Comparing the effects of classification techniques on landscape-level assessments: pixel-based versus object-based classification

Landscape-level assessments, particularly the quantification of forest fragmentation, often involve calculating landscape metrics from classified remotely sensed images. The utility of these derived metrics is often assumed to be dependent on the quality of the classified images. We compared conventional, pixel-based classification and a newer method of object-based classification to determine the effects of these two methods on fragmentation analysis of Cockpit Country, Jamaica, West Indies. Both methods showed similar trends in fragmentation metrics; however, there were significant differences between the methods for the metrics that quantified landscape configuration. The object-based classification allowed for the easy inclusion of roads into the analysis, which produced more accurate maps that showed a significant difference in the size of the largest forest patch. The object-based method also allowed classification of forests to show the location and extent of core forest areas; we were therefore able to identify an area of core forest that had remained consistent over the study period as a significant area for conservation focus. We recommend that the object-based method be the method chosen for landscape analyses, particularly forest-fragmentation studies.

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