Multi-scale image segmentation is a fundamental process of object-based image analysis (OBIA), and the segmentation scale is a key parameter. The selection of optimal scale parameter has attracted widely attentions in recent years, and many methods were proposed for this task. However, these methods usually need to set a scale sequence, which depends on the segmentation algorithm. When using another segmentation algorithm, the definition of the scale is different, these methods are less effective. In this study, we are aiming at developing a general framework for analyzing and estimating the optimal scale parameter for region merging segmentation, which is the most widely used approach for generating multi-scale objects in remote sensing. A stepwise evolution analysis (SEA) framework and its application in optimal scale parameter estimation are proposed. In the SEA framework, the evolutions of scale, local variance (LV) and Moran's I (MI) indexes are analyzed step-by-step, and then be used for automated scale parameterisations. Four LV and MI metrics based methods were technically integrated in this framework, and applied to a QuickBird and a WorldView-2 image to evaluate the performance. The results demonstrated that: The stepwise analysis of a hierarchical region merging provided the distribution information of the scale, which could be used to estimate the key parameters and integrate different scale parameterisation methods. Traditional LV- and MI-based scale estimation methods could be technically integrated, which showed the powerful ability and great potential in utilizing different scale paramterisation methods for region merging segmentation.