Method for delineating open water bodies based on the deeply clear waterbody delineation index

Yue, Hui; Liu, Ying

doi:10.1117/1.jrs.13.038504

Cited by 7 publications

(4 citation statements)

References 38 publications

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“…We used the Deeply Clear Water Extraction Index (DCWEI) (Yue and Liu, 2019b) to extract the lake area, DCWDI can be expressed as:…”

Section: Lake Area Extraction: Lamentioning

confidence: 99%

“…The continuous development of the coal mining project has a great impact on the groundwater supply for Hongjiannao Lake. In 2015, the area of Hongjiannao Lake has been reduced to its lowest level of 31.41 km 2 , and the continuous reduction of the lake has resulted in the continuous deterioration of the ecological environment (Yue and Liu, 2019a;Yue and Liu, 2019b). Therefore, it is necessary to monitor the variations of Hongjiannao Lake and to analyze its responses to climatic and anthropogenic factors, to provide a scientific reference for rational coal mining exploitation and protection of water resources, as well as regional sustainable development in the arid and semi-arid area of Northwest China.…”

Section: Introductionmentioning

confidence: 99%

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Water balance and influence mechanism analysis: a case study of Hongjiannao Lake, China

Yue

Liu

2021

Environ Monit Assess

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Hongjiannao groundwater exchange was the largest desert freshwater lake in China (57.25 km 2 in 1986). However, it shrank sharply over the past 34a (1986-2019), with the smallest lake area 31.41 km 2 in 2015. The objective of this study was to use the Landsat images, ASTER GDEM V2 data, and meteorology and statistics data, in combination with the water balance model to calculate the dynamics of water balance elements, quantify and characterize the interannual variations in lakegroundwater exchanges, and analyzed its influencing factors by using the geographical detector. The results showed that in the stable stage (1986-1997), the average rate of the lake area, water level, and lake volume change was-0.26 km 2 /a,-0.0483 m/a, and-0.0009 km 3 /a, respectively. Precipitation, river inflow, and groundwater were 0.0203 km 3 , 0.0485 km 3 , and 0.0098 km 3 , which accounts for the whole input were 25.83%, 61.70%, and 12.47%, respectively, evaporation was 0.0786 km 3. In the reduction stage (1998-2015), the average rate of the lake area, water level, and lake volume change was-1.21 km 2 /a,-0.2422 m/a, and-0.0101 km 3 /a, respectively. Before 2006, precipitation, river inflow, and groundwater were 0.0154 km 3 , 0.0475 km 3 , and-0.0025 km 3 , respectively; from 2006 to 2009, precipitation, river inflow, and groundwater were 0.0143 km 3 , 0.0334 km 3 , and 0.0058 km 3 , respectively; after 2009, precipitation, river inflow, and groundwater were 0.0139 km 3 , 0.0199 km 3 , and 0.0085 km 3 , respectively. Evaporation decreased from 0.0714 km 3 to 0.0480 km 3 from 1998 to 2015. In the growth stage (2016-2019), the average rate of the lake area, water level, and lake volume change were 1.38 km 2 /a, 0.27 m/a, and 0.0088 km 3 /a, respectively. Precipitation, river inflow, and groundwater were 0.0209 km 3 , 0.0005 km 3 , and 0.0373 km 3 , which accounts for the whole input were 46.63 %, 52.12 %, and 1.25 %, respectively, evaporation was 0.0187 km 3. Compared with the stable stage, groundwater in the growth stage reduced from 12.47 % (0.0098 km 3) to only 1.25 % (0.0005 km 3). From 1998 to 2004, Hongjiannao Lake experienced continuous losing conditions (discharge from the lake to groundwater), with a variable exchange volume of up to-0.01582 km 3 in 1999. Through geographical detector analysis, it was found that temperature was the dominant factor from 1988 to 1997, while human factors were the dominant factors from1998 to 2015.

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“…We used the Deeply Clear Water Extraction Index (DCWEI) (Yue and Liu, 2019b) to extract the lake area, DCWDI can be expressed as:…”

Section: Lake Area Extraction: Lamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Water balance and influence mechanism analysis: a case study of Hongjiannao Lake, China

Yue

Liu

2021

Environ Monit Assess

Self Cite

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“…However, selecting an appropriate threshold that yields the highest possible accuracy is a time consuming and challenging task because threshold values vary with location and image quality [27,28]. Therefore, great effort has been made to select optimal water-related indices with imagery from various sensors, thresholding rules (including sensitivity), and modifying or developing spectral indices to remove noise [4][5][6][27][28][29]. Even though open water has distinctive spectral characteristics compared to other land cover types, it is similar to shadow and built-up areas [27,30], creating challenges for accurate mapping of open surface water from optical multispectral images.…”

Section: Introductionmentioning

confidence: 99%

Automatic Extraction of Open Water Using Imagery of Landsat Series

Zhang

Shi

et al. 2020

Water

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Open surface freshwater is an important resource for terrestrial ecosystems. However, climate change, seasonal precipitation cycling, and anthropogenic activities add high variability to its availability. Thus, timely and accurate mapping of open surface water is necessary. In this study, a methodology based on the concept of spatial autocorrelation was developed for automatic water extraction from Landsat series images using Taihu Lake in south-eastern China as an example. The results show that this method has great potential to extract continuous open surface water automatically, even when the water surface is covered by floating vegetation or algal blooms. The results also indicate that the second shortwave-infrared band (SWIR2) band performs best for water extraction when water is turbid or covered by surficial vegetation. Near-infrared band (NIR), first shortwave-infrared band (SWIR1), and SWIR2 have consistent extraction success when the water surface is not covered by vegetation. Low filter image processing greatly overestimated extracted water bodies, and cloud and image salt and pepper issues have a large impact on water extraction using the methods developed in this study.

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“…Water masking First, we separate the water bodies using two popular water indices. Xu [22] developed a modified normalized difference water index (MNDWI), and Yue and Liu [23] developed a deeply clear waterbody delineation index (DCWDI).…”

Section: 32mentioning

confidence: 99%

Development of Global Impervious Surface Area Index for Automatic Spatiotemporal Urban Mapping

Javed¹,

Shao²,

Ara³

et al. 2022

Preprint

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Impervious surface area (ISA) is a crucial indicator for quantitative urban studies. It is also important for land use land cover classification, groundwater recharge, sustainable development, urban heat island effects, and more. Spectral ISA mapping suffers from mixed pixel problems, especially with bare soil. This study aims to develop an ISA index for spatiotemporal urban mapping from common multispectral bands by reducing soil signature better than in previous studies. This study proposed a global impervious surface area index (GISAI) enhancing ISA mapping accuracy using a temporal parameter of the remote sensing (RS) dataset. Bare soil spectral reflectance shows more fluctuation than urban ISA. Therefore, the study uses minimum composites of earlier urban indices to compile minimum soil signature. It is later improved by removing water, increasing the contrast between bare soil and urban ISA and reducing bright bare soil area. This study maps the ISA of all 12 megacities using the annual RS image collection from 2021. GISAI reduced the bare soil signature and achieved an overall accuracy of 87.29%, F1-score of 0.84, and Kappa coefficient of 0.75. However, it has some limitations with grey bare soil and barren hilly areas. By limiting bare soil signature, GISAI broadens the scope of inter-urban studies globally and lengthens potential urban time-series analysis using common multispectral bands.

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Method for delineating open water bodies based on the deeply clear waterbody delineation index

Cited by 7 publications

References 38 publications

Water balance and influence mechanism analysis: a case study of Hongjiannao Lake, China

Water balance and influence mechanism analysis: a case study of Hongjiannao Lake, China

Automatic Extraction of Open Water Using Imagery of Landsat Series

Development of Global Impervious Surface Area Index for Automatic Spatiotemporal Urban Mapping

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