Synergy of multi-temporal polarimetric SAR and optical image satellite for mapping of marsh vegetation using object-based random forest algorithm

The accurate classification of marsh vegetation is an important prerequisite for wetland management and protection. In this study, the Honghe National Nature Reserve was used as the research area. The VV and VH polarized backscattering coefficients of Sentinel-1B, the polarimetric decomposition para...

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Autores principales:	Bolin Fu, Shuyu Xie, Hongchang He, Pingping Zuo, Jun Sun, Lilong Liu, Liangke Huang, Donglin Fan, Ertao Gao
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Marsh vegetation classification Backscattering coefficient Polarimetric decomposition parameters Multi-scale inheritance segmentation Variable selection Random forest algorithm Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/18a3af00c79143d48e2327b57fa6d43a
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spelling	oai:doaj.org-article:18a3af00c79143d48e2327b57fa6d43a2021-12-01T05:00:01ZSynergy of multi-temporal polarimetric SAR and optical image satellite for mapping of marsh vegetation using object-based random forest algorithm1470-160X10.1016/j.ecolind.2021.108173https://doaj.org/article/18a3af00c79143d48e2327b57fa6d43a2021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21008384https://doaj.org/toc/1470-160XThe accurate classification of marsh vegetation is an important prerequisite for wetland management and protection. In this study, the Honghe National Nature Reserve was used as the research area. The VV and VH polarized backscattering coefficients of Sentinel-1B, the polarimetric decomposition parameters of Sentinel-1B, and Sentinel-2A multi-spectral images from June and September were selected to construct 18 multi-dimensional data sets. A highly correlated variable elimination algorithm, a recursive feature elimination variable selection algorithm (RFE-RF), and an optimized random forest algorithm (RF) were used to construct a marsh vegetation identification model. In this study, we searched for an RF model to achieve the accurate classification of marsh vegetation and find the best feature for identifying various types of vegetation. Additionally, the applicability of different optimized RF models to the task of the identification of wetland vegetation and the stability of the identification of marsh vegetation using different classification models were quantitatively analyzed. The results show the following: (1) RFE-RF variable selection and RF parameter optimization can reduce the data dimensionality, improve the accuracy and stability of the wetland vegetation classification model, and achieve a training accuracy of up to 85.39%. (2) The RF model integrating multi-spectral data, backscattering coefficients, and polarimetric decomposition parameters for June and September can obtain the highest overall accuracy (91.16%), and the model has the strongest applicability. (3) The importance of multi-spectral variables in wetland vegetation classification is higher than that of backscattering coefficients and polarimetric decomposition parameters. The visible bands and vegetation index are the most important variables, while the cross-polarized backscattering coefficient (Mean_VH), polarimetric decomposition eigenvalue (Mean_l1, Mean_l2), and calculated eigenvalues of the matrix (Mean_lambda) are the backscattering coefficient features and polarimetric decomposition parameters with the highest contributions. (4) The modified normalized difference water index in June (MNDWI_ Jun), blue band in September (Mean_B_Sep), location feature pixel coordinates (Y_Max_Pxl), and ratio vegetation index in September (RVI_Sep) have the highest contribution to the identification and classification of deep-water marsh vegetation, shallow-water marsh vegetation, forest, and shrubs, respectively. (5) The identification of forest is the strongest, and the classification accuracy for shrubs and deep-water marsh vegetation is greatly affected by the combination of time phase and data sources.Bolin FuShuyu XieHongchang HePingping ZuoJun SunLilong LiuLiangke HuangDonglin FanErtao GaoElsevierarticleMarsh vegetation classificationBackscattering coefficientPolarimetric decomposition parametersMulti-scale inheritance segmentationVariable selectionRandom forest algorithmEcologyQH540-549.5ENEcological Indicators, Vol 131, Iss , Pp 108173- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Marsh vegetation classification Backscattering coefficient Polarimetric decomposition parameters Multi-scale inheritance segmentation Variable selection Random forest algorithm Ecology QH540-549.5
spellingShingle	Marsh vegetation classification Backscattering coefficient Polarimetric decomposition parameters Multi-scale inheritance segmentation Variable selection Random forest algorithm Ecology QH540-549.5 Bolin Fu Shuyu Xie Hongchang He Pingping Zuo Jun Sun Lilong Liu Liangke Huang Donglin Fan Ertao Gao Synergy of multi-temporal polarimetric SAR and optical image satellite for mapping of marsh vegetation using object-based random forest algorithm
description	The accurate classification of marsh vegetation is an important prerequisite for wetland management and protection. In this study, the Honghe National Nature Reserve was used as the research area. The VV and VH polarized backscattering coefficients of Sentinel-1B, the polarimetric decomposition parameters of Sentinel-1B, and Sentinel-2A multi-spectral images from June and September were selected to construct 18 multi-dimensional data sets. A highly correlated variable elimination algorithm, a recursive feature elimination variable selection algorithm (RFE-RF), and an optimized random forest algorithm (RF) were used to construct a marsh vegetation identification model. In this study, we searched for an RF model to achieve the accurate classification of marsh vegetation and find the best feature for identifying various types of vegetation. Additionally, the applicability of different optimized RF models to the task of the identification of wetland vegetation and the stability of the identification of marsh vegetation using different classification models were quantitatively analyzed. The results show the following: (1) RFE-RF variable selection and RF parameter optimization can reduce the data dimensionality, improve the accuracy and stability of the wetland vegetation classification model, and achieve a training accuracy of up to 85.39%. (2) The RF model integrating multi-spectral data, backscattering coefficients, and polarimetric decomposition parameters for June and September can obtain the highest overall accuracy (91.16%), and the model has the strongest applicability. (3) The importance of multi-spectral variables in wetland vegetation classification is higher than that of backscattering coefficients and polarimetric decomposition parameters. The visible bands and vegetation index are the most important variables, while the cross-polarized backscattering coefficient (Mean_VH), polarimetric decomposition eigenvalue (Mean_l1, Mean_l2), and calculated eigenvalues of the matrix (Mean_lambda) are the backscattering coefficient features and polarimetric decomposition parameters with the highest contributions. (4) The modified normalized difference water index in June (MNDWI_ Jun), blue band in September (Mean_B_Sep), location feature pixel coordinates (Y_Max_Pxl), and ratio vegetation index in September (RVI_Sep) have the highest contribution to the identification and classification of deep-water marsh vegetation, shallow-water marsh vegetation, forest, and shrubs, respectively. (5) The identification of forest is the strongest, and the classification accuracy for shrubs and deep-water marsh vegetation is greatly affected by the combination of time phase and data sources.
format	article
author	Bolin Fu Shuyu Xie Hongchang He Pingping Zuo Jun Sun Lilong Liu Liangke Huang Donglin Fan Ertao Gao
author_facet	Bolin Fu Shuyu Xie Hongchang He Pingping Zuo Jun Sun Lilong Liu Liangke Huang Donglin Fan Ertao Gao
author_sort	Bolin Fu
title	Synergy of multi-temporal polarimetric SAR and optical image satellite for mapping of marsh vegetation using object-based random forest algorithm
title_short	Synergy of multi-temporal polarimetric SAR and optical image satellite for mapping of marsh vegetation using object-based random forest algorithm
title_full	Synergy of multi-temporal polarimetric SAR and optical image satellite for mapping of marsh vegetation using object-based random forest algorithm
title_fullStr	Synergy of multi-temporal polarimetric SAR and optical image satellite for mapping of marsh vegetation using object-based random forest algorithm
title_full_unstemmed	Synergy of multi-temporal polarimetric SAR and optical image satellite for mapping of marsh vegetation using object-based random forest algorithm
title_sort	synergy of multi-temporal polarimetric sar and optical image satellite for mapping of marsh vegetation using object-based random forest algorithm
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/18a3af00c79143d48e2327b57fa6d43a
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Synergy of multi-temporal polarimetric SAR and optical image satellite for mapping of marsh vegetation using object-based random forest algorithm

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