Urban vegetation mapping using sub‐pixel analysis and expert system rules: A critical approach

Since the traditional hard classifier can label each pixel only with one class, urban vegetation (e.g. trees) can only be recorded as either present or absent. The sub‐pixel analysis that can provide the relative abundance of surface materials within a pixel may be a potential solution to effectively identifying urban vegetation distribution. This study examines the effectiveness of a sub‐pixel classifier with the use of expert system rules to estimate varying distributions of different vegetation types in urban areas. The Spearman's rank order correlation between the vegetation output and reference data for wild grass, man‐made grass, riparian vegetation, tree, and agriculture were 0.791, 0.869, 0.628, 0.743, and 0.840 respectively. Results from this study demonstrated that the expert system rule using NDVI threshold procedure is reliable and the sub‐pixel processor picked the signatures relatively well. This study reports a checklist of the sources of limitation in the application of sub‐pixel approaches.

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