Introducing a rain-adjusted vegetation index (RAVI) for improvement of long-term trend analyses in vegetation dynamics

Christine Wessollek; Pierre Karrasch; Babatunde Osunmadewa

doi:10.1117/12.2192821

20 October 2015 Introducing a rain-adjusted vegetation index (RAVI) for improvement of long-term trend analyses in vegetation dynamics

Christine Wessollek, Pierre Karrasch, Babatunde Osunmadewa

Author Affiliations +

Proceedings Volume 9644, Earth Resources and Environmental Remote Sensing/GIS Applications VI; 96440M (2015) https://doi.org/10.1117/12.2192821
Event: SPIE Remote Sensing, 2015, Toulouse, France

Abstract

It seems to be obvious that precipitation has a major impact on greening during the rainy season in semi-arid regions. First results1 imply a strong dependence of NDVI on rainfall. Therefore it will be necessary to consider specific rainfall events besides the known ordinary annual cycle. Based on this fundamental idea, the paper will introduce the development of a rain adjusted vegetation index (RAVI). The index is based on the enhancement of the well-known normalized difference vegetation index (NDVI²) by means of TAMSAT rainfall data and includes a 3-step procedure of determining RAVI. Within the first step both time series were analysed over a period of 29 years to find best cross correlation values between TAMSAT rainfall and NDVI signal itself. The results indicate the strongest correlation for a weighted mean rainfall for a period of three months before the corresponding NDVI value. Based on these results different mathematical models (linear, logarithmic, square root, etc.) are tested to find a functional relation between the NDVI value and the 3-months rainfall period before (0.8). Finally, the resulting NDVI-Rain-Model can be used to determine a spatially individual correction factor to transform every NDVI value into an appropriate rain adjusted vegetation index (RAVI).

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Christine Wessollek, Pierre Karrasch, and Babatunde Osunmadewa "Introducing a rain-adjusted vegetation index (RAVI) for improvement of long-term trend analyses in vegetation dynamics", Proc. SPIE 9644, Earth Resources and Environmental Remote Sensing/GIS Applications VI, 96440M (20 October 2015); https://doi.org/10.1117/12.2192821

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KEYWORDS

Vegetation

Modeling

Statistical analysis

Near infrared

Data modeling

Mathematical modeling

Meteorology

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