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Mr Ahmad Fadaei, Dr Ali Soltani, Dr Mozhgan Abbasi,
Volume 4, Issue 1 (reserch article 2024)
Abstract

Background and Objective: the aim of this study was to investigate the effect of different cultivation densities and cutting turns on the wood yield of white poplar (Populus alba L.) trees in eastern part of Dehcheshmeh village in Farsan county. 

Materials and Methods: An area of approximately 5 hectares was divided into 12 more or less uniform and flat terraces. According to the age of the trees, the terraces were divided into two groups: the terraces with young trees (from 6 to 7 years old) and those with older trees (from 15 to 18 years old). The terraces were also labeled either they were uncut, or under the first (cut) or second turn (double-cut) of cutting; and if they were grown in thin or dense cultivation. Number of shoots and the dimensions of the trees were measured using a plot sampling approach. The width of the annual rings of fallen trees was estimated using the photogrammetry method.

 Results: Considering the age of trees as block, a split-plot design indicated that the trees grown in the cut terraces were significantly larger than those in uncut or double-cut terraces in sense of DBH, height, crown height and standing volume. The highest shoot number was observed in double-cut terraces and the highest crown height values were belonged to the uncut and the double-cut terraces. As the cultivations became thinner, the volume and dimensions of the trees increased significantly. The analysis of mean (MAI) and current (CAI) annual increment curves and the point where those curves intercepted, showed that the biological age for the thinned and cut cultivations (the superior treatments) were 12.1 and 13.0 years respectively.

 Conclusion: In the center of poplar cultivation in the province of Chaharmahal-Va-Bakhtiari, it is strongly recommended to cultivate the white poplars in density of less than 100 trees per are, with a single cut, and for 12.5 years of rotation period.


Niloufar Abbasi, Reza Basiri, Mostafa Moradi,
Volume 4, Issue 1 (reserch article 2024)
Abstract

Background and Objectives: Plant ecology is the scientific study of the interactions between plants and environmental factors that determine their distribution and abundance. Ecology provides the necessary framework for an accurate understanding of vegetation dynamics and species distribution, emphasizing the essential role of vegetation within ecosystems. The biodiversity of any habitat plays a crucial role in the stability and health of the natural environment. The assessment of different plant species and their distributions allows important steps to be taken towards biodiversity conservation. Researchers use parametric distribution models, also known as abundance distribution models, to evaluate vegetation diversity and evaluate species diversity. The four main models developed by ecologists include the geometric series model, the lognormal series model, the logarithmic series model, and the broken-stick model, which are used to adjust the distribution of species diversity. The application of parametric models in riparian forests - one of the most diverse and complex plant communities in the world - has provided a new perspective on methods for assessing ecological diversity. Therefore, the aim of this study was to measure biodiversity in the riparian forests of Maroon River in Behbahan district using biodiversity distribution models.

Materials and methods: In this study, transects were laid at 100-meter intervals across the study area to record vegetation. We surveyed 79 sample plots and recorded and analyzed the percentage of tree cover, number of individuals and species composition. The TWINSPAN analysis classified the sampled plots into two ecological groups based on the indicator species Populus euphratica Oliv. and Tamarix arceuthoides Bunge, resulting in 56 and 23 sample plots, respectively. Using the species abundance data and the chi-square test for goodness of fit, we tested the fit of the Geometric, Lognormal, Logarithmic and Broken-Stick parametric models for each ecological group.

Results: The results of the chi-square test showed significant differences between the observed and expected abundance of species in both ecological groups (Populus euphratica and Tamarix arceuthoides), leading to the rejection of the parametric models. However, no significant difference was found between the distribution curve of the observed data and the lognormal distribution curve.

Conclusion: The lognormal model provided the best fit for the distribution of species abundance in both ecological groups, suggesting that these communities are heterogeneous and stable, characterized by species with moderate abundance.


Zahra Amiri, Mozhgan Abbasi, Saleh Kahyani, Hamid Reza Riyahi Bakhtyari,
Volume 4, Issue 2 (9-2025)
Abstract

Background and objectives: The Zagros forests, one of Iran's natural ecosystems, possess significant ecological diversity, and understanding their typology is crucial for sustainable management. The forests of Flard County in Chaharmahal and Bakhtiari Province, primarily covered by oak (Quercus brantii) and pistachio (Pistacia mutica), are part of the central Zagros region. These forests play a prominent role in ecological sustainability, biodiversity conservation, and providing environmental services. However, the lack of current and accurate typological maps has hindered their scientific management. Traditional ground-based survey methods are inefficient due to high costs and difficult terrain. This study aimed to use modern remote sensing technologies and evaluate how spectral indices derived from a spectral library improve the accuracy of supervised classification of Sentinel-2 satellite imagery for discriminating forest types.
Materials and methods: To investigate the impact of spectral indices derived from the spectral library on distinguishing the region's dominant species, this study was conducted in three forest areas: two pure types and one mixed type. These areas were Mishan (pure oak), Kate-Siah (pure pistachio), and Vandun (mixed oak-pistachio cover) in Flard County, Chaharmahal and Bakhtiari Province. Three different classification scenarios were used. In Scenario 1, only Sentinel-2 images from October 15, 2024, were classified for all three study areas. In Scenario 2, in addition to the sensor's main bands, general spectral indices—NDVI, SAVI, MSAVI, GNDVI, and EVI—were applied to the Sentinel-2 data and incorporated into the classification process. These indices, respectively, assess greenness, reduce soil background effects, measure chlorophyll health, and enhance sensitivity to dense canopies. In Scenario 3, besides the main bands, specialized spectral indices—PRI, CRI, MCRI, CAI, NDVI800, NPCI, RENDVI, and ARI—extracted from the spectral library data of the oak and pistachio species were included in the classification process. Data classification in all three areas was performed using three supervised algorithms: Support Vector Machine (SVM), Maximum Likelihood Classification (MLC), and Minimum Distance (MinDist).
Accuracy was assessed using a ground truth map, the Kappa coefficient, and overall accuracy criteria.
Results: Results indicated that in Scenario 1, the Maximum Likelihood Classification (MLC) algorithm performed best with overall accuracies and Kappa coefficients of 98.4% and 0.71 in Mishan, 77.5% and 0.32 in Kate-Siah, and 86.7% and 0.67 in Vandun, respectively. In Scenario 2, the same algorithm was best in Mishan (97.7% overall accuracy, 0.65 Kappa coefficient) and Kate-Siah (75.9% overall accuracy, 0.38 Kappa coefficient). However, in Vandun, the SVM algorithm was superior, achieving an overall accuracy of 77.9% and a Kappa coefficient of 0.57. In Scenario 3, accuracy assessment results demonstrated that the SVM algorithm reported the highest accuracy in all three regions: Mishan (99.2% overall accuracy, 0.81 Kappa coefficient), Kate-Siah (98.2% overall accuracy, 0.74 Kappa coefficient), and Vandun (89.0% overall accuracy, 0.67 Kappa coefficient). A comparison of the three scenarios showed that specialized spectral indices derived from the spectral library were more effective in increasing classification accuracy than general indices. Despite the high accuracy of the algorithms, limitations such as spectral similarity between soil and vegetation in low-density areas affected the results.
Conclusion: Based on these findings, integrating valid spectral data with machine learning algorithms is an effective tool for preparing detailed forest cover maps. Using a spectral library alongside Sentinel-2 imagery enables more accurate discrimination of forest. The SVM algorithm is a suitable option for classifying forest types in areas with dense cover and complex structures. The findings of this research are applicable to conservation-management planning and the continuous monitoring of Zagros Forest ecosystems.
 


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