Forests exhibiting old-growth (OG) attributes represent only a small fraction of global forest cover, yet they harbor disproportionately high ecological value in terms of ecosystem functions, structural complexity, and biodiversity. Identifying and quantifying such forests is particularly challenging in anthropogenically disturbed landscapes, where they can serve as reference models for guiding managed forests toward more resilient and ecologically valuable conditions. We developed a multi-level, interdisciplinary methodological approach to detect and assess potential OG forests in the Italian Alps by integrating: (1) landscape-scale remote-sensing screening; (2) field-based structural indicators (i.e., tree-related microhabitats, coarse wood debris, standing volume, carbon stocks, and regeneration patterns); and (3) canopy height model-derived metrics to identify remote sensing variables for upscaling. We propose a diametric distribution complexity ratio (actual/potential) to position forests within an evolutionary trajectory framework. Application to our case study revealed that current structural complexity has reached ~59% of its potential maximum. Tree-related microhabitats were most abundant on large-diameter dead trees. Total standing volume (800 m³ ha⁻¹) was comparable to reference European OG forests, although standing deadwood accounted for only 10% of the total volume. Aboveground carbon stock reached 240 Mg ha⁻¹ . Canopy height model-derived structural metrics showed strong correlations with field measurements (R² > 0.75). Regeneration was dominated by shade-tolerant species under closed canopy. This replicable multi-parameter framework enables systematic OG forest assessment through validated ground-based and remote-sensing indicators, providing quantitative tools for identifying high conservation value forests, establishing structural targets for closer-to-nature silviculture, and monitoring forest development toward OG characteristics.
Quantifying old-growth forest attributes in anthropogenic landscapes: A methodological approach based on structural indicators
Pasqualotto, Gaia
;Anfodillo, Tommaso;Atzeni, Francesco;Campagnaro, Thomas;Hussain, Muzamil;Lingua, Emanuele;Menon, Nicola;Pividori, Mario;Sitzia, Tommaso
2026
Abstract
Forests exhibiting old-growth (OG) attributes represent only a small fraction of global forest cover, yet they harbor disproportionately high ecological value in terms of ecosystem functions, structural complexity, and biodiversity. Identifying and quantifying such forests is particularly challenging in anthropogenically disturbed landscapes, where they can serve as reference models for guiding managed forests toward more resilient and ecologically valuable conditions. We developed a multi-level, interdisciplinary methodological approach to detect and assess potential OG forests in the Italian Alps by integrating: (1) landscape-scale remote-sensing screening; (2) field-based structural indicators (i.e., tree-related microhabitats, coarse wood debris, standing volume, carbon stocks, and regeneration patterns); and (3) canopy height model-derived metrics to identify remote sensing variables for upscaling. We propose a diametric distribution complexity ratio (actual/potential) to position forests within an evolutionary trajectory framework. Application to our case study revealed that current structural complexity has reached ~59% of its potential maximum. Tree-related microhabitats were most abundant on large-diameter dead trees. Total standing volume (800 m³ ha⁻¹) was comparable to reference European OG forests, although standing deadwood accounted for only 10% of the total volume. Aboveground carbon stock reached 240 Mg ha⁻¹ . Canopy height model-derived structural metrics showed strong correlations with field measurements (R² > 0.75). Regeneration was dominated by shade-tolerant species under closed canopy. This replicable multi-parameter framework enables systematic OG forest assessment through validated ground-based and remote-sensing indicators, providing quantitative tools for identifying high conservation value forests, establishing structural targets for closer-to-nature silviculture, and monitoring forest development toward OG characteristics.
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