Details

Autor(en) / Beteiligte

• Mangiarotti, S.
• Mazzega, P.
• Hiernaux, P.
• Mougin, E.

Titel

Predictability of vegetation cycles over the semi-arid region of Gourma (Mali) from forecasts of AVHRR-NDVI signals

Ist Teil von

• Remote sensing of environment, 2012-08, Vol.123, p.246-257

Ort / Verlag

New York, NY: Elsevier Inc

Erscheinungsjahr

2012

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• The NOAA-AVHRR Normalised Difference Vegetation Index (NDVI) dataset is used to investigate the predictability of the vegetation cycle in an area centred on the Gourma region in Sahelian Mali at scales varying from 8km2 to 1024km2 over a period spanning from 1982 to 2004. The predictability of the vegetation cycle is analysed with a model based on a reconstruction approach that fully relies on the dataset. Two parameters deduced from the growth of the forecast error are considered: the horizon of effective predictability, HE, which is the horizon at which a satisfying prediction can be effectively forecasted at a given level of error, and the level of noise. Predictability is therefore analysed with regard to the horizon of prediction and the spatial scale; the influence of the model's dimensions is also discussed. The analysis clearly indicates that the signal predictability increases, and the level of noise decreases with an expanding area. However, even though the signal is more regular, its complexity increases within the narrowing entangled trajectory, setting the level of error of any prediction at a minimum of 15%, which matches the level of noise characteristic of the AVHRR-NDVI data series. The forecasting error quickly increases with the horizon of prediction, setting the optimum horizon of predictability in the range of 2 to 4decades, with high intra-annual variability. At the short horizon of 1decade, a resolution of 16km2 is reasonable to achieve an accuracy of approximately 20%. At the longer horizon of 3decades, only low resolutions (256km2 or lower) give an accuracy equal to or better than 35%. ► NDVI signal is taken as a proxy of the dynamics and used as a forecasting model. ► Predictability is estimated from the error growth of model forecasts. ► Horizon of predictability varies in time and with spatial scale. ► The model is especially efficient at intermediate scale (125×125km2). ► At larger scale complexity is higher but overall behaviour becomes more predictable.