Publications
Challenges Facing the Improvement of Forest Management in the Hyrcanian Forests of Iran
A hierarchical framework, the Sustainable Hyrcanian Forest Management Model (SHFMM), emerges as a promising solution to address challenges hindering sustainable forest management (SFM) in Iran's Hyrcanian forests. These challenges include inconsistent policies, reliance on outdated techniques, and conflicts between policymakers and local communities. The SHFMM proposes a collaborative approach that empowers state and private sectors to make decisions while fostering cooperation among all stakeholders. Developed through independent studies of forest management in the region, this model offers site-specific lessons with broader applicability.
Estimating Aboveground Biomass in Dense Hyrcanian Forests by the Use of Sentinel-2 Data
A novel approach using Sentinel-2 satellite data demonstrates high accuracy in estimating aboveground biomass (AGB) in dense Hyrcanian forests, particularly for Carpinus betulus (common hornbeam). The study found that band 6, located in the red-edge spectrum, exhibited the strongest correlation with AGB (r = −0.723), outperforming other spectral and derived indices. Among machine learning methods tested—Multiple Regression (MR), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), and Random Forest (RF)—ANN achieved the best performance, with a %RMSE of 19.9%. This method offers a cost-effective alternative to traditional field measurements, which are often hindered by challenging terrain. The findings suggest that simple vegetation indices derived from Sentinel-2 imagery can reliably estimate AGB in temperate forests, providing a scalable solution for biomass assessment in remote or difficult-to-access areas.
Modeling of trees failure under windstorm in harvested Hyrcanian forests using machine learning techniques
A machine learning model, specifically the multi-layer perceptron (MLP) neural network, has demonstrated high accuracy—97.7% across all datasets—in predicting tree failure under windstorm conditions in harvested Hyrcanian forests. This model outperformed radial basis function neural networks and support vector machines, identifying key factors such as tree height, crown diameter, and target tree height as critical determinants of susceptibility to wind-induced damage. The findings underscore the potential for integrating artificial intelligence into forest management strategies to mitigate tree failure risks during timber harvesting operations, thereby optimizing wood extraction while reducing unscheduled clear-cutting costs.
Classification of the Hyrcanian forest vegetation, Northern Iran
Researchers have developed the first comprehensive classification of Hyrcanian forest vegetation, identifying 21 associations and seven alliances across Northern Iran. This study, which analyzed 1,597 vegetation plots covering altitudes from -22 to 2,850 meters, revealed that the distribution of these syntaxa is primarily influenced by altitude and mean annual temperature. Notably, this classification scheme links Hyrcanian forests to European temperate forest vegetation, supported by an expert system for automatic plot classification. The findings propose a new syntaxonomic framework, distinguishing between lowland swamp forests, submontane mesic and wet forests, montane beech forests, and upper-montane oak and hornbeam forests.
Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran)
Combining Sentinel-2A optical imagery with ALOS-2 PALSAR-2 radar data significantly improves the accuracy of estimating forest aboveground biomass (AGB) compared to using either dataset alone. A case study in Iran’s Hyrcanian Forest demonstrated that Support Vector Regression (SVR) models, when trained on combined Sentinel-ALOS datasets, achieved the highest prediction accuracy (R² = 0.73, RMSE = 38.68), outperforming Gaussian Processes (GP), Random Forests (RF), and Multi-Layer Perceptron Neural Networks (MPL). While Sentinel-2A alone provided reasonable results, ALOS-2 PALSAR-2 data yielded poor performance when used independently. This study highlights the potential of integrating multi-sensor remote sensing with machine learning to enhance biomass estimation in UNESCO World Heritage Sites like the Hyrcanian Forests.
Genetic diversity of Castanea sativa an endangered species in the Hyrcanian forest
A study on the genetic diversity of Castanea sativa, an endangered tree species in Iran's Hyrcanian Forest, reveals significant genetic erosion and population differentiation. Using 18 simple sequence repeat (SSR) loci—10 nuclear and 8 chloroplastic—the research found low nuclear genetic diversity with observed heterozygosity ranging from 0.125 to 1.000, while chloroplast SSRs showed no polymorphism. Analysis of molecular variance (AMOVA) indicated high within-population variation (84%) but notable between-population differentiation (16%). Structure and UPGMA analyses separated the Shafaroud population from others, suggesting a genetic extinction vortex due to limited gene flow over short distances. The findings underscore the urgent need for stricter protection measures across all four known populations of this species in Iran.
Classifying Complex Mountainous Forests with L-Band SAR and Landsat Data Integration: A Comparison among Different Machine Learning Methods in the Hyrcanian Forest
Integrating L-band SAR and Landsat data significantly improves the classification accuracy of forest stand age classes in complex mountainous regions, such as the Hyrcanian Forest. The study found that combining these datasets achieves an overall accuracy (OA) of 86%, a notable improvement over using either dataset alone or traditional classifiers like maximum likelihood classification (MLC). Among machine learning methods, support vector machines (SVM) and random forest (RF) outperformed neural networks (NN), with SVM and RF showing the highest robustness and accuracy. Terrain correction was crucial, as non-corrected data failed to differentiate forest classes effectively (OA = 65%). The research highlights the potential of multisource remote sensing for precise forest classification in challenging terrains.
Regional and local patterns of ectomycorrhizal fungal diversity and community structure along an altitudinal gradient in the Hyrcanian forests of northern Iran
A study of the Hyrcanian Forests, one of Eurasia's last temperate old-growth forests, reveals that ectomycorrhizal fungal diversity declines monotonically with altitude. This pattern mirrors broader trends seen in macroorganisms and is influenced by environmental energy limitations, which reduce competitive ability for rare species. The research combined morphological and molecular identification methods to analyze 367 fungal species across three altitudinal transects, finding that host species and altitude were the primary drivers of community composition at both local and regional scales. Climatic variables like mean annual temperature and precipitation, strongly correlated with altitude, also played a significant role in shaping these patterns. The study suggests further investigation into direct effects of climatic variables and their seasonality, particularly at larger spatial scales.
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