Effects of Topographic Normalization on the Relationship Between Tropical Forest Biomass and Landsat TM Images

  • Cunjian YangEmail author
  • He Huang
  • Jing Ni
  • Defei Yang
Research Article


The tropical forest system of Xishuangbanna is of important value in global forest system, because it is the only green island in the tropical desert belt and the largest intact best-preserved tropical rain forest in China. Discovering the relationship between the tropical forest biomass of Xishuangbanna and Landsat TM data is very important for depicting the tropical forest biomass by using remote sensing. The study aimed to (1) discover the relationship between tropical forest biomass and Landsat Thematic Mapper (TM) images in Xishuangbanna Prefecture, Yunnan Province, China, by using forest permanent plot data and correlation analysis; (2) explore how to improve the relationship by using topographic correction models such as the Lambert cosine correction model (LCCM) and the sun–canopy–sensor topographic correction model (SCSTCM). The results revealed the tropical forest biomass characteristics and the relationship characteristics between the tropical forest biomass and Landsat TM data and its improvement by using LCCM and SCSTCM. The results are beneficial to monitoring the tropical forest biomass variation in Xishuangbanna.


Biomass Remote sensing Image processing 



This research was supported by NNSF (Contract Nos. 40771144, 40161007), 863 (Contract No. 2009AA12Z140) and 973 (Contract Nos. 2009CB42110 and 2007CB714401). Thanks go to Jiyuan Liu, Chenglin Dang and Shuying Lv for their kind help.


  1. Boyd, D. S., Foody, G. M., & Curran, P. J. (1999). The relationship between the biomass of Cameroonian tropical forests and radiation reflected in middle infrared wavelengths. International Journal of Remote Sensing, 20(5), 1017–1023.CrossRefGoogle Scholar
  2. Brown, S. (2002). Measuring carbon in forests: current status and future challenges. Environmental Pollution, 116(3), 363–372.CrossRefGoogle Scholar
  3. Curtis, R. O. (1967). Height–diameter and height–diameter–age equations for second-growth Douglas-fir. Forestry Science, 13(4), 365–375.Google Scholar
  4. Dang, C. L., & Wu, Z. L. (1992). Study on the biomass for castanopsis echidnocarpa community of monsoon evergreen broad-leaved forest. Journal of Yunnan University, 14(2), 95–107.Google Scholar
  5. Dang, C. L., Wu, Z. L., & Zhang, Q. (1997). A study on biomass of the ravine tropical rain forest in Xishuangbanna. Acta Botanica Yunnanica, 9(suppl.), 123–128.Google Scholar
  6. David, R., Emilio, C., Javier, S., & Inmaculada, A. (2003). Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types. IEEE Transactions on Geoscience and Remote Sensing, 41(5), 1056–1061.CrossRefGoogle Scholar
  7. Dong, J. R., Kaufmann, R. K., Myneni, R. B., Tucker, C. J., Kauppi, P. E., Liski, J., et al. (2003). Remote sensing estimates of boreal and temperate forest woody biomass: Carbon pools, sources, and sinks. Remote Sensing of Environment, 84(3), 393–410.CrossRefGoogle Scholar
  8. Ekstrand, S. (1996). Landsat TM-based forest damage assessment: correction for topographic effects. Photogrammetric Engineering and Remote Sensing, 62(2), 151–161.Google Scholar
  9. Fang, J. Y., Guo, Z. D., Pu, S. L., & Chen, A. P. (2007). Estimation of Chinese Land vegetation carbon sink between 1981 and 2000. Science in China: Series D in Earth Sciences, 37, 804–812.Google Scholar
  10. Freitas, S. R., Mello, M. C. S., & Cruz, C. B. M. (2005). Relationships between forest structure and vegetation indices in Atlantic rainforest. Forest Ecology and Management, 218(1–3), 353–362.CrossRefGoogle Scholar
  11. Gu, D. G., & Gillespie, A. (1998). Topographic normalization of Landsat TM images of forest based on subpixel sun–canopy–sensor geometry. Remote Sensing of Environment, 649(2), 166–175.CrossRefGoogle Scholar
  12. Hame, T., Salli, A., Andersson, K., & Lohi, A. (1997). A new methodology for the estimation of biomass of coniferdominated boreal forest using NOAA AVHRR data. International Journal of Remote Sensing, 18(15), 3211–3243.CrossRefGoogle Scholar
  13. Lefsky, M. A., Harding, D., Cohen, W., Parker, G., & Shugart, H. H. (1999). Surface lidar remote sensing of basal area and biomass in deciduous forests of eastern Maryland, USA. Remote Sensing of Environment, 67(1), 83–98.CrossRefGoogle Scholar
  14. Luckman, A., Baker, J., Honzak, M., & Lucas, R. (1998). Tropical forest biomass density estimation using JERS-1 SAR: Seasonal variation, confidence limits, and application to image mosaics. Remote Sensing of Environment, 63, 126–139. Scholar
  15. Meyer, P., Itten, K. I., Kellenberger, T., Sandmeier, S., & Sandmeier, R. (1993). Radiometric corrections of topographically induced effects on Landsat TM data in an alpine environment. ISPRS Journal of Photogrammetry and Remote Sensing, 48(4), 17–28.CrossRefGoogle Scholar
  16. Myneni, R. B., Dong, J., Tucker, C. J., Kaufmann, R. K., Kauppi, P. E., Liski, J., et al. (2001). A large carbon sink in the woody biomass of Northern forests. Proceedings of the National Academy of Sciences of the United States of America, 98, 14784–14789.CrossRefGoogle Scholar
  17. Penner, M., Power, K., Muhairwe, C., Tellier, R., & Wang, Y. (1997). Canada’s forest biomass resources, deriving estimates from Canada’s forest inventory. Victoria, BC: Natural Resources Canada, Canadian Forest Service-Pacific Forestry Centre, BC-X-370.Google Scholar
  18. Ranson, K., Sun, G., Lang, R., Chauhan, N., Cacciola, R., & Kilic, O. (1997). Mapping of boreal forest biomass from spaceborne synthetic aperture radar. Journal Geophysical Research, 102(D24), 29599–29610.CrossRefGoogle Scholar
  19. Smith, J. A., Lin, T. L., & Ranson, K. J. (1980). The Lambertian assumption and Landsat data. Photogrammetric Engineering and Remote Sensing, 46, 1183–1189.Google Scholar
  20. Soenena, S. A., Peddlea, D. R., Coburna, C. A., Hallba, R. J., & Hallc, F. G. (2008). Improved topographic correction of forest image data using a 3-D canopy reflectance model in multiple forward mode. International Journal of Remote Sensing, 29(4), 1007–1027.CrossRefGoogle Scholar
  21. Teillet, P. M., Guindon, B., & Goodenough, D. G. (1982). On the slope-aspect correction of multispectral scanner data. Canadian Journal of Remote Sensing, 8, 84–106.CrossRefGoogle Scholar
  22. Todd, S. W., Hoffer, R. M., & Milchunas, D. G. (1998). Biomass estimation on grazed and ungrazed rangelands using spectral indices. International Journal of Remote Sensing, 19(3), 427–438.CrossRefGoogle Scholar
  23. Tokola, T., Sarkeala, J., & Van Der Linden, M. (2001). Use of topographic correction in Landsat TM-based forest interpretation in Nepal. International Journal of Remote Sensing, 22(4), 551–563.CrossRefGoogle Scholar
  24. Tucker, C. J., Slayback, D. A., Pinzon, J. E., Los, S. O., Myneni, R. B., & Taylor, M. G. (2001). Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999. International Journal of Biometeorology, 45, 184–190.CrossRefGoogle Scholar
  25. Wirth, C., Schumacher, J., & Schulze, E. D. (2004). Generic biomass functions for Norway spruce in Central Europe: A meta-analysis approach toward prediction and uncertainty estimation. Tree Physiology, 24(2), 121–139.CrossRefGoogle Scholar
  26. Wu, Z. L., & Dang, C. L. (1992). The biomass and net primary productivity of pinus kesiya var langbianensis stands in Chang-nin district, Yunnan. Journal of Yunnan University, 14, 137–145.Google Scholar
  27. Yang, C. J., Huang, H., & Wang, S. Y. (2011). Estimation of tropical forest biomass using Landsat TM imagery and permanent plot data in Xishuangbanna, China. International Journal of Remote Sensing, 32(20), 5741–5756.CrossRefGoogle Scholar
  28. Yang, C. J., Liu, J. Y., Huang, H., Xu, H. X., & Dang, C. L. (2005). Correlation analysis of the biomass of the tropical forest vegetation, meteorological data and topographical data. Geographical Research, 24(3), 473–478.Google Scholar
  29. Yang, C. J., Liu, J. Y., & Luo, J. C. (2004). Correlation analysis of Landsat TM data and its derived data, meteorological data and topographica data with the biomass of different aged tropical forests. Acta Phytoecological Sinica, l28(6), 862–867.Google Scholar
  30. Yang, C. J., Zhou, J. M., Huang, H., & Chen, X. (2007). Correlations of the biomass of the main tropical forest vegetation types and Landsat TM data in Xishuangbanna of People’s Republic of China. In IEEE international geoscience and remote sensing symposium (pp. 4336–4338).Google Scholar
  31. Zheng, D. L., Rademacher, J., Chen, J. Q., Crow, T., Bresee, M., Moine, J. L., et al. (2004). Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA. Remote Sensing of Environment, 93(3), 402–411.CrossRefGoogle Scholar
  32. Zhou, L., Tucker, C. J., Kaufmann, R. K., Slayback, D., Shabanov, N. V., & Myneni, R. (2001). Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. Journal Geophysical Research, 106(D17), 20069–20083.CrossRefGoogle Scholar

Copyright information

© Indian Society of Remote Sensing 2018

Authors and Affiliations

  1. 1.Research Center of Remote Sensing and GIS Applications, Key Laboratory of Land Resources Evaluation and Monitoring in Southwest, Ministry of EducationSichuan Normal UniversityChengduPeople’s Republic of China
  2. 2.Fundamental Education CollegeSichuan Normal UniversityChengduPeople’s Republic of China
  3. 3.College of Landscape ArchitectureNanjing Forestry UniversityNanjingPeople’s Republic of China

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