Research on Vegetation Ecologic Quality Index of Rocky Desertification in Karst Area of Guangxi Province Based on NPP and Fractional Vegetation Cover Since 2000

  • Xin Yang
  • Haihong HuangEmail author
  • Shuan Qian
  • Hao Yan
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 546)


Karst rocky desertification is a kind of special and complicated surface form. Study established the Net primary productivity (NPP) estimation model based on the use of solar energy utilization of remote sensing data model in rocky desertification region of Guangxi province and the fractional vegetation cover (VFC) is obtained by the method of sub-pixel decomposition model. The comprehensive analysis method was used to establish vegetation ecologic quality index by using NPP and fractional vegetation cover to monitor the Rocky Desertification in the study area from 2000 to 2016, and mapping vegetation ecologic quality index grade classification. Results show that: (1) In 2016, vegetation ecologic quality index in rocky desertification region of Guangxi province is 87.6, the highest since 2000. Hechi city is the best in Guangxi Province. (2) From 2000 to 2016, vegetation ecologic quality index annual growth has a 1.04, the highest is 87.6 (in 2016) and the lowest is 63 (in 2004). The ecological restorations and environmental control projects have achieved remarkable results. (3) Great grade, Good grade and normal grade areas can account for 94.5%. And vegetation ecologic quality index decreased obviously in some relative development regions, such as Guilin city and Hezhou city.


Karst rocky desertification Vegetation ecologic quality index NPP Fractional vegetation cover 



This research was supported by Special Fund for meteorological scientific Research in the Public Interest (GYHY201506017). Sincerely thanks are also due to Guangxi Climate center and National Satellite Meteorology Center for providing the data for this study.


  1. Bonan, G.B.: Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444 (2008)CrossRefGoogle Scholar
  2. Chhabra, A., Dadhwal, V.K.: Estimating terrestrial net primary productivity over India using satellite data. Curr. Sci. 86(2), 269 (2004)Google Scholar
  3. Foster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (1999)Google Scholar
  4. Goetz, S.J., Prince, S.P., Smell, J., Gleason, A.C.: Inter annual variability of global terrestrial primary production: results of a model driven with global satellite observations. J. Geophys. Res. 105(D15), 20077–20091 (2000)CrossRefGoogle Scholar
  5. Fang, J., Piao, S., Tang, Z., Peng, C., Ji, W.: Inter annual variability in net primary production and precipitation. Science 293, 1723 (2001)CrossRefGoogle Scholar
  6. Knapp, K., Smith, M.D.: Variation among biomes in temporal dynamics of aboveground primary production. Science 291, 481 (2001)CrossRefGoogle Scholar
  7. Kumar, M., Monteith, J.L.: Remote sensing of crop growth. In: Smith, H. (ed.) Plants and the Daylight Spectrum, pp. 133–144. Academic Press, London (1981)Google Scholar
  8. Nemani, R.R., et al.: Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560 (2003)CrossRefGoogle Scholar
  9. Nemani, R., Running, S.W., Band, L.E., Peterson, D.L.: Regional hydroecological simulation system: an illustration of the integration of ecosystem models in a GIS. In: Goodchild, M.F., Parks, B.O., Steyaert, L.T. (eds.) Environmental modeling with GIS, pp. 296–304. Oxford University Press, New York (1993)Google Scholar
  10. Panigrahy, R.K., Panigrahy, S., Parihar, J.S.: Spatiotemporal pattern of agro ecosystem net primary productivity of India: a preliminary analysis using spot VGT data. In: International Symposium on Geospatial Databases for Sustainable Development, Goa, vol. 36, part 4, pp. 724–729 (2004)Google Scholar
  11. Prince, S.D.: A model of regional primary production to use with coarse resolution satellite data. Int. J. Remote Sens. 12(6), 1313–1330 (1991)CrossRefGoogle Scholar
  12. Running, S.W., Gower, S.T.: FOREST BGC, a general model of forest ecosystem processes for regional applications. II. Dynamic carbon allocation and nitrogen budgets. Tree Physiol. 9, 147–160 (1991)CrossRefGoogle Scholar
  13. Running, S.W., Coughlan, J.C.: A general model of forest ecosystem processes for regional applications I hydrological balance, canopy gas exchange and primary production processes. Ecol. Model. 42, 125–154 (1988)CrossRefGoogle Scholar
  14. Sabbe, H., Veroustraete, F.: Estimation of net primary and net ecosystem productivity of European terrestrial ecosystems by means of the C-Fix model and NOAA/AVHRR data. In: VEGETATION 2000 Conference, 2 Years of Operation to Prepare the Future, pp. 95–99. Space Application Institute, Joint Research Center, 21020 Ispra, Varese-Italy (2000)Google Scholar
  15. Gao, Z., Liu, J.: Simulation study of China’s net primary production. Chin. Sci. Bull. 53, 434–443 (2008)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Xin Yang
    • 1
    • 3
  • Haihong Huang
    • 1
    • 3
    Email author
  • Shuan Qian
    • 2
  • Hao Yan
    • 2
  1. 1.Remote Sensing Application and Test Base of National Satellite Meteorology CentreNanningChina
  2. 2.National Meteorological Center of CMABeijingChina
  3. 3.Institute of Meteorological Disaster MitigationNanningPeople’s Republic of China

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