Improved random forest algorithms for increasing the accuracy of forest aboveground biomass estimation using Sentinel-2 imagery

A simpler, unbiased, and comprehensive random forest (RF) model is needed to improve the accuracy of aboveground biomass (AGB) estimation. In this study, data were obtained from 128 sample plots of Pinus yunnanensis forest located in Chuxiong prefecture, Yunnan province, China. Sentinel-2 imagery da...
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Autor*in:	Xiaoli Zhang [verfasserIn] Hanwen Shen [verfasserIn] Tianbao Huang [verfasserIn] Yong Wu [verfasserIn] Binbing Guo [verfasserIn] Zhi Liu [verfasserIn] Hongbin Luo [verfasserIn] Jing Tang [verfasserIn] Hang Zhou [verfasserIn] Leiguang Wang [verfasserIn] Weiheng Xu [verfasserIn] Guanglong Ou [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Random Forest (RF) Regularized Random Forest (RRF) Quantile Random Forest (QRF) Forest aboveground biomass (AGB) estimation Sentinel-2 imagery

Übergeordnetes Werk:	In: Ecological Indicators - Elsevier, 2021, 159(2024), Seite 111752-
Übergeordnetes Werk:	volume:159 ; year:2024 ; pages:111752-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.ecolind.2024.111752

Katalog-ID:	DOAJ097032158

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allfields	10.1016/j.ecolind.2024.111752 doi (DE-627)DOAJ097032158 (DE-599)DOAJ6bf56c0c9c754bd181cad49b56fecfc8 DE-627 ger DE-627 rakwb eng QH540-549.5 Xiaoli Zhang verfasserin aut Improved random forest algorithms for increasing the accuracy of forest aboveground biomass estimation using Sentinel-2 imagery 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A simpler, unbiased, and comprehensive random forest (RF) model is needed to improve the accuracy of aboveground biomass (AGB) estimation. In this study, data were obtained from 128 sample plots of Pinus yunnanensis forest located in Chuxiong prefecture, Yunnan province, China. Sentinel-2 imagery data were applied to extract the important predictors of forest AGB, which were screened using the Boruta algorithm. We compared the fitting performance of two modified random forest models − regularized random forest (RRF) and quantile random forest (QRF) − with the random forest model. Moreover, we combined the smallest mean error of each quantile model as the best QRF (QRFb). The result showed: (1) Window sizes of 3 × 3 pixels and 5 × 5 pixels demonstrated greater sensitivity and suitability for estimating AGB than the 7 × 7 pixels window size. Enhanced vegetation indices derived from Red Edge 1 (B5) and Near-Infrared bands (B8A) were strongly correlated with AGB, indicating the heightened sensitivity of B5 and B8A bands to biomass and their potential in AGB estimation. (2) The RRF model outperformed both the standard RF and QRF in fitting performance, with an R2 of 0.56 and RMSE 57.14 Mg/ha. (3) The QRFb model exhibited the highest R2 of 0.88 and lowest RMSE of 29.56 Mg/ha, significantly reducing overestimation and underestimation issues. The modified RF regression supplies new insights into improving forest AGB estimation, which will be helpful for future research addressing carbon cycling. Random Forest (RF) Regularized Random Forest (RRF) Quantile Random Forest (QRF) Forest aboveground biomass (AGB) estimation Sentinel-2 imagery Ecology Hanwen Shen verfasserin aut Tianbao Huang verfasserin aut Yong Wu verfasserin aut Binbing Guo verfasserin aut Zhi Liu verfasserin aut Hongbin Luo verfasserin aut Jing Tang verfasserin aut Hang Zhou verfasserin aut Leiguang Wang verfasserin aut Weiheng Xu verfasserin aut Guanglong Ou verfasserin aut In Ecological Indicators Elsevier, 2021 159(2024), Seite 111752- (DE-627)338074163 (DE-600)2063587-4 18727034 nnns volume:159 year:2024 pages:111752- https://doi.org/10.1016/j.ecolind.2024.111752 kostenfrei https://doaj.org/article/6bf56c0c9c754bd181cad49b56fecfc8 kostenfrei http://www.sciencedirect.com/science/article/pii/S1470160X24002097 kostenfrei https://doaj.org/toc/1470-160X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 159 2024 111752-
spelling	10.1016/j.ecolind.2024.111752 doi (DE-627)DOAJ097032158 (DE-599)DOAJ6bf56c0c9c754bd181cad49b56fecfc8 DE-627 ger DE-627 rakwb eng QH540-549.5 Xiaoli Zhang verfasserin aut Improved random forest algorithms for increasing the accuracy of forest aboveground biomass estimation using Sentinel-2 imagery 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A simpler, unbiased, and comprehensive random forest (RF) model is needed to improve the accuracy of aboveground biomass (AGB) estimation. In this study, data were obtained from 128 sample plots of Pinus yunnanensis forest located in Chuxiong prefecture, Yunnan province, China. Sentinel-2 imagery data were applied to extract the important predictors of forest AGB, which were screened using the Boruta algorithm. We compared the fitting performance of two modified random forest models − regularized random forest (RRF) and quantile random forest (QRF) − with the random forest model. Moreover, we combined the smallest mean error of each quantile model as the best QRF (QRFb). The result showed: (1) Window sizes of 3 × 3 pixels and 5 × 5 pixels demonstrated greater sensitivity and suitability for estimating AGB than the 7 × 7 pixels window size. Enhanced vegetation indices derived from Red Edge 1 (B5) and Near-Infrared bands (B8A) were strongly correlated with AGB, indicating the heightened sensitivity of B5 and B8A bands to biomass and their potential in AGB estimation. (2) The RRF model outperformed both the standard RF and QRF in fitting performance, with an R2 of 0.56 and RMSE 57.14 Mg/ha. (3) The QRFb model exhibited the highest R2 of 0.88 and lowest RMSE of 29.56 Mg/ha, significantly reducing overestimation and underestimation issues. The modified RF regression supplies new insights into improving forest AGB estimation, which will be helpful for future research addressing carbon cycling. Random Forest (RF) Regularized Random Forest (RRF) Quantile Random Forest (QRF) Forest aboveground biomass (AGB) estimation Sentinel-2 imagery Ecology Hanwen Shen verfasserin aut Tianbao Huang verfasserin aut Yong Wu verfasserin aut Binbing Guo verfasserin aut Zhi Liu verfasserin aut Hongbin Luo verfasserin aut Jing Tang verfasserin aut Hang Zhou verfasserin aut Leiguang Wang verfasserin aut Weiheng Xu verfasserin aut Guanglong Ou verfasserin aut In Ecological Indicators Elsevier, 2021 159(2024), Seite 111752- (DE-627)338074163 (DE-600)2063587-4 18727034 nnns volume:159 year:2024 pages:111752- https://doi.org/10.1016/j.ecolind.2024.111752 kostenfrei https://doaj.org/article/6bf56c0c9c754bd181cad49b56fecfc8 kostenfrei http://www.sciencedirect.com/science/article/pii/S1470160X24002097 kostenfrei https://doaj.org/toc/1470-160X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 159 2024 111752-
allfields_unstemmed	10.1016/j.ecolind.2024.111752 doi (DE-627)DOAJ097032158 (DE-599)DOAJ6bf56c0c9c754bd181cad49b56fecfc8 DE-627 ger DE-627 rakwb eng QH540-549.5 Xiaoli Zhang verfasserin aut Improved random forest algorithms for increasing the accuracy of forest aboveground biomass estimation using Sentinel-2 imagery 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A simpler, unbiased, and comprehensive random forest (RF) model is needed to improve the accuracy of aboveground biomass (AGB) estimation. In this study, data were obtained from 128 sample plots of Pinus yunnanensis forest located in Chuxiong prefecture, Yunnan province, China. Sentinel-2 imagery data were applied to extract the important predictors of forest AGB, which were screened using the Boruta algorithm. We compared the fitting performance of two modified random forest models − regularized random forest (RRF) and quantile random forest (QRF) − with the random forest model. Moreover, we combined the smallest mean error of each quantile model as the best QRF (QRFb). The result showed: (1) Window sizes of 3 × 3 pixels and 5 × 5 pixels demonstrated greater sensitivity and suitability for estimating AGB than the 7 × 7 pixels window size. Enhanced vegetation indices derived from Red Edge 1 (B5) and Near-Infrared bands (B8A) were strongly correlated with AGB, indicating the heightened sensitivity of B5 and B8A bands to biomass and their potential in AGB estimation. (2) The RRF model outperformed both the standard RF and QRF in fitting performance, with an R2 of 0.56 and RMSE 57.14 Mg/ha. (3) The QRFb model exhibited the highest R2 of 0.88 and lowest RMSE of 29.56 Mg/ha, significantly reducing overestimation and underestimation issues. The modified RF regression supplies new insights into improving forest AGB estimation, which will be helpful for future research addressing carbon cycling. Random Forest (RF) Regularized Random Forest (RRF) Quantile Random Forest (QRF) Forest aboveground biomass (AGB) estimation Sentinel-2 imagery Ecology Hanwen Shen verfasserin aut Tianbao Huang verfasserin aut Yong Wu verfasserin aut Binbing Guo verfasserin aut Zhi Liu verfasserin aut Hongbin Luo verfasserin aut Jing Tang verfasserin aut Hang Zhou verfasserin aut Leiguang Wang verfasserin aut Weiheng Xu verfasserin aut Guanglong Ou verfasserin aut In Ecological Indicators Elsevier, 2021 159(2024), Seite 111752- (DE-627)338074163 (DE-600)2063587-4 18727034 nnns volume:159 year:2024 pages:111752- https://doi.org/10.1016/j.ecolind.2024.111752 kostenfrei https://doaj.org/article/6bf56c0c9c754bd181cad49b56fecfc8 kostenfrei http://www.sciencedirect.com/science/article/pii/S1470160X24002097 kostenfrei https://doaj.org/toc/1470-160X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 159 2024 111752-
allfieldsGer	10.1016/j.ecolind.2024.111752 doi (DE-627)DOAJ097032158 (DE-599)DOAJ6bf56c0c9c754bd181cad49b56fecfc8 DE-627 ger DE-627 rakwb eng QH540-549.5 Xiaoli Zhang verfasserin aut Improved random forest algorithms for increasing the accuracy of forest aboveground biomass estimation using Sentinel-2 imagery 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A simpler, unbiased, and comprehensive random forest (RF) model is needed to improve the accuracy of aboveground biomass (AGB) estimation. In this study, data were obtained from 128 sample plots of Pinus yunnanensis forest located in Chuxiong prefecture, Yunnan province, China. Sentinel-2 imagery data were applied to extract the important predictors of forest AGB, which were screened using the Boruta algorithm. We compared the fitting performance of two modified random forest models − regularized random forest (RRF) and quantile random forest (QRF) − with the random forest model. Moreover, we combined the smallest mean error of each quantile model as the best QRF (QRFb). The result showed: (1) Window sizes of 3 × 3 pixels and 5 × 5 pixels demonstrated greater sensitivity and suitability for estimating AGB than the 7 × 7 pixels window size. Enhanced vegetation indices derived from Red Edge 1 (B5) and Near-Infrared bands (B8A) were strongly correlated with AGB, indicating the heightened sensitivity of B5 and B8A bands to biomass and their potential in AGB estimation. (2) The RRF model outperformed both the standard RF and QRF in fitting performance, with an R2 of 0.56 and RMSE 57.14 Mg/ha. (3) The QRFb model exhibited the highest R2 of 0.88 and lowest RMSE of 29.56 Mg/ha, significantly reducing overestimation and underestimation issues. The modified RF regression supplies new insights into improving forest AGB estimation, which will be helpful for future research addressing carbon cycling. Random Forest (RF) Regularized Random Forest (RRF) Quantile Random Forest (QRF) Forest aboveground biomass (AGB) estimation Sentinel-2 imagery Ecology Hanwen Shen verfasserin aut Tianbao Huang verfasserin aut Yong Wu verfasserin aut Binbing Guo verfasserin aut Zhi Liu verfasserin aut Hongbin Luo verfasserin aut Jing Tang verfasserin aut Hang Zhou verfasserin aut Leiguang Wang verfasserin aut Weiheng Xu verfasserin aut Guanglong Ou verfasserin aut In Ecological Indicators Elsevier, 2021 159(2024), Seite 111752- (DE-627)338074163 (DE-600)2063587-4 18727034 nnns volume:159 year:2024 pages:111752- https://doi.org/10.1016/j.ecolind.2024.111752 kostenfrei https://doaj.org/article/6bf56c0c9c754bd181cad49b56fecfc8 kostenfrei http://www.sciencedirect.com/science/article/pii/S1470160X24002097 kostenfrei https://doaj.org/toc/1470-160X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 159 2024 111752-
allfieldsSound	10.1016/j.ecolind.2024.111752 doi (DE-627)DOAJ097032158 (DE-599)DOAJ6bf56c0c9c754bd181cad49b56fecfc8 DE-627 ger DE-627 rakwb eng QH540-549.5 Xiaoli Zhang verfasserin aut Improved random forest algorithms for increasing the accuracy of forest aboveground biomass estimation using Sentinel-2 imagery 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A simpler, unbiased, and comprehensive random forest (RF) model is needed to improve the accuracy of aboveground biomass (AGB) estimation. In this study, data were obtained from 128 sample plots of Pinus yunnanensis forest located in Chuxiong prefecture, Yunnan province, China. Sentinel-2 imagery data were applied to extract the important predictors of forest AGB, which were screened using the Boruta algorithm. We compared the fitting performance of two modified random forest models − regularized random forest (RRF) and quantile random forest (QRF) − with the random forest model. Moreover, we combined the smallest mean error of each quantile model as the best QRF (QRFb). The result showed: (1) Window sizes of 3 × 3 pixels and 5 × 5 pixels demonstrated greater sensitivity and suitability for estimating AGB than the 7 × 7 pixels window size. Enhanced vegetation indices derived from Red Edge 1 (B5) and Near-Infrared bands (B8A) were strongly correlated with AGB, indicating the heightened sensitivity of B5 and B8A bands to biomass and their potential in AGB estimation. (2) The RRF model outperformed both the standard RF and QRF in fitting performance, with an R2 of 0.56 and RMSE 57.14 Mg/ha. (3) The QRFb model exhibited the highest R2 of 0.88 and lowest RMSE of 29.56 Mg/ha, significantly reducing overestimation and underestimation issues. The modified RF regression supplies new insights into improving forest AGB estimation, which will be helpful for future research addressing carbon cycling. Random Forest (RF) Regularized Random Forest (RRF) Quantile Random Forest (QRF) Forest aboveground biomass (AGB) estimation Sentinel-2 imagery Ecology Hanwen Shen verfasserin aut Tianbao Huang verfasserin aut Yong Wu verfasserin aut Binbing Guo verfasserin aut Zhi Liu verfasserin aut Hongbin Luo verfasserin aut Jing Tang verfasserin aut Hang Zhou verfasserin aut Leiguang Wang verfasserin aut Weiheng Xu verfasserin aut Guanglong Ou verfasserin aut In Ecological Indicators Elsevier, 2021 159(2024), Seite 111752- (DE-627)338074163 (DE-600)2063587-4 18727034 nnns volume:159 year:2024 pages:111752- https://doi.org/10.1016/j.ecolind.2024.111752 kostenfrei https://doaj.org/article/6bf56c0c9c754bd181cad49b56fecfc8 kostenfrei http://www.sciencedirect.com/science/article/pii/S1470160X24002097 kostenfrei https://doaj.org/toc/1470-160X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 159 2024 111752-
language	English
source	In Ecological Indicators 159(2024), Seite 111752- volume:159 year:2024 pages:111752-
sourceStr	In Ecological Indicators 159(2024), Seite 111752- volume:159 year:2024 pages:111752-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Random Forest (RF) Regularized Random Forest (RRF) Quantile Random Forest (QRF) Forest aboveground biomass (AGB) estimation Sentinel-2 imagery Ecology
isfreeaccess_bool	true
container_title	Ecological Indicators
authorswithroles_txt_mv	Xiaoli Zhang @@aut@@ Hanwen Shen @@aut@@ Tianbao Huang @@aut@@ Yong Wu @@aut@@ Binbing Guo @@aut@@ Zhi Liu @@aut@@ Hongbin Luo @@aut@@ Jing Tang @@aut@@ Hang Zhou @@aut@@ Leiguang Wang @@aut@@ Weiheng Xu @@aut@@ Guanglong Ou @@aut@@
publishDateDaySort_date	2024-01-01T00:00:00Z
hierarchy_top_id	338074163
id	DOAJ097032158
language_de	englisch
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callnumber-first	Q - Science
author	Xiaoli Zhang
spellingShingle	Xiaoli Zhang misc QH540-549.5 misc Random Forest (RF) misc Regularized Random Forest (RRF) misc Quantile Random Forest (QRF) misc Forest aboveground biomass (AGB) estimation misc Sentinel-2 imagery misc Ecology Improved random forest algorithms for increasing the accuracy of forest aboveground biomass estimation using Sentinel-2 imagery
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topic_title	QH540-549.5 Improved random forest algorithms for increasing the accuracy of forest aboveground biomass estimation using Sentinel-2 imagery Random Forest (RF) Regularized Random Forest (RRF) Quantile Random Forest (QRF) Forest aboveground biomass (AGB) estimation Sentinel-2 imagery
topic	misc QH540-549.5 misc Random Forest (RF) misc Regularized Random Forest (RRF) misc Quantile Random Forest (QRF) misc Forest aboveground biomass (AGB) estimation misc Sentinel-2 imagery misc Ecology
topic_unstemmed	misc QH540-549.5 misc Random Forest (RF) misc Regularized Random Forest (RRF) misc Quantile Random Forest (QRF) misc Forest aboveground biomass (AGB) estimation misc Sentinel-2 imagery misc Ecology
topic_browse	misc QH540-549.5 misc Random Forest (RF) misc Regularized Random Forest (RRF) misc Quantile Random Forest (QRF) misc Forest aboveground biomass (AGB) estimation misc Sentinel-2 imagery misc Ecology
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title	Improved random forest algorithms for increasing the accuracy of forest aboveground biomass estimation using Sentinel-2 imagery
ctrlnum	(DE-627)DOAJ097032158 (DE-599)DOAJ6bf56c0c9c754bd181cad49b56fecfc8
title_full	Improved random forest algorithms for increasing the accuracy of forest aboveground biomass estimation using Sentinel-2 imagery
author_sort	Xiaoli Zhang
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author_browse	Xiaoli Zhang Hanwen Shen Tianbao Huang Yong Wu Binbing Guo Zhi Liu Hongbin Luo Jing Tang Hang Zhou Leiguang Wang Weiheng Xu Guanglong Ou
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title_sort	improved random forest algorithms for increasing the accuracy of forest aboveground biomass estimation using sentinel-2 imagery
callnumber	QH540-549.5
title_auth	Improved random forest algorithms for increasing the accuracy of forest aboveground biomass estimation using Sentinel-2 imagery
abstract	A simpler, unbiased, and comprehensive random forest (RF) model is needed to improve the accuracy of aboveground biomass (AGB) estimation. In this study, data were obtained from 128 sample plots of Pinus yunnanensis forest located in Chuxiong prefecture, Yunnan province, China. Sentinel-2 imagery data were applied to extract the important predictors of forest AGB, which were screened using the Boruta algorithm. We compared the fitting performance of two modified random forest models − regularized random forest (RRF) and quantile random forest (QRF) − with the random forest model. Moreover, we combined the smallest mean error of each quantile model as the best QRF (QRFb). The result showed: (1) Window sizes of 3 × 3 pixels and 5 × 5 pixels demonstrated greater sensitivity and suitability for estimating AGB than the 7 × 7 pixels window size. Enhanced vegetation indices derived from Red Edge 1 (B5) and Near-Infrared bands (B8A) were strongly correlated with AGB, indicating the heightened sensitivity of B5 and B8A bands to biomass and their potential in AGB estimation. (2) The RRF model outperformed both the standard RF and QRF in fitting performance, with an R2 of 0.56 and RMSE 57.14 Mg/ha. (3) The QRFb model exhibited the highest R2 of 0.88 and lowest RMSE of 29.56 Mg/ha, significantly reducing overestimation and underestimation issues. The modified RF regression supplies new insights into improving forest AGB estimation, which will be helpful for future research addressing carbon cycling.
abstractGer	A simpler, unbiased, and comprehensive random forest (RF) model is needed to improve the accuracy of aboveground biomass (AGB) estimation. In this study, data were obtained from 128 sample plots of Pinus yunnanensis forest located in Chuxiong prefecture, Yunnan province, China. Sentinel-2 imagery data were applied to extract the important predictors of forest AGB, which were screened using the Boruta algorithm. We compared the fitting performance of two modified random forest models − regularized random forest (RRF) and quantile random forest (QRF) − with the random forest model. Moreover, we combined the smallest mean error of each quantile model as the best QRF (QRFb). The result showed: (1) Window sizes of 3 × 3 pixels and 5 × 5 pixels demonstrated greater sensitivity and suitability for estimating AGB than the 7 × 7 pixels window size. Enhanced vegetation indices derived from Red Edge 1 (B5) and Near-Infrared bands (B8A) were strongly correlated with AGB, indicating the heightened sensitivity of B5 and B8A bands to biomass and their potential in AGB estimation. (2) The RRF model outperformed both the standard RF and QRF in fitting performance, with an R2 of 0.56 and RMSE 57.14 Mg/ha. (3) The QRFb model exhibited the highest R2 of 0.88 and lowest RMSE of 29.56 Mg/ha, significantly reducing overestimation and underestimation issues. The modified RF regression supplies new insights into improving forest AGB estimation, which will be helpful for future research addressing carbon cycling.
abstract_unstemmed	A simpler, unbiased, and comprehensive random forest (RF) model is needed to improve the accuracy of aboveground biomass (AGB) estimation. In this study, data were obtained from 128 sample plots of Pinus yunnanensis forest located in Chuxiong prefecture, Yunnan province, China. Sentinel-2 imagery data were applied to extract the important predictors of forest AGB, which were screened using the Boruta algorithm. We compared the fitting performance of two modified random forest models − regularized random forest (RRF) and quantile random forest (QRF) − with the random forest model. Moreover, we combined the smallest mean error of each quantile model as the best QRF (QRFb). The result showed: (1) Window sizes of 3 × 3 pixels and 5 × 5 pixels demonstrated greater sensitivity and suitability for estimating AGB than the 7 × 7 pixels window size. Enhanced vegetation indices derived from Red Edge 1 (B5) and Near-Infrared bands (B8A) were strongly correlated with AGB, indicating the heightened sensitivity of B5 and B8A bands to biomass and their potential in AGB estimation. (2) The RRF model outperformed both the standard RF and QRF in fitting performance, with an R2 of 0.56 and RMSE 57.14 Mg/ha. (3) The QRFb model exhibited the highest R2 of 0.88 and lowest RMSE of 29.56 Mg/ha, significantly reducing overestimation and underestimation issues. The modified RF regression supplies new insights into improving forest AGB estimation, which will be helpful for future research addressing carbon cycling.
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title_short	Improved random forest algorithms for increasing the accuracy of forest aboveground biomass estimation using Sentinel-2 imagery
url	https://doi.org/10.1016/j.ecolind.2024.111752 https://doaj.org/article/6bf56c0c9c754bd181cad49b56fecfc8 http://www.sciencedirect.com/science/article/pii/S1470160X24002097 https://doaj.org/toc/1470-160X
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doi_str	10.1016/j.ecolind.2024.111752
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up_date	2024-07-03T23:29:00.143Z
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Improved random forest algorithms for increasing the accuracy of forest aboveground biomass estimation using Sentinel-2 imagery

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