Advertisement

Land-Cover Change

Chapter

Abstract

In this chapter, land-cover change based on the Normalized Difference Vegetation Index (NDVI) derived from the NOAA AVHRR Global Vegetation Index (GVI) for the Lhasa area at the central Tibetan Plateau from 1985 to 1999 is presented, and its sensitivity to climate conditions is discussed, followed by analysis on vegetation phenologies and dynamics using the discrete Fourier transform (DFT). The time series of NDVI demonstrate a positive trend from 1985 to 1999, which means that general vegetation biomass on land surface presents increasing, and this trend is strongly associated with increased rainfall and temperature from the mid-1980s to 1990s. The correlation analysis shows that the NDVI is more sensitive to precipitation (r = 0.75, P < 0.01) than temperature (r = 0.63, P < 0.01) in this semiarid climate zone. The study also indicated that DFT is a very useful tool to understand vegetation phenologies and dynamic change through decomposition of temporal data to frequency domain.

Keywords

Land-cover change Climate variables AVHRR GVI Discrete Fourier Transform Central Tibetan Plateau 

Notes

Acknowledgment

This chapter is derived in part from the author’s article published in Arctic, Antarctic, and Alpine Research on January 28, 2018, available online:  https://doi.org/10.1657/1523-0430(07-501)[CHU]2.0.CO;2.

References

  1. Andres, L., W.A. Salas, and D. Skoles. 1994. Fourier analysis of multi-temporal AVHRR data applied to a land cover classification. International Journal of Remote Sensing 15 (5): 1115–1121.CrossRefGoogle Scholar
  2. Azzali, S., and M. Menenti. 2000. Mapping vegetation-soil-climate complexes in southern Africa using temporal Fourier analysis of NOAAAVHRR data. International Journal of Remote Sensing 21: 973–996.CrossRefGoogle Scholar
  3. Bontemps, S., M. Herold, L. Kooistra, et al. 2012. Revisiting land cover observation to address the needs of the climate modeling community. Biogeosciences 9: 2145–2157.CrossRefGoogle Scholar
  4. Briggs, W.L., and V.E. Hensen. 1995. The DFT: An Owner’s Manual for the Discrete Fourier Transform. Philadelphia: Society for Industrial and Applied Mathematics.CrossRefGoogle Scholar
  5. Burgess, D.W., P. Lewis, and J.P. Muller. 1995. Topographic effects in AVHRR NDVI data. Remote Sensing of Environment 54: 223–232.CrossRefGoogle Scholar
  6. Chapin, F., and S. Zavaleta. 2000. Consequences of changing biodiversity. Nature 405: 234–242.CrossRefGoogle Scholar
  7. Chu, D. 2002. Spatial and Temporal Land-Use/Land-Cover Changes in Lhasa Area, Tibet. PhD thesis. Graduate School of Chinese Academy of Sciences, 20–23.Google Scholar
  8. ———. 2003. Global climate change and management strategies. Meteorology in Tibet, 25–27.Google Scholar
  9. Chuvieco, E., and A. Huete. 2009. Fundamentals of Satellite Remote Sensing. Boca Raton: CRC Press.CrossRefGoogle Scholar
  10. Comprehensive Scientific Expedition Team to Tibetan Plateau of CAS. 1984. Climate of Tibet. Beijing: Science Press, 43–44.Google Scholar
  11. Defries, R., M. Hansen, and J. Townshend. 1995. Global discrimination of land cover types from metrices derived from AVHRR pathfinder data. Remote Sensing of Environment 54 (3): 209–222.CrossRefGoogle Scholar
  12. DeFries, R., M. Hansen, J. Townshend, et al. 1998. Global land cover classifications at 8 km spatial resolution: The use of training data derived from Landsat imagery in decision tree classifiers. International Journal of Remote sensing 19 (16): 3141–3168.CrossRefGoogle Scholar
  13. Douglas, I. 1999. Hydrological investigations of forest disturbance and land cover impacts in South-East Asia: A review. Philosophical Transactions of the Royal Society of London, Series B 354: 1725–1738.CrossRefGoogle Scholar
  14. Ehlers, M., M.A. Jadkowski, R.R. Howard, et al. 1990. Application of SPOT data for regional growth analysis and local planning. Photogrammetric Engineering and Remote Sensing 56: 175–180.Google Scholar
  15. Giles, M., P. Foody, and J. Curran. 1994. Environmental Remote Sensing from Regional to Global Scales, 238 pp. New York: Wiley.Google Scholar
  16. Giri, C.P. 2012. Remote Sensing of Land Use and Land Cover: Principles and Applications. Boca Raton: CRC Press.Google Scholar
  17. Gutman, G., and A. Ignatov. 1995. Global land monitoring from AVHRR: Potential and limitations. International Journal of Remote sensing 16: 2301–2309.CrossRefGoogle Scholar
  18. Gutman, G., D. Tarpley, A. Ignatov, et al. 1995. The enhanced NOAA global land datasets from the advanced very high resolution radiometer. Bulletin of the American Meteorological Society 76: 1141–1156.CrossRefGoogle Scholar
  19. Hansen, M.C., and R.S. DeFries. 2004. Detecting long-term global forest change using continuous fields of tree-cover maps from 8-km advanced very high resolution radiometer (AVHRR) data for the years 1982–99. Ecosystems 7 (7): 695–716.CrossRefGoogle Scholar
  20. Holben, B.N. 1986. Characteristics of maximum-value composite images from temporal AVHRR data. International Journal of Remote Sensing 7: 1417–1434.CrossRefGoogle Scholar
  21. Holben, B., and R.S. Frazer. 1984. Red and near-infrared sensor response to off-nadir viewing. International Journal of Remote Sensing 5: 5145–5160.CrossRefGoogle Scholar
  22. Immerzeel, W.W., R.A. Quiroz, and S.M. de Jong. 2005. Understanding complex spatiotemporal weather patterns and land use interaction in the Tibetan Autonomous Region using harmonic analysis of SPOT VGT-S10 NDVI time series. International Journal of Remote Sensing 11: 2281–2296.CrossRefGoogle Scholar
  23. Justice, C.O., and P.H.Y. Hiernaux. 1986. Monitoring the grasslands of the Sahel using NOAA AVHRR data: Niger 1983. International Journal of Remote Sensing 7: 1475–1497.CrossRefGoogle Scholar
  24. Kleemann, J., G. Baysal, H.N. Bulley, et al. 2017. Assessing driving forces of land use and land cover change by a mixed-method approach in north-eastern Ghana, West Africa. Journal of Environmental Management 196: 411–442.CrossRefGoogle Scholar
  25. Lambin, E.F., and P. Meyfroidt. 2010. Land use transitions: Socio-ecological feedback versus socio-economic change. Land Use Policy 27 (2): 108–118.CrossRefGoogle Scholar
  26. Lawler, J.J., D.J. Lewis, E. Nelson, et al. 2014. Projected land-use change impacts on ecosystem services in the united states. Proceedings of the National Academy of Sciences of the United States of America 111 (20): 7492.CrossRefGoogle Scholar
  27. Lhasa City Agricultural and Pastoral Bureau. 1993. Land Resources in Lhasa City, 16–17. Beijing: China Agricultural Science and Technology Press.Google Scholar
  28. Lin, R., C. Li, and Y. Zhang. 2001. Climatic Resources for Agriculture in Lhasa, Tibet, 19–68. Beijing: Meteorological Press.Google Scholar
  29. Loveland, T.R., and A.S. Belward. 1997a. The international geosphere biosphere programme data and information system global land cover data set (DISCover). Acta Astronautica 41 (410): 681–689.CrossRefGoogle Scholar
  30. ———. 1997b. The IGBP-DIS global 1 km land cover data set, DISCover: Results. International Journal of Remote Sensing 18: 3289–3295.CrossRefGoogle Scholar
  31. Loveland, T.R., B.C. Reed, J.F. Brown, et al. 2000. Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. International Journal of Remote Sensing 21: 1303–1330.CrossRefGoogle Scholar
  32. Mahmood, R., R.A. Pielke, K.G. Hubbard, et al. 2014. Land cover changes and their biogeophysical effects on climate. International Journal of Climatology 34 (4): 929–953.CrossRefGoogle Scholar
  33. Menenti, M., S. Azzali, A. de Vries, et al. 1993a. Vegetation monitoring in Southern Africa using temporal Fourier analysis of AVHRR/NDVI observations. In Proceedings, International Symposium on Remote Sensing in Arid and Semi-Arid Regions, 287–294. Lanzhou: LIGG.Google Scholar
  34. Menenti, M., S. Azzali, W. Verhoef, et al. 1993b. Mapping agro-ecological zones and time lag in vegetation growth by means of Fourier analysis of time series of NDVI images. Advances in Space Research 13 (5): 233–237.CrossRefGoogle Scholar
  35. Milich, L., and E. Weiss. 2000. GAC NDVI images: Relationship to rainfall and potential evaporation in the grazing lands of the Gourma (northern Sahel) and in the croplands of the Niger-Nigeria border (southern Sahel). International Journal of Remote Sensing 21: 261–280.CrossRefGoogle Scholar
  36. Moody, A., and D.M. Johnson. 2001. Land-surface phonologies from AVHRR using the discrete Fourier transform. Remote Sensing of Environment 75: 305–323.CrossRefGoogle Scholar
  37. NOAA GVI User’s Guide. 2001. http://www2.ncdc.noaa.gov/docs.
  38. Olsson, L., and L. Eklundh. 1994. Fourier series for analysis of temporal sequences of satellite sensor imagery. International Journal of Remote Sensing 15: 3735–3741.CrossRefGoogle Scholar
  39. Penner, J.E. 1994. Atmospheric chemistry and air quality. In Changes in Land Use and Land Cover: A Global Perspective, ed. W.B. Meyer and B.L. Turner II, 175–209. Cambridge: Cambridge University Press.Google Scholar
  40. Pfeifer, M., M. Disney, T. Quaife, et al. 2012. Terrestrial ecosystems from space: A review of earth observation products for macroecology applications. Global Ecology and Biogeography 21: 603–624.CrossRefGoogle Scholar
  41. Piao, S., J. Fang, H. Liu, et al. 2005. NDVI-indicated decline in desertification in China in the past two decades. Geophysical Research Letters 32: L06402.  https://doi.org/10.1029/2004GL021764.CrossRefGoogle Scholar
  42. Potter, C.S., and V. Brooks. 1998. Global analysis of empirical relations between annual climate and seasonality of NDVI. International Journal of Remote Sensing 19: 2921–2948.CrossRefGoogle Scholar
  43. Richard, Y., and I. Poccard. 1998. A statistical study of NDVI sensitivity to seasonal and interannual rainfall variations in Southern Africa. International Journal of Remote Sensing 19: 2907–2920.CrossRefGoogle Scholar
  44. Roerink, G.J., M. Menenti, W. Soepboer, et al. 2003. Assessment of climate impact on vegetation dynamics by using remote sensing. Physics and Chemistry of the Earth 28: 103–109.CrossRefGoogle Scholar
  45. Rogers, D.J., S.I. Hay, and M.J. Packer. 1996. Predicting the distribution of tsetse flies in West Africa using temporal Fourier processed meteorological satellite data. Annals of Tropical Medicine and Parasitology 90: 225–241.CrossRefGoogle Scholar
  46. Sellers, P.J., S.O. Tucker, G.J. Collatz, et al. 1994. A global 1 by 1 NDVI data set for climate Studies. Part 2: The generation of global fields of terrestrial biophysical parameters from the NDVI. International Journal of Remote Sensing 15 (17): 3519–3545.CrossRefGoogle Scholar
  47. Stöckli, R., and P.L. Vidale. 2004. European plant phenology and climate as seen in a 20-year AVHRR land-surface parameter dataset. International Journal of Remote Sensing 25: 3303–3330.CrossRefGoogle Scholar
  48. Tolessa, T., F. Senbeta, and M. Kidane. 2017. The impact of land use/land cover change on ecosystem services in the central highlands of Ethiopia. Ecosystem Services 23: 47–54.CrossRefGoogle Scholar
  49. Tucker, C.J., R.G. Townshend, and T.E. Goff. 1985. African land-cover classification using satellite data. Science 227 (4685): 369–375.CrossRefGoogle Scholar
  50. Tucker, C.J., J.E. Pinzon, M.E. Brown, et al. 2005. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. International Journal of Remote Sensing 26 (20): 4485–4498.CrossRefGoogle Scholar
  51. Turner, B.L., D. Skole, S. Sanderson, et al. 1995. Land-Use and Land-Cover Change (LUCC): Science/Research Plan. IGBP Report No. 35, HDP Report No.7, Stockholm, Geneva.Google Scholar
  52. Weng, Q. 2002. Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. Journal of Environmental Management 64: 273–284.CrossRefGoogle Scholar
  53. Yin, H., D. Pflugmacher, A. Li, et al. 2018. Land use and land cover change in inner Mongolia-understanding the effects of china’s re-vegetation programs. Remote Sensing of Environment 204: 918–930.CrossRefGoogle Scholar
  54. Zhang, H.K., and D.P. Roy. 2017. Using the 500 m MODIS land cover product to derive a consistent continental scale 30 m Landsat land cover classification. Remote Sensing of Environment 197: 15–34.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

Personalised recommendations