Flood mapping in the lower Mekong River Basin using daily MODIS observations

Fayne, Jessica V.; Bolten, J. D.; Doyle, Colin; Fuhrmann, Sven; Rice, Matthew; Houser, Paul R.; Lakshmi, V.

doi:10.1080/01431161.2017.1285503

Cited by 42 publications

(28 citation statements)

References 24 publications

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“…Further, high spatial resolution information can increase confidence in associated decision-making [17][18][19][20][21][22][23][24][25]. Broad spatial resolution can be downscaled to achieved finer spatial resolution, if access to high temporal resolution data is a pertinent factor [8,[26][27][28]. In addition, broad spatial resolution increases the likelihood of mixed pixels (e.g., pixels containing both inundated and dry areas), which can confuse classification attempts [4,9,29], although methods exist to reduce this issue [26,30,31].…”

Section: Introductionmentioning

confidence: 99%

Automated Inundation Mapping Over Large Areas Using Landsat Data and Google Earth Engine

Inman

Lyons

2020

Remote Sensing

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Accurate inundation maps for flooded wetlands and rivers are a critical resource for their management and conservation. In this paper, we automate a method (thresholding of the short-wave infrared band) for classifying peak inundation in the Okavango Delta, northern Botswana, using Landsat imagery and Google Earth Engine. Inundation classification in the Okavango Delta is complex owing to the spectral overlap between inundated areas covered with aquatic vegetation and dryland vegetation classes on satellite imagery, and classifications have predominately been implemented on broad spatial resolution imagery. We present the longest time series to date (1990–2019) of inundation maps for the peak flood season at a high spatial resolution (30 m) for the Okavango Delta. We validated the maps using image-based and in situ data accuracy assessments, with overall accuracy ranging from 91.5% to 98.1%. Use of Landsat imagery resulted in consistently lower (on average, 692 km2) estimates of inundation extent than previous studies that used Moderate Resolution Imaging Spectroradiometer (MODIS) and National Oceanic and Atmospheric Administration Advanced Very-High-Resolution Radiometer (NOAA AVHRR) imagery, likely owing to the increased number of mixed pixels that occur when using broad spatial resolution imagery, which can lead to overestimations of the size of inundated areas. We provide the inundation maps and Google Earth Engine code for public use. This classification method can likely be adapted for inundation mapping in other regions.

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Section: Introductionmentioning

confidence: 99%

Automated Inundation Mapping Over Large Areas Using Landsat Data and Google Earth Engine

Inman

Lyons

2020

Remote Sensing

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“…Previous studies have shown that the diverse water index methods have various characteristics and differ in regard to their ability to identify water bodies accurately. 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].…”

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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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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“…Some data, such as those provided through NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) system, can commonly be made available within 3 h of overpass, making it suitable for NRT applications (Davies et al, 2015). Examples of such initiatives include NASA's NRT Flood Mapping Fayne et al, 2017;Policelli et al, 2017), UMD's Global Flood Monitoring System (GFMS, Wu et al, 2014), and the Dartmouth Flood Observatory (DFO, Brakenridge and Anderson, 2006).…”

Section: The Value Of Applied Earth Observations In Emergency Managementmentioning

confidence: 99%

The Value of Near Real-Time Earth Observations for Improved Flood Disaster Response

Oddo

Bolten

2019

Front. Environ. Sci.

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Information is a critical resource in disaster response scenarios. Data regarding the geographic extent, severity, and socioeconomic impacts of a disaster event can help guide emergency responders and relief operations, particularly when delivered within hours of data acquisition. Information from remote observations provides a valuable tool for assessing conditions "on the ground" more quickly and efficiently. Here, we evaluate the social value of a near real-time flood impact system using a disaster response case study, and quantify the Value of Information (VOI) of satellite-based observations for rapid response using a hypothetical flooding disaster in Bangkok, Thailand. MODIS imagery from NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) system is used to produce operational estimates of inundation depths and economic damages. These rapid Earth observations are coupled with a decision-analytical model to inform decisions on emergency vehicle routing. Emergency response times from vehicles routed using flood damage data are compared with baseline routes without the benefit of advance information on road conditions. Our results illustrate how the application of near real-time Earth observations can improve the response time and reduce potential encounters with flood hazards when compared with baseline routing strategies. Results indicate a potential significant economic benefit (i.e., millions of dollars) from applying near real-time Earth observations for improved flood disaster response and management.

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Flood mapping in the lower Mekong River Basin using daily MODIS observations

Cited by 42 publications

References 24 publications

Automated Inundation Mapping Over Large Areas Using Landsat Data and Google Earth Engine

Automated Inundation Mapping Over Large Areas Using Landsat Data and Google Earth Engine

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

The Value of Near Real-Time Earth Observations for Improved Flood Disaster Response

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