Characterizing Degradation Gradients through Land Cover Change Analysis in Rural Eastern Cape, South Africa
AbstractLand cover change analysis was performed for three catchments in the rural Eastern Cape, South Africa, for two time steps (2000 and 2014), to characterize landscape conversion trajectories for sustained landscape health. Land cover maps were derived: (1) from existing data (2000); and (2) through object-based image analysis (2014) of Landsat 8 imagery. Land cover change analysis was facilitated using land cover labels developed to identify landscape change trajectories. Land cover labels assigned to each intersection of the land cover maps at the two time steps provide a thematic representation of the spatial distribution of change. While land use patterns are characterized by high persistence (77%), the expansion of urban areas and agriculture has occurred predominantly at the expense of grassland. The persistence and intensification of natural or invaded wooded areas were identified as a degradation gradient within the landscape, which amounted to almost 10% of the study area. The challenge remains to determine significant signals in the landscape that are not artefacts of error in the underlying input data or scale of analysis. Systematic change analysis and accurate uncertainty reporting can potentially address these issues to produce authentic output for further modelling. View Full-Text
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Münch, Z.; Okoye, P.I.; Gibson, L.; Mantel, S.; Palmer, A. Characterizing Degradation Gradients through Land Cover Change Analysis in Rural Eastern Cape, South Africa. Geosciences 2017, 7, 7.
Münch Z, Okoye PI, Gibson L, Mantel S, Palmer A. Characterizing Degradation Gradients through Land Cover Change Analysis in Rural Eastern Cape, South Africa. Geosciences. 2017; 7(1):7.Chicago/Turabian Style
Münch, Zahn; Okoye, Perpetua I.; Gibson, Lesley; Mantel, Sukhmani; Palmer, Anthony. 2017. "Characterizing Degradation Gradients through Land Cover Change Analysis in Rural Eastern Cape, South Africa." Geosciences 7, no. 1: 7.
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