show how different the NDVI values at the time of acquisition of each Landsat scene in the c.1990 and c.2000 datasets were from the seasonal mean for the range of dates covered by each dataset. show the NDVI standard deviation, and demonstrate how much NDVI values varied over the months and years covered by each Landsat dataset. By using the results in to rescale the data in , we control for the differing levels of seasonal NDVI variation across Africa to produce coefficient of variation maps. thus provide a measure of how different the NDVI value of the Landsat scene in question is from the mean annual NDVI, as a proportion of the annual range of NDVI values for that area.
Figure 1 (a) Mean difference from the 1984–96 NDVI mean for each scene from the c.1990 Landsat dataset; (b) Mean difference from the 1999–2002 NDVI mean for each scene from the c.2000 Landsat dataset; (c) Mean NDVI standard deviation over the 1984–96 (more ...)
The results reveal that much of both of the Africa Landsat datasets demonstrate large areas of spatial correlation in NDVI variation. This is a clear indication of the implementation of rules imposed on Landsat scene selection in creating the datasets (Tucker et al. 2004
). show some interesting spatial patterns in both c.1990 and c.2000 cases. Generally, the Landsat scenes chosen to represent the tropical belt above the Equator exhibit NDVI values below mean levels. In contrast, the majority of south-central Africa scenes exhibit above average NDVI values. These patterns are not completely consistent spatially or temporally, however. Landsat scenes acquired in above average NDVI periods also occur in the tropical belt and scenes acquired in below-average periods are seen in southern areas. The c.2000 image also has fewer scenes acquired in periods that were substantially different from the mean NDVI for each scene than the c.1990 dataset. Most likely, this results from the greater choice of scenes for dataset construction during this acquisition era (Tucker et al. 2004
highlight the scenes acquired during the greatest NDVI deviations from mean values. These appear in those areas with substantial annual NDVI variation. Therefore, an estimate of the position of the Landsat scene NDVI level within the normal seasonal NDVI deviation () was required. thus provide a more complete picture of between-scene and between-dataset NDVI differences in the form of coefficient of variation maps. For example, while shows that no scenes in the Landsat c.1990 dataset representing the Horn of Africa were acquired at a time when NDVI deviated more than 0.1 from mean levels, indicates that very little NDVI deviation occurs in the region. Scaling by the NDVI deviation that does occur, however (), reveals that the scenes chosen for the coastal section were acquired at a time of relatively high NDVI, although the absolute NDVI changes in such arid regions are small.
In construction of the NASA datasets, Landsat scenes were preferentially selected, where possible, from the growing season when NDVI is at a maximum (Tucker et al. 2004
). The coefficient of variation maps () show that in general, this was achieved across both northern and southern Africa, with the majority of scenes acquired when NDVI was above mean values. However, for the tropical belt, as described in Tucker (2004), the reverse is true. Here, most scenes used in the datasets were acquired when NDVI values were significantly lower than normal levels, as peak NDVI is frequently accompanied by significant cloud cover.
The implications of these results for both land cover/use mapping by creating mosaics of scenes from a single Landsat dataset, or for land cover/use change studies examining scene differences between datasets, are significant. Across both are Landsat scenes acquired when NDVI levels were substantially above mean levels, adjacent to scenes acquired when levels were substantially below mean. Without knowledge of such seasonal effects, construction of mosaics from such scenes to produce land cover/use maps introduces confounding effects from scene selection dates rather than land cover/use change processes. Similarly, problems may arise when using a single scene from each of the c.1990 and c.2000 datasets to infer land cover or use change between dates, without knowledge of the seasonal NDVI differences mapped. This may lead to land cover or use change being erroneously assigned to, for example, urbanisation, deforestation or desertification, when seasonal variation in vegetation levels is the cause.