Objective Parameterization of InVEST Habitat Quality Model Using Integrated PCA-SEM-Spatial Analysis: A Biotope Map-Based Framework

Dong Uk Kim () and Hye Yeon Yoon
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Dong Uk Kim: National Institute of Ecology, Ecology and Nature Map Team, 1210 Geumgang-ro, Maseo-myeon, Seocheon-gun 33657, Chungcheongnam-do, Republic of Korea
Hye Yeon Yoon: National Institute of Ecology, Ecology and Nature Map Team, 1210 Geumgang-ro, Maseo-myeon, Seocheon-gun 33657, Chungcheongnam-do, Republic of Korea

Land, 2025, vol. 14, issue 10, 1-23

Abstract: Current InVEST habitat quality assessments rely heavily on subjective expert judgment for parameter specification, introducing substantial uncertainty and limiting their regional applicability. To address this gap, we developed an objective, statistically rigorous framework for parameter derivation by integrating Principal Component Analysis (PCA), Structural Equation Modeling (SEM), and spatial analysis, supported by high-resolution biotope mapping. The methodology was applied to Gochang-gun, South Korea, where nine threat factors were analyzed using empirical data from 6633 sampling points. PCA identified threat groupings, SEM quantified habitat–threat relationships for sensitivity derivation, and variogram analysis determined maximum influence distances, while 1:5000 scale biotope maps incorporating 14 ecological indicators replaced conventional land cover classifications. These empirically derived parameters were directly incorporated into the InVEST Habitat Quality model, replacing default or expert-based values. As a result, the biotope-based InVEST HQ implementation achieved exceptional performance (R 2 = 0.892) with crops emerging as the dominant threat factor (sensitivity = 1.000, weight = 34.1%). Compared to the land use/land cover (LULC)-based approach using conventional parameterization, the biotope–PCA–SEM model demonstrated higher predictive accuracy (AUC = 0.805 vs. 0.755), stronger correlations with independent conservation indicators (protected area correlation: 0.457 vs. 0.201), and clearer ecological gradients across UNESCO Biosphere Reserve zones. This framework eliminates subjective bias while maintaining regional specificity, establishing a transferable foundation for evidence-based conservation planning. By demonstrating substantial improvements over conventional parameterization, the study highlights the inadequacy of transferred parameters and provides an objective standard for advancing InVEST applications worldwide.

Keywords: structural equation modeling; principal component analysis; ecosystem services; landscape ecology (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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