Advertisement

On Grass Yield Remote Sensing Estimation Models of China’s Northern Farming-Pastoral Ecotone

  • Xiuchun YangEmail author
  • Bin Xu
  • Jin Yunxiang
  • Li Jinya
  • Xiaohua Zhu
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 142)

Abstract

On the basis of grassland zoning, using the NASA MODIS data and the 668 ground sample data from mid July to September 2005, this paper took China’s northern farming-pastoral ecotone as its subject of study and built the linear, nonlinear models and BP neural network models by using 5 vegetation indexes (NDVI, EVI, MSAVI, OSAVI and SAVI) and thereby proposed a whole set of feasible methods to estimate the grass yields in China’s northern farming-pastoral ecotone. Some conclusions are drawn: (i) The zoned models are superior to the non-zoned models for reflecting the actual grass yield condition better in China’s northern farming-pastoral ecotone; (ii) The grass yield estimation models based on BP neural network are superior to the linear and nonlinear models, and more accurate and most suitable for estimation the grass yields of China’s northern farming-pastoral ecotone; (iii) NDVI and SAVI have the highest precision of fitting with the sample biomass and thereby, they are the vegetation indexes suited most to be applied in grass yield remote sensing estimation of China’s northern farming-pastoral ecotone.

Keywords

China’s northern farming-pastoral ecotone Grass yield Remote sensing Monitoring Model 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Deering, D.W.: Rangeland reflectance characteristics measured by aircraft and spacecraft sensors. Ph.D. Dissertation, Texas A&M University, College Station, TX. 338 (1978)Google Scholar
  2. 2.
    Gao, J.: Quantification of grassland properties: how it can benefit from geoinformatic technologies? International Journal of Remote Sensing 27, 1351–1365 (2006)CrossRefGoogle Scholar
  3. 3.
    Hornik, K.M., Stinchcombe, M., White, H.: Multilayer feed forward networks are universal approximators. Neural Networks 2, 359–366 (1989)CrossRefGoogle Scholar
  4. 4.
    Huang, J., Wang, X., Hu, X.: Studies on grass yield monitoring models for different natural grassland types using remote sensing data in Northern Xinjiang. Grassland of China 21, 7–11, 18 (1999)Google Scholar
  5. 5.
    Huang, J., Wang, X., Wang, R., et al.: Relation analysis between the production of natural grassland and satellite vegetation indices. Research of Agricultural Modernization 21, 33–36 (2000)Google Scholar
  6. 6.
    Huang, J., Wang, X., Wang, R., et al.: A study on monitoring and predicting models of grass yield in natural grassland using remote sensing data and meteorological data. Journal of Remote Sensing 5, 71–76 (2001)Google Scholar
  7. 7.
    Huete, A.R.: A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25, 295–309 (1988)CrossRefGoogle Scholar
  8. 8.
    Jiang, D., Yang, X., Clinton, N., et al.: An artificial neural network model for estimating crop yields using remotely sensed information. International Journal of Remote Sensing 25, 1723–1732 (2004)CrossRefGoogle Scholar
  9. 9.
    Kanemasu, E.T., Demetriades-Shah, T.H., Su, H., et al.: Estimating grassland biomass using remotely sensed data. In: Steven, M.D., Clark, J.A. (eds.) Applications of Remote Sensing in Agriculture, pp. 185–199. Butterworth-Heinemanm, London (1990)Google Scholar
  10. 10.
    Li, J., Jiang, P.: The Study on the Remote Sensing Technology in Estimating and Forecasting Grassland Field Applications. Journal of Wuhan Technical University of Surveying and Mapping 23, 153–157 (1998)Google Scholar
  11. 11.
    Li, X., Yeh, A.G.O.: Cellular automata for simulating complex land use systems using neural networks. Geographical Research 24, 19–27 (2005)Google Scholar
  12. 12.
    Liu, H.Q., Huete, A.R.: A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Trans. Geosci. Remote Sensing 33, 457–465 (1995)CrossRefGoogle Scholar
  13. 13.
    Niu, Z.C., Ni, S.X.: Study on Models for Monitoring of Grassland Biomass around Qinghai Lake Assisted by Remote Sensing. Acta Geographica Sinica 58, 395–702 (2003)Google Scholar
  14. 14.
    Piao, S.L., Fang, J.Y., He, J.S., Xiao, Y.: Spatial distribution of grassland biomass in China. Acta Phytoecologica Sinica 28, 491–498 (2004)Google Scholar
  15. 15.
    Purevdorj, T., Tateishi, R., Ishiyama, T., et al.: Relationship between percent vegetation cover and vegetation indices. International Journal of Remote Sensing 19, 3519–3535 (1998)CrossRefGoogle Scholar
  16. 16.
    Qi, J.A.: Modified soil adjusted vegetation index. Remote Sensing of Environment 25, 295–309 (1988)CrossRefGoogle Scholar
  17. 17.
    Rasmussen, M.S.: Developing simple, operational, consistent NDVI-vegetation models by apply environmental and climatic information: Part I. Assessment of net primary production. International Journal of Remote Sensing 19, 97–117 (1998)CrossRefGoogle Scholar
  18. 18.
    Ren, J.Z.: Scientific research methods of the grass industry, pp. 201–203. China Agriculture Press, Beijing (1998)Google Scholar
  19. 19.
    Rondeaux, G., Steven, M., Baret, F.: Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment 55, 95–107 (1996)CrossRefGoogle Scholar
  20. 20.
    Roy, P.S., Jonna, S., Pant, D.N.: Evaluation of grasslands and spectral reflectance relationship to its biomass in Kanha National Park (M P), India. Geocarto International 6, 39–45 (1991)CrossRefGoogle Scholar
  21. 21.
    Simpson, G.: Crop yield prediction using a CMAC neural network. In: Proceedings of the Society of Photo-Optical Instrumentation Engineers, vol. 2315, pp. 160–171 (1994)Google Scholar
  22. 22.
    Wang, Z., Liu, C., Zhao, B., et al.: ANPP estimate from MODIS-EVI for the grassland region of Xilingol, China. Journal of Lanzhou University (Natural Science Edition) 41, 10–16 (2005)zbMATHGoogle Scholar
  23. 23.
    Xu, B., Xin, X.P., Qin, Z.H., et al.: Development of spatial GIS databases for monitoring on dynamic state of grassland productivity and animal loading balance in northern China. In: Proceeding of the 12th International Conference on Geoinformatics 2004, pp. 585–592. University of Gavle Press, Sweeden (2004)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Xiuchun Yang
    • 1
    Email author
  • Bin Xu
    • 1
  • Jin Yunxiang
    • 1
  • Li Jinya
    • 1
  • Xiaohua Zhu
    • 2
  1. 1.Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural SciencesBeijingChina
  2. 2.Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina

Personalised recommendations