An Evaluation of Eight Machine Learning Regression Algorithms for Forest Aboveground Biomass Estimation from Multiple Satellite Data Products

Zhang, Yuzhen; Ma, Jun; Liang, Shunlin; Li, Xisheng; Li, Manyao

doi:10.3390/rs12244015

Cited by 67 publications

(23 citation statements)

References 112 publications

(154 reference statements)

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“…However, in broadleaf (n = 2111) and ALL (n = 2716), CatBoost runs much faster than RFR (Tables 5-7). This finding is in line with the results reported by Zhang et al, which show that the CatBoost algorithm outperformed the RFR and GBRT algorithms for the AGB estimation [37]. Li et al focused on the potential for machine learning algorithms to improve AGB estimation accuracy [23].…”

Section: Discussionsupporting

confidence: 86%

“…XGBoost and CatBoost, which are improvements of GBDT, have shown potential in fields such as biology and medicine [33][34][35][36]. Although some studies have attempted to use these methods for modeling forest properties from remote sensing data, the CatBoost method has been introduced very recently [13,37]. However, Pham et al found that extreme gradient boosting regression-genetic algorithm (XGBR-GA) outperformed CatBoost and RFR in estimating mangrove AGB [13].…”

Section: Introductionmentioning

confidence: 99%

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Combination of Feature Selection and CatBoost for Prediction: The First Application to the Estimation of Aboveground Biomass

Luo

Wang

Xie

et al. 2021

Forests

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Increasing numbers of explanatory variables tend to result in information redundancy and “dimensional disaster” in the quantitative remote sensing of forest aboveground biomass (AGB). Feature selection of model factors is an effective method for improving the accuracy of AGB estimates. Machine learning algorithms are also widely used in AGB estimation, although little research has addressed the use of the categorical boosting algorithm (CatBoost) for AGB estimation. Both feature selection and regression for AGB estimation models are typically performed with the same machine learning algorithm, but there is no evidence to suggest that this is the best method. Therefore, the present study focuses on evaluating the performance of the CatBoost algorithm for AGB estimation and comparing the performance of different combinations of feature selection methods and machine learning algorithms. AGB estimation models of four forest types were developed based on Landsat OLI data using three feature selection methods (recursive feature elimination (RFE), variable selection using random forests (VSURF), and least absolute shrinkage and selection operator (LASSO)) and three machine learning algorithms (random forest regression (RFR), extreme gradient boosting (XGBoost), and categorical boosting (CatBoost)). Feature selection had a significant influence on AGB estimation. RFE preserved the most informative features for AGB estimation and was superior to VSURF and LASSO. In addition, CatBoost improved the accuracy of the AGB estimation models compared with RFR and XGBoost. AGB estimation models using RFE for feature selection and CatBoost as the regression algorithm achieved the highest accuracy, with root mean square errors (RMSEs) of 26.54 Mg/ha for coniferous forest, 24.67 Mg/ha for broad-leaved forest, 22.62 Mg/ha for mixed forests, and 25.77 Mg/ha for all forests. The combination of RFE and CatBoost had better performance than the VSURF–RFR combination in which random forests were used for both feature selection and regression, indicating that feature selection and regression performed by a single machine learning algorithm may not always ensure optimal AGB estimation. It is promising to extending the application of new machine learning algorithms and feature selection methods to improve the accuracy of AGB estimates.

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Section: Discussionsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Combination of Feature Selection and CatBoost for Prediction: The First Application to the Estimation of Aboveground Biomass

Luo

Wang

Xie

et al. 2021

Forests

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“…CAT improves the accuracy of the algorithm and its generalizability [47]. It has been successfully applied in many fields such as weather forecasting, media popularity prediction, evapotranspiration, and biomass [48,49]. It is for this reason that the model is applied here to predict the performance of ultraprecision machining.…”

Section: Cat Boost Regression (Cat)mentioning

confidence: 99%

Applying Bayesian Optimization for Machine Learning Models in Predicting the Surface Roughness in Single-Point Diamond Turning Polycarbonate

Nguyen

Truong

et al. 2021

Mathematical Problems in Engineering

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This paper deals with the prediction of surface roughness in manufacturing polycarbonate (PC) by applying Bayesian optimization for machine learning models. The input variables of ultraprecision turning—namely, feed rate, depth of cut, spindle speed, and vibration of the X-, Y-, and Z-axis—are the main factors affecting surface quality. In this research, six machine learning- (ML-) based models—artificial neural network (ANN), Cat Boost Regression (CAT), Support Vector Machine (SVR), Gradient Boosting Regression (GBR), Decision Tree Regression (DTR), and Extreme Gradient Boosting Regression (XGB)—were applied to predict the surface roughness (Ra). The predictive performance of the baseline models was quantitatively assessed through error metrics: root means square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The overall results indicate that the XGB and CAT models predict Ra with the greatest accuracy. In improving baseline models such as XGB and CAT, the Bayesian optimization (BO) is next used to determine their best hyperparameters, and the results indicate that XGB is the best model according to the evaluation metrics. Results have shown that the performance of the models has been improved significantly with BO. For example, the values of RMSE and MAE of XGB have decreased from 0.0076 to 0.0047 and from 0.0063 to 0.0027, respectively, for the training dataset. Using the testing dataset, the values of RMSE and MAE of XGB have decreased from 0.4033 to 0.2512 and from 0.2845 to 0.2225, respectively. Moreover, the vibrations of the X, Y, and Z axes and feed rate are the most significant feature in predicting the results, which is in high accordance with the literature. We find that, in a specified value domain, the vibration of the axes has a greater influence on the surface quality than does the cutting condition.

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“…The ERTFM is an application of the ERT method to predict terrestrial LE, which could also be employed for other land surface parameters. For example, Zhang et al [64] used eight machine learning regression algorithms to estimate forest aboveground biomass and found the recently developed ERT and CatBoost methods achieved better performances, providing more stable results. Similar studies were conducted to predict streamflow [65] and air quality [66].…”

Section: The Application Of Ertfmmentioning

confidence: 99%

ERTFM: An Effective Model to Fuse Chinese GF-1 and MODIS Reflectance Data for Terrestrial Latent Heat Flux Estimation

et al. 2021

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Coarse spatial resolution sensors play a major role in capturing temporal variation, as satellite images that capture fine spatial scales have a relatively long revisit cycle. The trade-off between the revisit cycle and spatial resolution hinders the access of terrestrial latent heat flux (LE) data with both fine spatial and temporal resolution. In this paper, we firstly investigated the capability of an Extremely Randomized Trees Fusion Model (ERTFM) to reconstruct high spatiotemporal resolution reflectance data from a fusion of the Chinese GaoFen-1 (GF-1) and the Moderate Resolution Imaging Spectroradiometer (MODIS) products. Then, based on the merged reflectance data, we used a Modified-Satellite Priestley–Taylor (MS–PT) algorithm to generate LE products at high spatial and temporal resolutions. Our results illustrated that the ERTFM-based reflectance estimates showed close similarity with observed GF-1 images and the predicted NDVI agreed well with observed NDVI at two corresponding dates (r = 0.76 and 0.86, respectively). In comparison with other four fusion methods, including the widely used spatial and temporal adaptive reflectance fusion model (STARFM) and the enhanced STARFM, ERTFM had the best performance in terms of predicting reflectance (SSIM = 0.91; r = 0.77). Further analysis revealed that LE estimates using ERTFM-based data presented more detailed spatiotemporal characteristics and provided close agreement with site-level LE observations, with an R2 of 0.81 and an RMSE of 19.18 W/m2. Our findings suggest that the ERTFM can be used to improve LE estimation with high frequency and high spatial resolution, meaning that it has great potential to support agricultural monitoring and irrigation management.

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An Evaluation of Eight Machine Learning Regression Algorithms for Forest Aboveground Biomass Estimation from Multiple Satellite Data Products

Cited by 67 publications

References 112 publications

Combination of Feature Selection and CatBoost for Prediction: The First Application to the Estimation of Aboveground Biomass

Combination of Feature Selection and CatBoost for Prediction: The First Application to the Estimation of Aboveground Biomass

Applying Bayesian Optimization for Machine Learning Models in Predicting the Surface Roughness in Single-Point Diamond Turning Polycarbonate

ERTFM: An Effective Model to Fuse Chinese GF-1 and MODIS Reflectance Data for Terrestrial Latent Heat Flux Estimation

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