The NASA Global Flood Mapping System

Policelli, F.; Slayback, D. A.; Brakenridge, Bob; Nigro, Joseph; Hubbard, Alfred; Zaitchik, B. F.; Carroll, M.; Jung, Hahn Chul

doi:10.1007/978-3-319-43744-6_3

Cited by 36 publications

(23 citation statements)

References 10 publications

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“…Water is identified using the 250 m spatial resolution red (MODIS Band 1) and near infrared bands (MODIS band 2) data. A band threshold classification approach uses the low water reflectivity in band 2 as ratioed against band 1 to identify "water," and then further removes cloud shadow false positives by requiring the "water" threshold to be met in three of the six images available from Terra and Aqua for a three-day period (Policelli et al, 2016). The automated system then rolls forward in time, updating daily.…”

Section: Evaluation and Application Examplesmentioning

confidence: 99%

Estimating Floodwater Depths from Flood Inundation Maps and Topography

Cohen

Brakenridge

Kettner

et al. 2017

J American Water Resour Assoc

106

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Information on flood inundation extent is important for understanding societal exposure, water storage volumes, flood wave attenuation, future flood hazard, and other variables. A number of organizations now provide flood inundation maps based on satellite remote sensing. These data products can efficiently and accurately provide the areal extent of a flood event, but do not provide floodwater depth, an important attribute for first responders and damage assessment. Here we present a new methodology and a GIS‐based tool, the Floodwater Depth Estimation Tool (FwDET), for estimating floodwater depth based solely on an inundation map and a digital elevation model (DEM). We compare the FwDET results against water depth maps derived from hydraulic simulation of two flood events, a large‐scale event for which we use medium resolution input layer (10 m) and a small‐scale event for which we use a high‐resolution (LiDAR; 1 m) input. Further testing is performed for two inundation maps with a number of challenging features that include a narrow valley, a large reservoir, and an urban setting. The results show FwDET can accurately calculate floodwater depth for diverse flooding scenarios but also leads to considerable bias in locations where the inundation extent does not align well with the DEM. In these locations, manual adjustment or higher spatial resolution input is required.

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Section: Evaluation and Application Examplesmentioning

confidence: 99%

Estimating Floodwater Depths from Flood Inundation Maps and Topography

Cohen

Brakenridge

Kettner

et al. 2017

J American Water Resour Assoc

106

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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 using the rapid-response Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product is effective due to the product's global coverage and accuracy (Brakenridge and Anderson 2006). One of noticeable studies, the MODIS Water Product (MWP) was created using two bands, red and near-infrared (NIR), with a spatial resolution of 250 m (Policelli and Slayback 2017). When validating with 53 flood events in 2013 and 2014, MWP captured 44% of the events from good (which means about half of the water-covered area is detected) to almost perfect (which means just about all of the water-covered area is detected; Nigro et al 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Despite the importance of cloud removal, current studies attempting to remove clouds from flood maps are still in an early stage. To the best of our knowledge, the only effort to remove clouds from flood maps came from the same group (Policelli and Slayback 2017) when they created composite flood maps for 2, 3, and 7 days. While this approach was appropriate in some cases, it increased the product's latency (Nigro et al 2014), which might fail to capture events that occur between the composite days.…”

Section: Introductionmentioning

confidence: 99%

Improving Hydrologic Modeling Using Cloud-Free MODIS Flood Maps

Tran

Nguyen

Ombadi

et al. 2019

Journal of Hydrometeorology

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Flood mapping from satellites provides large-scale observations of flood events, but cloud obstruction in satellite optical sensors limits its practical usability. In this study, we implemented the Variational Interpolation (VI) algorithm to remove clouds from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) Snow-Covered Area (SCA) products. The VI algorithm estimated states of cloud-hindered pixels by constructing three-dimensional space–time surfaces based on assumptions of snow persistence. The resulting cloud-free flood maps, while maintaining the temporal resolution of the original MODIS product, showed an improvement of nearly 70% in average probability of detection (POD) (from 0.29 to 0.49) when validated with flood maps derived from Landsat-8 imagery. The second part of this study utilized the cloud-free flood maps for calibration of a hydrologic model to improve simulation of flood inundation maps. The results demonstrated the utility of the cloud-free maps, as simulated inundation maps had average POD, false alarm ratio (FAR), and Hanssen–Kuipers (HK) skill score of 0.87, 0.49, and 0.84, respectively, compared to POD, FAR, and HK of 0.70, 0.61, and 0.67 when original maps were used for calibration.

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The NASA Global Flood Mapping System

Cited by 36 publications

References 10 publications

Estimating Floodwater Depths from Flood Inundation Maps and Topography

Estimating Floodwater Depths from Flood Inundation Maps and Topography

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

Improving Hydrologic Modeling Using Cloud-Free MODIS Flood Maps

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