Abstract:

Forest stage classification is a core scientific issue for understanding forest dynamics and implementing sustainable forest management. In response to the challenges of conceptual ambiguity and methodological applicability in current research, this paper systematically reviews and evaluates mainstream classification methods for forest life-cycle stage classification, clarifying their theoretical foundations and application boundaries, so as to provide theoretical basis and practical guidance for precision forest management. The paper first dialectically distinguishes the connotation, difference and connection between the two core concepts of forest succession (species replacement at the community scale) and forest development (structural dynamics at the stand scale), thereby establishing the theoretical cornerstone for the following method review and classification. Furthermore, from the perspective of historical evolution, the paper categorizes existing methods into four major paradigms: 1) Chronological paradigm (age-group classification): with stand age as a single division indicator to serve sustainable timber production, this method is easy to apply, but insufficient to capture complex structures. 2) Structural paradigm (forest cycle stage classification): based on the gap dynamics theory, this method reveals the natural structural cycle of primeval forests and serves as an ecological benchmark for biodiversity conservation. 3) Ecological paradigm (successional stage classification): based on the substitution sequence of functional species groups, this method explains macro-level community succession laws and provides a theoretical framework for ecological restoration. 4) Integrative paradigm (close-to-nature developmental stage classification): oriented toward life-cycle management, combining structural dynamics with management objectives, this method serves as a bridge between ecological theory and management practice. Comprehensive analysis shows that each paradigm has its own historical background, applicable scale, and limitations, thus, the choice of method depends on specific application scenarios or management objectives. Currently, this field faces three core challenges: the complexity of conceptual integration, insufficient quantification and standardization, and the uncertainties introduced by climate change disrupting traditional development and succession pathways. Future research on forest life-cycle stage classification will evolve toward a new phase of deep integration characterized by precision, intelligence, and dynamics. Key development directions include: constructing accurate quantitative indicator systems that integrate structural and ecological functions, developing “climate-smart” dynamic classification frameworks that incorporate climate prediction models. Ultimately, through the deep integration of advanced sensing technologies and artificial intelligence algorithms, a paradigm shift from “manual judgment” to “intelligent perception” is expected, providing transformative technical support for precision monitoring and high-quality management of global forests.

Key words: forest stage classification, succession, development, classification paradigm, life-cycle management