Integrated Framework and Technical Path for Multi-level Nested Assessment of Landscape Character

<sec><title>Objective</title> The landscape character assessment system is an effective tool to help people understand the history and current status of landscapes. Its results are widely used in land decision-making and spatial planning control. Landscape character assessment (LCA) and landscape personality assessment (LPA) are two different perspectives and systems. LCA has a certain research and practical foundation, forming a relatively mature methodology system, emphasizing the characterization of the current status of landscape. In contrast, there is relatively little research and practice related to LPA, although attention has been paid to the trend of landscape changes. In addition, there is a nested relationship in terms of value implications, landscape spatial carrier, evaluation index system, and practical application of results, and there is a certain degree of complementarity between LPA and LCA. Integrating LCA and LPA and constructing a multi-level nested framework for landscape character assessment can sort out the multi-level relationships of landscape character representation, better meet practical needs at different scales, and help people comprehensively understand the past, present, and future of landscape. </sec><sec><title>Methods</title> Taking the landscape character assessment system as the research object, this research analyzes the concepts and connotations related to landscape character, and evaluates the shortcomings of existing LCA and LPA research and practice from the aspects of value dimension, indicator system, process characteristic, classification method, etc. There is considerable research and practical experience in the LCA research field, and scholars have also explored LPA. This research analyzes, evaluates, and horizontally compares relevant typical research and practical projects both domestically and internationally. The overall landscape carries the overall humanistic ecosystem, whose structurality and decomposability determine that landscape space is a complex composed of multiple relatively independent spaces, concatenation and nesting determine the multi-level nesting of the overall landscape space, and perceptibility and symbolism determine that identifying the characteristics of a landscape is a way to recognize the unique value of the landscape. Based on existing research content, the research summarizes the overall characteristics and future development trends of the two assessment methods, analyzes the differences and underlying connections between them, and explores possible integration methods for them. </sec><sec><title>Results</title> LCA can describe what a landscape is, while LPA can explore why it is. Comparing the methodology of LCA with that of LPA, LPA and LCA have certain complementarity in research perspective, indicator system, classification method, and other aspects. LCA focuses on the objective description of elements and their combination level features, which can depict local landscape differences and is supported by quantitative analysis techniques; LPA focuses on the comprehensive effects in the dynamic process of resource combination, which can characterize the value and personalized characteristics of the overall landscape, but lacks quantitative classification techniques. Landscape personality is formed by highly condensed landscape character with inherent attributes that are perceived by humans, and LCA is the foundation of LPA formation. LCA and LPA have certain complementarity in different stages of landscape character assessment, and are two sets of local character representation ideas suitable for different scenes in the landscape character assessment system. The integration of the two can help optimize the landscape character assessment system in both theory and practice. Based on the overall characteristics of landscape and the nesting of landscape spatial units, integrating the perspectives and systems of LCA and LPA, a multi-level nested framework for landscape character assessment is proposed as a reference for understanding the multi-scale local characteristics and spatial systems of landscape. By combining multi-scale segmentation and spatial clustering techniques of deep learning, a technical path for multi-level nested landscape character assessment is constructed as a new idea for characterizing the local characteristics of landscape at multiple scales. </sec><sec><title>Conclusion</title> In the process of developing landscape character assessment systems, there have been numerous methodological systems. The multi-level nested assessment framework and technical path integrating LCA and LPA can accurately grasp the local characteristics of landscape through quantitative assessment and spatial mapping using artificial intelligence technology at different scales of practical needs. This framework serves as a comprehensive framework in the landscape character assessment system, providing a holistic perspective for exploring the unique value of landscape. In addition, combining the integrated framework with digital technology analysis, a landscape character assessment approach that adapts to multi-scale practical needs is proposed, providing a technical path for analyzing the local characteristics of landscape based on different levels of landscape space. The landscape spatial unit system can be used as a carrier to characterize the characteristics of different levels of landscape places, and the assessment results can be integrated with applications at different scales, thus assisting in the practice of landscape planning and design, zoning control, and resource protection and utilization in different fields. The integrated framework and technical path for LCA and LPA can help future landscape character assessment research comprehensively understand the past, present, and future of landscape, systematically understand the local characteristics of landscape from both the local and overall perspectives, and combine practical needs at different scales of results. </sec>