Assessing scale‐dependent effects on Forest biomass productivity based on machine learning

He, Jingyuan; Fan, Chunyu; Geng, Yan; Zhao, Xiuhai; Gadow, Klaus von

doi:10.1002/ece3.9110

Cited by 8 publications

(2 citation statements)

References 93 publications

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“…For example, Chu et al (2020) reported the mean absolute error, mean bias error, mean relative error, mean square error, and RMSE for the proposed hybrid BBI rice prediction model. The aforementioned errors are scale-dependent errors (He et al, 2022) which are calculated and interpreted as the error metrics expressed in the units of the underlying data. Meanwhile, Khaki et al (2020) assessed their CNN-RNN maize yield prediction model based on RMSE and correlation coefficient percentage.…”

Section: Model Performance Comparabilitymentioning

confidence: 99%

Machine learning for major food crops breeding: Applications, challenges, and ways forward

et al. 2023

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Increasing the production of the three major food crops (MFCs), maize (Zea mays), rice (Oryza sativa), and wheat (Triticum aestivum), is essential to fulfilling the food demand for the growing human population. Increasing food production may require the integration of machine learning (ML) into plant breeding programs. However, developing ML tools to improve the production of MFCs is a daunting task due to the lack of quality data and the computation resources needed to process this information. Hence, this review discusses the recent applications of ML for improving MFCs production, including plant phenotyping, yield forecasting, and candidate gene prediction. Based on the challenges reported in recent ML experiments for MFCs, this review prescribes solutions to produce scalable ML models. This review provides valuable insights for future studies and promotes collective efforts among researchers implementing ML to enhance MFCs productivity.

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Section: Model Performance Comparabilitymentioning

confidence: 99%

Machine learning for major food crops breeding: Applications, challenges, and ways forward

et al. 2023

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“…Parametric models and machine learning methods have been applied to predict forest biomass and carbon density in previous studies. Among them, MLR, M5P, RF, ANN and SVM models are the most widely used with high prediction accuracy [34][35][36]. Therefore, this paper chooses these five models to predict the carbon density of sample plots in Shaoguan city.…”

Section: Prediction Model Of Forest Carbon Densitymentioning

confidence: 99%

Forest Carbon Density Estimation Using Tree Species Diversity and Stand Spatial Structure Indices

Lan

et al. 2023

Forests

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The forest spatial structure and diversity of tree species, as the important evaluation indicators of forest quality, are key factors affecting forest carbon storage. To analyze the impacts of biodiversity indices and stand spatial structure on forest carbon density, five tree diversity indices were calculated from three aspects of richness, diversity and evenness, and three indices (Reineke’s stand density index, Hegyi’s competition index and Simple mingling degree) were calculated from stand spatial structure. The relationships between these eight indices and forest carbon density were explored using the Structural Equation Model (SEM). Then, these eight indices were used as characteristic variables to predict the aboveground carbon density of trees (abbreviated as forest carbon density) in the sample plots of the National Forest Resources Continuous Inventory (NFCI) in Shaoguan City in 2017. Multiple Linear Regression (MLR) and four typical machine learning models of Random Forest (RF), Tree-based Piecewise Linear Model (M5P), Artificial Neural Network (ANN) and Support Vector Regression (SVR) were used to predict the forest carbon density. The results show that: (1) Based on the analysis results of the structural equation model (SED), the species diversity and forest stand spatial structure have greater impacts on carbon density. (2) The R2 of all the five prediction models is greater than 0.6, among which the random forest model is the highest. (3) Based on the calculation results of optimal model of RF, the mean forest carbon density of Shaoguan city in 2017 was 43.176 tC/ha. The forest carbon density can be accurately estimated based on the species diversity index and stand spatial structure with machine learning algorithms. Therefore, a new method for the prediction of forest carbon density and carbon storage using species diversity indices and stand spatial structure can be explored. By analyzing the impacts of different biodiversity indices and stand spatial structure on forest carbon density, a scientific reference for the making of management measures for increasing forest carbon sinks and reducing emissions can be provided.

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Estimating the dynamics of ecosystem functions under climate change in a temperate forest region

He,

Lu,

et al. 2024

Ecological Indicators

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Assessing scale‐dependent effects on Forest biomass productivity based on machine learning

Cited by 8 publications

References 93 publications

Machine learning for major food crops breeding: Applications, challenges, and ways forward

Machine learning for major food crops breeding: Applications, challenges, and ways forward

Forest Carbon Density Estimation Using Tree Species Diversity and Stand Spatial Structure Indices

Estimating the dynamics of ecosystem functions under climate change in a temperate forest region

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