Landscape metric scalograms (the response curves of landscape metrics to changing grain size) have been used to illustrate the scale effects of metrics for real landscapes. However, whether they detect the characteristic scale of hierarchically structured landscapes remains uncertain. To address this question, the scalograms of 26 class-level metrics were systematically examined for a simple random landscape, seven hierarchical neutral landscapes, and the real landscape of the Xilin River Basin of Inner Mongolia, China. The results show that when the fraction of the focal patch type (P) is below a critical value (P _c), most metric scalograms are sensitive to change in single-scale and lower-level hierarchical structure and insensitive to change in higher-level hierarchical structure. The scalograms of only a few metrics measuring spatial aggregation and connectedness are sensitive to change in intermediate-level hierarchical structure. Most metric scalograms explicitly identify the characteristic scale of a single-scale landscape and fine or intermediate characteristic scales of a multi-scale landscape for both simulated and real landscapes. When P exceeds P _c, only some metrics detect scale and change in structure. The scalograms of total class area and Euclidean nearest-neighbor distance cannot detect scale or change in structure in either case. Landscape metric scalograms are useful for addressing scale issues, including illustrating the scale effects of spatial patterns, detecting multi-scale patterns, and developing possible scaling relations.