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Browsing by Author "Hamidi, Seyedeh Kosar"

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    Assessing Biotic and Abiotic Effects on Biodiversity Index Using Machine Learning
    Bayat, Mahmoud; Burkhart, Harold E.; Namiranian, Manouchehr; Hamidi, Seyedeh Kosar; Heidari, Sahar; Hassani, Majid (MDPI, 2021-04-10)
    Forest ecosystems play multiple important roles in meeting the habitat needs of different organisms and providing a variety of services to humans. Biodiversity is one of the structural features in dynamic and complex forest ecosystems. One of the most challenging issues in assessing forest ecosystems is understanding the relationship between biodiversity and environmental factors. The aim of this study was to investigate the effect of biotic and abiotic factors on tree diversity of Hyrcanian forests in northern Iran. For this purpose, we analyzed tree diversity in 8 forest sites in different locations from east to west of the Caspian Sea. 15,988 trees were measured in 655 circular permanent sample plots (0.1 ha). A combination of machine learning methods was used for modeling and investigating the relationship between tree diversity and biotic and abiotic factors. Machine learning models included generalized additive models (GAMs), support vector machine (SVM), random forest (RF) and K-nearest–neighbor (KNN). To determine the most important factors related to tree diversity we used from variables such as the average diameter at breast height (DBH) in the plot, basal area in largest trees (BAL), basal area (BA), number of trees per hectare, tree species, slope, aspect and elevation. A comparison of RMSEs, relative RMSEs, and the coefficients of determination of the different methods, showed that the random forest (RF) method resulted in the best models among all those tested. Based on the results of the RF method, elevation, BA and BAL were recognized as the most influential factors defining variation of tree diversity.
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    Modeling Tree Growth Responses to Climate Change: A Case Study in Natural Deciduous Mountain Forests
    Bayat, Mahmoud; Knoke, Thomas; Heidari, Sahar; Hamidi, Seyedeh Kosar; Burkhart, Harold E.; Jaafari, Abolfazl (MDPI, 2022-10-31)
    Climate change has significant effects on forest ecosystems around the world. Since tree diameter increment determines forest volume increment and ultimately forest production, an accurate estimate of this variable under future climate change is of great importance for sustainable forest management. In this study, we modeled tree diameter increment under the effects of current and expected future climate change, using multilayer perceptron (MLP) artificial neural networks and linear mixed-effect model in two sites of the Hyrcanian Forest, northern Iran. Using 573 monitoring fixed-area (0.1 ha) plots, we measured and calculated biotic and abiotic factors (i.e., diameter at breast height (DBH), basal area in the largest trees (BAL), basal area (BA), elevation, aspect, slope, precipitation, and temperature). We investigated the effect of climate change in the year 2070 under two reference scenarios; RCP 4.5 (an intermediate scenario) and RCP 8.5 (an extreme scenario) due to the uncertainty caused by the general circulation models. According to the scenarios of climate change, the amount of annual precipitation and temperature during the study period will increase by 12.18 mm and 1.77 °C, respectively. Further, the results showed that the impact of predicted climate change was not very noticeable and the growth at the end of the period decreased by only about 7% annually. The effect of precipitation and temperature on the growth rate, in fact, neutralize each other, and therefore, the growth rate does not change significantly at the end of the period compared to the beginning. Based on the models’ predictions, the MLP model performed better compared to the linear mixed-effect model in predicting tree diameter increment.