005) Estado del Conocimiento de la Biodiversidad en El Salvador. Project Developing Capacities and Sharing Technology for the management of biodiversity in Central America. Ministerio de Medio Ambiente y Recursos Naturales, Costa RicaGoogle Scholar
  22. Galvincio JD, Popescu SC (2016) Measuring individual tree height and crown diameter for mangrove trees with airborne lidar data. Int J Adv Eng Manag Sci (IJAEMS) 2(5):431–443Google Scholar
  23. Gao J (1999) A comparative study on spatial and spectral resolutions of satellite data in mapping mangrove forests. Int J Remote Sens 20(14):2823–2833CrossRefGoogle Scholar
  24. Gilbert T, Stys B (2004) Descriptions of vegetation and land cover types mapped using Landsat imagery. Florida Fish and Wildlife Conservation Commission, Tallahassee, p 16Google Scholar
  25. Giri C (2016) Observation and monitoring of mangrove forests using remote sensing: opportunities and challenges. Multidisciplinary Digital Publishing InstituteGoogle Scholar
  26. Giri C, Ochieng E, Tieszen LL, Zhu Z, Singh A, Loveland T, Masek J, Duke N (2011) Status and distribution of mangrove forests of the world using earth observation satellite data. Glob Ecol Biogeogr 20(1):154–159.  https://doi.org/10.1111/j.1466-8238.2010.00584.xCrossRefGoogle Scholar
  27. Green EP, Mumby PJ, Edwards AJ, Clark CD, Ellis AC (1997) Estimating leaf area index of mangroves from satellite data. Aquat Bot 58(1):11–19CrossRefGoogle Scholar
  28. Green EP, Clark CD, Mumby PJ, Edwards AJ, Ellis A (1998) Remote sensing techniques for mangrove mapping. Int J Remote Sens 19(5):935–956CrossRefGoogle Scholar
  29. Hamilton SE (2012) The impact of shrimp farming on mangrove ecosystems and local livelihoods along the Pacific Coast of Ecuador, vol 1. Department of Geography and Geology, Doctoral dissertation, vol 3477166, 1st edn. ProQuest, UMI Dissertation Publishing, HattiesburgGoogle Scholar
  30. Hamilton SE, Lovette J (2015) Ecuador’s mangrove forest carbon stocks: a spatiotemporal analysis of living carbon holdings and their depletion since the advent of commercial aquaculture. PLoS One 10:e0118880CrossRefPubMedPubMedCentralGoogle Scholar
  31. Hamilton SE (2016) Introducing CGMFC-21 (continuous global mangrove forest cover for the 21st century). GLOMIS/ISME 14(3):11–14Google Scholar
  32. Hamilton SE, Casey D (2016) Creation of a high spatio-temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC-21). Glob Ecol Biogeogr 25(6):729–738.  https://doi.org/10.1111/geb.12449CrossRefGoogle Scholar
  33. Hamilton SE, Lovette JP, Borbor-Cordova MJ, Millones M (2017) The carbon holdings of northern Ecuador’s mangrove forests. Ann Am Assoc Geogr 107(1):54–71.  https://doi.org/10.1080/24694452.2016.1226160CrossRefGoogle Scholar
  34. Hansen M, DeFries R, Townshend J, Carroll M, Dimiceli C, Sohlberg R (2003) Global percent tree cover at a spatial resolution of 500 meters: first results of the MODIS vegetation continuous fields algorithm. Earth Interact 7(10):1–15CrossRefGoogle Scholar
  35. Hansen MC, Stehman SV, Potapov PV (2010) Quantification of global gross forest cover loss. Proc Natl Acad Sci 107(19):8650–8655.  https://doi.org/10.1073/pnas.0912668107CrossRefPubMedGoogle Scholar
  36. Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, Thau D, Stehman SV, Goetz SJ, Loveland TR, Kommareddy A, Egorov A, Chini L, Justice CO, Townshend JRG (2013) High-resolution global maps of 21st-century forest cover change. Science 342(6160):850–853.  https://doi.org/10.1126/science.1244693CrossRefPubMedGoogle Scholar
  37. Heumann BW (2011) Satellite remote sensing of mangrove forests: recent advances and future opportunities. Prog Phys Geogr 35(1):87–108CrossRefGoogle Scholar
  38. Hogarth PJ (2015) The biology of mangroves and seagrasses. Oxford University PressGoogle Scholar
  39. Homer CG, Dewitz JA, Yang L, Jin S, Danielson P, Xian G, Coulston J, Herold ND, Wickham J, Megown K (2015) Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogramm Eng Remote Sens 81(5):345–354Google Scholar
  40. Huber O, Oliveira-Miranda MA (2010) Ambientes terrestres de Venezuela. In: Rodríguez JP, Rojas-Suarez F, Giraldo-Hernández D (eds) Libro rojo de los ecosistemas terrestres de Venezuela, (Provita, Shell Venezuela y Lenovo Venezuela, Caracas). Provita, Shell Venezuela, Lenovo, Caracas, pp 29–89Google Scholar
  41. Hutchison J, Spalding M, zu Ermgassen P (2014) The role of mangroves in fisheries enhancement. The Nature Conservancy and Wetlands International, CambridgeGoogle Scholar
  42. Instituto de Hidrologia MyEA, (IDEAM), Instituto Geográfico Agustín Codazzi (IGAC), Instituto de Investigación de Recursos Biológicos Alexander von Humboldt (IAvH), Instituto de Investigaciones Marinas y Costeras José Benito Vives De Andréis (Invemar), Instituto Amazónico de Investigaciones Científicas Sinchi (I. Sinchi), (IIAP) IdIAdPJvN (2007) Ecosistemas Continentales, Costeros y Marinos de Colombia. Imprenta Nacional de Colombia, BogotáGoogle Scholar
  43. INVEMAR (2016) Informe del Estado de los Ambientes y Recursos Marinos y Costeros de Colombia Año 2015, Serie de Publicaciones Periódicas, vol 3, 2nd edn. INVEMAR, Santa MartaGoogle Scholar
  44. Juman R, Ramsewak D (2013) Status of mangrove forests in Trinidad and Tobago, West Indies. Caribb J Sci 47(2–3):291–304CrossRefGoogle Scholar
  45. Jusoff K (2006) Individual mangrove species identification and mapping in Port Klang using airborne hyperspectral imaging. J Sustain Sci Manag 1(2):27–36Google Scholar
  46. Kairo J, Kivyatu B, Koedam N (2002) Application of remote sensing and GIS in the management of mangrove forests within and adjacent to Kiunga Marine Protected Area, Lamu, Kenya. Environ Dev Sustain 4(2):153–166CrossRefGoogle Scholar
  47. Kanniah KD, Wai NS, Shin A, Rasib AW (2007) Per-pixel and sub-pixel classifications of high-resolution satellite data for mangrove species mapping. Appl GIS 3(8):1–22Google Scholar
  48. Kautz R, Stys B, Kawula R (2007) Florida vegetation 2003 and land use change between 1985–89 and 2003. Fla Sci 70(1):12Google Scholar
  49. Kovacs JM, Vandenberg CV, Wang J, Flores-Verdugo F (2008) The use of multipolarized spaceborne SAR backscatter for monitoring the health of a degraded mangrove forest. J Coast Res 24:248–254CrossRefGoogle Scholar
  50. Krause G, Bock M, Weiers S, Braun G (2004) Mapping land-cover and mangrove structures with remote sensing techniques: a contribution to a synoptic GIS in support of coastal management in North Brazil. Environ Manag 34(3):429–440CrossRefGoogle Scholar
  51. Kuenzer C, Bluemel A, Gebhardt S, Quoc TV, Dech S (2011) Remote sensing of mangrove ecosystems: a review. Remote Sens 3(5):878–928CrossRefGoogle Scholar
  52. Lazo R (2017) GIS and remote sensing: diagnosis and evaluation of mangrove areas in Venezuela. Rodrigo Lazo. http://www.rodrigolazo.com/projects/diagnosis-and-evaluation-of-mangrove-areas-in-venezuela/. Accessed 31 May 2017
  53. LINZ (2017) LINZ data service. Land Information New Zealand. https://data.linz.govt.nz/. Accessed 31 May 2017
  54. Lucas RM, Mitchell AL, Rosenqvist A, Proisy C, Melius A, Ticehurst C (2007) The potential of L-band SAR for quantifying mangrove characteristics and change: case studies from the tropics. Aquat Conserv Mar Freshwat Ecosyst 17(3):245–264CrossRefGoogle Scholar
  55. MacLean MG, Congalton RG (2012) Map accuracy assessment issues when using an object-oriented approach. In: Proceedings of the American Society for Photogrammetry and Remote Sensing 2012 annual conference, pp 1–5Google Scholar
  56. Magris RA, Barreto R (2010) Mapping and assessment of protection of mangrove habitats in Brazil. Pan Am J Aquat Sci 5(4):546–556Google Scholar
  57. Martin NM, Maldonado BD, Salinas-de-León P (2016) Inexpensive method to assess Galapagos’ mangrove forests through the use of Google Earth and Open Source GIS. Paper presented at the IUCN World Conservation Congress, Hawaii, USA, 09/03/2016Google Scholar
  58. Menéndez L (2013) El ecosistema de manglar en el archipiélago cubano: bases para su gestión. Universitat d’Alacant, AlicanteGoogle Scholar
  59. Menéndez L, Guzmán JM (2010) Los bosques de mangles del archipiélago cubano, caracterización, distribución y relación con el Cambio Climático. In: Hernández-Zanuy AC, Alcolado PM (eds) La biodiversidad en Ecosistemas Marinos y Costeros del Litoral de Iberoamérica y el Cambio Climático: I, vol 1. RED CYTED BIODIVMAR, Havanna, pp 90–107Google Scholar
  60. Meyer-Arendt KJ, Byrd S, Hamilton SE (2013) Mangrove deforestation in the Dominican Republic, 1969 to 2012. ISME/GLOMIS Electron J 11(1):1–4Google Scholar
  61. Meyrat A (2009) Ubicación, Estado y Valor de las Especies, Formaciones Vegetales y Ecosistemas de Nicaragua: Estudio de Ecosistemas y Formaciones Vegetales de Nicaragua, 1st edn. Ministerio del Ambiente y los Recursos Naturales (MARENA), ManaguaGoogle Scholar
  62. Ministerio de Ambiente y Desarrollo Sostenible de la República de Colombia (2017) Manglares: Los Manglares de Colombia. Ministerio de Ambiente y Desarrollo Sostenible. http://www.minambiente.gov.co/index.php/bosques-biodiversidad-y-servicios-ecosistematicos/ecosistemas-estrategicos/manglares. Accessed 1 May 2017
  63. Montreal Process Implementation Group for Australia, National Forest Inventory Steering Committee (2013) Australia’s state of the forests report: 2013. ABARES, CanberraGoogle Scholar
  64. Mutendeudzi M, Read S, Howell C, Davey S, Clancy T (2013) Improving Australia’s forest area estimate using a ‘Multiple Lines of Evidence’ approach. Australian Bureau of Agricultural and Resource Economics and Sciences, CanberraGoogle Scholar
  65. Nascimento WR, Souza-Filho PWM, Proisy C, Lucas RM, Rosenqvist A (2013) Mapping changes in the largest continuous Amazonian mangrove belt using object-based classification of multisensor satellite imagery. Estuar Coast Shelf Sci 117:83–93CrossRefGoogle Scholar
  66. Oliveira-Miranda MA, Huber O, Rodríguez JP, Rojas-Suarez F, De Oliveira-Miranda R, Hernández-Montilla M, Zambrano-Martínez S, Giraldo-Hernández D (2010) Riesgo de eliminación de los ecosistemas terrestres de Venezuela. In: Rodríguez JP, Rojas-Suarez F, Giraldo-Hernández D (eds) Libro rojo de los ecosistemas terrestres de Venezuela, (Provita, Shell Venezuela y Lenovo Venezuela, Caracas). Provita, Shell Venezuela, Lenovo, Caracas, pp 107–235Google Scholar
  67. Olson DM, Dinerstein E, Wikramanayake ED, Burgess ND, Powell GV, Underwood EC, D'amico JA, Itoua I, Strand HE, Morrison JC (2001) Terrestrial ecoregions of the world: a new map of life on earth a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. Bioscience 51(11):933–938CrossRefGoogle Scholar
  68. ONU-REDD (2015) La superficie boscosa y la tasa de deforestación en Panamá: Insumos para establecer datos oficiales a ser utilizados en las estadísticas nacionales, y para informar a convenciones y procesos internacionales, vol 1. Programa conjunto de las Naciones Unidas para la reducción de emisiones provenientes de deforestación y de degradación de los bosques en Panamá, Panama CityGoogle Scholar
  69. Paul TT, Dennis A, George G (2016) A review of remote sensing techniques for the visualization of mangroves, reefs, fishing grounds, and molluscan settling areas in tropical waters. In: Seafloor mapping along continental shelves. Springer, pp 105–123Google Scholar
  70. Pendleton L, Donato DC, Murray BC, Crooks S, Jenkins WA, Sifleet S, Craft C, Fourqurean JW, Kauffman JB, Marbà N, Megonigal P, Pidgeon E, Herr D, Gordon D, Baldera A (2012) Estimating global “Blue Carbon” emissions from conversion and degradation of vegetated coastal ecosystems. PLoS One 7(9):e43542.  https://doi.org/10.1371/journal.pone.0043542CrossRefPubMedPubMedCentralGoogle Scholar
  71. Pettorelli N, Laurance WF, O’Brien TG, Wegmann M, Nagendra H, Turner W (2014) Satellite remote sensing for applied ecologists: opportunities and challenges. J Appl Ecol 51(4):839–848CrossRefGoogle Scholar
  72. Por el Ministerio de Ambiente y Recursos Naturales (2013) Estudio de la cobertura de mangle en la República de Guatemala. MARN, GuatemalaGoogle Scholar
  73. Proisy C, Mougin E, Fromard F, Karam M (2000) Interpretation of polarimetric radar signatures of mangrove forests. Remote Sens Environ 71(1):56–66CrossRefGoogle Scholar
  74. Ramsey EW, Jensen JR (1996) Remote sensing of mangrove wetlands relating canopy spectra to site-specific data. Photogramm Eng Remote Sens 62(8):939–948Google Scholar
  75. Regional Centre for Mapping of Resources for Development (2015) Mitigating impacts of coastal hazards in mangrove. Ardhi University, TanzaniaGoogle Scholar
  76. Rodríguez JP, Rojas-Suarez F, Giraldo-Hernández D (2010) Libro rojo de los ecosistemas terrestres de Venezuela. Provita, Shell Venezuela, Lenovo, CaracasGoogle Scholar
  77. Rogan J, Franklin J, Stow D, Miller J, Woodcock C, Roberts D (2008) Mapping land-cover modifications over large areas: a comparison of machine learning algorithms. Remote Sens Environ 112(5):2272–2283CrossRefGoogle Scholar
  78. Rosati I, Prosperi P, Latham J, Kainuma M (2008) World atlas of mangroves. In: Sessa R (ed) Terrestrial observations of our planet, GTOS 50, vol 1. Food and Agricultural Organization of the United Nations, Rome, pp 30–31Google Scholar
  79. Ruefenacht B, Benton R, Johnson V, Biswas T, Baker C, Finco M, Megown K, Coulston J, Winterberger K, Riley M (2015) Forest service contributions to The National Land Cover Database (NLCD). In: Stanton SM, Christensen GA (eds) Pushing boundaries: new directions in inventory techniques & applications. Forest Inventory and Analysis (FIA) Symposium 2015, Portland, OR, 12/8/2015. U. S. Department of Agriculture, pp 241–243Google Scholar
  80. Saintilan N, Wilson NC, Rogers K, Rajkaran A, Krauss KW (2014) Mangrove expansion and salt marsh decline at mangrove poleward limits. Glob Chang Biol 20(1):147–157CrossRefPubMedGoogle Scholar
  81. Seto KC, Fragkias M (2007) Mangrove conversion and aquaculture development in Vietnam: a remote sensing-based approach for evaluating the Ramsar Convention on Wetlands. Glob Environ Chang 17(3):486–500CrossRefGoogle Scholar
  82. Shearman PL, Ash J, Mackey B, Bryan JE, Lokes B (2009) Forest conversion and degradation in Papua New Guinea 1972–2002. Biotropica 41(3):379–390CrossRefGoogle Scholar
  83. Siikamäki J, Sanchirico JN, Jardine SL (2012) Global economic potential for reducing carbon dioxide emissions from mangrove loss. Proc Natl Acad Sci (Early Ed).  https://doi.org/10.1073/pnas.1200519109
  84. Spalding M, Kainuma M, Collins L (2010) World atlas of mangroves. Earthscan, LondonCrossRefGoogle Scholar
  85. Valderrama-Landeros LH, Rodríguez-Zúñiga MT, Troche-Souza C, Velázquez-Salazar S, Villeda-Chávez E, Alcántara-Maya JA, Vázquez-Balderas B, Cruz-López MI, Ressl R (2017) Manglares de México: actualización y exploración de los datos del sistema de monitoreo 1970/1980–2015. Comisión Nacional para el Conocimiento y Uso de la Biodiversidad, Mexico CityGoogle Scholar
  86. Verheyden A, Dahdouh-Guebas F, Thomaes K, De Genst W, Hettiarachchi S, Koedam N (2002) High-resolution vegetation data for mangrove research as obtained from aerial photography. Environ Dev Sustain 4(2):113–133CrossRefGoogle Scholar
  87. Virly S (2008) Typologies et biodiversité des mangroves de Nouvelle-Calédonie, Cartographie des mangroves. In: Paper presented at the IFRECOR – French Initiative for Coral Reefs, Nouméa, Nouvelle CalédonieGoogle Scholar
  88. Wang Y, Imhoff M (1993) Simulated and observed L-HH radar backscatter from tropical mangrove forests. Int J Remote Sens 14(15):2819–2828CrossRefGoogle Scholar
  89. Wang Y, Bonynge G, Nugranad J, Traber M, Ngusaru A, Tobey J, Hale L, Bowen R, Makota V (2003) Remote sensing of mangrove change along the Tanzania coast. Mar Geod 26(1–2):35–48CrossRefGoogle Scholar
  90. Wang L, Sousa W, Gong P (2004) Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. Int J Remote Sens 25(24):5655–5668CrossRefGoogle Scholar
  91. Wannasiri W, Nagai M, Honda K, Santitamnont P, Miphokasap P (2013) Extraction of mangrove biophysical parameters using airborne LiDAR. Remote Sens 5(4):1787–1808CrossRefGoogle Scholar
  92. Zhu Z, Waller E (2003) Global forest cover mapping for the United Nations Food and Agriculture Organization forest resources assessment 2000 program. For Sci 49(3):369–380Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Stuart E. Hamilton
    • 1
  • Gustavo A. Castellanos-Galindo
    • 2
  • Marco Millones-Mayer
    • 3
  • Mara Chen
    • 1
  1. 1.Department of Geography and Geosciences, Henson Science HallSalisbury UniversitySalisburyUSA
  2. 2.Leibniz Centre for Tropical Marine Research (ZMT)BremenGermany
  3. 3.Department of GeographyUniversity of Mary WashingtonFredericksburgUSA

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