Water depth retrieval models of East Dongting Lake, China, using GF-1 multi-spectral remote sensing images
High-resolution satellite technology is playing an increasingly important role in environmental monitoring. As the first high-resolution satellite for earth observation in China, GaoFen-1 (GF-1) has great potential for collecting water depth data. Multispectral images from GF-1 (16 m) combined with...
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doaj-9d5ac285ee8648b0813c6191b240a0272020-11-25T03:26:41ZengElsevierGlobal Ecology and Conservation2351-98942020-06-0122Water depth retrieval models of East Dongting Lake, China, using GF-1 multi-spectral remote sensing imagesYang Nan0Li Jianhui1Mo Wenbo2Luo Wangjun3Wu Di4Gao Wanchao5Sun Changhao6College of Architecture & Urban Planning, Hunan City University, Yiyang, Hunan, 413000, China; Urban Planning Architectural Design Institute of Hunan City University, Yiyang, 413000, China; Central South University of Forestry and Technology, Changsha, 410004, ChinaHunan Spatiomap Information Technology Co., Ltd, Changsha, 410000, ChinaUrban Planning Architectural Design Institute of Hunan City University, Yiyang, 413000, China; Central South University of Forestry and Technology, Changsha, 410004, China; Hunan Urban and Rural Ecological Planning and Restoration Engineering Technology Research Center, China; Corresponding author. No. 518 Yingbin East Road, Yiyang City, Hunan Province, China.Hunan Spatiomap Information Technology Co., Ltd, Changsha, 410000, ChinaHunan Spatiomap Information Technology Co., Ltd, Changsha, 410000, ChinaHydrology & Water Resources Bureau of Hunan Province, Changsha, 410004, ChinaCollege of Architecture & Urban Planning, Hunan City University, Yiyang, Hunan, 413000, ChinaHigh-resolution satellite technology is playing an increasingly important role in environmental monitoring. As the first high-resolution satellite for earth observation in China, GaoFen-1 (GF-1) has great potential for collecting water depth data. Multispectral images from GF-1 (16 m) combined with water depth data from long-term monitoring at a hydrological station were used with the normalized difference waterbody index, density segmentation, band combination, models (single-band, multi-band, and dual-band ratio) to invert the water depth of East Dongting Lake in the dry season and to provide important references for remote sensing of water depth. The experimental results are as follows. (1) Red, green, blue, and combined bands had higher correlation with the measured data. Consequently, the green band was chosen as the factor in the single-band model; blue, green, and red bands in the multi-band model and the ratio of green to red bands in the dual-band ratio model. (2) The correlation coefficients of measured data and single-, multi-, and dual-band models were 0.848, 0.867, and 0.925, respectively. (3) The inversion results showed that average water level in East Dongting Lake was approximately 20 m in the dry season during the period 2013–2016 and it was highest in southern area, intermediate in eastern area, and lowest in the western area.http://www.sciencedirect.com/science/article/pii/S2351989419308832GF-1East Dongting LakeInversion modelMulti-spectral remote sensingWater depth |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yang Nan Li Jianhui Mo Wenbo Luo Wangjun Wu Di Gao Wanchao Sun Changhao |
spellingShingle |
Yang Nan Li Jianhui Mo Wenbo Luo Wangjun Wu Di Gao Wanchao Sun Changhao Water depth retrieval models of East Dongting Lake, China, using GF-1 multi-spectral remote sensing images Global Ecology and Conservation GF-1 East Dongting Lake Inversion model Multi-spectral remote sensing Water depth |
author_facet |
Yang Nan Li Jianhui Mo Wenbo Luo Wangjun Wu Di Gao Wanchao Sun Changhao |
author_sort |
Yang Nan |
title |
Water depth retrieval models of East Dongting Lake, China, using GF-1 multi-spectral remote sensing images |
title_short |
Water depth retrieval models of East Dongting Lake, China, using GF-1 multi-spectral remote sensing images |
title_full |
Water depth retrieval models of East Dongting Lake, China, using GF-1 multi-spectral remote sensing images |
title_fullStr |
Water depth retrieval models of East Dongting Lake, China, using GF-1 multi-spectral remote sensing images |
title_full_unstemmed |
Water depth retrieval models of East Dongting Lake, China, using GF-1 multi-spectral remote sensing images |
title_sort |
water depth retrieval models of east dongting lake, china, using gf-1 multi-spectral remote sensing images |
publisher |
Elsevier |
series |
Global Ecology and Conservation |
issn |
2351-9894 |
publishDate |
2020-06-01 |
description |
High-resolution satellite technology is playing an increasingly important role in environmental monitoring. As the first high-resolution satellite for earth observation in China, GaoFen-1 (GF-1) has great potential for collecting water depth data. Multispectral images from GF-1 (16 m) combined with water depth data from long-term monitoring at a hydrological station were used with the normalized difference waterbody index, density segmentation, band combination, models (single-band, multi-band, and dual-band ratio) to invert the water depth of East Dongting Lake in the dry season and to provide important references for remote sensing of water depth. The experimental results are as follows. (1) Red, green, blue, and combined bands had higher correlation with the measured data. Consequently, the green band was chosen as the factor in the single-band model; blue, green, and red bands in the multi-band model and the ratio of green to red bands in the dual-band ratio model. (2) The correlation coefficients of measured data and single-, multi-, and dual-band models were 0.848, 0.867, and 0.925, respectively. (3) The inversion results showed that average water level in East Dongting Lake was approximately 20 m in the dry season during the period 2013–2016 and it was highest in southern area, intermediate in eastern area, and lowest in the western area. |
topic |
GF-1 East Dongting Lake Inversion model Multi-spectral remote sensing Water depth |
url |
http://www.sciencedirect.com/science/article/pii/S2351989419308832 |
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