Simulation of over-bark tree bole diameters, through the RFr (Random Forest Regression) algorithm

Diamantopoulou, Maria J.

doi:10.2478/foecol-2022-0010

Cited by 3 publications

(3 citation statements)

References 34 publications

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“…Beltrán et al [69] state that the increase in diameter is more significant in better sites and tree canopy class, while it decreases with tree age, directly impacting forest yield by reducing vigor [70]. Since this study aimed to use measures of density, growth, structure, efficiency, and regeneration to enable management actions for sustainability, these measures serve to comprehend the dynamics of the forest in light of potential future environmental changes [71].…”

Section: Discussionmentioning

confidence: 99%

Sustainability Assessment of Araucaria Forest Remnants in Southern Brazil: Insights from Traditional Forest Inventory Surveys

Hess,

Demétrio,

de Sousa

et al. 2024

Sustainability

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Precise estimates of dendrometric and morphometric variables are indispensable for effective forest resource conservation and sustainable utilization. This study focuses on modeling the relationships between shape (morphometric), dimension (dendrometric) and density (N) to assess the sustainability of forest resources. It sheds light on the current state of site characteristics, reproduction, and the structure of Araucaria angustifolia trees at selected forest remnants across multiple sites in Santa Catarina, Southern Brazil. Individual trees and their dendrometric variables, such as the diameter at breast height (d), height (h), crown base height (cbh), annual periodic increment (API) in growth rings, and morphometric variables, including four radii of the crown in cardinal directions, were evaluated. These measurements allowed us to calculate various morphometric indices and crown efficiency, enabling the assessment of both vertical and horizontal structural conditions. Statistical analysis confirmed a positive relationship of the crown volume (cv) and crown surface area (csa) with the crown length (cl). Conversely, the crown efficiency, density, increment rate, and reproductive structure production declined. These morphometric relationships emphasize the complex dynamics within these forest ecosystems, irrespective of the chosen site, indicating that horizontal and vertical forest structures have stagnated and have been characterized by limited change in the last ten years. Such results raise concerns about sustainability, highlighting the need for proper conservation measures and sustainable forest management practices. Our findings underscore the need for substantial adjustments in the structure and dynamics of the forest, particularly on selected rural properties where this tree species is abundant, to ensure long-term sustainability.

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Section: Discussionmentioning

confidence: 99%

Sustainability Assessment of Araucaria Forest Remnants in Southern Brazil: Insights from Traditional Forest Inventory Surveys

Hess,

Demétrio,

de Sousa

et al. 2024

Sustainability

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show abstract

“…When carbon storage and CO 2 sequestration by cedar plantations can be obtained by accurate models, adequate forest management decisions can be taken allowing for both the improvement of forest sustainability and the implementation of multi-task reforestation designs aimed to mitigate the detrimental effects of climate change. Moreover, the development of new models and methods to improve the prediction of tree attributes related to forest productivity was deemed essential (Güner et al 2022, Diamantopoulou 2022. As displayed in Fig.…”

Section: Discussionmentioning

confidence: 99%

Exploring machine learning modeling approaches for biomass and carbon dioxide weight estimation in Lebanon cedar trees

Diamantopoulou,

Çömez,

Özçelik

et al. 2024

iForest

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Accurate estimates of total tree biomass are of critical importance to obtain reliable estimation of the carbon dioxide weight sequestered from the atmosphere by trees and forest stands. This information has the potential to guide appropriate forest management decisions which allow for both the improvement of forest sustainability and the implementation of multi-task reforestation designs aimed to mitigate the detrimental effects of climate change. The current laborious and tree-destructive procedures needed to attain such information has led to the development of machine learning (ML) models aimed at providing accurate estimations of the tree biomass sequestering the atmospheric carbon dioxide. We tested the Levenberg-Marquardt artificial neural network and the support vector machine for regression techniques as an alternative to non-linear allometric regression (NLR) modelling approaches commonly used for tree biomass estimation. We tested the developed ML models using primary ground-truth data from the Lebanon cedar forests in the Western Inner Anatolian regions of Turkey, and their predictions were compared to those of NLR models developed using the same dataset. The results showed that the ML approaches outperformed the NLR models in accurately estimating tree biomass and its components (above-and belowground dry biomass, dry branches biomass, etc.), and the support vector regression (SVR) models gave the highest accuracy of estimates. Therefore, the carbon dioxide weight sequestered in Lebanon cedar trees were reliably estimated, with the aim of supporting the best forest management practices to be applied in Lebanon cedar tree stands in Turkey.

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“…Each node has a specific satisfaction condition (impurity criterium), and if this objective is not met, the process advances to a new node and its corresponding children. The ensemble method used for ET o model construction was bootstrapped aggregation (bagging) [36,[39][40][41][42][43]. This approach entails training multiple independent models on random subsets (bootstraps) of the fitting data.…”

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confidence: 99%

Performance Evaluation of Regression-Based Machine Learning Models for Modeling Reference Evapotranspiration with Temperature Data

Diamantopoulou,

Papamichail

2024

Hydrology

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In this study, due to their flexibility in forecasting, the capabilities of three regression-based machine learning models were explored, specifically random forest regression (RFr), generalized regression neural network (GRNN), and support vector regression (SVR). The above models were assessed for their suitability in modeling daily reference evapotranspiration (ETo), based only on temperature data (Tmin, Tmax, Tmean), by comparing their daily ETo results with those estimated by the conventional FAO 56 PM model, which requires a broad range of data that may not be available or may not be of reasonable quality. The RFr, GRNN, and SVR models were subjected to performance evaluation by using statistical criteria and scatter plots. Following the implementation of the ETo models’ comparisons, it was observed that all regression-based machine learning models possess the capability to accurately estimate daily ETo based only on temperature data requirements. In particular, the RFr model outperformed the others, achieving the highest R value of 0.9924, while the SVR and GRNN models had R values of 0.9598 and 0.9576, respectively. Additionally, the RFr model recorded the lowest values in all error metrics. Once these regression-based machine learning models have been successfully developed, they will have the potential to serve as effective alternatives for estimating daily ETo, under current and climate change conditions, when temperature data are available. This information is crucial for effective water resources management and especially for predicting agricultural production in the context of climate change.

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Simulation of over-bark tree bole diameters, through the RFr (Random Forest Regression) algorithm

Cited by 3 publications

References 34 publications

Sustainability Assessment of Araucaria Forest Remnants in Southern Brazil: Insights from Traditional Forest Inventory Surveys

Sustainability Assessment of Araucaria Forest Remnants in Southern Brazil: Insights from Traditional Forest Inventory Surveys

Exploring machine learning modeling approaches for biomass and carbon dioxide weight estimation in Lebanon cedar trees

Performance Evaluation of Regression-Based Machine Learning Models for Modeling Reference Evapotranspiration with Temperature Data

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