Water Resources Management

, Volume 26, Issue 6, pp 1591–1613 | Cite as

The use of NDVI and its Derivatives for Monitoring Lake Victoria’s Water Level and Drought Conditions

Article

Abstract

Normalized Difference Vegetation Index (NDVI), which is a measure of vegetation vigour, and lake water levels respond variably to precipitation and its deficiency. For a given lake catchment, NDVI may have the ability to depict localized natural variability in water levels in response to weather patterns. This information may be used to decipher natural from unnatural variations of a given lake’s surface. This study evaluates the potential of using NDVI and its associated derivatives (VCI (vegetation condition index), SVI (standardised vegetation index), AINDVI (annually integrated NDVI), green vegetation function (Fg), and NDVIA (NDVI anomaly)) to depict Lake Victoria’s water levels. Thirty years of monthly mean water levels and a portion of the Global Inventory Modelling and Mapping Studies (GIMMS) AVHRR (Advanced Very High Resolution Radiometer) NDVI datasets were used. Their aggregate data structures and temporal co-variabilities were analysed using GIS/spatial analysis tools. Locally, NDVI was found to be more sensitive to drought (i.e., responded more strongly to reduced precipitation) than to water levels. It showed a good ability to depict water levels one-month in advance, especially in moderate to low precipitation years. SVI and SWL (standardized water levels) used in association with AINDVI and AMWLA (annual mean water levels anomaly) readily identified high precipitation years, which are also when NDVI has a low ability to depict water levels. NDVI also appears to be able to highlight unnatural variations in water levels. We propose an iterative approach for the better use of NDVI, which may be useful in developing an early warning mechanisms for the management of lake Victoria and other Lakes with similar characteristics.

Keywords

NDVI Lake Victoria Water levels Drought Catchment basin Lake variability 

References

  1. Anyamba A, Tucker CJ, Eastman JR (2001) NDVI anomaly patterns over Africa during the 1997/98 ENSO warm event. Int J Remote Sens 22(10):1847–1859. doi:10.1080/01431160010029156 CrossRefGoogle Scholar
  2. Anyamba A, Tucker CJ, Mahoney R (2002) From El nino to La nina: vegetation response patterns over East and Southern Africa during the 1997–2000 period. J Clim 15(21):3096–3103. doi:10.1175/1520-0442(2002)015<3096:FENOTL>2.0.CO;2 CrossRefGoogle Scholar
  3. Awange JL (2012) Environmental monitoring using GNSS. Springer, Heidelberg-BerlinGoogle Scholar
  4. Awange JL, Aluoch J, Ogallo LA, Omulo M, Omondi P (2007) Frequency and severity of drought in the Lake Victoria region, Kenya and its effects on food security. Clim Res 33(2):135–142CrossRefGoogle Scholar
  5. Awange JL, Ogalo L, Bae K-H., Were P, Omondi P, Omute P, Omulo M (2008) Falling Lake Victoria levels: is climate a contributing factor? Clim Change 89:281–297. doi:10.1007/s10584-008-9409-x CrossRefGoogle Scholar
  6. Awange JL, Ong’ang’a O (2006) Lake Victoria: ecology resource and 571 environment. Springer, BerlinGoogle Scholar
  7. Baret F, Guyot G (1991) Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sens Environ 35(2/3):161–173. 10.1016/0034-4257(91)90009-U CrossRefGoogle Scholar
  8. Bayarjargal Y, Karnieli A, Bayasgalan M, Khudulmur S, Gandush C, Tucker CJ (2006) A comparative study of NOAA-AVHRR derived drought indices using change vector analysis. Remote Sens Environ 105(1):9–22CrossRefGoogle Scholar
  9. Becker M, LLovel W, Cazenave A, Güntner A, Crétaux J-F (2010) Recent hydrological behavior of the East African great lakes region inferred from GRACE, satellite altimetry and rainfall observations. C. R. Geoscience 342:223–233. doi:10.1016/j.rse.2006.06.003 CrossRefGoogle Scholar
  10. Benada JR (1997) TOPEX/POSEIDON User’s Handbook. Technical report, Jet Propulsion Laboratory, California Institute of Technology Generation B (MGDR-B) Version 2.0.Google Scholar
  11. Birkett CM (1995) The contribution of TOPEX/POSEIDON to the global monitoring of climatically sensitive lakes. JGR-Oceans 100(12):25,179–25, 204. doi:10.1029/95JC02125 Google Scholar
  12. Camberlin P, Martiny N, Philippon N, Richard Y (2007) Determinants of the interannual relationships between remote sensed photosynthetic activity and rainfall in tropical Africa. Remote Sens Environ 106(2):199–216. 10.1016/j.rse.2006.08.009 CrossRefGoogle Scholar
  13. Carlson TN, Ripley DA (1997) On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens Environ 62(3):241–252. doi:10.1016/S0034-4257(97)00104-1 CrossRefGoogle Scholar
  14. Charon B, Brad D (2004) Anew remote sensing tool for water resource management. Earth Obs Mag 13(6). http://www.eomonline.com/Common/Archives/2004octnov/. Accessed 29 Apr 2006
  15. Charon B, Reynolds C (2005) Anew remote sensing tool for water resources management. http://www.pecad.fas.usda.gov/cropexplorer/global_reservoir/index.cfm. Accessed 21 Mar 2006
  16. Chen L, Qian X, Shi Y (2011) Critical area identification of potential soil loss in a typical watershed of the three Gorges reservoir region. Water Resour Manag 25(13):3445–3463. doi:10.1007/s11269-011-9864-4 CrossRefGoogle Scholar
  17. Chilar J, Chen JM, Li Z, Huang F, Latifovic R, Dixon R (1998) Can interannual land surface signal be discerned in composite AVHRR data? J Geophys Res 103:23163–23172. doi:10.1029/98JD00050 CrossRefGoogle Scholar
  18. Cretaux JF, Birkett C (2006) Lake studies from satellite radar altimetry. C R Geosci 338(14/15):1098–1112. doi:10.1016/j.physletb.2003.10.071 CrossRefGoogle Scholar
  19. FEWS NET (Famine Early Warning Systems Network) (2007) Africa data dissemination service. http://earlywarning.cr.usgs.gov/adds/index.php. Accessed 1 Mar 2008
  20. FAO (Food and Agriculture Organization) (1998) Food supply situation and crop prospects in Sub Saharan Africa: Africa report, no. 1/98. Rome Italy: FAO/GIEW http://www.fao.org/DOCREP/004/W8261E/w8261e03.htm. Accessed 28 Feb 2008
  21. Fu L-L, Christensen E, Yamarone C Jr, Lefebvre M, Escudier M, Menard P, Dorrer Y (1994) TOPEX/POSEIDON Mission Overview. http://hdl.handle.net/2014/34628
  22. Gontia NK, Tiwari KN (2010) Estimation of crop coefficient and evapotranspiration of wheat (Triticum aestivum) in an irrigation command using remote sensing and GIS. Water Resour Manag 24(7):1399–1414. doi:10.1007/s11269-009-9505-3 CrossRefGoogle Scholar
  23. Gutman GG (1999) On the use of long-term global data of land reflectances and vegetation indices derived from the advanced very high resolution radiometer. J Geophys Res 104:6241–6255. doi:10.1029/1998JD200106 CrossRefGoogle Scholar
  24. Gutman G, Ignatov A (1998) The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. Int J Remote Sens 19(8):1533–1543. doi:10.1080/014311698215333 CrossRefGoogle Scholar
  25. Hatfield JL, Prueger JH, Kustas WP (2004) Remote sensing of dryland crops. In: Ustin S (ed) Remote sensing for natural resources and environmental monitoring: manual of Remote Sensing, 3rd edn, vol 4. Wiley, New Jersey, pp 531–568Google Scholar
  26. Hay SI, Snow RW, Rogers DJ (1998) Predicting malaria seasons in Kenya using multi temporal meteorological satellite sensor data. Trans R Soc Trop Med Hyg 92(1):12–20CrossRefGoogle Scholar
  27. Kastens JH, Kastens TL, Kastens DLA, Price KP, Martinko EA, Lee R (2005) Image masking for crop yield forecasting using AVHRR NDVI time series imagery. Remote Sens Environ 99(3):341–356. doi:10.1016/j.rse.2005.09.010 CrossRefGoogle Scholar
  28. Kiage LM, Walker ND (2009) Using NDVI from MODIS to monitor duckweed bloom in Lake Maracaibo, Venezuela. Water Resour Manag 23:1125–1135. doi:10.1007/s11269-008-9318-9 CrossRefGoogle Scholar
  29. Kogan FN (1997) Global drought watch from space. Bull Am meteorol Soc 78(4):621–636. doi:10.1175/1520-0477(1997)078<0621:GDWFS>2.0.CO;2 CrossRefGoogle Scholar
  30. Kull D (2006) Connection between recent water level drops in Lake Victoria, dam operations and drought: Technical Report. International River Network. http://www.irn.org/programs/nile/
  31. Liang S (2004) Quantitative remote sensing of land surfaces. New Jersey: Wiley & SonsGoogle Scholar
  32. Liu WT, Negrón Juárez RI (2001) ENSO drought onset prediction in northeast Brazil using NDVI. Int J Remote Sens 22(17):3483–3501. doi:10.1080/01431160010006430 CrossRefGoogle Scholar
  33. McKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to time scales. In: Preprints, 8th Conference on Applied Climatology, January 17-22, 1993. Anaheim, CA. http://scholar.google.com/scholar?
  34. Mendicino G, Versace P (2007) Integrated drought watch system: a case study in Southern Italy. Water Resour Manag 21(8):1409–1428. doi:10.1007/s11269-006-9091-6 CrossRefGoogle Scholar
  35. McVicar TR, Jupp DLB (1998) The current and potential operational uses of remote sensing to aid decisions on drought exceptional circumstances in Australia: a review. Agric syst 57(3):399–468. doi:10.1016/S0308-521X(98)00026-2 CrossRefGoogle Scholar
  36. Nicholson SE, Davenport ML, Malo AR (1990) A comparison of the vegetation response to rainfall in the Sahel and East Africa, using normalized difference vegetation index from NOAA AVHRR. Clim Change 17(2/3):209–241CrossRefGoogle Scholar
  37. Nicholson SE (2001) Application of remote sensing to climatic and environmental studies in arid and semi-arid lands. In: Geoscience and Remote Sensing Symposium, 2001, 9–13 July 2001. Sydney, NSW: IGARSS ’01. IEEE International, vol 3, pp 985–987. doi:10.1109/IGARSS.2001.976722
  38. Picot N, Case K, Desai S, Vincent P (2003) AVISO and PODAAC user handbook. IGDR and GDR Jason products, SMM-MU-M5-OP-13184-CN (AVISO), JPL D-21352 (PODAAC)Google Scholar
  39. Pinzon J, Brown ME, Tucker CJ (2004) Satellite time series correction of orbital drift artifacts using empirical mode decomposition. In: Huang N (eds) Hilbert-Huang transform: introduction and applications, pp Chapter 10, Part II. Applications. World Scientific PublishingGoogle Scholar
  40. Privette JL, Fowler C, Wick GA, Baldwin D, Emery WJ (1995) Effects of orbital drift on advanced very high resolution radiometer products: normalized difference vegetation index and sea surface temperature. Remote Sens Environ 53(3):164–171CrossRefGoogle Scholar
  41. Propastin PA (2008) Application of remote sensing Simple model for monitoring Balkhash Lake water levels and Ili River discharges: Application of remote sensing. Lakes Reserv 13:77–81. doi:10.1111/j.1440-1770.2007.00354.x CrossRefGoogle Scholar
  42. Subash N, Mohan HSR (2011) A simple rationally integrated drought indicator for rice-wheat productivity. Water Resour Manag 25(10):2425–2447. doi:10.1007/s11269-011-9817-y CrossRefGoogle Scholar
  43. Swenson S, Wahr J (2009) Monitoring the water balance of Lake Victoria, East Africa, from space. J Hydrol 370(1-4):163–176. doi:10.1016/j.jhydrol.2009.03.008 CrossRefGoogle Scholar
  44. Tucker CJ (1980) Remote sensing of leaf water content in the near infrared. Remote Sens Environ 10(1):23–32. doi:10.1016/0034-4257(80)90096-6 CrossRefGoogle Scholar
  45. Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring Vegetation. Remote Sens Environ 8(2):127–150. doi:10.1016/0034-4257(79)90013-0 CrossRefGoogle Scholar
  46. Tucker CJ, Pinzon JE, Brown ME, Slayback D, Pak EW, Mahoney R, Vermote E, El Saleous N (2005) An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int J Remote Sens 26(20):4485–4498. doi:10.1080/01431160500168686 CrossRefGoogle Scholar
  47. USDA/FAS (United States Department of Agriculture, Foreign Agricultural Services) (2007) Global reservoir monitor. http://www.pecad.fas.usda.gov/cropexplorer/global_reservoir/index.cfm. Accessed 21 Mar 2008
  48. Verdin J, Funk C, Senay G, Choularton R (2005) Climate science and early warning. Philos Trans R Soc B 360(1463):2155–2168. doi:10.1098/rstb.2005.1754/10.1175/1520-0442(2002) CrossRefGoogle Scholar
  49. World Resources Institute (2006) Earth trends: the environmental information portal [Image]. http://earthtrends.wri.org/text/water-resources/map-300.html. Accessed 12 May 2008
  50. Yates DN, Strzepek KM (1998) Modelling the Nile Basin under climate change. J Hydrol Eng 3(2):98–108. doi:10.1061/(ASCE)1084-0699(1998)3:2(98) CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Paul Omute
    • 1
  • Rob Corner
    • 2
  • Joseph Langat Awange
    • 2
    • 3
  1. 1.Kyambogo UniversitiesKampalaUganda
  2. 2.Curtin University of TechnologyPerthAustralia
  3. 3.Karlsruhe Institute of TechnologyKarlsruheGermany

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