## 4 Discussion
It is interesting to see the fact that study sites with high ED2 scores (poor segmentation quality) have achieved higher classification accuracy than the sites with low ED2 scores. For example, overall classification accuracy (Table 3) of ZAVO has surpassed that of HOPE. By performing a segmentation quality evaluation, we aimed to rule out the possibility that the variations in classification accuracies across candidate study sites attributed not merely because of perturbations in fuzzy rules but because of inferior or superior segmentation. GEOBIA's wide adoption in VSHR image based land use land cover (LULC) mapping reflects the fact that object-based workflows are more transferable across image scenes than alternative image analysis methods. However, GEOBIA cannot explicitly link low-level image attributes encoded in image object candidates to high-level user expectations (semantics). While the challenge is universal to VHSR image based LULC mapping in general, within the scope of this study, the intriguing question is \"what is the degree of transferability of a master ruleset for guano across scenes and across sites?\" Designing a master ruleset based on a certain image scene and targeting a set of target classes is a time-, labor-, and knowledge-intensive process as the operator has to choose the optimal object and scene features and variables to meaningfully link class labels and image object candidates. Thus, in an operations context, reapplication of the master ruleset without adaptations across different scenes would be ideal; however, it is highly unrealistic in most image classification scenarios to achieve acceptable classification accuracy without ruleset modification. When the master ruleset is reapplied without adaptations (Table 3), the QuickBird scene over BAIL and WorldView-2 scene over BIRD showed relatively high detection of guano stains but still the classification accuracies were inferior to the reference QuickBird scene over HOPE. This is mainly due to the similar spectral, textural, and most importantly neighborhood characteristics (spatially-disjointpatches embedded with dark substrate) of the target class in these three study sites. On the other hand, the WorldView-2 scene over ZAVO showed a zero detection of guano stains when the master ruleset was re-tasked as is. One could argue that this is due to the different sensor characteristics, however; results from BAIL and BIRD challenge that argument. We suspect that notably high reflectance of clouds and wave crests, spectral characteristics of guano, and the hierarchical design (labelling of sub-objects based on super-objects) may have led to an ill detection of guano. From the view point of \"ruleset without adaptations\", our analysis reveals that the success rate of the master ruleset's repeatability is low and unpredictable even in instances where the classification is constrained to a particular semantic class. As seen in Table 3, scene- or site-specific adaptations inevitably enhance the classification accuracy at the expense of operator engagement in re-tuning of fuzzy rules. When revisiting the fine-scale adjustments associated with the fuzzy rules across all candidate sites (Tables 4-9), it is clear that a majority of changes fall onto the Type F\({}^{b}\) category, which harbors the changes associated with the underlying fuzzy membership function. In certain instances, for example SPIN and ZAVO, the border contrast rule was left inactive because of the spectral ambiguity between the guano and the surrounding geology. Across all candidate sites, Type F\({}^{b}\) deviations have been dominated by the \(\delta v\) component, which elucidates the fact that the shape (range \(v\)) of the fuzzy membership function (stretch or shrink) has changed rather than a shift in the mean value (\(a\)). This reflects the potential elasticity available to the operator to refine rules.