Comparative Analysis of Modeling Algorithms for Forest Aboveground Biomass Estimation in a Subtropical Region

Gao, Yukun; Lu, Dengsheng; Li, Guiying; Wang, Guangxing; Chen, Qi; Liu, Lijuan; Li, Dengqiu

doi:10.3390/rs10040627

Cited by 147 publications

(162 citation statements)

References 56 publications

(150 reference statements)

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“…Note that mangrove biosphere reserves each have their own stand structures and complex mix of species, leading to different data saturation issues in both SAR and multispectral data. Regarding multispectral data, such as Landsat TM, ETM+, OLI, and S-2, data saturation has caused weak prediction performance at high AGB values and dense forest canopy densities [84][85][86]. Optical sensors may be saturated at approximately 100-150 Mg·ha −1 in complex tropical forests and at approximately 152-159 Mg·ha −1 in mixed forests [83,87].…”

Section: Discussionmentioning

confidence: 99%

Comparison of Machine Learning Methods for Estimating Mangrove Above-Ground Biomass Using Multiple Source Remote Sensing Data in the Red River Delta Biosphere Reserve, Vietnam

et al. 2020

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This study proposes a hybrid intelligence approach based on an extreme gradient boosting regression and genetic algorithm, namely, the XGBR-GA model, incorporating Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2 data to estimate the mangrove above-ground biomass (AGB), including small and shrub mangrove patches in the Red River Delta biosphere reserve across the northern coast of Vietnam. We used the novel extreme gradient boosting decision tree (XGBR) technique together with genetic algorithm (GA) optimization for feature selection to construct and verify a mangrove AGB model using data from a field survey of 105 sampling plots conducted in November and December of 2018 and incorporated the dual polarimetric (HH and HV) data of the ALOS-2 PALSAR-2 L-band and the Sentinel-2 multispectral data combined with Sentinel-1 (C-band VV and VH) data. We employed the root-mean-square error (RMSE) and coefficient of determination (R 2 ) to evaluate the performance of the proposed model. The capability of the XGBR-GA model was assessed via a comparison with other machine-learning (ML) techniques, i.e., the CatBoost regression (CBR), gradient boosted regression tree (GBRT), support vector regression (SVR), and random forest regression (RFR) models. The XGBR-GA model yielded a promising result (R 2 = 0.683, RMSE = 25.08 Mg·ha −1 ) and outperformed the four other ML models. The XGBR-GA model retrieved a mangrove AGB ranging from 17 Mg·ha −1 to 142 Mg·ha −1 (with an average of 72.47 Mg·ha −1 ). Therefore, multisource optical and synthetic aperture radar (SAR) combined with the XGBR-GA model can be used to estimate the mangrove AGB in North Vietnam. The effectiveness of the proposed method needs to be further tested and compared to other mangrove ecosystems in the tropics.

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Section: Discussionmentioning

confidence: 99%

Comparison of Machine Learning Methods for Estimating Mangrove Above-Ground Biomass Using Multiple Source Remote Sensing Data in the Red River Delta Biosphere Reserve, Vietnam

et al. 2020

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“…AGC may have high or weak relationships with remote sensing variables. Because of the strong correlations among some explanatory variables, it was critical to eliminate the variables that have a high correlation between themselves and nonsignificant correlations between variables and AGC [55,69]. The advantage of stepwise regression is to determine the importance of explanatory variables and eliminate the influence of collinearity on accuracy of models.…”

Section: Methods Of Model Constructionmentioning

confidence: 99%

Spatiotemporal Estimation of Bamboo Forest Aboveground Carbon Storage Based on Landsat Data in Zhejiang, China

Han

et al. 2018

Remote Sensing

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China is one of the countries with the most abundant bamboo forest resources in the world, and Zhejiang province is among the top-3 Chinese provinces with richest bamboo forests. For rational bamboo forests management, it is of great significance to study the spatiotemporal dynamic changes of Aboveground Carbon (AGC) stocks of bamboo forest in Zhejiang. In this study, remote sensing variables, such as spectral, vegetation indices and texture features of bamboo forest in Zhejiang, were extracted from 32 Landsat TM and OLI images got from four different years (2000, 2004, 2008 and 2014). These variables were subsequently selected with stepwise regression method to build an estimation model of AGC of the bamboo forests. The results showed that (1) the accuracy of bamboo forest remote sensing information extracted from the four different years was high with a classification accuracy of >76.26% and an accuracy of users of >91.62%. The classification area of bamboo forest was highly consistent with the area from forest resource inventory, and the area accuracy was over 96.50%; (2) the estimation model performed well in predicting the AGC in Zhejiang for different years. The correlation coefficient for estimated and measured AGC was between 63% and 72% with low root mean square error; (3) the derived AGC of the bamboo forests in Zhejiang province increased gradually from 2000 to 2014, with the AGC density of 6.75 Mg·ha −1 , 10.95 Mg·ha −1 , 15.25 Mg·ha −1 and 19.07 Mg·ha −1 respectively, and the average annual growth of 0.88 Mg·ha −1 . The spatiotemporal evolution of bamboo forest AGC in Zhejiang province had a close relationship with the gradual expansion of bamboo forest in the province and the differentiation of management levels in different regions.

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“…When remote sensing data and sample plots are determined, selection of suitable variables and use of proper modeling algorithms are two critical steps for AGB studies in a given region [12,13]. Many studies have examined the mapping of bamboo forest distribution and modeling AGB using remote sensing data such as Landsat [2,14].…”

Section: Introductionmentioning

confidence: 99%

“…Due to the intensive management (e.g., fertilization, selective logging) and the short growth period from bamboo shoots to fully developed bamboo trees [9], remote sensing-based AGB estimation for bamboo forests becomes especially challenging. When remote sensing data and sample plots are determined, selection of suitable variables and use of proper modeling algorithms are two critical steps for AGB studies in a given region [12,13]. Many studies have examined the mapping of bamboo forest distribution and modeling AGB using remote sensing data such as Landsat [2,14].…”

Section: Introductionmentioning

confidence: 99%

“…2019, 11, 7 4 of 22 algorithms (e.g., regression-based methods) and non-parametric algorithms (e.g., k-nearest neighbor (kNN), artificial neural network (ANN), support vector regression (SVR)). The regression-based modeling approach is often used for developing AGB estimation; in particular, stepwise regression can automatically identify the variables for AGB modeling [13,14]. However, regression-based approaches require the assumption that the selected variables have linear relationships with AGB.…”

Section: Introductionmentioning

confidence: 99%

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Exploring Bamboo Forest Aboveground Biomass Estimation Using Sentinel-2 Data

Chen

et al. 2018

Remote Sensing

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Bamboo forests, due to rapid growth and short harvest rotation, play an important role in carbon cycling and local economic development. Accurate estimation of bamboo forest aboveground biomass (AGB) has garnered increasing attention during the past two decades. However, remote sensing-based AGB estimation for bamboo forests is challenging due to poor understanding of the mechanisms between bamboo forest growth characteristics and remote sensing data. The objective of this research is to examine the remote sensing characteristics of on-year and off-year bamboo forests at different dates and their AGB estimation performance. This research used multiple Sentinel-2 data to explore AGB estimation of bamboo forests in Zhejiang Province, China, by taking into account the unique characteristics of on-year and off-year bamboo forest growth features. Combining field survey data and Sentinel-2 spectral responses (spectral bands and vegetation indices) and textural images, random forest was used to identify key variables for AGB estimation. The results show that (1) the on-year and off-year bamboo forests have considerably different spectral signatures, especially in the wavelengths between red edge 2 and near-infrared wavelength (NIR2) (740-865 nm), making it possible to separate on-year and off-year bamboo forests; (2) on-year bamboo forests have similar spectral signatures although AGB increases from as small as 40 Mgha −1 to as high as 90 Mgha −1 , implying that optical sensor data cannot effectively model on-year bamboo AGB; (3) off-year bamboo AGB has significant relationships with red and shortwave infrared (SWIR) spectral bands in the April image and with red edge 2 in the July image, but the AGB saturation problem yields poor estimation accuracy; (4) stratification considerably improved off-year bamboo AGB estimation but not on-year, non-stratification using the April image is recommended; and (5) Sentinel-2 data cannot solve the bamboo AGB data saturation problem when AGB is greater than 70 Mgha −1 , similar to other optical sensor data such as Landsat. More research should be conducted in the future to integrate multiple sources-remotely sensed data (e.g., lidar, optical sensor data) and ancillary data (e.g., soil, topography)-into AGB modeling to improve the estimation. The use of very high spatial resolution images that can effectively extract tree density information may improve bamboo AGB estimation and yield new insights.

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Comparative Analysis of Modeling Algorithms for Forest Aboveground Biomass Estimation in a Subtropical Region

Cited by 147 publications

References 56 publications

Comparison of Machine Learning Methods for Estimating Mangrove Above-Ground Biomass Using Multiple Source Remote Sensing Data in the Red River Delta Biosphere Reserve, Vietnam

Comparison of Machine Learning Methods for Estimating Mangrove Above-Ground Biomass Using Multiple Source Remote Sensing Data in the Red River Delta Biosphere Reserve, Vietnam

Spatiotemporal Estimation of Bamboo Forest Aboveground Carbon Storage Based on Landsat Data in Zhejiang, China

Exploring Bamboo Forest Aboveground Biomass Estimation Using Sentinel-2 Data

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