Applications of the new Remote Sensing Method to the Forest Biomass Estimation

Applications of the new Remote Sensing Method to the Forest Biomass Estimation

Loading document ...
Loading page ...


Author(s): Wang Nan, Masato Katoh, Shinichi Yamamoto, Naoyuki Nishimura, Daisuke Hoshino

Download Full PDF Read Complete Article

789 1429 1-13 Volume 2 - Aug 2013


For accurate measurement of forest biomass in the Akazawa Forest Reserve, this study analyzed texture measures derived from GeoEye-1 satellite data using the individual tree crown (ITC) method. On this basis, canopy area, tree tops and tree species of individual trees were delineated. Canopy area was used to calculate the DBH of trees in canopy layer based on canopy-DBH curve in this stand. In this study, the estimation models, between DBH and height, and between canopy area and DBH were developed by linear regression using forest survey data. Then according to the results of satellite data interpreted the biomass of every tree was calculated by biomass expansion factor (BEF). This method was verified against the survey data from old–growth Chamaecyparis obtusa stand composed of various cover types. For Chamaecyparis obtusa, the accuracy of biomass estimation was higher than 84%. However, the accuracy of Chamaecyparis pisifera was less than 60%, because some Chamaecyparis pisifera trees were misidentified as Chamaecyparis obtusa, and canopy area of Chamaecyparis pisifera was underestimated in the high-density stand. For Thujopsis dolabrata, the accuracy ranged from 22.4 % to 78.9%, and from 63.4% to 84.6% for broad-leaved trees, because many of them were understory. These results indicated that estimation of old-growth forest biomass based on high resolution satellite data, might be validated for estimating biomass at the individual tree level improved by developing and applying forest stratum–specific models with the ITC-survey data as a bridging reference in addition to spectral information. This approach is useful for biomass estimation whether is used to calculate biomass of individual tree or forest.


Biomass estimation, Individual Tree Crown (ITC) method, High resolution satellite data, Old-growth forest


  1. Bortolot, Z.J.; Wynne, R.H., 2005. Estimating forest biomass using small footprint lidar data: An individual tree-based approach that incorporates training data. ISPRS J. Photogramm. 2005, 59, 342-360
  2. Curtis Edson and Michael G. Wing., 2011. Airborne Light Detection and Ranging (LiDAR) for Individual Tree Stem Location, Height, and Biomass Measurements. Remote Sens. 2011, 3, 2494-2528pp
  3. ERDAS, 2012. ERDAS Imagine 8.6 On-Line Help Manual; Available online: (accessed on 6 March 2012)
  4. Forestry Agency Planning Division., 1970. Stem volume table of east Japan.261-283pp, Japan forestry investigation committee, Tokyo
  5. Geomatica 9. EASI User Guide; PCI Geomatics Enter., 2005. Inc.: Richmond, ON, Canada,
  6. Gougeon, F.A., 1995. A crown following approach to the automatic delineation of individual tree crowns in high spatial resolution aerial images. Can. J. Remote Sens. 1995, 21, 274–284
  7. Gougeon, F.A.; Leckie, D.G., 2003. Forest Information Extraction from High Spatial Resolution Images Using an Individual Tree Crown Approach; Information Report BC-X-396; Canadian Forest Service, Pacific Forestry Centre, Victoria, BC, Canada, 2003
  8. Hese, S.; Lucht, W.; Schmullius, C.; Barnsley, M.; Dubayah, R.; Knorr, D.; Neumann, K.; Riedel, T.; Schröter, K., 2005. Global biomass mapping for an improved understanding of the CO2 balance—The Earth observation mission carbon-3D. Remote Sens. Environ. 2005, 94, 94-104
  9. Hoshino D, Nishimura N and Yamamoto S., 2002. Dynamics of major conifer and deciduous broad-leaved tree species in an old-growth Chamaecyparis obtusa forest, central Japan Forest Ecology and Management. 159:133-144
  10. Hoshino D, Nishimura N and Yamamoto S., 2003. Effects of canopy conditions on the regeneration of major tree species in an old-growth Chamaecyparis in central Japan Forest Ecology and Management. 175:141-152
  11. Houghton, R.A., 2005. Aboveground forest biomass and the global carbon balance. Glob. Chang. Biol 11, 945-958
  12. Jalal Amini and Josaphat Tetuko Sri Sumantyo., 2011. SAR and Optical Images for Forest Biomass Estimation.5-15pp. Earth and Planetary Sciences
  13. Masato Katoh., 2007. Forest remote sensing- From fundamentals to applications Revision, In: Nobuyuki Abe (Eds), Forest environment, 12th ed. Japan forestry investigation committee, Tokyo, pp.180
  14. Masato Katoh, Gougeon, F.A. and Leckie, D.G., 2009. Application of high-resolution airborne data using individual tree crowns in Japanese conifer plantations. Journal of Forestry Research 14(1): 10-19
  15. NRFO (Nagano Regional Forest Office)., 1985. Report on the management of the Akasawa Chamaecyparis obtusa forest (enlarged). 102 pp, Nagano Regional Forest Office, Nagano, in Japan
  16. Oosumi, S., 1987. Forest Metrology Lecture. 287pp, Yokendo, Tokyo
  17. UNFCCC., 2010. Outcome of the Work of the Ad Hoc Working Group on Long-Term Cooperative Action under the Convention—Policy Approaches and Positive Incentives on Issues Relating to Reducing Emissions from Deforestation and Forest Degradation in
  18. Yamamoto S., 1993. Structure and dynamics of an old-growth Chamaecyparis forest in the Akasawa Forest Researve, Kisodistrict, central Japan. Jpn.J.For, Environ. 35: 32-41

Cite this Article:

International Journal of Sciences is Open Access Journal.
This article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License.
Author(s) retain the copyrights of this article, though, publication rights are with Alkhaer Publications.

Search Articles

Issue June 2023

Volume 12, June 2023

Table of Contents

World-wide Delivery is FREE

Share this Issue with Friends:

Submit your Paper