A comparison of Gaussian process regression, random forests and support vector regression for burn severity assessment in diseased forests

Hultquist, Carolynne; Chen, Gang; Zhao, Kaiguang

doi:10.1080/2150704x.2014.963733

Cited by 95 publications

(63 citation statements)

References 28 publications

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“…Our study corroborated these research works; the RF method was able to use different kinds of data for forest biomass estimation, and the prediction result is more optimal than these general methods. The RF method performed very well compared to many other regressions, which may be due to the fact that each node in the random forest is split using the best among a subset of predictors randomly chosen at that node [76]. In this study, we only systematically compared five common modeling methods (RF, SVM, BPNN, KNN and GLMM), but the model has its own disadvantage in different degrees.…”

Section: Influence Of the Statistical Methods On Estimationmentioning

confidence: 99%

Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China

Luodan

Pan

et al. 2018

Remote Sensing

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Forest Aboveground Biomass (AGB) is a key parameter for assessing forest productivity and global carbon content. In previous studies, AGB has been estimated using various prediction methods and types of remote sensing data. Increasingly, there is a trend towards integrating various data sources such as Light Detection and Ranging (LiDAR) and optical data. In this study, we constructed and compared the accuracies of five models for estimating AGB of forests in the upper Heihe River Basin in Northwest China. The five models were constructed using field and remotely-sensed data (optical and LiDAR) and algorithms including Random Forest (RF), Support Vector Machines (SVM), Back Propagation Neural Networks (BPNN), K-Nearest Neighbor (KNN) and the Generalized Linear Mixed Model (GLMM). Models based on the RF algorithm emerged as being the best among the five algorithms irrespective of the datasets used. The Random Forest AGB model, using only LiDAR data (R 2 = 0.899, RMSE = 14.0 t/ha) as the input data, was more effective than the one using optical data (R 2 = 0.835, RMSE = 22.724 t/ha). Compared to LiDAR or optical data alone, the AGB model (R 2 = 0.913, RMSE = 13.352 t/ha) that used the RF algorithm and integrated LiDAR and optical data was found to be optimal. Incorporation of terrain variables with optical data resulted in only slight improvements in accuracy. The models developed in this study could be useful for using integrated airborne LiDAR and passive optical data to accurately estimate forest biomass.

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Section: Influence Of the Statistical Methods On Estimationmentioning

confidence: 99%

Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China

Luodan

Pan

et al. 2018

Remote Sensing

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“…For the RF model, n tree was set to 500 (after exploratory trials using the training data). For m try , the results from previous studies [59,64] showed that m try optimization resulted in minimal improvements in RF predictions. We therefore used a predictive variable number of 2 in this analysis.…”

Section: Lai Spatial Prediction Based On the Four Reflectance Bandsmentioning

confidence: 95%

Predicting Grassland Leaf Area Index in the Meadow Steppes of Northern China: A Comparative Study of Regression Approaches and Hybrid Geostatistical Methods

Wang

Tang

et al. 2016

Remote Sensing

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Leaf area index (LAI) is a key parameter used to describe vegetation structures and is widely used in ecosystem biophysical process and vegetation productivity models. Many algorithms have been developed for the estimation of LAI based on remote sensing images. Our goal was to produce accurate and timely predictions of grassland LAI for the meadow steppes of northern China. Here, we compare the predictive power of regression approaches and hybrid geostatistical methods using Chinese Huanjing (HJ) satellite charge coupled device (CCD) data. The regression methods evaluated include partial least squares regression (PLSR), artificial neural networks (ANNs) and random forests (RFs). The two hybrid geostatistical methods were regression kriging (RK) and random forests residuals kriging (RFRK). The predictions were validated for different grassland types and different growing stages, and their performances were also examined by adding several groups of vegetation indices (VIs). The two hybrid geostatistical models (RK and RFRK) yielded the most accurate predictions (root mean squared error (RMSE) = 0.21 m 2 /m 2 and 0.23 m 2 /m 2 for RK and RFRK, respectively), followed by the RF model (RMSE = 0.27 m 2 /m 2 ), which was the most accurate among the regression models. These three models also exhibited the best temporal performance across the duration of the growing season. The PLSR and ANN models were less accurate (RMSE = 0.33 m 2 /m 2 and 0.35 m 2 /m 2 for ANN and PLSR, respectively), and the PLSR model performed the worst (exhibiting varied temporal performance and unreliable prediction accuracy that was susceptible to ground conditions). By adding VIs to the predictor variables, the predictions of the PLSR and ANN models were obviously improved (RMSE improved from 0.35 m 2 /m 2 to 0.28 m 2 /m 2 for PLSR and from 0.33 m 2 /m 2 to 0.28 m 2 /m 2 for ANN); the RF and RFRK models did not generate more accurate predictions and the performance of the RK model declined (RMSE decreased from 0.21 m 2 /m 2 to 0.32 m 2 /m 2 ).

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“…Compared to other regression methods, GP regression has several advantages: (1) its prediction equation is much simpler and it is given in an analytical form; (2) to each predicted output it associates a confidence measure and (3) thanks to its Bayesian formulation, the model selection issue is handled in an automatic way, as demonstrated by different works recently published in the literature, including Pasolli, Melgani, and Blanzieri (2010); Bazi, Alajlan, and Melgani (2012) and Hultquist, Chen, and Zeng (2014). It represents a powerful and interesting theoretical framework for Bayesian regression and classification.…”

Section: Introductionmentioning

confidence: 99%

Spatial contextual Gaussian process learning for remote-sensing image classification

Hassouna

Melgani

Mokhtari

2015

Remote Sensing Letters

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Compared to state-of-the-art classifiers, the Gaussian process classifier (GPC) offers several attractive properties such as the possibility to estimate the hyperparameters or to learn the best input features in a fully automatic way. However, till now, the integration of spatial contextual information in a GPC model for classifying remote sensing imagery has not yet received a sufficient attention compared to other classification approaches for which it has been shown that the classification accuracy can benefit from the exploitation of such information. In this context, in order to improve the GPC capabilities, we propose here to reformulate the GPC learning model so as to integrate spatial contextual information. All the mathematical developments leading to the proposed spatial GPC (SGPC), which embeds iteratively the spatial contextual information in the classification process, is described. Experimental results show that the SGPC can help in improving the classification accuracy compared to the baseline GPC.

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A comparison of Gaussian process regression, random forests and support vector regression for burn severity assessment in diseased forests

Cited by 95 publications

References 28 publications

Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China

Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China

Predicting Grassland Leaf Area Index in the Meadow Steppes of Northern China: A Comparative Study of Regression Approaches and Hybrid Geostatistical Methods

Spatial contextual Gaussian process learning for remote-sensing image classification

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