On Grass Yield Remote Sensing Estimation Models of China’s Northern Farming-Pastoral Ecotone
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.
KeywordsChina’s northern farming-pastoral ecotone Grass yield Remote sensing Monitoring Model
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- 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
- 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.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.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
- 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.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.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
- 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.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
- 18.Ren, J.Z.: Scientific research methods of the grass industry, pp. 201–203. China Agriculture Press, Beijing (1998)Google Scholar
- 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
- 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