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

  • Xinchuang Wang
  • Shidong Wang
  • Limin Dai
Original Paper


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.


Forest biomass Biomass density Spatial distribution Human disturbance Remote sensing 



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.


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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

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