Understanding the various characteristics of multiobjective optimization problems (MOPs) is crucial for designing and configuring optimization algorithms to efficiently solve them. This paper introduces a method that uses the estimation of local correlation between objectives to transform MOP landscapes into single-objective problem (SOP) landscapes. With this transformation, we make it possible to apply SOP landscape features to MOPs, thereby extracting valuable information about problem properties, such as modality. Our approach integrates both sample-based and search-based features, which are assessed for their ability to distinguish between unimodal, moderately multimodal, and highly multimodal MOPs. The proposed method is validated through a two-phase experimental setup. In the first phase, we select features that can reliably identify problem modality under ideal conditions with abundant data. The second phase evaluates their performance in more realistic scenarios with smaller samples and higher problem dimensions. The results show that features computed on the local correlation landscape achieve comparable or better performance than existing MOP features. These findings demonstrate the capability of SOP features to generalize to MOPs, showcasing their potential for characterizing MOP landscapes and inspiring future research on extending this approach to uncover additional problem properties.