The remote sensing of vegetation, which has predominantly applied methods that analyze each image pixel as independent observations, has recently seen the development of several methods that identify groups of pixels that share similar spectral or structural properties as objects. The outputs of “per-object” rather than “per-pixel” methods represent characteristics of vegetation objects, such as location, size, and volume, in a spatially explicit manner. Before decisions can be influenced by data products derived from per-object remote sensing methods, it is first necessary to adopt methodologies that can quantify the spatial and temporal trends in vegetation structure in a quantitative manner. In this study, we present one such methodological framework where (i) marked point patterns of vegetation structure are produced from two per-object methods, (ii) new spatial-structural data layers are developed via moving-window statistics applied to the point patterns, (iii) the layers are differenced to highlight spatial-structural change over a 60 year period, and (iv) the resulting difference layers are evaluated within an ecological context to describe landscape-scale changes in vegetation structure. Results show that this framework potentially provides information on the population, growth, size association (nonspatial distribution of large and small objects), and dispersion. We present an objective methodological comparison of two common per-object approaches, namely image segmentation and classification using Definiens software and two-dimensional wavelet transformations.