A two-scale approach for estimating forest aboveground biomass with optical remote sensing images in a subtropical forest of Nepal

  • Upama A. Koju
  • Jiahua Zhang
  • Shashish Maharjan
  • Sha Zhang
  • Yun Bai
  • Dinesh B. I. P. Vijayakumar
  • Fengmei Yao
Original Paper


Forests account for 80% of the total carbon exchange between the atmosphere and terrestrial ecosystems. Thus, to better manage our responses to global warming, it is important to monitor and assess forest aboveground carbon and forest aboveground biomass (FAGB). Different levels of detail are needed to estimate FAGB at local, regional and national scales. Multi-scale remote sensing analysis from high, medium and coarse spatial resolution data, along with field sampling, is one approach often used. However, the methods developed are still time consuming, expensive, and inconvenient for systematic monitoring, especially for developing countries, as they require vast numbers of field samples for upscaling. Here, we recommend a convenient two-scale approach to estimate FAGB that was tested in our study sites. The study was conducted in the Chitwan district of Nepal using GeoEye-1 (0.5 m), Landsat (30 m) and Google Earth very high resolution (GEVHR) Quickbird (0.65 m) images. For the local scale (Kayerkhola watershed), tree crowns of the area were delineated by the object-based image analysis technique on GeoEye images. An overall accuracy of 83% was obtained in the delineation of tree canopy cover (TCC) per plot. A TCC vs. FAGB model was developed based on the TCC estimations from GeoEye and FAGB measurements from field sample plots. A coefficient of determination (R2) of 0.76 was obtained in the modelling, and a value of 0.83 was obtained in the validation of the model. To upscale FAGB to the entire district, open source GEVHR images were used as virtual field plots. We delineated their TCC values and then calculated FAGB based on a TCC versus FAGB model. Using the multivariate adaptive regression splines machine learning algorithm, we developed a model from the relationship between the FAGB of GEVHR virtual plots with predictor parameters from Landsat 8 bands and vegetation indices. The model was then used to extrapolate FAGB to the entire district. This approach considerably reduced the need for field data and commercial very high resolution imagery while achieving two-scale forest information and FAGB estimates at high resolution (30 m) and accuracy (R2 = 0.76 and 0.7) with minimal error (RMSE = 64 and 38 tons ha−1) at local and regional scales. This methodology is a promising technique for cost-effective FAGB and carbon estimations and can be replicated with limited resources and time. The method is especially applicable for developing countries that have low budgets for carbon estimations, and it is also applicable to the Reducing Emissions from Deforestation and Forest Degradation (REDD +) monitoring reporting and verification processes.


Forest aboveground biomass Google Earth imagery Multi-scale remote sensing Virtual plot Optical imagery 



The secondary data, as well as field inventory data for the Kayerkhola watershed in the study area, were obtained from the International Centre for Integrated Mountain Development (ICIMOD). We would like to express our humble gratitude for this great contribution. The authors would also like to acknowledge funding from the CAS-TWAS Fellowship Program for the research.


  1. Abubakari Z, Molen P, Bennett RM, Kuusaana ED (2016) Land consolidation, customary lands, and Ghana’s Northern Savannah Ecological Zone: an evaluation of the possibilities and pitfalls. Land Use Policy 54(2):386–398CrossRefGoogle Scholar
  2. Avitabile V, Baccini A, Friedl MA, Schmullius C (2012) Capabilities and limitations of Landsat and land cover data for aboveground woody biomass estimation of Uganda. Remote Sens Environ 117(3):366–380CrossRefGoogle Scholar
  3. Baccini A, Goetz SJ, Walker WS, Laporte NT, Sun M, Sulla D (2012) Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat Clim Change 2(1):182–185CrossRefGoogle Scholar
  4. Baig MHA, Zhang LF, Tong Shuai, Tong QX (2014) Derivation of a tasselled cap transformation based on Landsat 8 at- satellite reflectance. Remote Sens Lett 5(5):423–431CrossRefGoogle Scholar
  5. Barbier N, Couteron P, Proisy C, Malhi Y, Gastellu JP (2010) The variation of apparent crown size and canopy heterogeneity across lowland Amazonian forests. Glob Ecol Biogeogr 19(1):72–84CrossRefGoogle Scholar
  6. Bastin JF, Barbier N, Couteron P, Adams B, Shapiro A, Bogaert J, Cannière C (2014) Aboveground biomass mapping of African forest mosaics using canopy texture analysis: toward a regional approach. Ecol Appl 24(8):1984–2001CrossRefPubMedGoogle Scholar
  7. Breiman L (2001) Random forests. Mach Learn 45(1):5–32CrossRefGoogle Scholar
  8. Chalise D, Rawal N, Shriwastac CP (2014) Characterization and mapping of soils of Chitwan. Accessed 16 May 2017Google Scholar
  9. Chojnacky DC, Heath LS (2002) Estimating down deadwood from FIA forest inventory variables in Maine. Environ Pollut 116(1):25–30CrossRefGoogle Scholar
  10. Culvenor DS (2002) TIDA: an algorithm for the delineation of tree crowns in high spatial resolution remotely sensed imagery. Comput Geosci 28(1):33–44CrossRefGoogle Scholar
  11. Cutler MEJ, Boyd DS, Foody GM, Vetrivel A (2012) Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: an assessment of predictions between regions. ISPRS J Photogramm Remote Sens 70(4):66–77CrossRefGoogle Scholar
  12. DeFries R, Achard F, Brown S, Herold M, Murdiyarso D, Schlamadinger B, Souza C (2007) Earth observations for estimating greenhouse gas emissions from deforestation in developing countries. Environ Sci Policy 10(4):385–394CrossRefGoogle Scholar
  13. Dorais A, Cardille J (2011) Strategies for incorporating high-resolution google earth databases to guide and validate classifications : understanding deforestation in borneo. Remote Sens 3(6):1157–1176CrossRefGoogle Scholar
  14. Eckert S (2012) Improved forest biomass and carbon estimations using texture measures from worldView-2 satellite data. Remote Sens 4(12):810–829CrossRefGoogle Scholar
  15. Elshayal (2017) Elshayal Smart GIS software. Accessed 18 May 2017Google Scholar
  16. FAO (2010) Global forest resources assessment 2010. FAO, Rome, p 122Google Scholar
  17. Frazier RJ, Coops NC, Wulder MA, Kennedy R (2014) Characterization of aboveground biomass in an unmanaged boreal forest using Landsat temporal segmentation metrics. ISPRS J Photogramm Remote Sens 92(6):137–146CrossRefGoogle Scholar
  18. Fuchs H, Magdon P, Kleinn C, Flessa H (2009) Estimating aboveground carbon in a catchment of the Siberian forest tundra: combining satellite imagery and field inventory. Remote Sens Environ 113(3):518–531CrossRefGoogle Scholar
  19. Gates DM (1990) Climate change and the response of forests. Int J Remote Sens 11(7):1095–1107CrossRefGoogle Scholar
  20. Gibbs HK, Brown S, Niles JO, Foley J (2007) Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environ Res Lett 2(4):45–49Google Scholar
  21. Gilani H, Gautam SK, Murthy MSR, Koju UA, Uddin K, Karky B (2014) Monitoring the performance of community forestry to achieve REDD+ Goals through geospatial methods. Int Arch Photogramm Remote Sens Spat Inf Sci 8(1):1295–1300CrossRefGoogle Scholar
  22. Glenn NF, Neuenschwander A, Vierling LA, Spaete L, Shinneman DJ, McIlroy SK (2016) Landsat 8 and ICESat-2: performance and potential synergies for quantifying dryland ecosystem vegetation cover and biomass. Remote Sens Environ 185(11):233–242CrossRefGoogle Scholar
  23. Goetz SJ, Baccini A, Laporte NT, Johns T, Walker W, Kellndorfer J, Sun M (2009) Mapping and monitoring carbon stocks with satellite observations: a comparison of methods. Carbon Balance Manage 4(1):112–121CrossRefGoogle Scholar
  24. Goodman RC, Phillips OL, Baker TR (2014) The importance of crown dimensions to improve tropical tree biomass estimates. Ecol Appl 24(4):680–698CrossRefPubMedGoogle Scholar
  25. Guneralp I, Filippi AM, Randall J (2014) Estimation of floodplain aboveground biomass using multispectralremote sensing and nonparametric modelling. Int J Appl Earth Obs Geoinf 33(1):119–126CrossRefGoogle Scholar
  26. Hanes JM (2014) Biophysical applications of satellite remote sensing. Accessed 8 May 2017Google Scholar
  27. Healey SP, Yang Z, Cohen WB, Pierce DJ (2006) Application of two regression-based methods to estimate the effects of partial harvest on forest structure using Landsat data. Remote Sens Environ 101(91):115–126CrossRefGoogle Scholar
  28. Hu Q, Wu WB, Xia T, Yu QY, Yang P, Li ZG, Song Q (2013) Exploring the use of google earth imagery and object-based methods in land use/cover mapping. Remote Sens 5(11):6026–6042CrossRefGoogle Scholar
  29. Husch B, Beers TW, Kershaw JA (2003) Forest mensuration. Wiley, New YorkGoogle Scholar
  30. Hussin YA, Gilani H (2011) Mapping carbon stocks in community forests of Nepal using high spatial resolution satellite images. ICIMOD, Sustainable Mountain Development, Patan, pp 125–135Google Scholar
  31. Hussin YA, Gilani H, Leeuwen L, Murthy MSR, Shah R, Baral S, Qamer FM (2014) Evaluation of object-based image analysis techniques on very high-resolution satellite image for biomass estimation in a watershed of hilly forest of Nepal. Appl Geomatics 6(1):59–68CrossRefGoogle Scholar
  32. Ismail R, Mutanga O (2010) A comparison of regression tree ensembles: Predicting Sirex noctilio induced water stress in Pinus patula forests of KwaZulu-Natal, South Africa. Int J Appl Earth Obs Geoinf 12(1):45–51CrossRefGoogle Scholar
  33. Kamusoko C, Gamba J, Murakami H (2014) Mapping woodland cover in the Miombo ecosystem: a comparison of machine learning classifiers. Land 3(2):524–540CrossRefGoogle Scholar
  34. Kankeu RS, Jean D, Richard S, Marle N (2016) Quantifying post logging biomass loss using satellite images and ground measurements in Southeast Cameroon. J For Res 27(6):1415–1426CrossRefGoogle Scholar
  35. Kapfer PM, Streby HM, Gurung B, Simcharoen A, McDougal CC, Smith JLD (2011) Fine-scale spatio-temporal variation in tiger Panthera tigris diet: effect of study duration and extent on estimates of tiger diet in Chitwan National Park, Nepal. Wildl Biol 17(3):277–285CrossRefGoogle Scholar
  36. Karlson M, Ostwald M, Reese H, Sanou J, Tankoano B, Karlson M, Tankoano B (2015) Mapping tree canopy cover and aboveground biomass in Sudano-Sahelian woodlands using landsat 8 and random forest. Remote Sens 7(8):10017–10041CrossRefGoogle Scholar
  37. Karna YK, Hussin YA, Gilani H, Bronsveld MC, Murthy MSR, Qamer FM, Baniya CB (2015) Integration of WorldView-2 and airborne LiDAR data for tree species level carbon stock mapping in Kayar Khola watershe, Nepal. Int J Appl Earth Obs Geoinf 38(6):280–291CrossRefGoogle Scholar
  38. Ke YH, Quackenbush LJ (2008) Comparison of individual tree crown detection and delineation methods. In: ASPRS 2008 annual conference proceedings, 11. Accessed 28 May 2017Google Scholar
  39. Ke YH, Quackenbush LJ (2011) A review of methods for automatic individual tree-crown detection and delineation from passive remote sensing. Int J Remote Sens 32(17):4725–4747CrossRefGoogle Scholar
  40. Kim SR, Kwak DA, Lee WK, Son Y, Bae W, Yoo S (2010) Estimation of carbon storage based on individual tree detection in Pinus densiflora stands using a fusion of aerial photography and LiDAR data. Sci China. Life Sci 53(7):885–897CrossRefPubMedGoogle Scholar
  41. Landsat (2017) Using the USGS Landsat 8 Product. Accessed 08 Jan 2017Google Scholar
  42. Larsson H (2007) Linear regressions for canopy cover estimation in Acacia woodlands using Landsat-TM, -MSS and SPOT HRV XS data. Int J Remote Sens 48(1):37–41Google Scholar
  43. Leckie DG, Gougeon FA, Tinis S, Nelson T, Burnett CN, Paradine D (2005) Automated tree recognition in old growth conifer stands with high resolution digital imagery. Remote Sens Environ 94(3):311–326CrossRefGoogle Scholar
  44. Lu DS (2006) The potential and challenge of remote sensing—based biomass estimation. Int J Remote Sens 27(7):1297–1328CrossRefGoogle Scholar
  45. Lu DS, Mausel P, Brond E, Moran E (2004) Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin. For Ecol Manag 198(3):149–167CrossRefGoogle Scholar
  46. Ludwig A, Meyer H, Nauss T (2016) Automatic classification of Google Earth images for a larger scale monitoring of bush encroachment in South Africa. Int J Appl Earth Obs Geoinf 50(3):89–94CrossRefGoogle Scholar
  47. MacDicken KG (1997) A guide to monitoring carbon storage in forestry and agroforestry projects. Winnrocak Int. Special publication, Morrilton, p 182Google Scholar
  48. Malarvizhi K, Kumar SV, Porchelvan P (2016) Use of high resolution google earth satellite imagery in landuse map preparation for urban related applications. Procedia Technol 24(2):1835–1842CrossRefGoogle Scholar
  49. Mcroberts RE, Tomppo EO, Czaplewski RL (1992) Sampling designs for national forest assessments. FAO, Rome, p 121Google Scholar
  50. Ming QZ, Guo SR, Jiao YM (2011) High gradient effects of forest biomass energy in mountainous region: a case of Meili Snow Mountain. Procedia Earth Planet Sci 2(1):315–320CrossRefGoogle Scholar
  51. Murthy MSR, Wesselman SG (2015) Multi-scale forest biomass assessment and monitoring in the Hindu Kush Himalayan region: a geospatial perspective. ICIMOD, p 16Google Scholar
  52. Platt RV, Schoennagel T (2017) An object-oriented approach to assessing changes in tree cover in the Colorado Front. For Ecol Manag 258(7):1342–1349CrossRefGoogle Scholar
  53. Ploton P, Pélissier R, Proisy C, Flavenot T, Barbier N, Rai SN, Couteron P (2012) Assessing aboveground tropical forest biomass using Google Earth canopy images. Ecol Appl 22(3):993–1003CrossRefPubMedGoogle Scholar
  54. Pot J (2016) Google Earth Pro used to cost $400 a year—here’s how to get it for free. Accessed 11 Nov 2016Google Scholar
  55. Rahman EM, Berg M, Way MJ, Ahmed FB (2009) Hand-held spectrometry for estimating thrips (Fulmekiola serrata) incidence in sugarcane. Geosci Remote Sens 63(1):268–271Google Scholar
  56. Shao G (2016) Optical remote sensing. In: Richardson D, Castree N, Goodchild MF, Kobayashi A, Liu W, Marston RA (eds) International encyclopedia of geography: people, the earth, environment, and technology. Wiley, Hoboken, pp 2390–2395Google Scholar
  57. Sharma ER, Pukkala T (1990) Volume and biomass prediction equations of forest trees of Nepal. Forest Survey and Statistical Division. Ministry of Forest and Soil Conservation, Kathmandu, pp 82–88Google Scholar
  58. Shih FY, Cheng SX (2005) Automatic seeded region growing for color image segmentation. Image Vis Comput 23(10):877–886CrossRefGoogle Scholar
  59. Singh M, Evans D, Friess DA, Tan BS (2015) Google Earth. Remote Sens 7(5):5057–5076CrossRefGoogle Scholar
  60. Smil V (2010) Energy myths and realities: bringing science to the energy policy debate. Government Institutes Press, Lanham, p 182Google Scholar
  61. Spanner M, Johnson L, Miller J, Mccreight R, Applications SE, May N, Gong P (1994) Remote sensing of seasonal leaf area index across the Oregon Transect Freemantle. Ecol Appl 4(2):258–271CrossRefGoogle Scholar
  62. Stocker TF, Qin GK, Plattner M, Tignor SK, Allen J, Boschung A, Nauels Y, Xia VB (2013) IPCC, 2013: climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, p 1535Google Scholar
  63. Takahashi T, Awaya Y, Hirata Y, Furuya N, Sakai T, Sakai A (2010) Stand volume estimation by combining low laser-sampling density LiDAR data with QuickBird panchromatic imagery in closed-canopy Japanese cedar ( Cryptomeria japonica ) plantations. Int J Remote Sens 31(5):1281–1301CrossRefGoogle Scholar
  64. Uddin K, Lal H, Murthy MSR, Bajracharya B, Shrestha B, Gilani H, Dango B (2014) Development of 2010 national land cover database for the Nepal. J Environ Manag 148(1):82–90Google Scholar
  65. Uddin K, Gilani H, Murthy MSR, Kotru R, Qamer FM (2015) Forest condition monitoring using very-high-resolution satellite imagery in a Remote Mountain watershed in Nepal. Mt Res Dev 35(3):264–277CrossRefGoogle Scholar
  66. Wu WC, Pauw E, Helldén U (2013) Assessing woody biomass in African tropical savannahs by multiscale remote sensing. Int J Remote Sens 34(13):4525–4549CrossRefGoogle Scholar
  67. Wu CF, Shen HH, Wang K, Shen AH, Deng JS, Gan MY (2016) Landsat imagery-based above ground biomass estimation and change investigation related to human activities. Sustainability 8(2):159–165CrossRefGoogle Scholar
  68. Wulder MA, Skakun WAK (2004) Estimating time since forest harvest using segmented landsat ETM+ imagery. Remote Sens Environ 93(1–2):179–187CrossRefGoogle Scholar
  69. Yan EP, Lin H, Wang GX, Sun H (2014) Multi-scale simulation and accuracy assessment of forest carbon using Landsat and MODIS data. Conference paper on Earth observation and Remote sensing, Changsha, China. Accessed 28 Aug 2017Google Scholar
  70. Yunus U, Ibrahim O, Tufan O, Serhun D (2017) Comparison of satellite images with different spatial resolutions to estimate stand structural diversity in urban forests. J For Res 185(11):233–242Google Scholar
  71. Zhu XL, Liu DS (2015) Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series. ISPRS J Photogramm Remote Sens 102(4):222–231CrossRefGoogle Scholar

Copyright information

© Northeast Forestry University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Upama A. Koju
    • 1
    • 2
  • Jiahua Zhang
    • 1
  • Shashish Maharjan
    • 3
  • Sha Zhang
    • 1
    • 2
  • Yun Bai
    • 1
    • 2
  • Dinesh B. I. P. Vijayakumar
    • 4
  • Fengmei Yao
    • 2
  1. 1.Key Laboratory of Digital Earth SciencesInstitute of Remote Sensing and Digital Earth, CASBeijingPeople’s Republic of China
  2. 2.University of Chinese Academy of SciencesBeijingPeople’s Republic of China
  3. 3.International Maize and Wheat Improvement CenterKathmanduNepal
  4. 4.The Laboratory of Forest InventoryThe National Institute of Geographic and Forest InformationNancyFrance

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