Skip to main content

A Review of Remote Sensing Techniques for the Visualization of Mangroves, Reefs, Fishing Grounds, and Molluscan Settling Areas in Tropical Waters

  • Chapter
  • First Online:
Seafloor Mapping along Continental Shelves

Part of the book series: Coastal Research Library ((COASTALRL,volume 13))

Abstract

Globally there has been tremendous progress in space technology especially in the field of satellite remote sensing applications during the past five decades. Satellite based sensors provide a repetitive and synoptic coverage of inaccessible/larger areas which generated a time series database useful in identification and mapping of environment and resources. These databases form a scientific tool for various stakeholders to device suitable strategies for management of coastal and marine resources. This chapter analyses the various applications of satellite remote sensing and numerical modelling on identification and mapping of mangroves, coral reefs, fishing and molluscan grounds in the coastal marine ecosystems with relevant case studies and illustrations. The mapping methods for mangroves explains the classification protocols, advantages in using different remote sensing techniques and the comparison of different mapping techniques. In case of reef mapping, the vulnerability mapping of reefs due to extreme events is also discussed. Fish movement in a dynamic environment and the mapping of these movements with the help of proxy indicators are also detailed. Molluscan mapping is done based on the biomass differences during different seasons and their physical attributes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Aguilar-Manjarrez J, Kapetsky JM, Soto D (2010) The potential of spatial planning tools to support the ecosystem approach to aquaculture. FAO/Rome Expert Workshop. In: FAO fisheries and aquaculture proceedings, No. 17, 19–21 November 2008. FAO, Rome, p 176

    Google Scholar 

  • Ahern FN, Teckie DG (1987) Digital remote sensing for forestry: requirement and capability. To-day and tomorrow. Geocarto Int 3, pp 43–52

    Google Scholar 

  • Ajithkumar TT (1998) Characterization of aquaculture impact on mangrove environment with special reference to Muthupet mangroves, southeast coast of India. In: Annual progress report of the CSIR, New Delhi, 19 pp

    Google Scholar 

  • Alex H, Ticehurst C, Lymburner L (2003) High resolution mapping of tropical mangrove ecosystems using hyperspectral and radar remote sensing. Int J Remote Sens 24(13):2739–2759

    Article  Google Scholar 

  • Anand A, Krishnan P, Grinson G, Goutham BMP, Kaliyanamoorthy M, Hareef BSK, Suryavanshi AS, Srinivasa KT, Joshi AK (2014) Influence of mesoscale eddies on commercial fishery in the coastal waters of Andaman and Nicobar Islands, India. Int J Remote Sens 35(17):6418–6443

    Article  Google Scholar 

  • Artigas FJ, Yang J (2006) Spectral discrimination of marsh vegetation types in the New Jersey Meadowlands, USA. Wetlands 26:271–277

    Article  Google Scholar 

  • Baghdadi N, Bernier M, Gauthier R (2001) Evaluation of C-band SAR data for wetland mapping. Int J Remote Sens 22:71–88

    Article  Google Scholar 

  • Baker C, Lawrence R, Montagne C, Patten D (2006) Mapping wetlands and riparian areas using landsat ETMþ imagery and decision-tree-based models. Wetlands 26:465–474

    Article  Google Scholar 

  • Barbier EB, Sathiratai S (2004) Shrimp farming and mangrove loss in Thailand. Edward Elgar, Cheltenham

    Book  Google Scholar 

  • Bartheloma A (2006) Acoustic bottom detection and seabed classification in the German Bight, southern North Sea. Geo Mar. Lett 26(3):177–184

    Google Scholar 

  • Bastin J (1988) Measuring areas of coral reefs using satellite imagery. In: Symposium on remote sensing of the coastal zone, Gold Coast Queensland, Vii-1.1- vii 1.9

    Google Scholar 

  • Benediktsson JA, Swain PH, Ersoy OK (1993) Conjugate-gradient neural networks in classification of multisource and very-high-dimensional remote sensing data. Int J Remote Sens 14:2883–2903

    Article  Google Scholar 

  • Blasco F, Janodet E, Bellan MF (1994) Impacts of coastal hazards on mangroves in the Bay of Bengal. J Coast Res 12:277–288

    Google Scholar 

  • Blasco F, Aizpuru M, Gers C (2001) Depletion of the mangroves of continential Asia. Wetl Ecol Manag 9:245–256

    Article  Google Scholar 

  • Bolte D (2011) Mapping oyster reef habitats in mobile bay, NASA USRP- Internship final report. University of Alabama, Huntsville, pp 1–12

    Google Scholar 

  • Borel D (1985) Monitoring of mangrove areas through high resolution remote sensing techniques: the SPOT simulation campaign over Bangladesh. Bakawan 4:6–8

    Google Scholar 

  • Brakel WH (1984) Seasonal dynamics of the suspended sediment plumes from Tano and Sabaki Rivers, Kenya: analysis of coastal imagery. Remote Sens Environ 18:165–173

    Article  Google Scholar 

  • Bruce AD, Jensen JR (1998) Remote sensing of mangrove biophysical characteristics. Geocarto Int 13(4):55–64

    Article  Google Scholar 

  • Bustamante JF, Pacios RD, Aragones D (2009) Predictive models of turbidity and water depth in the Donana Marshes using landsat TM and ETM+ images. J Environ Manage 90:2219–2225

    Article  Google Scholar 

  • Chadwick J (2011) Integrated LiDAR and IKONOS multispectral imagery for mapping mangrove distribution and physical properties. Int J Remote Sens 32(21):6765–6781

    Article  Google Scholar 

  • 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):3625–3639

    Article  Google Scholar 

  • Chaves AB, Lakshumanan C (2008) Remote sensing and GIS based integrated study and analysis of mangrove- wetland restoration in Ennore creek, Chennai, South India. In: Senugupta M, Dalwani R (eds) Proceedings of Taal 2007: The 12th world lake conference, pp 685–690

    Google Scholar 

  • Chen JM, Cihlar J (1996) Retrieving leaf area index of boreal conifer forests using landsat TM images. Remote Sens Environ 55:153–162

    Article  Google Scholar 

  • Choi JK, Oh H, Koo BJ et al (2011) Macrobenthos habitat mapping in a tidal flat using remotely sensed data and a GIS-based probabilistic model. Mar Pollut Bull 62(3):564–572

    Article  Google Scholar 

  • Dahdouh-Guebas F (2001) Mangrove vegetation structure dynamics and regeneration. Dissertation, vrije universiteit, Brussel

    Google Scholar 

  • Dahdouh-Guebas F, Verheyden A, De Genst W (2000) Four decade vegetation dynamics in Sri Lankan mangroves as detected from sequential aerial photography: a case study in Galle. Bull Mar Sci 67:741–759

    Google Scholar 

  • Dahdouh-Guebas F, De Bondt R, Abeysinghe PD (2004a) Comparative study of the disjunct zonation pattern of the grey mangrove Avicennia marina (Forsk.) Vierh. in Gazi Bay Kenya. Bull Mar Sci 74:237–252

    Google Scholar 

  • Dahdouh-Guebas F, Pottelbergh VI, Kairo JG (2004b) Human-impacted mangroves in Gazi (Kenya): predicting future vegetation based on retrospective remote sensing, social surveys, and distribution of trees. Mar Ecol Prog Ser 272:77–92

    Article  Google Scholar 

  • Dahdouh-Guebas F, Hettiarachchi S, Lo Seen D (2005a) Transitions in ancient inland freshwater resource management in Sri Lanka affect biota and human populations in and around coastal lagoons. Curr Biol 15:579–586

    Article  Google Scholar 

  • Dahdouh-Guebas F, Jayatissa LP, Dinitto D (2005b) How effective were mangroves as a defence against the recent tsunami? Curr Biol 15:R443–R447

    Article  Google Scholar 

  • Dale PE, Chandica AL, Evans M (1996) Using image subtraction and classification to evaluate change in sub-tropical intertidal wetlands. Int J Remote Sens 17:703–719

    Article  Google Scholar 

  • Daniel B, van RC, David LB Jupp (1986) Mapping shallow water: application of remote sensing techniques in coastal zone management in the Great Barrier Reef region, Australia. In: Proceedings of the regional seminar on “Application of remote sensing techniques in coastal zone management and environmental management”, Dhaka, pp 145–153

    Google Scholar 

  • Demuro M, Chisholm L (2003) Assessment of hyperion for characterizing mangrove communities. In: Proceedings of the 12th JPL AVIRIS airborne earth science workshop, Pasadena, 24–28 Feb 2003

    Google Scholar 

  • Everitt JH, Escobar DE, Judd FW (1991) Evaluation of airborne video imagery for distinguishing black mangrove (Avicennia germinans) on the lower Texas Gulf coast. J Coast Res 7:1169–1173

    Google Scholar 

  • Filippi AM, Jensen JR (2006) Fuzzy learning vector quantization for hyperspectral coastal vegetation classification. Remote Sens Environ 100:512–530

    Article  Google Scholar 

  • Francisco FS, Kovacs JM, Lafrance P (2013) An object-oriented classification method for mapping mangroves in Guinea, West Africa, using multipolarized ALOS PALSAR L-band data. Int J Remote Sens 34(2):563–586

    Article  Google Scholar 

  • Fulton JW, Wagner CR, Rogers ME, Zimmerman GF (2010) Hydraulic modeling of mussel habitat at a bridge-replacement site, Allegheny River, Pennsylvania. USA Ecol Model 221:540–554

    Article  Google Scholar 

  • Gao J (1998) A hybrid method toward accurate mapping of mangroves in a marginal habitat from SPOT multispectral data. Int J Remote Sens 19:1887–1899

    Article  Google Scholar 

  • Gao J (1999) A comparative study on spatial and spectral resolutions of satellite data in mapping mangrove forests. Int J Remote Sens 20:2823–2833

    Article  Google Scholar 

  • Giri C, Pengra B, Zhu Z (2007) Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000. Estuar Coast Shelf Sci 73:91100

    Article  Google Scholar 

  • Gitelson A, Kaufman Y (1998) MODIS NDVI optimization to fit the AVHRR data series: spectral considerations. Remote Sens Environ 66:343–350

    Article  Google Scholar 

  • Gopal S, Woodcock CE, Strahler AH (1999) Fuzzy neural network classification of global land cover from a 10 AVHRR data set. Remote Sens Environ 67:230–243

    Article  Google Scholar 

  • Green EP, Mumby PJ, Edwards AJ (1997) Estimating leaf area index of mangroves from satellite data. Aquat Bot 58:11–19

    Article  Google Scholar 

  • Green EP, Mumby PJ, Edwards AJ, Clark CD, Ellis AC (1998) The assessment of mangrove areas using high resolution multispectral airborne imagery. J Coast Res 14:433–443

    Google Scholar 

  • Green EP, Clark CD, Edwards AJ (2000) Image classification and habitat mapping. In: Edwards AJ (ed) Remote sensing handbook for tropical coastal management. UNESCO, Paris, pp 141–154

    Google Scholar 

  • Grinson G (2014) Numerical modelling and satellite remote sensing as tools for research and management of marine fishery resources. In: Finkl CW, Makowski C (eds) Remote sensing and modeling: advances in coastal and marine resources. Coastal Research Library 9. Springer, pp 431–452

    Google Scholar 

  • Grinson G, Krishnan P, Kamal S, Kirubasankar R, Bharathi GMP, Kaliyamoorthy M, Krishnamurthy V, Kumar ST (2011a) Integrated potential fishing zone forecasts: a promising information and communication technology tool for promotion of green fishing in the islands. Indian J Agric Econ 66(3):513–519

    Google Scholar 

  • Grinson G, Vethamony P, Sudheesh K, Madavana TB (2011b) Fish larval transport in a micro tidal regime Gulf of Kachchh. Fish Res 110(1):160–169

    Article  Google Scholar 

  • Grinson G, Meenakumari B, Mini R, Srinivasa K, Vethamony P, Babu MT, Verlecar X (2012) Remotely sensed chlorophyll: a putative trophic link for explaining variability in Indian oil sardine stocks. J Coast Res 28(1A):105–113

    Google Scholar 

  • Harvey KR, Hill JE (2001) Vegetation mapping of a tropical freshwater swamp in the northern territory, Australia: a comparison of aerial photography, landsat TM and SPOT satellite imagery. Remote Sens Environ 22:2911–2925

    Article  Google Scholar 

  • Herwitz SR, Peterson DL, Eastman JR (1990) Thematic mapper detection of changes in the leaf area of closed canopy pine plantations in central Massachusetts. Remote Sens Environ 29:129–140

    Google Scholar 

  • Hirano A, Madden M, Welch R (2003) Hyperspectral image data for mapping wetland vegetation. Geo Mar. Lett 23:436–448

    Google Scholar 

  • Huete AR, Didan K, Miura T (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83:195–213

    Article  Google Scholar 

  • IOCCG (2009) Remote sensing in fisheries and aquaculture. In: Forget MH, Stuart V, Platt T (eds) Reports of the International Ocean Colour Coordinating Group, No. 8 IOCCG, Dartmouth

    Google Scholar 

  • Jeganathan C, Dash J, Atkinson PM (2010) Mapping the phenology of natural vegetation in India using a remote sensing-derived chlorophyll index. Int Remote Sens 31(22):5777–5796

    Article  Google Scholar 

  • Jensen JR, Ramsey EW, Mackey HE Jr (1987) Inland wetland change detection using aircraft MSS data. Photogramm Eng Remote Sens 53(5):521–529

    Google Scholar 

  • Jensen R, Lin H, Yang X (1991) The measurement of mangrove characteristics in southwest Florida using SPOT multispectral data. Geocarto Int 6:13–21

    Article  Google Scholar 

  • Jensen RR, Mausel P, Dias N (2007) Spectral analysis of coastal vegetation and land cover using AISA+ hyperspectral data. Geocarto Int 22:17–28

    Article  Google Scholar 

  • John WJ (2011) Remote sensing of vegetation pattern and condition to monitor changes in everglades biogeochemistry. Crit Rev Environ Sci Technol 41(S1):64–91

    Google Scholar 

  • Kairo JG, 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):153–166

    Article  Google Scholar 

  • Kanniah KD, Wai NS, Shin ALM (2007) Per-pixel and sub-pixel classifications of high-resolution satellite data for mangrove species mapping. Appl GIS 3(8):1–22

    Google Scholar 

  • Klemas V (2011) Remote sensing of wetlands: case studies comparing practical techniques. J Coast Res 27:418–427

    Article  Google Scholar 

  • Kovacs JM, Wang J, Blanco-Correa M (2001) Mapping disturbances in a mangrove forest using multidate landsat TM imagery. Environ Manage 27:763–776

    Article  Google Scholar 

  • Kovacs JM, Wang J, Flores F (2005) Mapping mangrove leaf area index at the species level using IKONOS and LAI-2000 sensors for the Agua Brava Lagoon, Mexican Pacific. Estuar Coast Shelf Sci 62:377–384

    Article  Google Scholar 

  • Krishnan P, Roy DS, George G, Srivastava RC, Anand A, Murugesan S, Kaliyamoorthy M, Vikas N, Soundararajan R (2011) Elevated sea surface temperature during May 2010 induces mass bleaching of corals in the Andaman. Curr Sci 100(1):111–117

    Google Scholar 

  • Krishnan P, George G, Vikas N, Titus I, Goutham BMP, Anand A, Vinod KK, Senthil KS (2012) Tropical storm off Myanmar coast sweeps reefs in Ritchie’s Archipelago, Andaman. Environ Monit Assess 123:1–12

    Google Scholar 

  • Lawrence RL, Ripple WJ (1998) Comparisons among vegetation indices and bandwise regression in a highly disturbed, heterogeneous landscape: Mount St Helens, Washington. Remote Sens Environ 64:91–102

    Article  Google Scholar 

  • Lee JH, Park HS (1998) Community structures of macrobenthos in chonsu bay, Korea. J Korean Soc Oceanogr 33:18–27

    Google Scholar 

  • Lefsky MA, Cohen WB, Parker GG (2002) LiDAR remote sensing for ecosystem studies. Bioscience 52:19–30

    Article  Google Scholar 

  • Lefsky M, Harding D, Keller M (2005) Estimates of forest canopy height and aboveground biomass using ICE Sat. Geophys Res Lett 32:1–4

    Google Scholar 

  • Liu Q, Huete A (1995) A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Trans Geosci Remote Sens 33:457–465

    Article  Google Scholar 

  • Lucas RM, Mitchell A, Proisy C (2002) The use of polarimetric AIRSAR (POLSAR) data for characterising mangrove communities. In: Proceedings of AIRSAR earth science and application workshop, Pasadena, 4–6 Mar 2002

    Google Scholar 

  • Lymburner L, Beggs PJ, Jacobson CR (2000) Estimation of canopy-average surface-specific leaf area using landsat TM data. Photogramm Eng Remote S 66:183–191

    Google Scholar 

  • Manson FJ, Loneragan NR, Mcleod IM (2001) Assessing techniques for estimating the extent of mangroves: topographic maps, aerial photographs and landsat TM images. Mar Freshwat Res 52:787–792

    Article  Google Scholar 

  • Markert E, Holler P, Kröncke I et al (2013) Benthic habitat mapping of sorted bedforms using hydroacoustic and ground-truthing methods in a coastal area of the German Bight/North Sea. Estuar Coast Shelf Sci 129:94–104

    Article  Google Scholar 

  • Martines JM, Toan T (2007) Mapping of flood dynamics and spatial distribution of vegetation in the Amazon floodplain using multitemporal SAR data. Remote Sens Environ 108:209–223

    Article  Google Scholar 

  • Melagni F, Alhashemy BAR, Taha SMR (2001) An evaluation of the explicit fuzzy method using parametric and non-parametric approaches for supervised classification of multispectral remote sensing data. Eng J Univ Qatar 14:77–104

    Google Scholar 

  • Meza Diaz B, Blackburn GA (2003) Remote sensing of mangrove biophysical properties: evidence from a laboratory simulation of the possible effects of background variation on spectral vegetation indices. Int J Remote Sens 24:53–73

    Article  Google Scholar 

  • Mumby PJ, Green EP, Edwards AJ (1999) The cost-effectiveness of remote sensing for tropical coastal resources assessment and management. J Environ Manag 55:157–166

    Article  Google Scholar 

  • Nath SS, Bolte JP, Ross LG (2000) Applications of geographical information systems (GIS) for spatial decision support in aquaculture. Aquac Eng 23:233–278

    Article  Google Scholar 

  • Nayar S, Bahuguna A (2001) Application of remote sensing to monitor mangroves and other coastal vegetation of India. Indian J Mar Sci 30(4):195–214

    Google Scholar 

  • Neukermans G, Dahdouh-Guebas F, Kairo JG et al (2008) Mangrove species and stand mapping in Gazi Bay (Kenya) using Quickbird satellite imagery. J Spat Sci 53:75–86

    Article  Google Scholar 

  • Novo EMLM, Costa MFP, Mantovani JE (2002) Relationship between macrophyte stand variables and radar backscatter at L and C band, Tucurui reservoir, Brasil. Int J Remote Sens 23:1241–1260

    Article  Google Scholar 

  • Ozesmi SL, Bauer ME (2002) Satellite remote sensing of wetlands. Wetl Ecol Manag 10:381–402

    Article  Google Scholar 

  • Pawar TA, Kolapkar R (2013) Mapping of mangrove area of Curtorim Village-South Goa District-GoaIndia-using remote sensing and GIS techniques. In: National conference on biodiversity: status and challenges in conservation – ‘FAVEO’ 2013, pp 94–96

    Google Scholar 

  • Pengra BW, Johnston CA, Loveland TR (2007) Mapping an invasive plant, Phragmites australis, in coastal wetlands using the EO-1 Hyperion hyperspectral sensor. Remote Sens Environ 108:74–81

    Article  Google Scholar 

  • Perry CR, Lautenschlager LF (1984) Functional equivalence of spectral vegetation indices. Remote Sens Environ 14:169–182

    Article  Google Scholar 

  • Phillips RL, Beeri O, Dekeyser ES (2005) Remote wetland assessment for Missouri Coteau prairie glacial basins. Wetlands 25:335–349

    Article  Google Scholar 

  • Radiarta IN, Saitoh SI (2008) Satellite-derived measurements of spatial and temporal chlorophyll-a variability in Funka Bay, southwestern Hokkaido, Japan. Estuar Coast Shelf Sci 79(3–79):400–408

    Article  Google Scholar 

  • Richardson AJ, Everitt JH (1992) Using spectral vegetation indices to estimate rangeland productivity. Geocarto Int 1:63–77

    Article  Google Scholar 

  • Robertson AI, Duke NC (1987) Mangroves as nursery sites, comparisons of the abundance of fish and crustaceans in mangroves and other near shore habitats in tropical Australia. Mar Biol 96:193–205

    Article  Google Scholar 

  • Rosland R, Strand O, Alunno-Bruscia M (2009) Applying dynamic energy budget (DEB) theory to simulate growth and bio-energetics of blue mussels under low seston conditions. J Sea Res 62:49–61

    Article  Google Scholar 

  • Rouse JW, Haas RH, Schell JA (1974) Monitoring the vernal advancement and retrogradation (greenwave effect) of natural vegetation. NASA/GSFC Type III Final Report, Greenbelt, p 371

    Google Scholar 

  • Saenger P, Hegerl EJ, Davie JDS (1983) Global status of mangrove systems. Commission on Ecology Papers No. 3. Gland, IUCN

    Google Scholar 

  • Saito H, Bellan MF, Al-Habshi A (2003) Mangrove research and coastal ecosystem studies with SPOT4 HRVIR and TERRA ASTER in the Arabian Gulf. Int J Remote Sens 24(21):4073–4092

    Article  Google Scholar 

  • Satyanarayana B (2007) Application of remote sensing: an approach for distinguishing vegetation structure and decadal changes in mangroves. Universiti Malaysia Terengganu, Kuala Terengganu

    Google Scholar 

  • Selvam V, Ravichandran KK, Gnanappazham L (2003) Assessment of community-based restoration of Pichavaram mangrove wetland using remote sensing data. Curr Sci 85(6):794–798

    Google Scholar 

  • Silapathong C, Blasco F (1992) The application of geographic information system to mangrove forest management: Khlung, Thailand. Asian Pac Remote Sens J 5:97–104

    Google Scholar 

  • Simard M, Zhang K, Rivera-Monroy V (2006) Mapping height and biomass of mangrove forest in everglades national park with SRTM elevation data. Photogramm Eng Remote Sens 72:299–311

    Article  Google Scholar 

  • Simard M, Rivera-Monroy VH, Mancera-Pineda JE et al (2008) A systematic method for 3D mapping of mangrove forests based on shuttle radar topography mission elevation data, ICESat/GLAS waveforms and field data: application to Cienaga Grande De Santa Marta, Colombia. Remote Sens Environ 112:2131–2144

    Article  Google Scholar 

  • Spalding M (1997) The global distribution and status of mangrove ecosystems. Int News Lett Coast Manag 1:20–21

    Google Scholar 

  • Steven RS, Dwayne EP, Coen LD (2006) Development of an automated mapping technique for monitoring and managing shellfish distributions. The NOAA/UNH Cooperative Institute for Coastal and Estuarine Environmental Technology (CICEET). NOAA Grant Number NA17OZ2507

    Google Scholar 

  • Tanre D, Holben BN, Kaufman YJ (1992) Atmospheric correction algorithm for NOAA-AVHRR products: theory and application. IEEE Trans Geosci Remote Sens 30:231–248

    Article  Google Scholar 

  • Terchunian A, Klemas V, Asegovia M (1986) Mangrove mapping in Ecuador: the impact of shrimp pond construction. Environ Manage 10:345–350

    Article  Google Scholar 

  • Thomas Y, Mazurié J, Alunno-Bruscia M et al (2011) Modelling spatio-temporal variability of Mytilus edulis (L.) growth by forcing a dynamic energy budget model with satellite-derived environmental data. J Sea Res 66:308–317

    Article  Google Scholar 

  • Tong PH, Auda Y, Populus J (2004) Assessment from space of mangroves evolution in the Mekong Delta; in relation to extensive shrimp farming. Int J Remote Sens 25:4795–4812

    Article  Google Scholar 

  • Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8:127–150

    Article  Google Scholar 

  • Vaiphasa C, Ongsomwang S (2004) Hyperspectral data for tropical mangrove species discrimination. ACRS Chiang Mai, Thailand

    Google Scholar 

  • Vaiphasa C, Ongsomwang S, Vaiphasa T (2005) Tropical mangrove species discrimination using hyperspectral data: a laboratory study. Estuar Coast Shelf Sci 65:371–379

    Article  Google Scholar 

  • Venkataratnam L, Thammappa SS (1993) Mapping and monitoring areas under prawn farming. Interface Bull NRSA Data Cent 4:4–7

    Google Scholar 

  • Wang Y, Imhoff ML (1993) Simulated and observed L-HH radar backscatter from tropical mangrove forests. Int J Remote Sens 14:2819–2828

    Article  Google Scholar 

  • Wang L, Sousa WP, Gong P (2004) Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of Panama. Remote Sens Environ 91:432–440

    Article  Google Scholar 

  • Weiss M, Baret F (1999) Evaluation of canopy biophysical variable retrieval performances from the accumulation of large swath satellite data. Remote Sens Environ 70:293–306

    Article  Google Scholar 

  • Wright C, Gallant A (2007) Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data. Remote Sens Environ 107:582–605

    Article  Google Scholar 

  • Yang X (2007) Integrated use of remote sensing and geographic information systems in riparian vegetation delineation and mapping. Int J Remote Sens 28(2):353–370

    Article  Google Scholar 

  • Yang J, Artigas F (2010) Mapping salt marsh vegetation by integrating hyperspecttral and LiDAR remote sensing. In: Wang Y (ed) Remote sensing of coastal environments. CRC Press, Boca Raton

    Google Scholar 

  • Yap CK, Ismail RA, Ismail A (2003) Species diversity of macrobenthic invertebrates in the Semenyih River, Selangor, Peninsular Malaysia. Pertanika J Trop Agric Sci 26:139–146

    Google Scholar 

  • Yoshida T, Omatu S (1994) Neural network approach to land cover mapping. IEEE Trans Geosci Remote Sens 32:1103–1109

    Article  Google Scholar 

Download references

Acknowledgement

The authors acknowledge the support from Dr. A. Gopalakrishnan, Director, Central Marine Fisheries Research Institute, India and the ChloRIFFS project which sponsored this work. Financial assistance from National Centre of Sustainable Coastal Management, Ministry of Environment and Forest, India is also acknowledged herewith. The first author would like to thank Dr. A.P. Sharma, Director, Central Inland Fisheries Research Institute for supporting the work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Grinson George .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Paul, T.T., 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: Finkl, C., Makowski, C. (eds) Seafloor Mapping along Continental Shelves. Coastal Research Library, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-319-25121-9_4

Download citation

Publish with us

Policies and ethics