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CONTEXT: Landscape transition drives environmental change across the globe. However, landscape and its change are complex with high spatial heterogeneity, which challenges strategic decision-making. OBJECTIVES: This paper aims to derive management meaningful units based on landscape status and change analysis and the generalization of landscape spatial heterogeneity. METHODS: Based on contrasting cases from Finland (Vanajavesi) and China (Baota District), this paper analyzed the landscape attributes and change since 2000. A k-means clustering approach was used to generalize the landscape types based on indicators of landscape composition and its change, spatial pattern, population, and income. RESULTS: Most significant change in land covers was the expansion of artificial surfaces, and the bi-directional transitions between agricultural areas and forests and semi-natural areas in Vanajavesi, while the expansion of artificial land and shrinkage of cropland were most significant in Baota District. Larger changes in landscape metrics were also observed in Baota District. Finally, three landscape clusters were generalized in both of the case areas. For each cluster, the landscapes and their change characteristics were interpreted as pertinent to the average land cover pattern and its change and socioeconomic conditions. Brief landscape management recommendations were also given for the resulting clusters. CONCLUSIONS: This paper contributes to enriching the understanding of the analysis and management of landscape spatial heterogeneity based on the information from both landscape status and change. The contrasting case analyses from an international perspective indicate the usefulness of clustering approach in accommodating spatial heterogeneity, which imply a regionalized need for landscape monitoring, assessment, and management.
