Landscape spaces such as gardens and parks are composed of various landscape components, creating diverse landscapes. In general, the quality of the landscape in these spaces is often judged subjectively by visitors. On the other hand, if landscapes can be evaluated objectively, they can be used to create better spaces in the management and creation of landscaped spaces. In recent years, point cloud data has been acquired in urban and natural spaces. In landscaped spaces, point cloud data is increasingly used for landscape simulation and current state planning. In this study, point cloud data acquired with a terrestrial laser scanner (TLS) in the target space were used to quantitatively characterize the entire landscape using fractal analysis and visual and ecological environmental quality models (VQM). We also segmented these data into components of the point cloud data and calculated the relationship between the data and the occupancy of the components. On the other hand, focusing on environmental visual information received passively from a wide range of environments, we conducted an analysis based on panoramic images created from point cloud data. As a result, both fractal analysis and VQM showed a high correlation with previous research methods in understanding the landscape using point cloud data. In addition, the analysis of the landscape was made more efficient than the conventional photographic analysis by segmenting the components in advance at the data processing stage, demonstrating the usefulness of landscape analysis from data acquired by laser scanners.