Using Landsat 7 TM data acquired days after a flood event to delineate the maximum flood extent on a coastal floodplain

Wang, Y.

doi:10.1080/0143116031000150022

Cited by 78 publications

(46 citation statements)

References 15 publications

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“…The fact that only 0.075 km 2 of woodland was detected as flooded in this study supported this point of view. This is in accordance with Wang's study [10], which found that scattered "holes" or "islands" exited in the submerged forest areas. To tackle this issue, radar data should be incorporated and future study should focus on the fusion of multi-sensor (optical and radar) data to increase flood mapping accuracy.…”

Section: Discussionsupporting

confidence: 80%

“…Although optical sensors are unable to penetrate thick clouds which is a major drawback in flood monitoring, the images acquired under cloud free conditions can still be utilized to extract flooded areas with high accuracy [10][11][12]. Besides, optical remote sensing can provide true color images which are much easier for visual interpretation than radar data.…”

Section: Introductionmentioning

confidence: 99%

“…Due to its synoptic view and continuous coverage of flooding events, remote sensing has been recognized as a powerful and effective tool to provide inundation maps in near real time according to many researches [1][2][3][4][5][6][7][8][9][10][11]. Generally, remotely sensed data used for flood monitoring are mainly collected from radar and optical satellites.…”

Section: Introductionmentioning

confidence: 99%

“…Widely used optical remote sensing data are mainly from Landsat series, i.e., Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), SPOT (Systeme Probatoire d'Observation de la Terre) series (i.e., SPOT-4, SPOT-5), and Ikonos due to their ideal combination of spatial and temporal resolutions, easy access, and ease in data processing and analysis [4]. Wang et al [10] used medium resolution (30 m) TM images to delineate the maximum flood event caused by Hurricane Floyd in North Carolina. Animi [11] proposed a model based on Artificial Neural Network to generate a floodplain map using high-resolution (1 m) Ikonos imagery and digital elevation model (DEM).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Flood Mapping Based on Multiple Endmember Spectral Mixture Analysis and Random Forest Classifier—The Case of Yuyao, China

et al. 2015

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Remote sensing is recognized as a valuable tool for flood mapping due to its synoptic view and continuous coverage of the flooding event. This paper proposed a hybrid approach based on multiple endmember spectral analysis (MESMA) and Random Forest classifier to extract inundated areas in Yuyao City in China using medium resolution optical imagery. MESMA was adopted to tackle the mixing pixel problem induced by medium resolution data. Specifically, 35 optimal endmembers were selected to construct a total of 3111 models in the MESMA procedure to derive accurate fraction information. A multi-dimensional feature space was constructed including the normalized difference water index (NDWI), topographical parameters of height, slope, and aspect together with the fraction maps. A Random Forest classifier consisting of 200 decision trees was adopted to classify the post-flood image based on the above multi-features. Experimental results indicated that the proposed method can extract the inundated areas precisely with a classification accuracy of 94% and a Kappa index of 0.88. The inclusion of fraction information can help improve the mapping accuracy with an increase of 2.5%. Moreover, the proposed method also outperformed the maximum likelihood classifier and the NDWI thresholding method. This research provided a useful reference for flood mapping using medium resolution optical remote sensing imagery. OPEN ACCESSRemote Sens. 2015, 7 12540

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Section: Discussionsupporting

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Flood Mapping Based on Multiple Endmember Spectral Mixture Analysis and Random Forest Classifier—The Case of Yuyao, China

et al. 2015

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“…When banks are overtopped, water spreads over flood plain and generally causes problem for inhabitants, crops and vegetation. During extreme flood event, it is important to determine quickly the extent of flooding and land use under water (Wang 2004). Flood map can be applied to develop comprehensive relief effort immediately after flooding.…”

Section: Introductionmentioning

confidence: 99%

Exposure, hazard and risk mapping during a flood event using open source geospatial technology

Aggarwal¹

2015

Geomatics, Natural Hazards and Risk

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After a flood event there is a need to delineate the hazard footprint as quickly as possible in order to assess the magnitude of losses and to plan for the relief operations. Delineation of such hazard footprint is generally hindered by the lack of geospatial data, technology and related software packages. This paper demonstrates the use of open source data and software packages which can be used to implement most recent technology available for flood hazard footprint delineation. This study utilizes open source software packages and web applications like Geographic Resource Analysis Support System, Quantum geographic information system and Google Earth to implement a complete process of hazard mapping using remotely sensed data which include preprocessing, mapping (both hazard and exposure) and accuracy assessment. In this study, Brisbane flood event of 2011 has been taken as a case study. For built-up extraction, the Landsat 7-band image has been transformed to a stack of 3-band image using vegetation, water and built-up indices. It has been observed by scattergram analysis that these transformations make vegetation, water and builtup classes more separable. Built-up area has been delineated using supervised maximum likelihood classification on the new 3-band image. For flood hazard mapping, thresholding of near-infrared band has been utilized along with the assistance of mid-infrared band to discriminate water from built-up classes. After delineating both exposure and hazard map, final risk map due to flood event has been generated to assess the urban exposure under the flood hazard impact.

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Detection of Hazard‐Damaged Bridges Using Multitemporal High‐Resolution SAR Imagery

Inoue

Yamazaki

2022

Remote Sensing of Water‐Related Hazards

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Using Landsat 7 TM data acquired days after a flood event to delineate the maximum flood extent on a coastal floodplain

Cited by 78 publications

References 15 publications

Flood Mapping Based on Multiple Endmember Spectral Mixture Analysis and Random Forest Classifier—The Case of Yuyao, China

Flood Mapping Based on Multiple Endmember Spectral Mixture Analysis and Random Forest Classifier—The Case of Yuyao, China

Exposure, hazard and risk mapping during a flood event using open source geospatial technology

Detection of Hazard‐Damaged Bridges Using Multitemporal High‐Resolution SAR Imagery

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