$Extraction of fractional vegetation cover in arid desert area based on Chinese GF-6 satellite$
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Extraction of fractional vegetation cover in arid desert area based on Chinese GF-6 satellite

The red edge band is considered as one of the diagnosable characteristics of green plants, but the large-scale remote sensing retrieval of fractional vegetation coverage (FVC) based on the red edge band is still rare. To explore the application of the red edge band in the remote sensing estimation o...

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Autores principales:	Deng Zhengdong, Lu Zhao, Wang Guangyuan, Wang Daqing, Ding Zhibin, Zhao Hongfei, Xu Haoli, Shi Yue, Cheng Zijian, Zhao Xiaoning
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	vegetation index pixel dichotomy model fractional vegetation cover red edge band Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/39bc0a26fdc7494581fc5501477c3b73
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id	oai:doaj.org-article:39bc0a26fdc7494581fc5501477c3b73
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spelling	oai:doaj.org-article:39bc0a26fdc7494581fc5501477c3b732021-12-05T14:10:48ZExtraction of fractional vegetation cover in arid desert area based on Chinese GF-6 satellite2391-544710.1515/geo-2020-0241https://doaj.org/article/39bc0a26fdc7494581fc5501477c3b732021-04-01T00:00:00Zhttps://doi.org/10.1515/geo-2020-0241https://doaj.org/toc/2391-5447The red edge band is considered as one of the diagnosable characteristics of green plants, but the large-scale remote sensing retrieval of fractional vegetation coverage (FVC) based on the red edge band is still rare. To explore the application of the red edge band in the remote sensing estimation of FVC, this study proposed a new vegetation index (normalized difference red edge index, RENDVI) based on the two red edge bands of Chinese GaoFen-6 satellite (GF-6). The FVC estimated by using three vegetation indices (NDVI, RENDVI1, and RENDVI2) were evaluated based on the field survey FVC obtained in Minqin Basin of Gansu Province. The results showed that there was a good linear correlation between the FVC estimated by GF-6 WFV data and the FVC investigated in the field, and the most reasonable estimation of FVC was obtained based on RENDVI2 model (R 2 = 0.97611 and RMSE = 0.07075). Meanwhile, the impact of three confidence levels (1, 2, and 5%) on FVC was also analyzed in this study. FVC obtained from NDVI and RENDVI2 has the highest accuracy at 2% confidence, while FVC based on RENDVI1 achieved the best accuracy at 5% confidence. It could be concluded that it is feasible and reliable to estimate FVC based on red edge bands, and the GF-6 Wide Field View (WFV) data with high temporal and spatial resolution provide a new data source for remote sensing estimation of FVC.Deng ZhengdongLu ZhaoWang GuangyuanWang DaqingDing ZhibinZhao HongfeiXu HaoliShi YueCheng ZijianZhao XiaoningDe Gruyterarticlevegetation indexpixel dichotomy modelfractional vegetation coverred edge bandGeologyQE1-996.5ENOpen Geosciences, Vol 13, Iss 1, Pp 416-430 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	vegetation index pixel dichotomy model fractional vegetation cover red edge band Geology QE1-996.5
spellingShingle	vegetation index pixel dichotomy model fractional vegetation cover red edge band Geology QE1-996.5 Deng Zhengdong Lu Zhao Wang Guangyuan Wang Daqing Ding Zhibin Zhao Hongfei Xu Haoli Shi Yue Cheng Zijian Zhao Xiaoning Extraction of fractional vegetation cover in arid desert area based on Chinese GF-6 satellite
description	The red edge band is considered as one of the diagnosable characteristics of green plants, but the large-scale remote sensing retrieval of fractional vegetation coverage (FVC) based on the red edge band is still rare. To explore the application of the red edge band in the remote sensing estimation of FVC, this study proposed a new vegetation index (normalized difference red edge index, RENDVI) based on the two red edge bands of Chinese GaoFen-6 satellite (GF-6). The FVC estimated by using three vegetation indices (NDVI, RENDVI1, and RENDVI2) were evaluated based on the field survey FVC obtained in Minqin Basin of Gansu Province. The results showed that there was a good linear correlation between the FVC estimated by GF-6 WFV data and the FVC investigated in the field, and the most reasonable estimation of FVC was obtained based on RENDVI2 model (R 2 = 0.97611 and RMSE = 0.07075). Meanwhile, the impact of three confidence levels (1, 2, and 5%) on FVC was also analyzed in this study. FVC obtained from NDVI and RENDVI2 has the highest accuracy at 2% confidence, while FVC based on RENDVI1 achieved the best accuracy at 5% confidence. It could be concluded that it is feasible and reliable to estimate FVC based on red edge bands, and the GF-6 Wide Field View (WFV) data with high temporal and spatial resolution provide a new data source for remote sensing estimation of FVC.
format	article
author	Deng Zhengdong Lu Zhao Wang Guangyuan Wang Daqing Ding Zhibin Zhao Hongfei Xu Haoli Shi Yue Cheng Zijian Zhao Xiaoning
author_facet	Deng Zhengdong Lu Zhao Wang Guangyuan Wang Daqing Ding Zhibin Zhao Hongfei Xu Haoli Shi Yue Cheng Zijian Zhao Xiaoning
author_sort	Deng Zhengdong
title	Extraction of fractional vegetation cover in arid desert area based on Chinese GF-6 satellite
title_short	Extraction of fractional vegetation cover in arid desert area based on Chinese GF-6 satellite
title_full	Extraction of fractional vegetation cover in arid desert area based on Chinese GF-6 satellite
title_fullStr	Extraction of fractional vegetation cover in arid desert area based on Chinese GF-6 satellite
title_full_unstemmed	Extraction of fractional vegetation cover in arid desert area based on Chinese GF-6 satellite
title_sort	extraction of fractional vegetation cover in arid desert area based on chinese gf-6 satellite
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/39bc0a26fdc7494581fc5501477c3b73
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Extraction of fractional vegetation cover in arid desert area based on Chinese GF-6 satellite

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