Monitoring forest landcover changes in the Eastern Sundarban of Bangladesh from 1989 to 2019

• Autorzy: Kumar Mithun , Mondal Ismail , Pham Quoc Bao

Identyfikatory

DOI

10.1007/s11600-021-00551-3

• Języki publikacji: EN

Abstrakty

EN

The present study aims to estimate areal extent of the mangrove forest cover in the eastern Sundarban of Bangladesh from 1989 to 2019 to understand mangrove dynamic over the last 30 years. Freely available Landsat TM of 1989, 2014, and L8 OLI imagery were used to generate land use/land cover (LU/LC) map for the study area using maximum likelihood (MaxLike) algorithm. Results of previous investigations among diferent scientists and researchers were used to develop a conceptual background and also included in this paper to fnd out the causes that relate to forest cover change in the study area. Study results show that the vegetation cover of Sharankhola range in Sundarban has decreased by 0.44% over last 30 years (from 1989 to 2019). Water body has increased (1.30%) with the decrease in vegetation cover. Classifed map of 2014 and 2019 shows that 2.66% vegetation cover of the study area was lost in 2014 based on 1989 while 2.22% vegetation cover was gained in 2019 based on 2014. The overall accuracy of Landsat TM (1989), TM (2014), and L8 OLI (2019) were 80%, 82.85%, and 84.28%, respectively. Its accuracy would increase if it is supplemented by extensive ground verifcation data and hybrid satellite data of diferent spectral and spatial resolution.

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2021
• Tom: Vol. 69, no. 2
• Strony: 561--577
• Opis fizyczny: Bibliogr. 55 poz.

Twórcy

autor

Kumar Mithun

• Space and Environment Research Center (SERC), Rajshahi 6205, Bangladesh

autor

Mondal Ismail

• School of Oceanographic Studies, Jadavpur University, Kolkata 700032, India

autor

Pham Quoc Bao

• Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam
• Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam

Bibliografia

• 1. Ajai Bahuguna A, Chauhan HB (2017) Mangrove inventory, monitoring, and health assessment. Coastal wetlands: alteration and remediation pp 573–630, Part of the Coastal Research Library book series (COASTALRL, volume 21)
• 2. Alam Mohammed Moninul (2008) Monitoring of forest cover change in Sundarban Mangrove forest using remote sensing and GIS.
• 3. Ali S (2002) Use of spectral and temporal unmiximing for crop identification using multi-spectral data. International Institute for Geo-Information Science and Earth Observation, Netherlands
• 4. Alongi DM (2002) Present state and future of the world’s mangrove forests. Environ Conserv 2002(29):331–349
• 5. Alongi DM (2012) Carbon sequestration in mangrove forests . Carbon Manage 3(3):313–322. https://doi.org/10.4155/CMT.12.20©2012FutureScienceLtdISSN1758-3004
• 6. Alongi DM (2014) Carbon cycling and storage in mangrove forests, Annu. Rev. Mar. Sci. 2014. The annual review of marine science is online at marine.annualreviews.org this article’s. https://doi.org/10.1146/annurev-marine-010213-135020 6:195–219.
• 7. Ball MC (1998) Mangrove species richness in relation to salinity and water logging. Global Ecol Biogeogr Lett 7:73–82
• 8. Bandyopadhyay J, Mondal I, Samanta N (2014) Shore line shifting of namkhana island of indian Sundarban, south 24 parganas, West Bengal, India, Using Remote Sensing & GIS Techniques. International Journal of Engineering Sciences & Research Technology (IJESRT) ISSN: 2277–9655 Scientific Journal Impact Factor: 3.449 (ISRA), Vol. 3 Issue 5 - May, 2014, pp. 162–169
• 9. Bandyopadhyay J. Mondal I. Maiti KK. Biswas A. Acharyya A. Sarkar S. Paul A. Das P. (2017): Forest canopy density mapping for natural resource management of Jangalmahal area, India, Using Geospatial Technology, International Journal of Current Research, Vol. 9, Issue, 08, pp.56073–56082, August, 2017, ISSN-0975–833X.
• 10. Bangladesh Population Census (2011) http://203.112.218.65:8008/WebTestApplication/userfiles/Image/National%20Reports/Union%20Statistics.pdf
• 11. Biswas SR, Choudhury JK, Nishat A, Rahman MM (2007) Do invasive plants threaten the Sundarbans mangrove forest of Bangladesh? For Ecol Manag 245:1–9
• 12. Blasco F, Aizpuru M, Gers C (2001) Depletion of the mangroves of Continental Asia. Wetl Ecol Manag 9:255–266
• 13. CEGIS (2005) Impacts of sea level rise (SLR) on landuse suitability and adaptation options. Center for Environmental and Geographic Information Services, Dhaka
• 14. Chander G, Markham BL, Helder DL (2009) Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens Environ 113(2009):893–903
• 15. Bangladesh sundarban delta vision 2050 (IUCN-2014). https://www.iucn.org/es/content/bangladesh-sundarban-delta-vision-2050-first-step-its-formulation-document-1-vision.
• 16. Dwivedi R, Rao B, Bhattacharya S (1999) Mapping wetlands of the Sundarban delta and it’s environs using ERS-1 SAR data. Int J Remote Sens 20:2235–2247
• 17. Emch M, Peterson M (2006) Mangrove forest cover change in the Bangladesh Sundarbans from 1989–2000: A remote sensing approach. Geocarto Int 21:5–12
• 18. FAO (2010) Global forest resources assessment 2010—main report. FAO Forestry Paper No. 163. Rome. http://www.fao.org/docrep/ 013/i1757e/i1757e00.html.
• 19. Global Forest Observations Initiative (GFOI). Review of priority research & development topics: R&D related to the use of remote sensing in national forest monitoring. Geneva: GEO; 2013. ISBN 978–92–990047–5–3.
• 20. Ghosh MK, Kumar L, Roy C (2016) Mapping long-term changes in mangrove species composition and distribution in the Sundarbans. Forests 7:305. https://doi.org/10.3390/f7120305
• 21. Ghosh A, Schmidt S, Fickert T, Nüsser M (2015) The Indian Sundarban mangrove forests: history, utilization, conservation strategies and local perception. Diversity 7:149–169. https://doi.org/10.3390/d7020149
• 22. Giri C, Muhlhausen J (2008) Mangrove forest distributions and dynamics in Madagascar (1975–2005). Sensors 8:2104–2117
• 23. Giri C, Pengra B, Zhu Z, Singh A, Tieszen LL (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 73:91–100
• 24. Hagan J (1998) User’s guide. Clark Lab, Clark University, Massachusetts, Cartalinx, pp 92–97
• 25. Hall RJ, Skakun RS, Arsenault EJ, Case BS (2006) Modeling forest stand structure attributes using Landsat ETM+ data: application to mapping of aboveground biomass and stand volume. Forest Ecol Manag Elsevier 225(2006):378390
• 26. Healey SP, Cohen WB, Zhiqiang Y, Krankina ON (2005) Comparison of tasseled cap-based Landsat data structures for use in forest disturbance detection. Remote Sens Environ 97(3):301–310
• 27. Hirschmugl M, Steinegger M, Gallaun H, Schardt M (2014) Mapping forest degradation due to selective logging by means of time series analysis: case studies in central Africa. Remote Sens 6:756–775
• 28. Hussain Z, Acharya G (1994) Mangroves of the Sundarbans. Volume 2: Bangladesh; International Union for Conservation of Nature AND Natural Resources (IUCN): Gland, Switzerland Volume 2, p. 257.
• 29. Iftekhar M, Islam M (2004) Managing mangroves in Bangladesh: a strategy analysis. J Coast Conserv 10:139–146
• 30. Islam MJ, Alam MS, Elahi KM (1997) Remote sensing for change detection in the Sunderbands Bangladesh. Geocarto Int 12:91–100
• 31. Islam MT, Broström G, Christensen K, Drivdal M, Weber J, Shendryk I, Alwmark C (2014) Vegetation changes of Sundarbans based on Landsat Imagery analysis between 1975 and 2006. Landsc Environ 8:1–9
• 32. Islam M, Borgqvist H, Kumar L (2019) Monitoring Mangrove forest landcover changes in the coastline of Bangladesh from 1976 to 2015. Geocarto Int 34(13):1458–1476 https://doi.org/10.1080/10106049.2018.1489423
• 33. IWM (2013) Final report of ganges basin development challenge: assessment of the impact of anticipated external drivers of change on water resources of coastal zone, Institute of Water Modelling (IWM), Dhaka.
• 34. Kumar L, Ghosh MK (2012) Land cover change detection of Hatiya Island, Bangladesh, using remote sensing techniques. J Appl Remote Sens 6(1):3608. https://doi.org/10.1117/1.JRS.6.063608
• 35. Levin Noam (1999) Fundamental of Remote Sensing, http://www.geoservis.ftn.uns.ac.rs/downloads/ISP/1999-fundamentals-of-remote-sensing.pdf.
• 36. Manna S (2012) Vegetation Cover Change Analysis from Multi-Temporal Satellite Data in Jharkhali Island Sundarbans, India. Indian J Geo-Marine Sci 42(3):331–342
• 37. Miettinen J, Stibig H-J, Achard F (2014) Remote sensing of forest degradation in Southeast Asia—aiming for a regional view through 5–30 m satellite data. Glob Ecol Conserv 2:24–36
• 38. Mitchell AL, Rosenqvist A, Mora B (2017) Current remote sensing approaches to monitoring forest degradation in support of countries measurement, reporting and verification (MRV) systems for REDD+. Carbon Balance Manage 12:9. https://doi.org/10.1186/s13021-017-0078-9
• 39. Mondal I, Bandyopadhyay J (2014) Coastal zone mapping through geospatial technology for resource management of Indian Sundarban, West Bengal, India. International Journal of Remote Sensing Applications (IJRSA), ISSN Online: 2226–4353, ISSN Print: 2226–4361, Vol. 4 Issue 2 https://doi.org/10.14355/ijrsa.2014.0402.04
• 40. Mondal I, Bandyopadhyay J (2014) Environmental change of trans international boundary indo-Bangladesh Border of SundarbanIchamati River Catchment Area Using Geoinformatics Techniques, West Bengal, India, Universal Journal of Environmental Research and Technology All Rights Reserved Euresian Publication © 2014 eISSN 2249 0256 Available Online at: www.environmentaljournal.org 2014 Volume 4, Issue 3: 143–154.
• 41. Mondal I Bandyopadhyay J, Jana M (2013) Mangrove zonation and succession pattern of Fazergange and Bakkhali Area, At Sundarban, W.B, India, Using RS & GIS Techniques.Indian Cartographer, Vol. XXXIII, , 2013, Vol.33|2013–311–315| http://inca33jodhpur2013.org/html/SubTheme.html, ISSN-0927–8392
• 42. Mondal I, Thakur S, Bandyopadhyay J (2020) (2019): Delineating lateral channel migration and risk zones of Ichamati River West Bengal, India. J Clean Product Elsevier 244:118740. https://doi.org/10.1016/j.jclepro.2019.11874
• 43. Myint SW, Giri C, Wang L, Zhu Z, Gillette SC (2008) 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 45:188–208
• 44. Nandy S, Joshi PK, Das KK (2003) Forest canopy density stratification using biophysical modeling. J Indian Soc Remote Sens 31(4):2003
• 45. Rahman M, Asaduzzaman M (2013) Ecology of Sundarban Bangladesh . J Sci Found 8:35–47
• 46. Rouse JW, Haas RH, Schell JA, Deering DW (1973) Monitoring vegetation systems in the Great Plains with ERTS. In: Fraden S.C., Marcanti E.P., Becker M.A. (eds): 3rd ERTS-1 Symposium, NASA SP-351, Washington, D.C., Dec 10–14, 1973: 309–317.
• 47. Simula M, Mansur E (2011) A global challenge needing local response—a common approach to defining and measuring forest degradation can lead to unique solutions for addressing it. Unasylva 238:62
• 48. Spalding M, Kainuma M, Collins L, (2010) World atlas of mangroves; Earthscan: London, UK, p. 319.
• 49. Teillet PM, Fedosejevs G (1995) On the dark target approach to atmospheric correction of remotely sensed data. Can J Remote Sensing 21:374387
• 50. Temfli Klaus et.al. (2009) Principles of Remote Sensing, An introductory text book, International Institute for Geo-Information Science and Earth Observation, Hengelosestra at 99, P.O. Box 6, 7500 AA, Enschede, The Netherlands
• 51. Thakur S, Mondal IG, Das PB, De P, T.K. (2019) A review of the application of multispectral remote sensing in the study of mangrove ecosystems with special emphasis on image processing techniques. Journal of Spatial Information Research, Springer Nature. https://doi.org/10.1007/s41324-019-00268-y,pp1-13,ISSN-2366-3294
• 52. Thakur S, Maity D, Mondal I, Basumatary G, Ghosh PB, De TK (2020) Assessment of changes in land use, land cover, and land surface temperature in the mangrove forest of Sundarbans, northeast coast of India. Environ Dev Sustain. https://doi.org/10.1007/s10668-020-00656-7
• 53. UNESCO, 1997. The Sundarban World Heritage Site. http://whc.unesco.org/en/list/798.
• 54. USGS-2014 https://archive.usgs.gov/archive/sites/eo1.usgs.gov/index.html
• 55. Valiela I, Bowen JL, York JK (2001) Mangrove Forests: One of the World’s Threatened Major Tropical Environments. Bioscience 51:807–815