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Fractional Vegetation Cover

Chapter

Abstract

Fractional vegetation cover (FVC) is an important parameter in the study of ecosystem balance, soil erosion, and climate change and is often used to evaluate and monitor vegetation degradation and desertification. Remote sensing provides the only feasible way to estimate FVC at regional and global scales. In the present study, an empirical model of FVC estimation is developed for central Tibetan Plateau (TP) based on the relationships between vegetation indices from Terra/Moderate Resolution Imaging Spectroradiometer (MODIS) and corresponding field measurements derived from digital camera, which is followed by in-depth analysis on the spatial distribution of vegetation coverage using proposed method. Study shows that a linear relationship exists between vegetation coverage from the field observation and MODIS NDVI with coefficient of determination of R2 = 0.90, which is slightly better than MODIS SAVI performance with R2 = 0.89 and is an optimal regression model for FVC estimation. Vegetation coverage ranges 20–90% in the most part of central TP, presenting moderate to high as a whole, and generally decreases from east to west with strong regional differences due to discrepancies in land-cover types, plant species, topography and water resources availability, and so on.

Keywords

Fractional vegetation cover Ground remote sensing Field measurement MODIS Central Tibetan Plateau 

References

  1. Agricultural and Pastoral Bureau of Lhasa Municipality. 1991. Land resources in Lhasa area, 181–213. Beijing: China Agricultural Science and Technology Press.Google Scholar
  2. Aman, A., H.P. Randriamanantena, A. Podaire, et al. 1992. Upscale integration of normalized difference vegetation index: The problem of spatial heterogeneity. IEEE Transactions on Geoscience and Remote Sensing 30: 326–338.CrossRefGoogle Scholar
  3. Bonan, G.B. 2008. Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science 320: 1444–1449.CrossRefGoogle Scholar
  4. Carlson, T.N., and D.A. Ripley. 1997. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment 62 (3): 241–252.CrossRefGoogle Scholar
  5. Chen, J., S. Yi, Y. Qin, and X. Wang. 2016. Improving estimates of fractional vegetation cover based on UAV in alpine grassland on the Qinghai-Tibetan Plateau. International Journal of Remote Sensing 37 (8): 1922–1936.CrossRefGoogle Scholar
  6. Coy, A., D. Rankine, M. Taylor, D.C. Nielsen, and J. Cohen. 2016. Increasing the accuracy and automation of fractional vegetation cover estimation from digital photographs. Remote Sensing 8: 474.CrossRefGoogle Scholar
  7. Díaz, B.M., and G.A. Blackburn. 2003. Remote sensing of mangrove biophysical properties: From a laboratory simulation of the possible effects of background variation on spectral vegetation indices. International Journal of Remote Sensing 24: 53–73.CrossRefGoogle Scholar
  8. Duan, H., C. Yan, A. Tsunekawa, X. Song, S. Li, and J. Xie. 2011. Assessing vegetation dynamics in the three-north shelter forest region of China using AVHRR NDVI data. Environmental Earth Sciences 64 (4): 1011–1020.CrossRefGoogle Scholar
  9. Godinez-Alvarez, H., J.E. Herrick, M. Mattocks, D. Toledo, and J. Van Zee. 2009. Comparison of three vegetation monitoring methods: Their relative utility for ecological assessment and monitoring. Ecological Indicators 9 (5): 1001–1008.CrossRefGoogle Scholar
  10. Gutman, G., and A. Ignatov. 1998. The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. International Journal of Remote Sensing 19 (8): 1533–1543.CrossRefGoogle Scholar
  11. Huete, A.R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25: 53–70.CrossRefGoogle Scholar
  12. Huete, A.R., R.D. Jackson, and D.F. Post. 1985. Spectral response of a plant canopy with different soil backgrounds. Remote Sensing of Environment 17: 37–53.CrossRefGoogle Scholar
  13. Huete, A.R., H. Liu, and W.V. Leeuwen. 1997. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment 59: 440–451.CrossRefGoogle Scholar
  14. Jensen, J.R. 1996. Introductory digital image processing: A remote sensing perspective. Upper Saddle: Prentice-Hall.Google Scholar
  15. Jia, K., S. Liang, S. Liu, Y. Li, Z. Xiao, Y. Yao, et al. 2015. Global land surface fractional vegetation cover estimation using general regression neural networks from MODIS surface reflectance. IEEE Transactions on Geoscience and Remote Sensing 53 (9): 4787–4796.CrossRefGoogle Scholar
  16. Jiang, Z.Y., A.R. Huete, J. Chen, et al. 2006. Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sensing of Environment 101 (3): 366–378.CrossRefGoogle Scholar
  17. Jiang, B., S. Liang, and W. Yuan. 2015. Observational evidence for impacts of vegetation change on local surface climate over northern China using the Granger causality test. Journal of Geophysical Research: Biogeosciences 120: 1–12.Google Scholar
  18. Jiapaer, G., X. Chen, and A.M. Bao. 2011. A comparison of methods for estimating fractional vegetation cover in arid regions. Agricultural and Forest Meteorology 151 (12): 1698–1710.CrossRefGoogle Scholar
  19. Li, X., Y. Chen, and P. Shi. 2003. Detecting vegetation fractional coverage of typical steppe in northern China based on multi-scale remotely sensed data. Journal of Plant Ecology 45 (10): 1146–1156.Google Scholar
  20. Li, X.B., Y.H. Chen, H. Yang, and Y.X. Zhang. 2005. Improvement, comparison, and application of field measurement methods for grassland vegetation fractional coverage. Journal of Integrative Plant Biology 47 (9): 1074–1083.CrossRefGoogle Scholar
  21. Li, J., Y. Cui, J. Liu, W. Shi, and Y. Qin. 2013. Estimation and analysis of net primary productivity by integrating MODIS remote sensing data with a light use efficiency model. Ecological Modelling 252 (1): 3–10.CrossRefGoogle Scholar
  22. Li, F., W. Chen, Y. Zeng, Q. Zhao, and B. Wu. 2014. Improving estimates of grassland fractional vegetation cover based on a pixel dichotomy model: A case study in inner Mongolia, China. Remote Sensing 6 (6): 4705–4722.CrossRefGoogle Scholar
  23. Lillesand, T.M., and R.W. Kiefer. 1987. Remote sensing and image interpretation. Ottawa: Wiley.Google Scholar
  24. Liu, Y.J., and Z.D. Yang. 2001. Processing principle and algorithm of MODIS data, 187–192. Beijing: Science Press.Google Scholar
  25. Liu, Y., X. Mu, H. Wang, and G. Yan. 2012. A novel method for extracting green fractional vegetation cover from digital images. Journal of Vegetation Science 23: 406–418.CrossRefGoogle Scholar
  26. Liu, D., L. Yang, K. Jia, S. Liang, et al. 2018. Global fractional vegetation cover estimation algorithm for VIIRS reflectance data based on machine learning methods. Remote Sensing 10: 1648.CrossRefGoogle Scholar
  27. Lu, H., M.R. Raupach, T.R. McVicar, et al. 2003. Decomposition of vegetation cover into woody and herbaceous components using AVHRR NDVI time series. Remote Sensing of Environment 86: 1–18.CrossRefGoogle Scholar
  28. Morsdorf, F., B. Kötz, E. Meier, K.I. Itten, and B. Allgöwer. 2006. Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction. Remote Sensing of Environment 104 (1): 50–61.CrossRefGoogle Scholar
  29. Mu, X., S. Huang, H. Ren, G. Yan, W. Song, and G. Ruan. 2015. Validating GEOV1 fractional vegetation cover derived from coarse-resolution remote sensing images over croplands. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (2): 439–446.CrossRefGoogle Scholar
  30. Nemani, R., and S.W. Running. 1996. Global vegetation cover changes from coarse resolution satellite data. Journal of Geophysical Research 101: 7157–7162.CrossRefGoogle Scholar
  31. North, P.R.J. 2002. Estimation of f APAR, LAI, and vegetation fractional cover from ATSR-2 imagery. Remote Sensing of Environment 80 (1): 114–121.CrossRefGoogle Scholar
  32. Okin, G.S., K.D. Clarke, and M.M. Lewis. 2013. Comparison of methods for estimation of absolute vegetation and soil fractional cover using MODIS normalized BRDF-adjusted reflectance data. Remote Sensing of Environment 130: 266–279.CrossRefGoogle Scholar
  33. Peng, J., Y.H. Liu, H. Shen, Y.N. Han, and Y.J. Pan. 2012. Vegetation coverage change and associated driving forces in mountain areas of northwestern Yunnan, China using RS and GIS. Environmental Monitoring and Assessment 184 (8): 4787–4798.CrossRefGoogle Scholar
  34. Purevdorj, T.S., R. Tateishi, T. Ishiyama, and Y. Honda. 1998. Relationships between percent vegetation cover and vegetation indices. International Journal of Remote Sensing 19 (18): 3519–3535.CrossRefGoogle Scholar
  35. Song, W., X. Mu, G. Ruan, Z. Gao, L. Li, and G. Yan. 2017. Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method. International Journal of Applied Earth Observation and Geoinformation 58: 168–176.CrossRefGoogle Scholar
  36. Townshend, J.R.G., and C.O. Justice. 2002. Towards operational monitoring of terrestrial systems by moderate-resolution remote sensing. Remote Sensing of Environment 83: 351–359.CrossRefGoogle Scholar
  37. White, M.A., G.P. Asner, R.R. Nemani, J.L. Privette, and S.W. Running. 2000. Measuring fractional cover and leaf area index in arid ecosystems: digital camera, radiation transmittance, and laser altimetry methods. Remote Sensing of Environment 74 (1): 45–57.CrossRefGoogle Scholar
  38. Xiao, J., and A. Moody. 2005. A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA. Remote Sensing of Environment 98 (2): 237–250.CrossRefGoogle Scholar
  39. Xiao, Z., T. Wang, S. Liang, and R. Sun. 2016. Estimating the fractional vegetation cover from GLASS leaf area index product. Remote Sensing 8: 337.CrossRefGoogle Scholar
  40. Yang, L., K. Jia, S. Liang, X. Wei, Y. Yao, and X. Zhang. 2017. A robust algorithm for estimating surface fractional vegetation cover from Landsat data. Remote Sensing 9: 857.CrossRefGoogle Scholar
  41. Zhang, X., C. Liao, J. Li, and Q. Sun. 2012. Fractional vegetation cover estimation in arid and semi-arid environments using HJ-1 satellite hyperspectral data. International Journal of Applied Earth Observation and Geoinformation 21: 506–512.CrossRefGoogle Scholar
  42. Zhang, D., L.R. Mansaray, H. Jin, H. Sun, Z. Kuang, and J. Huang. 2018. A universal estimation model of fractional vegetation cover for different crops based on time series digital photographs. Computers and Electronics in Agriculture 151: 93–103.CrossRefGoogle Scholar
  43. Zhou, Q., and M. Robson. 2001. Automated rangeland vegetation cover and density estimation using ground digital images and a spectral-contextual classifier. International Journal of Remote Sensing 22 (17): 3457–3470.CrossRefGoogle Scholar
  44. Zhou, Q., M. Robson, and P. Pilesjo. 1998. On the ground estimation of vegetation cover in Australian Rangelands. International Journal of Remote Sensing 19 (9): 1815–1820.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Duo Chu
    • 1
  1. 1.Tibet Institute of Plateau Atmospheric and Environmental SciencesTibet Meteorological BureauLhasaChina

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