Normalized Difference Flood Index for rapid flood mapping: Taking advantage of EO big data

Cian, Fabio; Marconcini, Mattia; Ceccato, Pietro

doi:10.1016/j.rse.2018.03.006

Cited by 171 publications

(146 citation statements)

References 69 publications

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“…We consider here the F 1 score (i.e., the harmonic mean of precision and recall measures). For the sake of comparison, we also report results from two existing methods: AP-based change detection [8] that also relies on spatial attributes extracted from morphological hierarchies, and the Normalized Difference Flood Index (NDFI) [14] thresholding approach. Let us note that the latter is more successful with long time series.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Min-Trees over Sentinel-1 Time Series for Flood Detection

Tuna¹,

Merciol²,

Lefèvre³

2019

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)

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Section: Resultsmentioning

confidence: 99%

“…Difference between these maps indicate where the floods occur. Nevertheless, we observe that some artifacts [14] can still occur due to double bounce effect, backscatter similarity of dry soil, etc. In order to overcome these errors, we post-process the binary change detection map with small area filtering (e.g.…”

Section: B Spatial Detectionmentioning

confidence: 91%

Analysis of Min-Trees over Sentinel-1 Time Series for Flood Detection

Tuna¹,

Merciol²,

Lefèvre³

2019

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)

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“…BEACON uses a methodology for flood mapping based on multi-temporal SAR data analysis and the computation of two indices, i.e. the Normalized Difference Flood Index (NDFI) for highlighting flooded areas, and the Normalized Difference Flood in Vegetated areas Index (NDFVI) for highlighting shallow water in short vegetation [15,16]. According to the method, two SAR multi-temporal layer stacks are created.…”

Section: Flood Damage Spatial Distributionmentioning

confidence: 99%

“…After the computation of the two indices, a threshold of 0.70 for NDFI and 0.75 for NDFVI is applied to extract flooded areas [15]. In BEACON, the sigma nought for VV polarization is used for both NDFI and NDFVI.…”

Section: Flood Damage Spatial Distributionmentioning

confidence: 99%

Redefining Agricultural Insurance Services Using Earth Observation Data. The Case of Beacon Project

Lekakis¹,

Kotsopoulos²,

Mygdakos³

et al. 2020

IFIP Advances in Information and Communication Technology

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BEACON is a market-led project that couples cutting edge Earth Observation (EO) technology with weather intelligence and blockchain to deliver a toolbox for the Agricultural Insurance (AgI) sector with timely cost-efficient and actionable insights for the agri-insurance industry. BEACON enables insurance companies to exploit the untapped market potential of AgI, while contributing to the redefinition of existing AgI products and services. The Damage Assessment Calculator of BEACON employs remote sensing techniques in order to improve the quality and cost-effectiveness of agri-insurance by: i) increasing the objectivity of the experts field inspections; ii) reducing the cost of field visits and iii) increasing farmers' confidence in the estimation results, given the significant economic impact of erroneous estimation. This paper provides an analysis of different type of EO data and remote sensing techniques implemented in the operational workflow of BEACON that can be used by AgI companies to provide safe and reliable results on storms, floods, wildfires and droughts damage on crops.

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“…Another type of image data involves multispectral optical surface reflectance imagery which contains consistent and distinct spectral information associated with floodwaters [2,[12][13][14][15]. Li et al [12] performed the discrete particle swarm optimization (DPSO) for sub-pixel flood mapping using satellite multispectral reflectance imagery, the Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) data.…”

mentioning

confidence: 99%

Patch Similarity Convolutional Neural Network for Urban Flood Extent Mapping Using Bi-Temporal Satellite Multispectral Imagery

et al. 2019

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Urban flooding is a major natural disaster that poses a serious threat to the urban environment. It is highly demanded that the flood extent can be mapped in near real-time for disaster rescue and relief missions, reconstruction efforts, and financial loss evaluation. Many efforts have been taken to identify the flooding zones with remote sensing data and image processing techniques. Unfortunately, the near real-time production of accurate flood maps over impacted urban areas has not been well investigated due to three major issues. (1) Satellite imagery with high spatial resolution over urban areas usually has nonhomogeneous background due to different types of objects such as buildings, moving vehicles, and road networks. As such, classical machine learning approaches hardly can model the spatial relationship between sample pixels in the flooding area. (2) Handcrafted features associated with the data are usually required as input for conventional flood mapping models, which may not be able to fully utilize the underlying patterns of a large number of available data. (3) High-resolution optical imagery often has varied pixel digital numbers (DNs) for the same ground objects as a result of highly inconsistent illumination conditions during a flood. Accordingly, traditional methods of flood mapping have major limitations in generalization based on testing data. To address the aforementioned issues in urban flood mapping, we developed a patch similarity convolutional neural network (PSNet) using satellite multispectral surface reflectance imagery before and after flooding with a spatial resolution of 3 meters. We used spectral reflectance instead of raw pixel DNs so that the influence of inconsistent illumination caused by varied weather conditions at the time of data collection can be greatly reduced. Such consistent spectral reflectance data also enhance the generalization capability of the proposed model. Experiments on the high resolution imagery before and after the urban flooding events (i.e., the 2017 Hurricane Harvey and the 2018 Hurricane Florence) showed that the developed PSNet can produce urban flood maps with consistently high precision, recall, F1 score, and overall accuracy compared with baseline classification models including support vector machine, decision tree, random forest, and AdaBoost, which were often poor in either precision or recall. The study paves the way to fuse bi-temporal remote sensing images for near real-time precision damage mapping associated with other types of natural hazards (e.g., wildfires and earthquakes).

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Normalized Difference Flood Index for rapid flood mapping: Taking advantage of EO big data

Cited by 171 publications

References 69 publications

Analysis of Min-Trees over Sentinel-1 Time Series for Flood Detection

Analysis of Min-Trees over Sentinel-1 Time Series for Flood Detection

Redefining Agricultural Insurance Services Using Earth Observation Data. The Case of Beacon Project

Patch Similarity Convolutional Neural Network for Urban Flood Extent Mapping Using Bi-Temporal Satellite Multispectral Imagery

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