Flood detection and monitoring with the Autonomous Sciencecraft Experiment onboard EO-1

Ip, Felipe; Dohm, J. M.; Baker, Victor R.; Doggett, T.; Davies, A. G.; Castaño, Rebecca; Chien, Steve; Cichy, B.; Greeley, R.; Sherwood, Robert; Tran, Daniel; Rabideau, Gregg

doi:10.1016/j.rse.2005.12.018

Cited by 76 publications

(29 citation statements)

References 5 publications

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“…Satellite remote sensing allows for timely investigation of areas of large regional extent and provides frequent imaging of the region of interest (Felipe et al, 2006). Until recently, near real-time flood detection was not possible, but with sensors such as Hyperion on board the EO-1 satellite this has been vastly improved (Felipe et al, 2006).…”

Section: Applications Of Hyperspectral Remote Sensing In Water Resourcesmentioning

confidence: 99%

“…Until recently, near real-time flood detection was not possible, but with sensors such as Hyperion on board the EO-1 satellite this has been vastly improved (Felipe et al, 2006). According to research conducted by Felipe et al (2006) automated spacecraft technology reduced the time to detect and react to flood events to a few hours. Advances in remote sensing, have resulted in the investigation of early warning systems with potential global applications.…”

Section: Applications Of Hyperspectral Remote Sensing In Water Resourcesmentioning

confidence: 99%

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A review of hyperspectral remote sensing and its application in vegetation and water resource studies

Govender¹,

Chetty²,

Bulcock³

2009

WSA

306

222

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Multispectral imagery has been used as the data source for water and land observational remote sensing from airborne and satellite systems since the early 1960s. Over the past two decades, advances in sensor technology have made it possible for the collection of several hundred spectral bands. This is commonly referred to as hyperspectral imagery. This review details the differences between multispectral and hyperspectral data; spatial and spectral resolutions and focuses on the application of hyperspectral imagery in water resource studies and, in particular the classification and mapping of land uses and vegetation.

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Section: Applications Of Hyperspectral Remote Sensing In Water Resourcesmentioning

confidence: 99%

Section: Applications Of Hyperspectral Remote Sensing In Water Resourcesmentioning

confidence: 99%

A review of hyperspectral remote sensing and its application in vegetation and water resource studies

Govender¹,

Chetty²,

Bulcock³

2009

WSA

306

222

View full text Add to dashboard Cite

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“…The list of optical river mapping applications can be extended if one includes work based on coarse resolution satellite imagery, measurement of parameters outside the low flow channel or topics receiving relatively little research. Notable applications that fit these more general criteria include forecasting and detection of ice breakup on large rivers (Gatto, 1990;Pavelsky et al, 2004;Morse and Hicks, 2005), flood detection and monitoring (Barton and Bathols, 1989;Kishi et al, 2001;Brakenridge et al, 2005;Ip et al, 2006), detection of levee slides (Hossain et al, 2006), estimating erosion and deposition (Brasington et al, 2003;, mapping general habitat types such as standing water and forest (Novo et al, 1997), measuring suspended chlorophyll (Karaska, 2004) and mapping derived variables such as stream power (Jordan and Fonstad, 2005).…”

Section: Applicationsmentioning

confidence: 99%

Optical remote mapping of rivers at sub‐meter resolutions and watershed extents

Marcus

Fonstad

2007

Earth Surf Processes Landf

250

198

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At watershed extents, our understanding of river form, process and function is largely based on locally intensive mapping of river reaches, or on spatially extensive but low density data scattered throughout a watershed (e.g. cross sections). The net effect has been to characterize streams as discontinuous systems. Recent advances in optical remote sensing of rivers indicate that it should now be possible to generate accurate and continuous maps of in‐stream habitats, depths, algae, wood, stream power and other features at sub‐meter resolutions across entire watersheds so long as the water is clear and the aerial view is unobstructed. Such maps would transform river science and management by providing improved data, better models and explanation, and enhanced applications. Obstacles to achieving this vision include variations in optics associated with shadows, water clarity, variable substrates and target–sun angle geometry. Logistical obstacles are primarily due to the reliance of existing ground validation procedures on time‐of‐flight field measurements, which are impossible to accomplish at watershed extents, particularly in large and difficult to access river basins. Philosophical issues must also be addressed that relate to the expectations around accuracy assessment, the need for and utility of physically based models to evaluate remote sensing results and the ethics of revealing information about river resources at fine spatial resolutions. Despite these obstacles and issues, catchment extent remote river mapping is now feasible, as is demonstrated by a proof‐of‐concept example for the Nueces River, Texas, and examples of how different image types (radar, lidar, thermal) could be merged with optical imagery. The greatest obstacle to development and implementation of more remote sensing, catchment scale ‘river observatories’ is the absence of broadly based funding initiatives to support collaborative research by multiple investigators in different river settings. Copyright © 2007 John Wiley & Sons, Ltd.

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“…We are particularly interested in developing these analytics for the detection of water and constructing flood maps to complement the onboard EO-1 flood detection system [22]. This analytic is applied to both ALI and Hyperion Level 1G data and classifies each pixel in an image as a member of a given class in a provided training set.…”

Section: Supervised Spectral Classifiermentioning

confidence: 99%

The Matsu Wheel: a reanalysis framework for Earth satellite imagery in data commons

Patterson

Anderson

Bennett³

et al. 2017

Int J Data Sci Anal

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Project Matsu is a collaboration between the Open Commons Consortium and NASA focused on developing open source technology for the cloud-based processing of Earth satellite imagery and for detecting fires and floods to help support natural disaster detection and relief. We describe a framework for efficient analysis and reanalysis of large amounts of data called the Matsu "Wheel" and the analytics used to process hyperspectral data produced daily by NASA's Earth Observing-1 (EO-1) satellite. The wheel is designed to be able to support scanning queries using cloud computing applications, such as Hadoop and Accumulo. A scanning query processes all, or most, of the data in a database or data repository. In contrast, standard queries typically process a relatively small percentage of the data. The wheel is a framework in which multiple scanning queries are grouped together and processed in turn, over chunks of data from the database or repository. Over time, the framework brings all data to each group of scanning queries. With this approach, contention and the overall time to process all scanning queries can be reduced. We describe our Wheel analytics, including an anomaly detector for rare spectral signatures or anomalies in hyperspectral data and a land cover classifier that can be This paper is an extension version of the BDS2016 accepted paper "The Matsu Wheel: A Cloud-based Framework for the Efficient Analysis and Reanalysis of Earth Satellite Imagery" [1].

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Flood detection and monitoring with the Autonomous Sciencecraft Experiment onboard EO-1

Cited by 76 publications

References 5 publications

A review of hyperspectral remote sensing and its application in vegetation and water resource studies

A review of hyperspectral remote sensing and its application in vegetation and water resource studies

Optical remote mapping of rivers at sub‐meter resolutions and watershed extents

The Matsu Wheel: a reanalysis framework for Earth satellite imagery in data commons

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