Vegetation Condition Index according to Kogan et al. (1990). Two-weeks normalized Differenced Vegetation Index (NDVI) composites based on 250m MODIS data are freely available for download from the MODIS/NDVI Time Series Database from the Global Agriculture Monitoring (GLAM) Project provided via the website of Geographic Department of Maryland University.
This practice shows how to monitor the impacts of meteorological drought on natural vegetation using MODIS optical satellite imagery. The purpose of this recommended practice is to monitor impacts of meteorological drought on natural vegetation (rain fed, range land & forest). The methodology as such can be applied globally.
Vegetation Condition Index based on normalized Differenced Vegetation Index (NDVI) composites calculated according to Kogan et al. (1990). Full step-by-step description and workflow are given here.
The results can be used for the development of a regional drought monitoring and risk assessment system. However, the choice of the months of the MODIS data will vary depending on the timing of the vegetation period.
Availability, simplicity, free of charge data, good research literature and citation, minimum requirements of inputs are main criteria, which have been considered to define the methodology.
Jain, SK, Keshri , R, Goswami , A, Sarkar , A, Chaudhry , A (2009). Identification of drought vulnerable areas using NOAA AVHRR data, International Journal of Remote Sensing, 30(10).
Mokhtari, M.H., (2005). Agricultural Drought Impact Assessment Using Remote sensing: A case study Borkhar district –Iran, M.sc Thesis, ITC University.
Brown, J. et al, (2008). Using eMODIS Vegetation Indices for Operational Drought Monitoring.
W.T.LIU, W.t., KOGAN, F.N. (1996). Monitoring regional drought using the Vegetation Condition Index. INT.J. Remote Sensing, vol. 17, NO. 14,2761-2782