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Weiqi Zhou, Kirsten Schwarz, Mary Cadenasso

2008 BES Annual Meeting. Agreement Assessment of Visual Interpretation and Digital Classification for Mapping Urban Landscape Heterogeneity. Weiqi Zhou, Kirsten Schwarz, Mary Cadenasso. Motivations (1). Visual interpretation of remotely sensed images is extensively used for urban analysis.

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Weiqi Zhou, Kirsten Schwarz, Mary Cadenasso

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  1. 2008 BES Annual Meeting Agreement Assessment of Visual Interpretation and Digital Classification for Mapping Urban Landscape Heterogeneity Weiqi Zhou, Kirsten Schwarz, Mary Cadenasso

  2. Motivations (1) • Visual interpretation of remotely sensed images is extensively used for urban analysis. • Patch mapping • Patch classification: Within-patch composition estimation. • However, few studies evaluate accuracy of the within-patch composition estimations, particularly in urban settings.

  3. Motivations (2) • Digital classification of high resolution image • Object-based classification greatly increases the accuracy of digital classification • Digital classification as reference data

  4. Cadenasso et al., 2007 Visual interpretation

  5. Digital classification

  6. Research Questions • What is the relative agreement of percent cover estimation between the two methods? • What are the spatial patterns of the patches with large disagreement?

  7. Visual Interpretation Digital Classification Degree of Disagreement # of HERCULES patches: 2250

  8. What is the relative agreement between the two classification methods? • Standard Procedure: the strictest agreement assessment method • Plus-one Method: A modification of the standard procedure, and accepts plus or minus one class of the actual class as agreement • Fuzzy Set Theory: Create fuzzy rules to account for fuzzy class boundaries.

  9. Results: Overall Agreement

  10. Observations from Agreement Assessment • Largest agreement when patches were dominated by one type of cover • Largest disagreement for cover ranges 10-35%, 35-75% • Vegetation: underestimated when cover <35% • Pavement: Underestimated • Buildings: Overestimated.

  11. Do the patches with large disagreement cluster spatially in the watershed? • Getis-Ord General G index: test whether the patches with large disagreement tend to cluster spatially • Anselin local Moran’s I index: detect spatial clusters (i.e., hot spots) of large disagreement within the watershed.

  12. Pattern Analysis: General G Index • Except for fine vegetation, patches with large disagreement clustered spatially (p<0.05).

  13. FV Bare Soil Pave Building CV

  14. Next step: How does patch heterogeneity affect the degree of disagreement? • Patch complexity metrics: e.g. patch size, shape, etc. • Within-patch heterogeneity metrics • Patch composition • Within-patch configuration

  15. Acknowledgements This research was funded by the National Science Foundation LTER program (grant DEB 042376) and biocomplexity program (grant BCE 0508054). Many thanks to a lot of BES people and colleagues in the Cadenasso lab.

  16. Questions & comments?

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