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This paper presents a novel methodological approach to countrywide vegetation mapping. We used green vegetation biomass over the year as captured by coarse resolution hyper-temporal NDVI satellite-imagery, to generate vegetation mapping units at the biome, ecoregion and at the next lower hierarchical level for Namibia, excluding the Zambezi Region. Our method was based on a time series of 15 years of SPOT-VGT-MVC images each representing a specific 10-day period (dekad). The ISODATA unsupervised clustering technique was used to separately create 2–100 NDVI-cluster maps. The optimal number of temporal NDVI-clusters to represent the information on vegetation contained in the imagery was established by divergence separability statistics of all generated NDVI-clusters. The selected map consisted of legend of 81 cluster-specific temporal NDVI-profiles covering each a 15-year period of averaged NDVI data representing all pixels classified to that cluster. Then, by legend-entry using the dekad-medians of all 15 annual repeats, we produced generalized legend-entries without year-specific anomalies for each cluster. Subsequently, a hierarchical cluster analysis of these temporal NDVI-profiles was used to produce a dendrogram that generated grouping options for the 81 legend-entries. Maps with cluster-groups of 8 and 4 legend-entries resulted. The 81-cluster map and its 65 legend-entries vector version have no equivalent in published vegetation maps. The 8 cluster-group map broadly corresponds with published ecoregion level maps and the 4 cluster-group map with the published biome maps in their number of legend units. The published vegetation maps varied considerably from our NDVI-profile maps in the location of mapping unit boundaries. The agreement index between our map and published biome maps ranges from 70−93. For the ecoregion level, the agreement index is much lower, namely 51−75. Our methodological approach showed a considerably higher discretionary power for hierarchical levels and the number of vegetation mapping units than the approaches applied to previously published maps. We recommended an approach to transform our three hyper-temporal NDVI-profiles based legend-entries into more specific vegetation units. This might be accomplished by re-analysis of available, spatially-comprehensive plant species occurrence data.

A novel approach to optimize hierarchical vegetation mapping from hyper-temporal NDVI imagery, demonstrated at national level for Namibia