Abstract
Abstract. Most regional scale studies of vegetation in the Sahel have been based on Earth observation (EO) imagery due to the limited number of sites providing continuous and long term in situ meteorological and vegetation measurements. From a long time series of coarse resolution normalized difference vegetation index (NDVI) data a greening of the Sahel since the 1980s has been identified. However, it is poorly understood how commonly applied remote sensing techniques reflect the influence of extensive grazing (and changes in grazing pressure) on natural rangeland vegetation. This paper analyses the time series of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI metrics by comparing it with data from the Widou Thiengoly test site in northern Senegal. Field data include grazing intensity, end of season standing biomass (ESSB) and species composition from sizeable areas suitable for comparison with moderate – coarse resolution satellite imagery. It is shown that sampling plots excluded from grazing have a different species composition characterized by a longer growth cycle as compared to plots under controlled grazing or communal grazing. Also substantially higher ESSB is observed for grazing exclosures as compared to grazed areas, substantially exceeding the amount of biomass expected to be ingested by livestock for this area. The seasonal integrated NDVI (NDVI small integral; capturing only the signal inherent to the growing season recurrent vegetation), derived using absolute thresholds to estimate start and end of growing seasons, is identified as the metric most strongly related to ESSB for all grazing regimes. However plot-pixel comparisons demonstrate how the NDVI/ESSB relationship changes due to grazing-induced variation in annual plant species composition and the NDVI values for grazed plots are only slightly lower than the values observed for the ungrazed plots. Hence, average ESSB in ungrazed plots since 2000 was 0.93 t ha−1, compared to 0.51 t ha−1 for plots subjected to controlled grazing and 0.49 t ha−1 for communally grazed plots, but the average integrated NDVI values for the same period were 1.56, 1.49, and 1.45 for ungrazed, controlled and communal, respectively, i.e. a much smaller difference. This indicates that a grazing-induced development towards less ESSB and shorter-cycled annual plants with reduced ability to turn additional water in wet years into biomass is not adequately captured by seasonal NDVI metrics.
Highlights
The need for a long time series of data on a regional scale to monitor vegetation development in the semi-arid Sahel is crucial, since this region has been characterized by high variability in rainfall (Nicholson et al, 1990) combined with an increasing population (Ickowicz et al, 2012) over the last few decades
Plot-pixel comparisons demonstrate how the normalized difference vegetation index (NDVI)/end of season standing biomass (ESSB) relationship changes due to grazinginduced variation in annual plant species composition and the NDVI values for grazed plots are only slightly lower than the values observed for the ungrazed plots
Average ESSB in ungrazed plots since 2000 was 0.93 t ha−1, compared to 0.51 t ha−1 for plots subjected to controlled grazing and 0.49 t ha−1 for communally grazed plots, but the average integrated NDVI values for the same period were 1.56, 1.49, and 1.45 for ungrazed, controlled and communal, respectively, i.e. a much smaller difference
Summary
The need for a long time series of data on a regional scale to monitor vegetation development in the semi-arid Sahel is crucial, since this region has been characterized by high variability in rainfall (Nicholson et al, 1990) combined with an increasing population (Ickowicz et al, 2012) over the last few decades. Long term EO data sets of vegetation indices (VI’s) derived from satellite-based optical sensors have been used over many years to estimate ground-based vegetation metrics such as composition, biomass and Sahelian vegetation resource availability (Tucker, 1978, 1979; Anyamba and Tucker, 2005; Herrmann et al, 2005; Olsson et al, 2005; Seaquist et al, 2006; Heumann et al, 2007; Fensholt and Rasmussen, 2011; Fensholt and Proud, 2012). For an adequate interpretation of vegetation change studies, the dependency on remote sensing for large-scale and long-term studies makes it important to have a clear understanding of how vegetation properties are derived from the often coarse spatial resolution data and the potential implications of working with EO-based proxies for vegetation productivity
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