Methods, Techniques and Sensors for Precision Flood Monitoring Through Remote Sensing

Refice, Alberto; D’Addabbo, A.; Capolongo, Domenico

doi:10.1007/978-3-319-63959-8_1

Cited by 12 publications

(15 citation statements)

References 111 publications

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“…Floods are known to affect societies, economies, and ecosystems and, at certain times and places, can have devastating impacts [7,8]. They cause casualties and property damage [3] on every inhabited continent and cause severe losses that negatively affect regional socio-economic development, industry, agriculture, and infrastructure [5], as well as cultural heritage [9][10][11]. The reason for flood generation is mainly due to heavy or prolonged rainfall and can have significant impacts on the water load of rivers, streams, and canals.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Sentinel-2 and Geographical Information Systems in Mapping Flooded Regions around the Sesia River, Piedmont, Italy

Petropoulos,

Georgiadi,

Kalogeropoulos

2024

GeoHazards

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Sentinel-2 data are crucial in mapping flooded areas as they provide high spatial and spectral resolution but under cloud-free weather conditions. In the present study, we aimed to devise a method for mapping a flooded area using multispectral Sentinel-2 data from optical sensors and Geographical Information Systems (GISs). As a case study, we selected a site located in Northern Italy that was heavily affected by flooding events on 3 October 2020, when the Sesia River in the Piedmont region was hit by severe weather disturbance, heavy rainfall, and strong winds. The method developed for mapping the flooded area was a thresholding technique through spectral water indices. More specifically, the Normalized Difference Water Index (NDWI) and the Modified Normalized Difference Water Index (MNDWI) were chosen as they are among the most widely used methods with applications across various environments, including urban, agricultural, and natural landscapes. The corresponding flooded area product from the Copernicus Emergency Management Service (EMS) was used to evaluate the flooded area predicted by our method. The results showed that both indices captured the flooded area with a satisfactory level of detail. The NDWI demonstrated a slightly higher accuracy, where it also appeared to be more sensitive to the separation of water from soil and areas with vegetation cover. The study findings may be useful in disaster management linked to flooded-area mapping and area rehabilitation mapping following a flood event, and they can also valuably assist decision and policy making towards a more sustainable environment.

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Section: Introductionmentioning

confidence: 99%

Leveraging Sentinel-2 and Geographical Information Systems in Mapping Flooded Regions around the Sesia River, Piedmont, Italy

Petropoulos,

Georgiadi,

Kalogeropoulos

2024

GeoHazards

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“…Satellite imagery analysis is one of the tools that is able to provide a fast initial mapping of the flooded region [5,14] (sometimes combined with other information sources e.g. from social networks [21]) and to help monitoring the situation after the disaster [16,19]. Remote sensing is also used to map areas with high flood risk [3,24], to forecast floods [2,13] and their evolution [12], and assess post-flood effects [1].…”

Section: Introductionmentioning

confidence: 99%

SpaceNet 8 - The Detection of Flooded Roads and Buildings

Hänsch

Arndt

Lunga

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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The frequency and intensity of natural disasters (i.e. wildfires, storms, floods) has increased over recent decades. Extreme weather can often be linked to climate change, and human population expansion and urbanization have led to a growing risk. In particular floods due to large amounts of rainfall are of rising severity and are causing loss of life, destruction of buildings and infrastructure, erosion of arable land, and environmental hazards around the world. Expanding urbanization along rivers and creeks often includes opening flood plains for building construction and river straightening and dredging speeding up the flow of water. In a flood event, rapid response is essential which requires knowledge which buildings are susceptible to flooding and which roads are still accessible. To this aim, SpaceNet 8 is the first remote sensing machine learning training dataset combining building footprint detection, road network extraction, and flood detection covering 850km 2 , including 32k buildings and 1,300km roads of which 13% and 15% are flooded, respectively.

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“…Flood area mapping is one of the most important applications of the satellite imagery in natural hazard monitoring [46][47][48][49]. Recently, much research has been done for flood mapping based on multispectral optical dataset and SAR satellite imagery.…”

Section: Introductionmentioning

confidence: 99%

Fusion of the Multisource Datasets for Flood Extent Mapping Based on Ensemble Convolutional Neural Network (CNN) Model

Seydi

Saeidi²,

Kalantar

et al. 2022

Journal of Sensors

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Floods, as one of the natural hazards, can affect the environment, damage the infrastructures, and threaten human lives. Due to climate change and anthropogenic activities, floods occur in high frequency all over the world. Therefore, mapping of the flood areas is of prime importance in disaster management. This research presents a novel framework for flood area mapping based on heterogeneous remote sensing (RS) datasets. The proposed framework fuses the synthetic aperture radar (SAR), optical, and altimetry datasets for mapping flood areas, and it is applied in three main steps: (1) preprocessing, (2) deep feature extraction based on multiscale residual kernel convolution and convolution neural network’s (CNN) parameter optimization by fusing the datasets, and (3) flood detection based on the trained model. This research exploits two large-scale area datasets for mapping the flooded areas in Golestan and Khuzestan provinces, Iran. The results show that the proposed methodology has a high performance in flood area detection. The visual and numerical analyses verify the effectiveness and ability of the proposed method to detect the flood areas with an overall accuracy (OA) higher than 98% in both study areas. Finally, the efficiency of the designed architecture was verified by hybrid-CNN and 3D-CNN methods.

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Methods, Techniques and Sensors for Precision Flood Monitoring Through Remote Sensing

Cited by 12 publications

References 111 publications

Leveraging Sentinel-2 and Geographical Information Systems in Mapping Flooded Regions around the Sesia River, Piedmont, Italy

Leveraging Sentinel-2 and Geographical Information Systems in Mapping Flooded Regions around the Sesia River, Piedmont, Italy

SpaceNet 8 - The Detection of Flooded Roads and Buildings

Fusion of the Multisource Datasets for Flood Extent Mapping Based on Ensemble Convolutional Neural Network (CNN) Model

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