Objective Parameterization of InVEST Habitat Quality Model Using Integrated PCA-SEM-Spatial Analysis: A Biotope Map-Based Framework
Abstract
1. Introduction
2. Materials and Methods
2.1. Analytical Framework Overview
2.2. Study Area
2.3. Data Sources and Biotope-Based Habitat Classification
2.3.1. Spatial Data Sources and Specifications
2.3.2. Data Processing and Integration
2.4. Integrated Threat Analysis Approach
2.4.1. Principal Component Analysis for Threat Dimensionality Reduction
2.4.2. Structural Equation Modeling for Causal Relationships
2.4.3. Spatial Parameter Optimization
2.5. InVEST Habitat Quality Implementation
2.5.1. Model Configuration and Parameter Integration
2.5.2. Model Execution and Quality Control
2.6. Comparative Performance Evaluation Framework
2.6.1. Reference Data Preparation and Sampling Strategy
2.6.2. Multi-Domain Performance Assessment
2.6.3. Ecological Gradient Verification
2.7. Statistical Analysis and Implementation
2.7.1. Comprehensive Software Framework
2.7.2. Analysis Workflow and Quality Control
2.7.3. Reproducibility and Quality Assurance
2.7.4. Statistical Significance and Effect Sizes
2.7.5. Performance Integration Framework
3. Result
3.1. Threat Variable Analysis and Reduction
3.1.1. PCA Results
3.1.2. SEM Results
3.2. Spatial Parameter Optimization
3.2.1. Distance Parameter Selection and Performance
3.2.2. Model Stability and Precision
3.3. Biotope vs. LULC Performance Comparison
3.3.1. Correlation Analysis with Validation Indicators
3.3.2. ROC Analysis for Protected Area Prediction
3.3.3. UNESCO Biosphere Reserve Validation
3.3.4. Spatial Overlap Analysis
3.4. Model Performance Summary
3.4.1. Comprehensive Performance Evaluation
3.4.2. Practical Significance Assessment
- NDVI correlation: +0.078 points (0.247 vs. 0.169);
- Biosphere Reserve correlation: +0.232 points (0.390 vs. 0.158);
- Special Management Zone correlation: +0.075 points (0.179 vs. 0.104);
- Any protected area correlation: +0.256 points (0.457 vs. 0.201).
4. Discussion
4.1. Methodological Development and Validation
4.2. Regional Parameterization and Ecological Insights
4.3. Addressing Methodological Limitations
4.4. Policy and Conservation Planning Implications
4.5. Future Research Directions
- (1)
- (2)
- (3)
- Integration with climate change projections will enable scenario-based conservation planning that accounts for both bioclimatic shifts and species responses [52,53]. In particular, linking bioclimate and population models offers improved forecasts for extinction risk, while iterative scenario testing may strengthen adaptive planning [52,53].
- (4)
- Development of automated parameter updating procedures is essential for operational applications. Advances in iterative near-term ecological forecasting [46] and Bayesian updating methods [64,65] provide promising pathways, and recent workflow frameworks demonstrate how automation can enhance reproducibility and transparency [66,67].
5. Conclusions
5.1. Research Accomplishments
5.2. Scientific and Methodological Contributions
- Objective parameter derivation substantially reducing expert judgment bias;
- Enhanced ecological realism through detailed biotope mapping;
- Comprehensive validation across independent ecological indicators;
- Transferable methodology applicable to diverse regional contexts.
5.3. Policy and Conservation Implications
5.4. Future Research and Applications
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Data Type | Description | Resolution | Source | Encoding |
---|---|---|---|---|
HQ Outputs | ||||
Biotope base HQ | biotope-based InVEST | 30 m | This Study | Continuous (0–1) |
Biotope base DG | ||||
LULC base HQ | LULC-based InVEST | |||
LULC base DG | ||||
Reference Data | ||||
Biosphere Reserve | UNESCO Biosphere Reserve zones | 30 m | UNESCO MAB | Categorical (0, 1, 2) |
Management Area | Separately managed conservation area | Min. of Environment | Binary (0, 1) | |
Ecological Grade | Grade 1 ecological natural map | Binary (0, 1) | ||
Vegetation Index | Normalized Difference Vegetation Index (NDVI) | This Study | Continuous (−1, 1) |
Category | Evaluation Indicator | Score | Description |
---|---|---|---|
Structure | Area | 3/2/1 | ≥10 ha/1–10 ha/<1 ha |
Slope | ≥25°/8–25°/<8° | ||
Elevation | ≥100 m/50–100 m/<50 m | ||
Shape Index | ≥2.0/1.5–2.0/<1.5 | ||
Naturalness | Vegetation Layers | Multi/Two/Single layer | |
Land Use Intensity | Conservation/Mixed/Developed | ||
Distance from Road | ≥100 m/50–100 m/<50 m | ||
Green Coverage | ≥60%/40–60%/<40% | ||
Permeable Surface | ≥60%/40–60%/<40% | ||
Function | Biodiversity Core | Highest | Priority conservation zones |
Buffer Zones | High | Secondary conservation areas | |
Cultural Heritage | High | Designated cultural properties | |
Species Habitat | High | Endangered species locations | |
Connectivity | Variable | ≥50% connectivity index |
Domain | Metric | Method | Interpretation |
---|---|---|---|
Ecological Coherence | NDVI correlation | Pearson r | Higher values indicated ecological alignment |
Policy Alignment | Protection correlations | Pearson r (4 type) | Higher values indicate policy concordance |
Prediction Accuracy | AUC values | ROC analysis | Values > 0.7 indicate good discrimination |
Spatial Precision | Overlap Rate, Precision | Spatial overlay | Higher values indicate spatial accuracy |
Analysis Component | Package | Version | Key Function | Primary Purpose |
---|---|---|---|---|
Data Processing | Dply | 1.1.0 | Select (), filter (), mutate () | Data manipulation |
Readr | 2.1.4 | Read_csv (), read_rds () | Data import/export | |
tibble | 3.2.1 | Tibble (), as_tibble () | Data structure management | |
PCA Analysis | FactoMineR | 2.8 | PCA (), get_eigenvalue () | Principal component analysis |
Facroextra | 1.0.7 | Fviz_pca_var (), fviz_contrib () | PCA visualization | |
Corrplot | 0.92 | Corrplot (), corrplot.mixed () | Correlation matrix visualization | |
SEM Modeling | Lavaan | 0.6-15 | Sem (), cfa (), fitMeasures () | Structural equation modeling |
semPlot | 1.1.6 | semPaths (), semPlotModel () | SEM path diagrams | |
Spatial analysis | Terra | 1.7-29 | Rast (), extract (), global () | Raster data processing |
Sf | 1.0-12 | St_read (), st_trensform () | Vector spatial operations | |
Gstat | 2.1-1 | Variogram (), fit.variogram () | Variogram analysis | |
Automap | 1.1-9 | autofitVariogram () | Automated variogram fitting | |
Performance Evaluation | pROC | 1.18.0 | Roc (), auc (), roc.test () | ROC analysis |
Caret | 6.0-94 | trainControl (), confusionMatrix () | Model validation | |
MLmetrics | 1.1.1 | RMSE (), MAE (), Accuracy () | Performance metrics |
Analysis Step | Sample Size | Method | Statistical Test | Quality Control |
---|---|---|---|---|
Data Sampling | 6000 pixels | Stratified random (seed = 42) | Distribution normality | Shapiro–Wilk test |
Missing Data | [Final N] pixel | Listwise deletion | Completeness Assessment | Missing pattern analysis |
PCA Execution | Full sample | Standardized variables | Kaiser-Maeyer- Olkin test | KMO > 0.8 Threshold |
SEM Fitting | Full sample | Maximum likelihood | Model fit indices | RMSEA < 0.08 CFI > 0.95 |
Variogram Analysis | Systematic sample | Exponential/Spherical models | Range parameter estimation | Cross-validation |
Correlation Analysis | Full sample | Pearson coefficients | Significance testing | Cor.test () |
ROC Analysis | Full sample | Binary classification | AUC calculation | 95% confidence intervals |
Spatial Concordance | Top 20% HQ | Quantile threshold | Overlap analysis | Precision/recall metrics |
Gradient Analysis | 3 protection levels | Group comparison | ANOVA | Descriptive statics |
Component | Eigenvalue | % of Variance | Cumulative % | Dominant Loading |
---|---|---|---|---|
PC1 | 3.82 | 42.47 | 42.47 | Urban (0.89), Industrial (0.84), Road (0.76) |
PC2 | 1.75 | 19.45 | 61.92 | Crop (0.91), Pasture (0.68) |
PC3 | 1.19 | 13.26 | 75.18 | Hydropower (0.82), Recreation (0.74) |
PC4 | 0.946 | 11.42 | 86.60 | Greenspace (0.88) |
PC5 | 0.67 | 7.42 | 94.02 | Bareground (0.91) |
Variable | PC1 | PC2 | PC3 | PC4 | PC5 | Cluster Value |
---|---|---|---|---|---|---|
Urban | 0.89 | 0.12 | −0.08 | 0.15 | 0.09 | 0.89 |
Industrial | 0.84 | 0.19 | 0.21 | −0.12 | 0.18 | 0.92 |
Road | 0.76 | 0.34 | 0.28 | 0.19 | −0.08 | 0.91 |
Crop | 0.23 | 0.91 | −0.15 | 0.12 | 0.08 | 0.94 |
Pasture | 0.41 | 0.68 | 0.18 | −0.21 | 0.31 | 0.89 |
Hydropower | 0.19 | −0.12 | 0.82 | 0.08 | 0.15 | 0.96 |
Recreation | 0.28 | 0.31 | 0.74 | 0.23 | −0.11 | 0.93 |
Greenspace | 0.15 | −0.19 | 0.23 | 0.88 | 0.12 | 0.94 |
Bareground | −0.08 | 0.21 | 0.09 | 0.18 | 0.91 | 0.92 |
Fit Index | Value | 90% CI | Acceptable Threshold |
---|---|---|---|
RMSEA | 0.042 | 0.038~0.047 | <0.05 |
CFI | 0.968 | - | >0.90 |
TLI | 0.954 | - | >0.90 |
χ2/df | 1.99 | - | <3.0 |
Principal Component | β | SE | t-Value | p-Value | InVEST Weight |
---|---|---|---|---|---|
PC1 (Development pressure) | −0.47 | 0.018 | −26.11 | <0.001 | 0.47 |
PC2 (Agricultural Pressure) | −0.31 | 0.021 | −14.76 | <0.001 | 0.31 |
PC3 (Infrastructure development) | −0.28 | 0.019 | −14.74 | <0.001 | 0.28 |
PC4 (Greenspace management) | −0.19 | 0.020 | −9.50 | <0.001 | 0.19 |
PC5 (Bareground disturbance) | −0.15 | 0.022 | −6.82 | <0.001 | 0.15 |
Distance (m) | R2 | AIC | BIC | Performance Category |
---|---|---|---|---|
150 | 0.610 | −4503.07 | −4475.06 | Good |
300 | 0.611 | −4504.67 | −4476.66 | Optimal |
450 | 0.610 | −4503.12 | −4475.11 | Good |
600 | 0.592 | −4485.23 | −4457.22 | Moderate |
900 | 0.523 | −4431.45 | −4403.44 | Poor |
1200 | 0.478 | −4389.67 | −4361.66 | Poor |
1800 | 0.412 | −4323.89 | −4295.88 | Very Poor |
2400 | 0.381 | −4278.12 | −4250.11 | Very Poor |
3000 | 0.354 | −4234.56 | −4206.55 | Very Poor |
Metric | Value | Interpretation |
---|---|---|
Average Confidence Interval Width | 0.00544 | Minimal uncertainty |
Average Standard Error | 0.00139 | Optimal precision |
Bootstrap CV (n = 1000) | <5% | High stability |
Total Coefficient Magnitude | 0.0742 | Maximum signal detection |
Moran’s I (residuals) | 0.031 (p = 0.124) | No spatial autocorrelation |
Distance Range | CV for Threat Weights | Reliability Assessment |
---|---|---|
150~450 m | <5% | High reliability |
450~900 m | 5~8% | Moderate reliability |
900~1200 m | 8~12% | Low reliability |
>1200 m | >12% | Poor reliability |
Validation Indicator | Biotope Model | LULC Model | Improvement |
---|---|---|---|
NDVI | 0.247 | 0.169 | +0.078 |
Biosphere Reserve Status | 0.390 | 0.158 | +0.232 |
Special Management Zone | 0.179 | 0.104 | +0.075 |
Natural Monument | −0.023 | 0.038 | −0.061 |
Protect Area | 0.457 | 0.201 | +0.256 |
Protection Type | Biotope AUC | LULC AUC | Improvement | Performance Level |
---|---|---|---|---|
Biosphere Reserve | 0.794 | 0.744 | +0.050 | Good -> Excellent |
Protected Area | 0.805 | 0.755 | +0.050 | Good -> Excellent |
Zone Type | Sample Size | Biotope HQ Mean | LULC HQ Mean | Biotope DG Mean | LULC DG Mean |
---|---|---|---|---|---|
Core | 268 | 0.982 | 0.869 | 0.067 | 0.100 |
Buffer | 2757 | 0.956 | 0.841 | 0.115 | 0.143 |
General | 861 | 0.881 | 0.721 | 0.211 | 0.216 |
Method | High HQ in BR | Total High HQ | Total BR | Overlap Rate | Precision |
---|---|---|---|---|---|
Biotope | 761 | 811 | 3025 | 0.938 | 0.252 |
LULC | 763 | 811 | 3025 | 0.941 | 0.252 |
Performance Category | Biotope Winner | LULC Winner | No Difference |
---|---|---|---|
Correlation Metrics | 4/5 | 1/5 | 0/5 |
AUC Metrics | 2/2 | 0/2 | 0/2 |
Spatial Metrics | 0/3 | 2/3 | 1/3 |
Overall | 6/10 | 3/10 | 1/10 |
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Kim, D.U.; Yoon, H.Y. Objective Parameterization of InVEST Habitat Quality Model Using Integrated PCA-SEM-Spatial Analysis: A Biotope Map-Based Framework. Land 2025, 14, 2050. https://doi.org/10.3390/land14102050
Kim DU, Yoon HY. Objective Parameterization of InVEST Habitat Quality Model Using Integrated PCA-SEM-Spatial Analysis: A Biotope Map-Based Framework. Land. 2025; 14(10):2050. https://doi.org/10.3390/land14102050
Chicago/Turabian StyleKim, Dong Uk, and Hye Yeon Yoon. 2025. "Objective Parameterization of InVEST Habitat Quality Model Using Integrated PCA-SEM-Spatial Analysis: A Biotope Map-Based Framework" Land 14, no. 10: 2050. https://doi.org/10.3390/land14102050
APA StyleKim, D. U., & Yoon, H. Y. (2025). Objective Parameterization of InVEST Habitat Quality Model Using Integrated PCA-SEM-Spatial Analysis: A Biotope Map-Based Framework. Land, 14(10), 2050. https://doi.org/10.3390/land14102050