Water Resource Monitoring Exploiting Sentinel-2 Satellite and Sentinel-2 Satellite Like Time Series; Application in Yangtze River Water Bodies

doi:10.11947/j.JGGS.2020.0404

Abstract

Abstract:

As part of the Dragon 4 project, the water extents of Wuchang and Shengjin lakes have been extracted from Sentinel-2 time series, using all exploitable images since the beginning of the acquisitions in 2015. The aim of the study is to assess the capability of the Sentinel-2 constellation and Landsat 8 over the Anhui region, especially the high temporal resolution. A total of 32 dates have been used and 10 Landsat 8 images (Libra) have been added to try to reduce the temporal gaps in the Sentinel-2 acquisitions caused by cloudy conditions. Extractions were done using a SERTIT-ICube automatized routine based on a supervised Maximum Likelihood Classification. These extractions allow to recreate the dynamic of the two lakes and show the drought and wet periods. During the 3 years interval, the surface peaks in July 2016 for both lakes. The lowest level appears at two different dates for each lake; in January 2018 for Wuchang, in February 2017 for Shengjin. Wuchang Lake surface area appears to be more variable than Shengjin Lake, with many local maximum and minimum between the end of 2017 and April 2018. In the case of Wuchang Lake, floating vegetation is a problem for automatic water surface area extraction. The lake is covered by vegetation during long periods of time and the water below can’t be detected by automatic radiometric means. Nevertheless, Sentinel-2 stays a pertinent and powerful tool for hydrological monitoring of lakes confirming the expectation from the remote sensing wetland community before launch. The presence of NIR and SWIR bands induces a strong discrimination between water and other classes, and the systematic acquisitions create dense time series, making analysis more consistent. It makes possible to sensor events occurring over short periods of time. Thanks to this a link can be done between endangered bird species, such as the Siberian Crane and the Lesser White-Fronted Goose and periodically flooded areas. These midterm results illustrated the pertinence and powerful of multi-source optical satellite data for environmental analysis.

Key words: Wuchang Lake; Sentinel-2; Landsat 8; endangered bird species

Julien BRIANT,Huber CLAIRE,Studer MATHIAS,Lei CAO,Kunpeng YI,Yésou HERVÉ. Water Resource Monitoring Exploiting Sentinel-2 Satellite and Sentinel-2 Satellite Like Time Series; Application in Yangtze River Water Bodies[J]. Journal of Geodesy and Geoinformation Science, 2020, 3(4): 41-49.

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References 13

[1]	BARTERM, CAO L, CHEN L, et al. Results of a survey for waterbirds in the lower Yangtze floodplain, China, in January-February 2004[J]. Forktail, 2005,21.
[2]	CHENG Y Q, CAO L, BARTER M, et al. Wintering waterbird survey at the Anhui Shengjin Lake National Nature Reserve, China 2008/9[J]. Journal of University of Science and Technology of China, 2009.
[3]	CAO L, ZHANG Y, BARTER M, et al. Anatidae in eastern China during the non-breeding season: Geographical distributions and protection status[J]. Biological Conservation, 2010,143(3):650-659.
[4]	ZHANG Xiaoke, WAN An, WANG Huili, et al. The overgrowth of Zizania latifolia in a subtropical floodplain lake: Changes in its distribution and possible water level control measures[J]. Ecological Engineering, 2016,89:114-120.
[5]	JIA Q, CAO L, YÉSOU H, et al. Combating aggressive macrophyte encroachment on a typical Yangtze River lake: lessons from a long-term remote sensing study of vegetation[J]. Aquatic Ecology, 2016. pmid: 21516253
[6]	DRUSCH M, DEL BELLO U, CARLIER S, et al. Sentinel-2: ESA’s optical high-resolution mission for GMES operational services[J]. Remote sensing of Environment, 2012,120:25-36.
[7]	XU Hanqiu. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery[J]. International Journal of Remote Sensing, 2006,27(12/14):3025-3033.
[8]	YÉSOU H, POTTIER É, MERCIER G, et al. Synergy of Sentinel-1 and Sentinel-2 imagery for wetland monitoring information extraction from continuous flow of sentinel images applied to water bodies and vegetation mapping and monitoring [C]//2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Beijing: IEEE, 2016: 162-165.
[9]	HUBER C, BATTISTON S, YÉSOU H, et al. Synergy of VHR pleiades data and SWIR spectral bands for flood detection and impact assessment in urban areas: Case of Krymsk, Russian Federation, in July 2012 [C]//2013 IEEE International Geoscience and Remote Sensing Symposium-IGARSS. Australia: IEEE, 2013: 4538-4541.
[10]	PEKEL J F, COTTAM A, GORELICK N, et al. High-resolution mapping of global surface water and its long-term changes[J]. Nature, 2016,540(7633):418-422. doi: 10.1038/nature20584 pmid: 27926733
[11]	AKAIKE H. Information theory and an extension of the maximum likelihood principle[M] //Selected papers of hirotugu akaike. New York: Springer, 1998: 199-213.
[12]	YUAN Yuan, GAO Hui, LI Weijing, et al. The 2016 summer floods in China and associated physical mechanisms: A comparison with 1998[J]. Journal of Meteorological Research, 2017,31(2):261-277.
[13]	ZHANG Y, CAO L, BARTER M, et al. Changing distribution and abundance of Swan Goose Anser cygnoides in the Yangtze River floodplain: the likely loss of a very important wintering site[J]. Bird Conservation International, 2011,21(1):36-48.

Validation	Water body	Urban area	Mineral	Bare soils	Wet bare soils	Wooded area	Green cropland	Other cropland	Grassland	Omission error/(%)
Water body	114	0	0	0	1	0	0	0	0	1
Urban area	0	24	0	0	0	2	0	5	0	23
Mineral	0	2	4	0	0	1	0	0	0	43
Bare soils	0	1	1	72	5	2	0	9	0	20
Wet bare soils	0	0	0	0	8	2	0	1	0	27
Wooded area	0	1	0	1	0	112	0	12	0	11
Green cropland	0	0	0	0	0	1	3	2	0	33
Other cropland	2	6	0	2	1	10	2	84	0	21
Grassland	0	0	0	0	0	1	0	1	5	29
Commission error/(%)	2	29	20	4	47	15	40	26	0	KAPPA 0.706