A Landscape Pattern Characterization Method Based on the Natural Complex: A Case Study of Songhua River Basin, China

The ‘human-land relationship’ has long been a central topic in geography, ecology, and social sciences. And dynamic monitoring of the land use/cover change (LUCC) and landscape pattern studies of land use provide essential theoretical support for this field. On the basis of physicogeographical regionalization and landscape ecology theory, this study proposed a method for characterizing landscape pattern by using the minmium natural complex as the basic evaluation unit, constructing a four-level classification framework that included natural area, landscape region, landscape subregion, and landscape group. The method was tested in the Songhua River Basin, China, and the results revealed that: 1) the landscape pattern characterization in the Songhua River Basin validated the feasibility of the proposed method, and the use of the minimum natural complex as the basic measurement unit for landscape pattern characterization benefited research in this field. 2) The multiscale landscape pattern characterization system, which was framed around natural area, landscape region, landscape subregion, and landscape group, accurately reflected the spatial heterogeneity of the physical geographic environment and the impact of human activities on the landscape pattern in the Songhua River Basin. The nature-dominated landscape region was mainly concentrated in high-altitude mountainous and hilly areas, the human-dominated landscape region was located on plains and platforms, and the human-land interaction landscape region exhibited a landscape pattern that is closely related to natural factors such as temperature, precipitation, and soil. 3) The proposed method not only revealed the complex interactions between the physical geographic environment and human activities but also provided scientific support for land use planning and ecological environment management. Future research should incorporate higher-resolution data and socioeconomic factors to further explore the dynamic changes in landscape patterns and their impacts on the regional ecological environment.