Biodiversity and Conservation

, Volume 28, Issue 5, pp 1129–1149 | Cite as

Quantifying and predicting multi-decadal forest cover changes in Myanmar: a biodiversity hotspot under threat

  • C. Sudhakar ReddyEmail author
  • S. Vazeed Pasha
  • K. V. Satish
  • Anjaly Unnikrishnan
  • Sapana B. Chavan
  • C. S. Jha
  • P. G. Diwakar
  • V. K. Dadhwal
Original Paper


The focus of the study was to develop a nation-wide forest cover database of Myanmar by assessing and predicting the forest cover changes in the period of 1950 to 2027. This study estimated the net changes in forests at regional level along with spatial patterns of forest fragmentation using multi-source data. The results indicate forest area representing as 77.1%, 65.3%, 54.1% and 50.6% of the total geographical area of Myanmar during 1950, 1975, 2005 and 2016 respectively. This study predicted the forest cover changes in Myanmar using Module for Land use change evaluation. The five spatial variables were used to determine the relationship between deforestation and explanatory variables. The predicted forest cover of Myanmar for 2027 shows 48.4% of total geographical area under forest. The model predicted a further decrease of 14,878 km2 of forest area in Myanmar between 2016 and 2027. The forest cover loss analysed using the classified maps of 1950 and 2016 indicated an overall loss of 34.4% of the forest cover. Ayeyarwady, Mandalay and Nayi Pyi Taw were found to be showing the highest rate of deforestation in the recent period of 2005–2016. This study has provided an insight for understanding of long-term deforestation trends of Myanmar. It offers a valuable inputs for effective management of forest resources and restoration programs as it delineates and forecast the spatial changes in forests from past to future.


Forest Deforestation Remote sensing Modelling Myanmar 



This study has been carried out as part of ISRO’s National Carbon Project. We thank ISRO-DOS Geosphere Biosphere Programme for financial support. We are thankful to Director, NRSC and Deputy Director, RSA, NRSC for suggestions and encouragement. We are grateful to U.S. Geological Survey and University of Texas for providing access to the Landsat data and topographical maps respectively.


  1. Bhagwat T, Hess A, Horning N, Khaing T, Thein ZM, Aung KM, Neil A (2017) Losing a jewel—rapid declines in Myanmar’s intact forests from 2002–2014. PLoS ONE 12(5):e0176364CrossRefGoogle Scholar
  2. FAO (2000) Global Forest Resources Assessment. UN Food and Agriculture Organization, RomeGoogle Scholar
  3. FAO (2005) Global forest resources assessment. UN Food and Agriculture Organization, RomeGoogle Scholar
  4. FAO (2007) Nations Fire management global assessment. UN Food and Agriculture Organization, Rome, p 64Google Scholar
  5. FAO (2009) Myanmar forestry sector outlook study. Asis pacific forestry sector outlook study working paper series. p 11Google Scholar
  6. FAO (2010) Global forest resources assessment. UN Food and Agriculture Organization, RomeGoogle Scholar
  7. FAO (2015) Global forest resources assessment. UN Food and Agriculture Organization, RomeGoogle Scholar
  8. FAO (2016) Global forest resources assessment. UN Food and Agriculture Organization, RomeGoogle Scholar
  9. FRA (2015) Global Forest Resources Assessment 2015. How Are the World’s Forests Changing?. Food and Agriculture Organization of the United Nation, RomeGoogle Scholar
  10. Geist HJ, Lambin EF (2002) Proximate causes and underlying driving forces of tropical deforestation tropical forests are disappearing as the result of many pressures, both local and regional, acting in various combinations in different geographical locations. Bioscience 52(2):143–150CrossRefGoogle Scholar
  11. Hansen MC et al (2013) High-resolution global maps of 21st-century forest cover change. Science 342(6160):850–853CrossRefGoogle Scholar
  12. Htun NZ, Mizoue N, Kajisa T, Yoshida S (2009) Deforestation and forest degradation as measures of Popa Mountain Park (Myanmar) effectiveness. Environ Conserv 36(3):218–224CrossRefGoogle Scholar
  13. Kumar S, Radhakrishnan N, Mathew S (2014) Land use change modelling using a Markov model and remote sensing. Geomatics, Natural Hazards and Risk 5(2):145–156CrossRefGoogle Scholar
  14. Lambin EF (1994) Modelling deforestation processes. Modelling deforestation processes: a review tropical ecosystem environment observations by satellites, TREES Series B (No. 1). Research reportGoogle Scholar
  15. Lim CL, Prescott GW, De Alban JDT, Ziegler AD, Webb EL (2017) Untangling the proximate causes and underlying drivers of deforestation and forest degradation in Myanmar. Conserv Biol 31(6):1362–1372CrossRefGoogle Scholar
  16. Liu FJ, Huang C, Pang Y, Li M, Song DX, Song XP, Guo Y (2016) Assessment of the three factors affecting Myanmar’s forest cover change using Landsat and MODIS vegetation continuous fields data. Int J Digital Earth 9(6):562–585CrossRefGoogle Scholar
  17. Mahajan Y, Venkatachalam P (2009) Neural network based cellular automata model for dynamic spatial modeling in GIS. In international conference on computational science and its applications. Springer, Berlin, pp 341–352Google Scholar
  18. Mas JF, Kolb M, Paegelow M, Olmedo MTC, Houet T (2014) Inductive pattern-based land use/cover change models: a comparison of four software packages. Environ Model Softw 51:94–111CrossRefGoogle Scholar
  19. Matthews E (2001) Understanding the FRA 2000. Forest Briefing 1:1–12Google Scholar
  20. MOECAF (2011) National Biodiversity Strategy and Action Plan. The Republic of the Union of MyanmarGoogle Scholar
  21. MOECAF (2014) Fifth National Report to the Convention on Biological Diversity. The Republic of the Union of Myanmar. Ministry of Environmental Conservation and Forestry, Nay Pyi TawGoogle Scholar
  22. Mon M, Mizoue N, Htun N, Kajisa T, Yoshida S (2012) Factors affecting deforestation and forest degradation in selectively logged production forest: a case study in Myanmar. For Ecol Manag 267:190–198CrossRefGoogle Scholar
  23. Myers N (2000) Biodiversity hotspots for conservation priorities. Nature 403(6772):853–858CrossRefGoogle Scholar
  24. Pijanowski BC, Tayyebi A, Doucette J, Pekin BK, Braun D, Plourde J (2014) A big data urban growth simulation at a national scale: configuring the GIS and neural network based land transformation model to run in a high performance computing (HPC) environment. Environ Model Softw 51:250–268CrossRefGoogle Scholar
  25. Puyravaud J (2003) Standardizing the calculation of the annual rate of deforestation. For Ecol Manag 177:593–596CrossRefGoogle Scholar
  26. Rahman MTU et al (2017) Temporal dynamics of land use/land cover change and its prediction using CA-ANN model for southwestern coastal Bangladesh. Environ Monit Assess 189(11):565CrossRefGoogle Scholar
  27. Rao M, Htun S, Platt SG, Tizard R, Poole C, Myint T, Watson JE (2013) Biodiversity conservation in a changing climate: a review of threats and implications for conservation planning in Myanmar. Ambio 42(7):789–804CrossRefGoogle Scholar
  28. Reddy CS, Jha CS, Dadhwal VK (2013) Assesment and monitroing of longterm forest cover changes in Odisha, India, using remote sensing and GIS. Environ Monit Assess 185(60):4399–4415CrossRefGoogle Scholar
  29. Reddy CS, Khuroo AA, Krishna PH, Saranya KRL, Jha CS, Dadhwal VK (2014) Threat evaluation for biodiversity conservation of forest ecosystems using geospatial techniques: a case study of Odisha, India. Ecol Eng 69:287–303CrossRefGoogle Scholar
  30. Reddy CS, Jha CS, Dadhwal VK, Krishna PH, Pasha SV, Satish KV, Diwakar PG (2016) Quantification and monitoring of deforestation in India over eight decades (1930–2013). Biodivers Conserv 25(1):93–116CrossRefGoogle Scholar
  31. Reddy CS, Saranya KRL, Pasha SV, Satish KV, Jha CS, Diwakar PG, Dadhwal VK, Rao PVN, Krishna Murthy YVN (2018) Assessment and Monitoring of Deforestation and Forest fragmentation in South Asia since the 1930s. Glob Planet Change 161:132–148. CrossRefGoogle Scholar
  32. Renner SC, Rappole JH, Leimgruber P, Kelly DS, Shwe NM, Aung T, Aung M (2007) Land cover in the Northern Forest Complex of Myanmar: new insights for conservation. Oryx 41(1):27–37CrossRefGoogle Scholar
  33. Riitters KH, O’Neill RV, Hunsaker CT, Wickham JD, Yankee DH, Timmins SP, Jones KB, Jackson BL (1995) A factor analysis of landscape pattern and structure metrics. Landsc Ecol 10:23–40CrossRefGoogle Scholar
  34. Roy PS et al (2012) Biodiversity characterisation at landscape level: national assessment. Indian Institute of Remote Sensing, Dehra Dun, pp 1–254Google Scholar
  35. Songer M, Aung M, Senior B, DeFries R, Leimgruber P (2009) Spatial and temporal deforestation dynamics in protected and unprotected dry forests: a case study from Myanmar (Burma). Biodivers Conserv 18(4):1001–1018CrossRefGoogle Scholar
  36. Stibig HJ, Achard F, Carboni S, Rasi R, Miettinen J (2014) Change in tropical forest cover of Southeast Asia from 1990 to 2010. Biogeosciences 11:247–258CrossRefGoogle Scholar
  37. Tucker CJ, Townshend JRG (2000) Strategies for monitoring tropical deforestation using satellite data. Int J Remote Sens 21:1461–1471CrossRefGoogle Scholar
  38. UNCCD (2005) National Action Programme of Myanmar to combat desertification. The Context of United Nations Convention to Combat Desertification (UNCCD), ParisGoogle Scholar
  39. Verburg PH, Schot PP, Dijst MJ, Veldkamp A (2004) Land use change modelling: current practice and research priorities. GeoJournal 61(4):309–324CrossRefGoogle Scholar
  40. Vogt P, Riitters KH, Estreguil C, Kozak J, Wade T, Wickham J (2007) Mapping spatial patterns with morphological image processing. Landsc Ecol 22:171–177CrossRefGoogle Scholar
  41. Wang C, Myint SW (2016) Environmental concerns of deforestation in Myanmar 2001–2010. Remote Sensing 8(9):728CrossRefGoogle Scholar
  42. Wildlife Conservation Society (WCS) (2012) Myanmar Biodiversity Conservation Investment Vision. Wildlife Conservation Society, YangonGoogle Scholar
  43. Yang X, Zhao Y, Chen R, Zheng X (2016) Simulating land use change by integrating landscape metrics into ANN-CA in a new way. Front Earth Sci 10(2):245–252CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • C. Sudhakar Reddy
    • 1
    Email author
  • S. Vazeed Pasha
    • 1
  • K. V. Satish
    • 1
  • Anjaly Unnikrishnan
    • 1
    • 2
  • Sapana B. Chavan
    • 1
  • C. S. Jha
    • 1
  • P. G. Diwakar
    • 1
    • 3
  • V. K. Dadhwal
    • 1
    • 4
  1. 1.National Remote Sensing CentreIndian Space Research OrganisationHyderabadIndia
  2. 2.Indian Institute of Information Technology and Management - KeralaThiruvananthapuramIndia
  3. 3.Indian Space Research Organisation, Antariksh BhavanBengaluruIndia
  4. 4.Department of Earth and Space SciencesIndian Institute of Space Science and TechnologyThiruvananthapuramIndia

Personalised recommendations