Mapping coastal wetlands of China using time series Landsat images in 2018 and Google Earth Engine

Wang, Xinxin; Xiao, Xiangming; Zou, Zhenhua; Hou, Lili; Qin, Yuanwei; Dong, Jinwei; Chen, Bangqian; Zhang, Xi; Chen, Ying; Ma, Jun; Zhao, Bin; Li, Bo

doi:10.1016/j.isprsjprs.2020.03.014

Cited by 193 publications

(104 citation statements)

References 88 publications

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“…Accuracy assessment of surface water body maps. Stratified random sampling approach, along with very high spatial resolution (VHSR) images from Google Earth, were most widely used and robust approach in accuracy assessment of land cover classification, such as the global surface water data set 2 , the global tidal flats data set 64 , and other maps at national and regional scales 27,65,66 . In this study, stratified random sampling approach was used to validate the year-long and seasonal surface water body maps, respectively, following the strategy used in the JRC data set 2 .…”

Section: Methodsmentioning

confidence: 99%

Gainers and losers of surface and terrestrial water resources in China during 1989–2016

et al. 2020

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Data and knowledge of the spatial-temporal dynamics of surface water area (SWA) and terrestrial water storage (TWS) in China are critical for sustainable management of water resources but remain very limited. Here we report annual maps of surface water bodies in China during 1989-2016 at 30m spatial resolution. We find that SWA decreases in waterpoor northern China but increases in water-rich southern China during 1989-2016. Our results also reveal the spatial-temporal divergence and consistency between TWS and SWA during 2002-2016. In North China, extensive and continued losses of TWS, together with small to moderate changes of SWA, indicate long-term water stress in the region. Approximately 569 million people live in those areas with deceasing SWA or TWS trends in 2015. Our data set and the findings from this study could be used to support the government and the public to address increasing challenges of water resources and security in China.

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Section: Methodsmentioning

confidence: 99%

Gainers and losers of surface and terrestrial water resources in China during 1989–2016

et al. 2020

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“…The use of Landsat satellite images has an important place in numerous studies where the water surface areas of wetlands are extracted or the temporal changes are determined [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43]. The ability of cloud computing programs has been significantly improved in recent years, and they have shown great application potential in large-scale land cover mapping [44]. Cloud computing platforms such as Google Earth Engine (GEE) provide the infrastructure to access and process large amounts of regularly updated Earth Observation data rapidly in a systematic and reproducible manner [45].…”

Section: Criteriamentioning

confidence: 99%

Analysis of the Temporal Changes of Inland Ramsar Sites in Turkey Using Google Earth Engine

Dervisoglu

2021

IJGI

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Ramsar Convention (RC) is the first of modern intergovernmental agreement on the conscious use and conservation of natural resources. It provides a platform for contracting parties working together to develop the best available data, advice, and policy recommendations to increase awareness of the benefits of wetlands in nature and society. Turkey became a party of the RC in 1994, and in the years 1994 to 2013, 14 wetlands that reached the Ramsar criteria were recognized as Ramsar sites (RS). With this study, all inland RS in Turkey from 1985 to 2020 were examined, and changes in the water surface areas were evaluated on the GEE cloud computing platform using Landsat satellite images and the NDWI index. The closest meteorological station data to each RS were evaluated and associated with the surface area changes. The reasons for the changes in these areas, besides the meteorological effects, have been scrutinized using management plans and publications. As a result, inland wetlands decreased at different rates from 1985 to 2020, with a total loss of 31.38% and 21571.0 ha for the spring months. Since the designation dates of RS, the total amount of water surface area reduction was 27.35 %, constituting 17,758.90 ha.

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“…Because of the limited quality of the QA band and the simple cloud score algorithm, such misclassifications arising from poor-quality observations from the S2 image cannot be removed (X. Wang et al, 2020b;Zhu et al, 2015). The rainbow in the cloud is the result of the push-broom design of S2 (Fig.…”

Section: Development Of a Phenology-and Pixel-based Algorithm For Mapping Rapeseedmentioning

confidence: 99%

The RapeseedMap10 database: annual maps of rapeseed at a spatial resolution of 10 m based on multi-source data

Han

Zhang

Luo

et al. 2021

Earth Syst. Sci. Data

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Abstract. Large-scale, high-resolution maps of rapeseed (Brassica napus L.), a major oilseed crop, are critical for predicting annual production and ensuring global energy security, but such maps are still not freely available for many areas. In this study, we developed a new pixel- and phenology-based algorithm and produced a new data product for rapeseed planting areas (2017–2019) in 33 countries at 10 m spatial resolution based on multiple data. Our product is strongly consistent at the national level with official statistics of the Food and Agricultural Organization of the United Nations. Our rapeseed maps achieved F1 spatial consistency scores of at least 0.81 when compared with the Cropland Data Layer in the United States, the Annual Crop Inventory in Canada, the Crop Map of England, and the Land Cover Map of France. Moreover, F1 scores based on independent validation samples ranged from 0.84 to 0.91, implying a good consistency with ground truth. In almost all countries covered in this study, the rapeseed crop rotation interval was at least 2 years. Our derived maps suggest, with reasonable accuracy, the robustness of the algorithm in identifying rapeseed over large regions with various climates and landscapes. Scientists and local growers can use the freely downloadable derived rapeseed planting areas to help predict rapeseed production and optimize planting structures. The product is publicly available at https://doi.org/10.17632/ydf3m7pd4j.3 (Han et al., 2021).

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Mapping coastal wetlands of China using time series Landsat images in 2018 and Google Earth Engine

Cited by 193 publications

References 88 publications

Gainers and losers of surface and terrestrial water resources in China during 1989–2016

Gainers and losers of surface and terrestrial water resources in China during 1989–2016

Analysis of the Temporal Changes of Inland Ramsar Sites in Turkey Using Google Earth Engine

The RapeseedMap10 database: annual maps of rapeseed at a spatial resolution of 10 m based on multi-source data

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