Skip to main content
SpringerLink
Log in

Estimating forest aboveground biomass using HJ-1 Satellite CCD and ICESat GLAS waveform data

  • Research Paper
  • Published:
Science China Earth Sciences Aims and scope Submit manuscript

Abstract

The ecosystem in northeastern China and the Russian Far East is a hotspot of scientific research into the global carbon balance. Forest aboveground biomass (AGB) is an important component in the land surface carbon cycle. In this study, using forest inventory data and forest distribution data, the AGB was estimated for forest in Daxinganlin in northeastern China by combining charge-coupled device (CCD) data from the Small Satellite for Disaster and Environment Monitoring and Forecast (HJ-1) and Geoscience Laser Altimeter System (GLAS) waveform data from the Ice, Cloud and land Elevation Satellite (ICESat). The forest AGB prediction models were separately developed for different forest types in the research area at GLAS footprint level from GLAS waveform parameters and field survey plot biomass in the Changqing (CQ) Forest Center, which was calculated from forest inventory data. The resulted statistical regression models have a R 2=0.68 for conifer and R 2=0.71 for broadleaf forests. These models were used to estimate biomass for all GLAS footprints of forest located in the study area. All GLAS footprint biomass coupled with various spectral reflectivity parameters and vegetation indices derived from HJ-1 satellite CCD data were used in multiple regression analyses to establish biomass prediction models (R 2=0.55 and R 2=0.52 for needle and broadleaf respectively). Then the models were used to produce a forest AGB map for the whole study area using the HJ-1 data. Biomass data obtained from forest inventory data of the Zhuanglin (ZL) Forest Center were used as independent field measurements to validate the AGB estimated from HJ-1 CCD data (R 2=0.71). About 80% of biomass samples had an error less than 20 t ha−1, and the mean error of all validation samples is 5.74 t ha−1. The pixel-level biomass map was then stratified into different biomass levels to illustrate the AGB spatial distribution pattern in this area. It was found that HJ-1 wide-swath data and GLAS waveform data can be combined to estimate forest biomass with good precision, and the biomass data can be used as input data for future carbon budget analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Lieth H, Whittaker R H. Primary Productivity of the Biosphere. New York: Springer-Verlag, 1975

    Google Scholar 

  2. Bown S, Lugo A E. Biomass of tropical forest: A new estimate based on forest volumes. Science, 1984, 223: 1290–1293

    Article  Google Scholar 

  3. Kira T, Ona Y, Hosokawa T. Biological production in a warm temperate evergreen Oak forest of Japan. JIBP Synthesis, 1978, 18: 69–82

    Google Scholar 

  4. Fang J Y, Chen A P, Peng C H, et al. Changes in forest biomass carbon storage in China between 1949 and 1998. Science, 2001, 292: 2320–2322

    Article  Google Scholar 

  5. Curran P L, Dungan J L, Gholz H L. Seasonal LAI in slash pine estimated with Landsat TM. Remote Sens Environ, 1992, 39: 3–13

    Article  Google Scholar 

  6. Zheng D, Radenacger J, Chen J, et al. Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin USA. Remote Sens Environ, 2004, 93: 402–411

    Article  Google Scholar 

  7. Guo Z H, Peng S L, Wang B S. Estimating forest biomass in western Guangdong using Landsat TM data (in Chinese). Acta Ecol Sin, 2002, 22: 1832–1840

    Google Scholar 

  8. Hame T, Salli A, Andersson K, et al. A new methodology for the estimation of biomass of conifer dominated boreal forest using NOAA AVHRR data. Int J Remote Sens, 1997, 18: 11–32

    Article  Google Scholar 

  9. Phua M H, Saito H. Estimation of biomass of a mountainous tropical forest using Landsat TM data. Can J Remote Sens, 2003, 29: 429–440

    Google Scholar 

  10. Houghton R A, Lawrence K T, Hackler J L, et al. The spatial distribution of forest biomass in the Brazilian Amazon: A comparison of estimates. Glob Change Biol, 2008, 7: 731–746

    Article  Google Scholar 

  11. Dong J, Kaufmann R K, Myneni R B, et al. Remote sensing estimates of boreal and temperate forest woody biomass: Carbon pools, sources, and sinks. Remote Sens Environ, 2003, 84: 393–410

    Article  Google Scholar 

  12. Lu D, Mateus B. Exploring TM image texture and its relationships with biomass estimation in Rondoia, Brazilian Amazon. Acta Amazon, 2005, 35: 261–268

    Google Scholar 

  13. Kasischke E S, Christensen Jr, Haney E M. Modeling of geometric properties of loblolly pine tree and stand characteristics for use in radar backscatter studies. IEEE Trans Geosci Remote Sens, 1994, 32: 800–822

    Article  Google Scholar 

  14. Imhoff M. Theoretical analysis of the effect of forest structure on synthetic aperture radar backscatter and the remote sensing of biomass. IEEE Trans Geosci Remote Sens, 1995, 33: 341–352

    Article  Google Scholar 

  15. Dobson M C, Ulaby F T, Le Toan T, et al. Dependence of radar backscatter on coniferous forest biomass. IEEE Trans Geosci Remote Sens, 1992, 30: 412–415

    Article  Google Scholar 

  16. Ranson K J, Sun G, Lang R H, et al. Mapping of boreal forest biomass from spaceborne synthetic aperture radar. J Geophys Res, 1997, 102: 29599–29610

    Article  Google Scholar 

  17. Sun G, Ranson K J, Kharuk V I. Radiometric slope correction for forest biomass estimation from SAR data in Western Sayani mountains, Siberia. Remote Sens Environ, 2000, 79: 279–287

    Article  Google Scholar 

  18. Harding D J, Carabajal C C. ICESat waveform measurements of within-footprint topographic relief and vegetation vertical structure. Geophys Res Lett, 2005, 32: S10

    Article  Google Scholar 

  19. Lefsky M A, Cohen W B, Acker S A, et al. Lidar remote sensing of the canopy structure and biophysical properties of Douglas-Fir Western Hemlock Forests. Remote Sens Environ, 1999, 70: 339–361

    Article  Google Scholar 

  20. Means J E, Acker S A, Harding D J, et al. Use of large-footprint scanning airborne lidar to estimate forest stand characteristics in the Western Cascades of Oregon. Remote Sens Environ, 1999, 67: 298–308

    Article  Google Scholar 

  21. Lefsky M A, Harding D, Cohen W B, et al. Surface lidar Remote Sensing of basal area and biomass in deciduous forest of Eastern Maryland, USA. Remote Sens Environ, 1999, 67: 83–98

    Article  Google Scholar 

  22. Drake J B, Dubayah R O, Clark D B, et al. Estimation of tropical forest structural characteristics using large-footprint lidar. Remote Sens Environ, 2002, 79: 305–319

    Article  Google Scholar 

  23. Drake J B, Knox R G, Dubayah R O, et al. Above-ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: Factors affecting the generality of relationships. Glob Ecol Biogeogr, 2003, 12: 147–159

    Article  Google Scholar 

  24. Zhou Y L. China Daxinganlin Vegetation (in Chinese). Beijing: Science Press, 1991

    Google Scholar 

  25. Feng Z W, Wang X K. Biomass and Productivity of Chinese Forest Ecosystem (in Chinese). Beijing: Science Press, 1999

    Google Scholar 

  26. Xu Z B, Dai H C. Daxinganling main forest types of biological production (in Chinese). Chin J Ecol, 1988, 7(Suppl): 49–54

    Google Scholar 

  27. Sun G, Ranson K J, Kimes D S, et al. Forest vertical structure from GLAS: An evaluation using LVIS and SRTM data. Remote Sens Environ, 2008, 112: 107–117

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing, 100101, China

    ZhiFeng Guo, Hong Chi & GuoQing Sun

  2. Graduate University of Chinese Academy of Sciences, Beijing, 100049, China

    Hong Chi

  3. Department of Geography University of Maryland, College Park, MD, 20742, USA

    GuoQing Sun

Authors
  1. ZhiFeng Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hong Chi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. GuoQing Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to ZhiFeng Guo.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Guo, Z., Chi, H. & Sun, G. Estimating forest aboveground biomass using HJ-1 Satellite CCD and ICESat GLAS waveform data. Sci. China Earth Sci. 53 (Suppl 1), 16–25 (2010). https://doi.org/10.1007/s11430-010-4128-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11430-010-4128-3

Keywords

Search

Navigation