A Comparison of Land Surface Water Mapping Using the Normalized Difference Water Index from TM, ETM+ and ALI

Li, Wenbo; Du, Zhiqiang; Ling, Feng; Zhou, Dongbo; Wang, Hailei; Gui, Yuanmiao; Sun, Bingyu; Zhang, Xiaoming

doi:10.3390/rs5115530

Cited by 343 publications

(244 citation statements)

References 49 publications

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“…The NDVI enables rapid and simple extraction of water bodies, and is thus considered to be one of the most successful water index methods [19,20]. The NDWI and MNDWI have been widely used to extract water body information in a highly targeted manner [21][22][23][24]. In some studies, NDWI has been recommended for extracting water body information; however, NDWI may be unreliable for shallow-water areas with a varied boundary [4,25].…”

Section: Introductionmentioning

confidence: 99%

“…In some studies, NDWI has been recommended for extracting water body information; however, NDWI may be unreliable for shallow-water areas with a varied boundary [4,25]. Li et al [24] found that MNDWI is more effective than NDWI at distinguishing water bodies, and this may be attributed to the ability of MNDWI to distinguish built-up land areas from water bodies effectively [18]. Tonle Sap Lake is a shallow-water lake that is inundated by periodic flooding and contains aquatic plants as well as shoreside plants.…”

Section: Introductionmentioning

confidence: 99%

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Changes in the Lake Area of Tonle Sap: Possible Linkage to Runoff Alterations in the Lancang River?

Luo

et al. 2018

Remote Sensing

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Tonle Sap Lake is the largest freshwater lake in Southeast Asia. Water development infrastructures are increasingly being constructed in the Lancang-Mekong River Basin, which is a major concern considering its potential impact on Tonle Sap Lake. This study aimed to investigate variations in the area of the lake and discuss their possible linkage to runoff alterations in the Lancang River (Upper Mekong) by comparing runoff at the Yunjinghong hydrological station before and after significant changes in runoff trends that occurred in 2008. First, four commonly used water body extraction methods (MNDWI, NDWI, NDVI, and EVI) were compared and MNDWI was found to provide a better and more stable performance. Based on MOD09A1 data, MNDWI was used to extract the water area of the lake from 2000 to 2014, and characteristics of variations in the area before and after 2008 were analyzed. The water area of Tonle Sap Lake displayed an overall decreasing trend, and specifically decreased by 8.3% during the flood season and by 1.5% on average during the dry season after 2008. Seasonal variations in the water area of Tonle Sap Lake were dominantly influenced by runoff from the Mekong River. Compared with the period 2000-2007, runoff at Yunjinghong station were increased during the dry season (20.74%) and decreased during the flood season (34.25%) between 2008 and 2014. Changes in upstream runoff contributed to runoff at the Stung Treng station in the lower Mekong River by 6.17% (dry season) and −2.41% (flood season). Evidently, the operation of dams in the Lancang River does not primarily account for the area decrease of Tonle Sap Lake during the flood season. In contrast, runoff increase during the dry season mitigates the area decrease of Tonle Sap Lake to a certain extent.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Changes in the Lake Area of Tonle Sap: Possible Linkage to Runoff Alterations in the Lancang River?

Luo

et al. 2018

Remote Sensing

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“…MVWR indicates more green cover at positive values, and more water cover at negative values. We selected MNDWI instead of the LSWI to calculate the MVWR because it has been shown in a number of studies that MNDWI is a more reliable index compared to other water indices including LSWI [41,42,46].…”

Section: Spectral Indicesmentioning

confidence: 99%

Remotely-Sensed Early Warning Signals of a Critical Transition in a Wetland Ecosystem

et al. 2017

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Abstract:The response of an ecosystem to external drivers may not always be gradual and reversible. Discontinuous and sometimes irreversible changes, called 'regime shifts' or 'critical transitions', can occur. The likelihood of such shifts is expected to increase for a variety of ecosystems, and it is difficult to predict how close an ecosystem is to a critical transition. Recent modelling studies identified indicators of impending regime shifts that can be used to provide early warning signals of a critical transition. The identification of such transitions crucially depends on the ability to monitor key ecosystem variables, and their success may be limited by lack of appropriate data. Moreover, empirical demonstrations of the actual functioning of these indicators in real-world ecosystems are rare. This paper presents the first study which uses remote sensing data to identify a critical transition in a wetland ecosystem. In this study, we argue that a time series of remote sensing data can help to characterize and determine the timing of a critical transition. This can enhance our abilities to detect and anticipate them. We explored the potentials of remotely sensed vegetation (NDVI), water (MNDWI), and vegetation-water (VWR) indices, obtained from time series of MODIS satellite images to characterize the stability of a wetland ecosystem, Dorge Sangi, near the lake Urmia, Iran, that experienced a regime shift recently. In addition, as a control case, we applied the same methods to another wetland ecosystem in Lake Arpi, Armenia which did not experience a regime shift. We propose a new composite index (MVWR) based on combining vegetation and water indices, which can improve the ability to anticipate a critical transition in a wetland ecosystem. Our results revealed that MVWR in combination with autocorrelation at-lag-1 could successfully provide early warning signals for a critical transition in a wetland ecosystem, and showed a significantly improved performance compared to either vegetation (NDVI) or water (MNDWI) indices alone.

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“…We sourced 1291 Landsat 8 OLI images that were acquired over the Volta basin (Landsat paths 192 to 197; rows 050 to 056) between 1 May 2013 and 31 October 2015, corresponding to the earliest complete month of data available in GEE and the temporal limit of GSW data. We used imagery that was pre-processed by USGS to Surface Reflectance and for each image, computed the Normalised Difference Water Index (NDWI) [40], Modified NDWI [27] using band 6 (referred to here as MNDWI1), and using band 7 (MNDWI2), indices that are commonly applied in peer-reviewed literature for surface water mapping [14,[41][42][43]. The relevant bands in Landsat 8 OLI imagery used to compute the three water indices are:…”

Section: Landsat-derived Surface Water Mapsmentioning

confidence: 99%

Big Data and Multiple Methods for Mapping Small Reservoirs: Comparing Accuracies for Applications in Agricultural Landscapes

et al. 2017

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Abstract:Whether or not reservoirs contain water throughout the dry season is critical to avoiding late season crop failure in seasonally-arid agricultural landscapes. Locations, volumes, and temporal dynamics, particularly of small (<1 Mm 3 ) reservoirs are poorly documented globally, thus making it difficult to identify geographic and intra-annual gaps in reservoir water availability. Yet, small reservoirs are the most vulnerable to drying out and often service the poorest of farmers. Using the transboundary Volta River Basin (~413,000 sq km) in West Africa as a case study, we present a novel method to map reservoirs and quantify the uncertainty of Landsat derived reservoir area estimates, which can be readily applied anywhere in the globe. We applied our method to compare the accuracy of reservoir areas that are derived from the Global Surface Water Monthly Water History (GSW) dataset to those that are derived when surface water is classified on Landsat 8 OLI imagery using the Normalised Difference Water Index (NDWI), Modified NDWI with band 6 (MNDWI1), and Modified NDWI with band 7 (MNDWI2). We quantified how the areal accuracies of reservoir size estimates vary with the water classification method, reservoir properties, and environmental context, and assessed the options and limitations of using uncertain reservoir area estimates to monitor reservoir dynamics in an agricultural context. Results show that reservoir area estimates that are derived from the GSW data are 19% less accurate for our study site than MNDWI1 derived estimates, for a sample of 272 reservoir extents of 0.09 to 72 ha. The accuracy of Landsat-derived estimates improves with reservoir size and perimeter-area ratio, while accuracy may decline as surface vegetation increases. We show that GSW derived reservoir area estimates can provide an upper limit for current reservoir capacity and seasonal dynamics of larger reservoirs. Data gaps and uncertainties make GSW derived reservoir extents unsuitable for monitoring reservoirs that are smaller than 5.1 ha (holding~49,759 m 3 ), which constitute 674 (56%) reservoirs in the Volta basin, or monitoring seasonal fluctuations of most small reservoirs, limiting its utility for agricultural planning. This study is one of the first to test the utility and limitations of the newly available GSW dataset and provides guidance on the conditions under which this, and other Landsat-based surface water maps, can be reliably used to monitor reservoir resources.

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A Comparison of Land Surface Water Mapping Using the Normalized Difference Water Index from TM, ETM+ and ALI

Cited by 343 publications

References 49 publications

Changes in the Lake Area of Tonle Sap: Possible Linkage to Runoff Alterations in the Lancang River?

Changes in the Lake Area of Tonle Sap: Possible Linkage to Runoff Alterations in the Lancang River?

Remotely-Sensed Early Warning Signals of a Critical Transition in a Wetland Ecosystem

Big Data and Multiple Methods for Mapping Small Reservoirs: Comparing Accuracies for Applications in Agricultural Landscapes

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