Estimating aboveground biomass in Avicennia marina plantation in Indian Sundarbans using high-resolution satellite data

Journal of Applied Remote Sensing

Volumn 8, Issue 1, 2014, Pages

  Manna, Sudip ^a   Nandy, Subrata ^b   Chanda, Abhra ^a   Akhand, Anirban ^a   Hazra, Sugata ^a   Dadhwal, Vinay Kumar ^c  

^a JADAVPUR UNIVERSITY   (India)

^b INDIAN INSTITUTE OF REMOTE SENSING   (India)

^c INDIAN SPACE RESEARCH ORGANISATION   (India)

Author keywords

aboveground biomass; Avicennia marina; normalized difference vegetation index; optimized soil adjusted vegetation index; Sundarbans; transformed difference vegetation index

Indexed keywords

BIOMASS; CARBON; COST EFFECTIVENESS; ECOSYSTEMS; FORESTRY; MARINAS; REFLECTION; SATELLITES; SOILS;

ABOVE GROUND BIOMASS; CARBON SEQUESTRATION POTENTIAL; HIGH RESOLUTION SATELLITE DATA; LINEAR RELATIONSHIPS; MONITORING AND MANAGEMENT; NORMALIZED DIFFERENCE VEGETATION INDEX; SUNDARBANS; VEGETATION INDEX;

VEGETATION;

EID: 84901295744     PISSN: None     EISSN: 19313195     Source Type: Journal    
DOI: 10.1117/1.JRS.8.083638     Document Type: Article

References (52)

1. F. Blasco, P. Saenger, and E. Janodet, "Mangroves as indicators of coastal change," Catena 27(3-4), 67-178 (1996), http://dx.doi.org/10.1016/ 0341-8162(96)00013-6.
2. S. Bouillon et al., "Mangrove production and carbon sinks: a revision of global budget estimates," Global Biogeochem. Cycles 22(2), 1-12 (2008), http://dx.doi.org/10.1029/2007GB003052.
3. D. M. Alongi, "Carbon sequestration in mangrove forests," Carbon Manage. 3(3), 313-322 (2012), http://dx.doi.org/10.4155/cmt.12.20.
4. J. G. Kairo et al., "Structural development and productivity of replanted mangrove plantations in Kenya," For. Ecol. Manage. 255(7), 2670-2677 (2008), http://dx.doi.org/10.1016/j.foreco.2008.01.031.
5. M. L. G. Soares, "Estudo da biomassa aérea de manguezais do sudeste do Brasil e análise de modelos," Vol. 2. Ph.D. Thesis, Instituto Oceanográfico, Universidade de Sao Paulo, Brazil (1997).
6. T. E. Fatoyinbo and A. H. Armstrong, "Remote characterization of biomass measurements: case study of mangrove forests," M. Momba and F. Bux, Eds., Biomass, Sciyo, Croatia, p. 202 (2010).
7. N. H. Ravindranath and B. S. Somashekhar, "Potential and economics of forestry options for carbon sequestration in India," Biomass Bioenergy 8(5), 323-342 (1995), http://dx.doi.org/10.1016/0961-9534(95)00025-9.
8. S. Nilsson and W. Schopfhauser, "The carbon-sequestration potential of a global afforestation program," Clim. Change 30(3), 267-293 (1995), http://dx.doi.org/10.1007/BF01091928. (Pubitemid 26435714)
9. D. M. Alongi, "Carbon payments for mangrove conservation: ecosystem constraints and uncertainties of sequestration potential," Environ. Sci. Policy 14(4), 462-470 (2011), http://dx.doi.org/10.1016/j.envsci.2011.02.004.
10. A. Komiyama, J. E. Ong, and S. Poungparn, "Allometry, biomass, and productivity of mangrove forests: a review," Aquat. Bot. 89(2), 128-137 (2008), http://dx.doi.org/10.1016/j.aquabot.2007.12.006.
11. P. K. R. Choudhuri, "Biomass production of mangrove plantation in Sunderbans, West Bengal (India)-a case study," Indian. For. 117(1), 3-12 (1991).
12. A. Chhabra and V. K. Dadhwal, "Assessment of major pools and fluxes of carbon in Indian forests," Clim. Change 64(3), 341-360 (2004), http://dx.doi.org/10.1023/B:CLIM.0000025740.50082.e7. (Pubitemid 38983063)
13. M. Kaul, G. M. J. Mohren, and V. K. Dadhwal, "Phytomass carbon pool of trees and forests in India," Clim. Change 108(1-2), 243-259 (2011), http://dx.doi.org/10.1007/s10584-010-9986-3.
14. C. Kuenzer et al., "Remote sensing of mangrove ecosystems: a review," Remote Sens. 3(5), 878-928 (2011), http://dx.doi.org/10.3390/ rs3050878.
15. R. Madugundu, V. Nizalapur, and C. S. Jha, "Estimation of LAI and above-ground biomass in deciduous forests: Western Ghats of Karnataka, India," Int. J. App. Ear. Obs. Geoinf. 10 (2), 211-219 (2008), http://dx.doi.org/10.1016/j.jag.2007.11.004.
16. J. Kamaruzaman and I. Kasawani, "Imaging spectrometry on mangrove species identification and mapping in Malaysia," WSEAS Trans. Bio. Biomed. 8(4), 118-126 (2007).
17. A. R. Sharma, K. V. S. Badarinath, and P. S. Roy, "Corrections for atmospheric and adjacency effects on high resolution sensor data-a case study using IRS-P6 LISS-IV data," in The Int. Archives Photogramming, Remote Sensing and Spatial Information Sciences, XXXVII Part B8, pp. 497-502, Beijing (2008).
18. P. M. Mather, Computer Processing of Remotely-Sensed Images 2nd ed., John Wiley & Sons, Chichester, England (1999).
19. G. M. Foody et al., "Mapping the biomass of Bornean tropical rain forest from remotely sensed data," Global Ecol. Biogeogr. 10(4), 379-386 (2001), http://dx.doi.org/10.1046/j.1466-822X.2001.00248.x.
20. X. Li et al., "Chen X regression and analytical models for estimating mangrove wetland biomass in South China using Radarsat images," Int. J. Remote Sens. 28(24), 5567-5582 (2007), http://dx.doi.org/10.1080/ 01431160701227638.
21. H. Santin-Janin et al., "Assessing the performance of NDVI as a proxy for plant biomass using non-linear models: a case study on the Kerguelen archipelago," Pol. Biol. 32(6), 861-871 (2009), http://dx.doi.org/10.1007/ s00300-009-0586-5.
22. C. Kalaitzidis, V. Heinzel, and D. Zianis, "A review of multispectral vegetation indices for biomass estimation," in Imagin[e/g] Europe, Proceedings of the 29th Symposium of the European Assoc. Remote. Sens. Lab., Chania, Greece (2009).
23. A. Mitra, K. Sengupta, and K Banerjee, "Standing biomass and carbon storage of aboveground structures in dominant mangrove trees in the Sundarbans," For. Ecol. Manage. 261 (7), 1325-1335 (2011), http://dx.doi.org/10.1016/j.foreco.2011.01.012.
24. R. Ray et al., "Carbon sequestration and annual increase of carbon stock in a mangrove forest," Atmos. Environ. 45(28), 5016-5024 (2011), http://dx.doi.org/10.1016/j.atmosenv.2011.04.074.
25. FAO, State of the World's Forests, Rome, Italy (2001).
26. P. Clarke and P. J. Myerscough, "The intertidal distribution of the grey mangrove (Avicennia marina) in southeastern Australia: the effects of physical conditions, interspecific competition, and predation on propagule establishment and survival," Aus. J. Ecol. 18 (3), 307-315 (1993), http://dx.doi.org/10.1111/aec.1993.18.issue-3. (Pubitemid 24407433)
27. A. I. Robertson and D. M. Alongi, "Coastal and Estuarine Studies," Tropical Mangrove Ecosystems, A. I. Robertson and D. M. Alongi, Eds., American Geophysical Union, Washington (1992).
28. S. Nandy and S. P. S. Kushwaha, "Study on the utility of IRS 1D LISS-III data and the classification techniques for mapping of Sunderban mangroves," J. Coast. Conser. 15(1), 123-137 (2011), http://dx.doi.org/10. 1007/s11852-010-0126-z.
29. B. Husch, T. W. Beers, and J. A. J. Kershaw, "Sampling Design in Forest Inventories," Chap. 13 in Forest Mensuration, 4th ed., pp. 299-336, John Wiley and Sons Inc., Hoboken, New Jersey (2003).
30. G. Chander, B. L. Markham, and D. L. Helder, "Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors," Remote Sens. Environ. 113(5), 893-903 (2009), http://dx.doi.org/10.1016/j.rse.2009.01.007.
31. M. R. Pandya et al., "Spectral characteristics of sensors onboard IRS-1D and P6 satellites: estimation and their influence on surface reflectance and NDVI," J. Indian Soc. Remote Sens. 35(4), 333-350 (2007), http://dx.doi.org/10.1007/BF02990789.
32. C. J. Tucker, "Red and photographic infrared linear combinations for monitoring vegetation," Remote Sens. Environ. 8(2), 127-150 (1979), http://dx.doi.org/10.1016/0034-4257 (79)90013-0.
33. M. D. Steven, "The sensitivity of the OSAVI vegetation index to observational parameters," Remote Sens. Environ. 63(1), 49-60 (1998), http://dx.doi.org/10.1016/S0034-4257(97) 00114-4. (Pubitemid 28056302)
34. A. Bannari, H. Asalhi, and P. M. Teillet, "Transformed difference vegetation index (TDVI) for vegetation cover mapping," in IGARSS IEEE Int. Geosci. Remote Sens. Symp., Vol. 5, pp. 3053-3055 (2002). (Pubitemid 35228480)
35. P. S. Roy and S. A. Ravan, "Biomass estimation using satellite remote sensing data-an investigation on possible approaches for natural forest," J. Biosci. 21(4), 535-561 (1996), http://dx.doi.org/10.1007/ BF02703218.
36. S. Manna et al., "Vegetation cover change analysis from multi-temporal satellite data in Jharkhali Island, Sundarbans, India," Indian J. Geo-Mar. Sci. 42(3), 331-342 (2013).
37. R. N. Mandal and K. R. Naskar, "Diversity and classification of Indian mangroves: a review," Trop. Ecol. 49(2), 131-146 (2008).
38. D. Ganguly et al., "Geomorphological study of Sundarban deltaic estuary," J. Indian Soc. Remote Sens. 34(4), 431-435 (2006), http://dx.doi.org/10.1007/BF02990928.
39. R. Oren et al., "Katul, soil fertility limits carbon sequestration by forest ecosystems in a CO2-enriched atmosphere," Nature 411, 469-472 (2001), http://dx.doi.org/10.1038/35078064. (Pubitemid 32494390)
40. A. K. Koshal, "Spectral characteristics of soil salinity areas in parts of South-West Punjab through remote sensing and GIS," Int. J. Remote Sens. GIS 1(2), 84-89 (2012).
41. Y. Y. Aldakheel, A.M. Elprince, and M. A. Aatti, "Mapping vegetation and saline soil using NDVI in arid irrigated lands," in ASPRS 2006 Ann. Conf., Reno, Nevada (2006).
42. F. V. D. Meer et al., "Vegetation indices, above ground biomass estimates and the red edge from MERIS," Int. Arc. Photogramm. Remote. Sens. XXXIII(Part B7), 1580-1587 (2000).
43. G. Cui et al., "Vegetation classification and biomass estimation using IKONOS imagery in Mt. ChangBai mountain area," J. Korean For. Soc. 101(3), 356-364 (2012).
44. T. Borowik et al., "Normalized difference vegetation index (NDVI) as a predictor of forage availability for ungulates in forest and field habitats," Eur. J. Wildl. Res. 59(5), 675-682 (2013), http://dx.doi.org/10. 1007/s10344-013-0720-0.
45. Y. Gua et al., "NDVI saturation adjustment: a new approach for improving cropland performance estimates in the Greater Platte River Basin, USA," Ecol. Indic. 30, 1-6 (2013), http://dx.doi.org/10.1016/j.ecolind. 2013.01.041.
46. X. Li et al., "Inventory of mangrove wetlands in the Pearl River Estuary of China using remote sensing," J. Geog. Sci. 16(2), 155-164 (2006), http://dx.doi.org/10.1007/s11442-006-0203-2. (Pubitemid 44451516)
47. S. A. Sader et al., "Tropical forest biomass and successional age class relationships to a vegetation index derived from Landsat TM data," Remote Sens. Environ. 28, 143-156 (1989), http://dx.doi.org/10.1016/0034- 4257(89)90112-0. (Pubitemid 20402486)
48. A. Huete, H. Liu, and W. Leeuwen, "The use of vegetation indices in forested regions: issues of linearity and saturation," in IEEE Int. Geosci. Remote. Sens. IGARSS, Remote Sensing-A Scientific Vision for Sustainable Development, Vol. 4, pp. 1966-1968 (1997).
49. A. Psomas et al., "Hyperspectral remote sensing for estimating aboveground biomass and for exploring species richness patterns of grassland habitats," Int. J. Remote Sens. 32(24), 9007-9031 (2011), http://dx.doi.org/10.1080/01431161.2010.532172.
50. "Land cover assessment and monitoring, Pakistan," Volume 10-A, Environment assessment technical reports, Report No. UNEP/EAP.TR/95-06 (1998).
51. A. Mitra, K. Banerjee, and D. P. Bhattacharyya, The Other Face of Mangroves, Govt. of West Bengal, India (2004).
52. J. A. Anaya, E. Chuvieco, and A. Palacios-Orueta, "Above ground biomass assessment in Colombia: a remote sensing approach," For. Ecol. Manage. 257(4), 1237-1246 (2009), http://dx.doi.org/10.1016/j.foreco.2008.11. 016.