Modeling structural attributes of Hyrcanian forests using spaceborne LiDAR, machine learning algorithms, and a hierarchical field data collection approach
Year: 2025
Type: article
Abstract: Most conservation plans do not incorporate forest structural attributes, prioritizing species-based criteria instead. We integrated GEDI LiDAR canopy metrics with hierarchical field inventories and management-unit aggregation to model basal area (G, m² ha⁻¹), standing volume (V, m³ ha⁻¹), and tree density (N, trees ha⁻¹) at plot and compartment levels. Using 1195 field plots, we benchmarked five learners: linear regression, support vector regression, random forest, gradient boosting, and a neural network under cross-validation; we then applied a two-phase design for regional estimation and compared high-V areas with UNESCO core zones. At the plot-level, V reached R² ≈ 0.75 (RMSE ≈ 65–66 m³ ha⁻¹), G was moderate (R² = 0.47; RMSE ≈ 6.1 m² ha⁻¹), and N was weak (R² = 0.10; RMSE ≈ 266 trees ha⁻¹). At the compartment-level, V achieved R² = 0.82 (RMSE = 37.7 m³ ha⁻¹), G reached R² = 0.54 (RMSE = 2.5 m² ha⁻¹), and N remained difficult to predict even after aggregation. Linear and SVR models matched or exceeded tree-based and neural models for G and V. Regional means were V = 241.05 m³ ha⁻¹ and G = 22.33 m² ha⁻¹ . Of the 60 1 × 1 km cells with V > 500 m³ ha⁻¹ , only 8 were inside UNESCO core zones, indicating a protection gap. Collectively, a compact set of GEDI height metrics, coupled with routine inventories and unit-level aggregation, enables timely updates of forest thematic maps and supports zoning that prioritizes high-biomass stands in the Hyrcanian region.
Source: Forest Ecology and Management
Authors: Hormoz Sohrabi, Reza Akhavan
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
SDG: Life on land
Open Access status: hybrid
Estimated APC paid: $3,560
OpenAlex ID: W4415623966