Journal of Forestry Research

, Volume 29, Issue 3, pp 797–811 | Cite as

Estimating and mapping forest biomass in northeast China using joint forest resources inventory and remote sensing data

Original Paper

Abstract

Being able to accurately estimate and map forest biomass at large scales is important for a better understanding of the terrestrial carbon cycle and for improving the effectiveness of forest management. In this study, forest plot sample data, forest resources inventory (FRI) data, and SPOT Vegetation (SPOT-VGT) normalized difference vegetation index (NDVI) data were used to estimate total forest biomass and spatial distribution of forest biomass in northeast China (with 1 km resolution). Total forest biomass at both county and provincial scales was estimated using FRI data of 11 different forest types obtained by sampling 1156 forest plots, and newly-created volume to biomass conversion models. The biomass density at the county scale and SPOT-VGT NDVI data were used to estimate the spatial distribution of forest biomass. The results suggest that the total forest biomass was 2.4 Pg (1 Pg = 1015 g), with an average of 77.2 Mg ha−1, during the study period. Forests having greater biomass density were located in the middle mountain ranges in the study area. Human activities affected forest biomass at different elevations, slopes and aspects. The results suggest that the volume to biomass conversion models that could be developed using more plot samples and more detailed forest type classifications would be better suited for the study area and would provide more accurate biomass estimates. Use of both FRI and remote sensing data allowed the down-scaling of regional forest biomass statistics to forest cover pixels to produce a relatively fine-resolution biomass map.

Keywords

Forest biomass Biomass density Spatial distribution Human disturbance Remote sensing 

Notes

Acknowledgements

The authors thank all staff members in the Natural Forest Conservation Group (NFCG) of the Institute of Applied Ecology (IAE) for their help. We are grateful to the authors of Xu et al. (2007) and Chen (2003) who allowed us to use their data for the 609 plots.

References

  1. Anaya JA, Chuvieco E, Palacios-Orueta A (2009) Above-ground biomass assessment in Colombia: a remote sensing approach. For Ecol Manag 257:1237–1246CrossRefGoogle Scholar
  2. Blackard JA, Finco MV, Helmer EH, Holden GR, Hoppus ML, Jacobs DM, Lister AJ, Moisen GG, Nelson MD, Riemann R, Ruefenacht B, Salajanu D, Weyermann DL, Winterberger KC, Brandeis TJ, Czaplewski RL, McRoberts RE, Patterson PL, Tymcio RP (2008) Mapping US forest biomass using nation wide forest inventory data and moderate resolution information. Remote Sens Environ 112:1658–1677CrossRefGoogle Scholar
  3. Botkin DB, Simpson LG (1990) Biomass of the North-American boreal forest: a step toward accurate global measures. Biogeochemistry 9:161–174Google Scholar
  4. Boyd DS, Foody GM, Curran PJ (1999) The relationship between the biomass of Cameroonian tropical forest and radiation reflected in middle infrared wavelength. Int J Remote Sens 20(5):1017–1023CrossRefGoogle Scholar
  5. Brown S (2002) Measuring carbon in forests: current status and future challenges. Environ Pollut 116:363–372CrossRefPubMedGoogle Scholar
  6. Brown SL, Schroeder PE (1999) Spatial patterns of aboveground production and mortality of woody biomass for eastern U.S. forests. Ecol Appl 9:968–980Google Scholar
  7. Brown SJ, Gillespie R, Lugo AE (1989) Biomass estimates for tropical moist forests of Brazilian Amazon. Interciencia 17:8–18Google Scholar
  8. Chen XL (2003) The distribution patterns and modeling of biomass and net primary production in China main forests. Doctor of Philosophy Thesis, Beijing Forestry University, Beijing, China (in Chinese)Google Scholar
  9. Chen CG, Guo XF (1986) Mathematical models for predicting broadleaved-Korean pine mixed forest biomass. J Liaoning For Sci Technol 3:27–37 (in Chinese) Google Scholar
  10. Dixon RK, Brown S, Houghton RA, Trexier MC, Winsniewski J (1994) Carbon pools and flux of global forest ecosystems. Science 263:185–190CrossRefPubMedGoogle Scholar
  11. Du L, Zhou T, Zou ZH, Zhao X, Huang KC, Wu H (2014) Mapping forest biomass using remote sensing and national forest inventory in China. Forests 5:1267–1283CrossRefGoogle Scholar
  12. EBVMC (Editorial Board of Vegetation Map of China, Chinese Academy of Sciences) (2001) Vegetation Atlas of China. Science Press, Beijing (in Chinese) Google Scholar
  13. Fang JY, Wang GG, Liu GH, Xu SL (1998) Forest biomass of China: an estimate based on the biomass-volume relationship. Ecol Appl 8(4):1984–1991Google Scholar
  14. Fang JY, Chen AP, Peng CH, Zhao SQ, Ci LJ (2001) Changes in forest biomass carbon storage in China between 1949 and 1998. Science 292:2320–2322CrossRefPubMedGoogle Scholar
  15. González-Alonso F, Merino-De-Miguel S, Roldán-Zamarrón A, García-Gigorro S, Cuevas JM (2006) Forest biomass estimation through NDVI composites. The role of remotely sensed data to assess Spanish forests as carbon sinks. Int J Remote Sens 27:5409–5415CrossRefGoogle Scholar
  16. Holben BN (1986) Characteristics of maximum-value composite images for temporal AVHRR data. Int J Remote Sens 7:1435–1445CrossRefGoogle Scholar
  17. Houghton RA, Lawrence KT, Hackler JL, Brown S (2001) The spatial distribution of forest biomass in the Brazilian Amazon: a comparison of estimates. Glob Change Biol 7:731–746CrossRefGoogle Scholar
  18. Jiao Y, Hu HQ (2005) Carbon storage and its dynamics of forest vegetations in Heilongjiang Province. Chin J Appl Ecol 16(12):2248–2252 (in Chinese) Google Scholar
  19. Kramer PJ (1982) Carbon dioxide concentration, photosynthesis, and dry mater production. Bioscience 31:29–33CrossRefGoogle Scholar
  20. Le Toan T, Quegan S, Davidson MWJ, Balzter H, Paillou P, Papathanassiou K, Plummer S, Rocca F, Saatchi S, Shugart H, Ulander L (2011) The BIOMASS mission: mapping global forest biomass to better understand the terrestrial carbon cycle. Remote Sens Environ 115:2850–2860CrossRefGoogle Scholar
  21. Li HK, Lei YC (2010) Estimation and evaluation of forest biomass carbon storage in China. Chinese Forestry Publishers, Beijing (in Chinese; Manag 222, 191–201)Google Scholar
  22. Liu J, Liu S, Loveland TR (2006) Temporal evolution of carbon budgets of the Appalachian forests in the U.S. from 1972 to 2000. For Ecol Manag 222:191–201.CrossRefGoogle Scholar
  23. Liu C, Zhang LJ, Li FR, Jin XJ (2014) Spatial modeling of the carbon stock of forest trees in Heilongjiang Province, China. J For Res 25(2):269–280CrossRefGoogle Scholar
  24. Lu DS (2007) The potential and challenge of remote sensing-based biomass estimation. Int J Remote Sens 27:1297–1328CrossRefGoogle Scholar
  25. Lu DS, Mausel P, Brondizio ES, Moran E (2004) Relationships between forest stand parameters and Landsat Thematic Mapper spectral responses in the Brazilian Amazon basin. For Ecol Manag 198:149–167CrossRefGoogle Scholar
  26. Luo T (1996) Research on carbon sequestration functions of main forest types in northern China. Doctor of Philosophy Thesis, Chinese Academy of Sciences, Beijing, China, p 211 (in Chinese)Google Scholar
  27. Melillo JM, Steudler PA, Aber JD, Newkirk K, Lux H, Bowles FP, Catricala C, Magill A, Ahrens T, Morrisseau S (2002) Soil warming and carbon cycle feedbacks to the climate system. Science 298:2173–2176CrossRefPubMedGoogle Scholar
  28. Myneni RB, Hall FG, Sellers PJ, Marshak AL (1995) The interpretation of spectral vegetation indexes. IEEE Trans Geosci Remote Sens 33:481–486CrossRefGoogle Scholar
  29. Pan YD, Luo TX, Birdsey R, Hom J, Melillo J (2004) New estimates of carbon storage and sequestration in China’s forests: effects of age-class and method on inventory-based carbon estimation. Clim Change 67:211–236CrossRefGoogle Scholar
  30. Phillips OL, Malhi Y, Higuchi N, Laurance WF, Nunez PV, Vasquez RM, Laurance SG, Ferreira LV, Stern M, Brown S, Grace J (1998) Changes in the carbon balance of tropical forests: evidence from long-term plots. Science 282:440–442CrossRefGoogle Scholar
  31. Prince SD, Goward SN (1995) Global primary production: a remote sensing approach. J Biogeogr 22:815–835CrossRefGoogle Scholar
  32. Ren Y, Chen SS, Wei XH, Xi WM, Luo YJ, Song XD, Zuo SD, Yang YS (2016) Disentangling the factors that contribute to variation in forest biomass increments in the mid-subtropical forests of China. J For Res 27(4):919–930CrossRefGoogle Scholar
  33. Running SW, Justice CO, Salomonson V, Hall D, Barker J, Kaufmann YJ, Strahler AH, Huete AR, Muller JP, Vanderbilt V, Wan ZM, Teillet P, Carneggie D (1994) Terrestrial remote sensing science and algorithms for EOS/MODIS. Int J Remote Sens 15:3587–3620CrossRefGoogle Scholar
  34. Smith JE, Heath LS, Jenkins JS (2003) Forest volume to biomass models and estimates of mass for live and standing dead trees of U.S. forests. [EB/OL], 17 Oct 2010. http://www.arborsearch.fs.fed.us/pubs/5179
  35. Sun YH, Meng L, Tian L, Li GL, Sun OJ (2016) Assessing current stocks and future sequestration potential of forest biomass carbon in Daqing Mountain Nature Reserve of Inner Mongolia, China. J For Res 27(4):931–938CrossRefGoogle Scholar
  36. Tan K, Piao SL, Peng CH, Fang JY (2007) Satellite-based estimation of biomass carbon storages for northeast China’s forests between 1982 and 1999. Forest Ecol Manage 240:114–121CrossRefGoogle Scholar
  37. Tucker CJ, Sellers PJ (1986) Satellite remote sensing of primary production. Int J Remote Sens 7(11):1395–1416CrossRefGoogle Scholar
  38. Wang XJ, Huang GS, Sun YJ, Fu X, Han AH (2008) Forest carbon storage and dynamics in Liaoning Province from 1984 to 2000. Acta Ecol Sin 28(10):4757–4764 (in Chinese) Google Scholar
  39. Wang YF, Liu L, Shangguan ZP (2017) Dynamics of forest biomass carbon stocks from 1949 to 2008 in Henan Province, east-central China. J For Res.  https://doi.org/10.1007/s11676-017-0459-7 (published online) Google Scholar
  40. West PW (2004) Arbor and forest measurement. Springer, BerlinCrossRefGoogle Scholar
  41. Woodwell GM, Whittaker RH, Reiners WA, Likens GE, Delwiche CC, Botkin DB (1978) The biota and the world carbon budget. Science 199:141–146CrossRefPubMedGoogle Scholar
  42. Wulder MA, White JC, Fournier RA, Luther JE, Magnussen S (2008) Spatially explicit large area biomass estimation: three approaches using forest inventory and remotely sensed imagery in a GIS. Sensors. 8:529–560CrossRefPubMedPubMedCentralGoogle Scholar
  43. Xu XL, Cao MK, Li KR (2007) Temporal spatial dynamics of carbon storage of forest vegetation in China. Prog Geog 26(6):1–10 (in Chinese) Google Scholar
  44. Yemshanov D, McKenney DW, Pedlar JH (2012) Mapping forest composition from the Canadian National Forest Inventory and land cover classification maps. Environ Monit Assess 184:4655–4669CrossRefPubMedGoogle Scholar
  45. Zhang XY, Kondragunta S (2006) Estimating forest biomass in the USA using generalized allometric models and MODIS land products. Geophys Res Lett 33:1–5Google Scholar
  46. Zhang J, Huang S, Hogg EH, Lieffers V, Qin Y, He F (2014) Estimating spatial variation in Alberta forest biomass from a combination of forest inventory and remote sensing data. Biogeosciences 11:2793–2808CrossRefGoogle Scholar
  47. Zhao M, Zhou GS (2004) Forest inventory data based biomass models and their prospects. Chin J Appl Ecol 15(8):1468–1472 (in Chinese) Google Scholar
  48. Zheng DL, Rademacher J, Chen JQ, Crow T, Bresee M, Le Moine J, Ryu S-R (2004) Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA. Remote Sens Environ 93:402–411CrossRefGoogle Scholar

Copyright information

© Northeast Forestry University and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Henan Polytechnic UniversityJiaozuoPeople’s Republic of China
  2. 2.State Key Laboratory of Forest and Soil Ecology, Institute of Applied EcologyChinese Academy of SciencesShenyangPeople’s Republic of China

Personalised recommendations