A Review of the Internet of Floods: Near Real-Time Detection of a Flood Event and Its Impact

Ackere, Samuel Van; Verbeurgt, Jeffrey; Sloover, Lars De; Gautama, Sidharta; Wulf, Alain De; Maeyer, Philippe De

doi:10.3390/w11112275

Cited by 23 publications

(15 citation statements)

References 169 publications

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“…Remote sensing technologies have evolved rapidly during the recent years and their advantages in analyzing the Earth surface by its spectral properties have been utilized in environmental monitoring and emergency and disaster relief [41]. Automated methods for waterbodies segmentation with satellite imagery can be divided in two categories, namely rule-based systems and machine learning models.…”

Section: B Remote Sensing and Flood Detectionmentioning

confidence: 99%

OmbriaNet—Supervised Flood Mapping via Convolutional Neural Networks Using Multitemporal Sentinel-1 and Sentinel-2 Data Fusion

Drakonakis

Tsagkatakis

Fotiadou

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Regions around the world experience adverse climate change induced conditions which pose severe risks to the normal and sustainable operations of modern societies. Extreme weather events, such as floods, rising sea-levels and storms, stand as characteristic examples that impair the core services of the global ecosystem. Especially floods have a severe impact on human activities, hence early and accurate delineation of the disaster is of top-priority since it provides environmental, economic, and societal benefits and eases relief efforts. In this work, we introduce OmbriaNet, a deep neural network architecture, based on Convolutional Neural Networks (CNNs), that detects changes between permanent and flooded water areas by exploiting the temporal differences among flood events extracted by different sensors. To demonstrate the potential of the proposed approach, we generated OMBRIA, a bitemporal and multimodal satellite imagery dataset for image segmentation through supervised binary classification. It consists of a total number of 3.376 images, Synthetic Aperture Radar (SAR) imagery from Sentinel-1, and multispectral imagery from Sentinel-2, accompanied with ground truth binary images produced from data derived by experts and provided from the Emergency Management Service of the European Space Agency Copernicus Program. The dataset covers 23 flood events around the globe, from 2017 to 2021. We collected, co-registrated and pre-processed the data in Google Earth Engine. To validate the performance of our method, we performed different benchmarking experiments on the OMBRIA dataset and we compared with several competitive state-of-theart techniques. The experimental analysis demonstrated that the proposed formulation is able to produce high-quality flood maps, achieving a superior performance over the state-of-theart.We provide OMBRIA dataset, as well as OmbriaNet code at: https://github.com/geodrak/OMBRIA.

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Section: B Remote Sensing and Flood Detectionmentioning

confidence: 99%

OmbriaNet—Supervised Flood Mapping via Convolutional Neural Networks Using Multitemporal Sentinel-1 and Sentinel-2 Data Fusion

Drakonakis

Tsagkatakis

Fotiadou

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Results of the model are thus relevant as they serve to accumulate and analyze historical, cartographical, and other types of data, leading to a better understanding of controls on and drivers of fire activity in the CMA at a high resolution. However, the model can be substantially improved with nearreal-time (NRT) information from terrestrial platforms (e.g., vehicles, towers, cranes), airborne platforms (e.g., aircraft, unmanned aerial vehicles (UAVs), helicopters), or spaceborne platforms (e.g., satellites) using electromagnetic sensors (Van Ackere et al, 2019) and leading us to a truly smart metropolitan area (Costa et al, 2020). In that sense, it becomes necessary to put in more effort in the future to extending the timeframe of the present study, as Chuvieco et al (2011, p. 54) accurately said, "Since fire occurrence changes in space and time, the validation of integrated indices should be done with long time series, because short periods may bias some of the theoretical assumptions that are required to build the model".…”

Section: Categorymentioning

confidence: 99%

Data-based wildfire risk model for Mediterranean ecosystems – case study of the Concepción metropolitan area in central Chile

Castillo¹,

Fernández²,

Robles³

et al. 2021

Nat. Hazards Earth Syst. Sci.

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Abstract. Wildfire risk is latent in Chilean metropolitan areas characterized by the strong presence of wildland–urban interfaces (WUIs). The Concepción metropolitan area (CMA) constitutes one of the most representative samples of that dynamic. The wildfire risk in the CMA was addressed by establishing a model of five categories (near zero, low, moderate, high, and very high) that represent discernible thresholds in fire occurrence, using geospatial data and satellite images describing anthropic–biophysical factors that trigger fires. Those were used to deliver a model of fire hazard using machine learning algorithms, including principal component analysis and Kohonen self-organizing maps in two experimental scenarios: only native forest and only forestry plantation. The model was validated using fire hotspots obtained from the forestry government organization. The results indicated that 12.3 % of the CMA's surface area has a high and very high risk of a forest fire, 29.4 % has a moderate risk, and 58.3 % has a low and very low risk. Lastly, the observed main drivers that have deepened this risk were discussed: first, the evident proximity between the increasing urban areas with exotic forestry plantations and, second, climate change that threatens triggering more severe and large wildfires because of human activities.

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“…The high velocity of space‐based data enables using satellite imagery to detect the abrupt occurrence of global change events and their biological impacts over large areas (Verbesselt, Zeileis, & Herold, 2012). Recently, some near real‐time monitoring systems based on space‐based data have been applied to forest conservation (Musinsky et al., 2018; Pratihast et al., 2016), flood event (Van Ackere et al., 2019), fire mapping (Pulvirenti et al., 2020), and tree mortality due to insect outbreak (He, Chen, Potter, & Meentemeyer, 2019; Olsson, Lindström, & Eklundh, 2016). Furthermore, global analyses of the big remote‐sensing data have revealed many emergent properties of ecosystems, such as the average optimum air temperature for ecosystem gross primary productivity (Huang, Piao, et al, 2019), high stability of evergreen broadleaf forests (Huang & Xia, 2019) and collapse of rain‐use efficiency in semi‐arid ecosystems (Du et al., 2018) under extreme droughts, diminishment of vegetation seasonality over northern lands (Xu et al, 2013), and constrained tropical photosynthetic seasonality by hydroclimate (Guan et al, 2015).…”

Section: Emergent Biological Mechanisms and Phenomena Based On Regionmentioning

confidence: 99%

Research challenges and opportunities for using big data in global change biology

Xia

Wang

Niu

2020

Global Change Biology

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Global change biology has been entering a big data era due to the vast increase in availability of both environmental and biological data. Big data refers to large data volume, complex data sets, and multiple data sources. The recent use of such big data is improving our understanding of interactions between biological systems and global environmental changes. In this review, we first explore how big data has been analyzed to identify the general patterns of biological responses to global changes at scales from gene to ecosystem. After that, we investigate how observational networks and space-based big data have facilitated the discovery of emergent mechanisms and phenomena on the regional and global scales. Then, we evaluate the predictions of terrestrial biosphere under global changes by big modeling data. Finally, we introduce some methods to extract knowledge from big data, such as meta-analysis, machine learning, traceability analysis, and data assimilation. The big data has opened new research opportunities, especially for developing new datadriven theories for improving biological predictions in Earth system models, tracing global change impacts across different organismic levels, and constructing cyberinfrastructure tools to accelerate the pace of model-data integrations. These efforts will uncork the bottleneck of using big data to understand biological responses and adaptations to future global changes. K E Y W O R D S big data, Earth system model, global change biology, machine learning, model uncertainty | 6041 XIA et Al.

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A Review of the Internet of Floods: Near Real-Time Detection of a Flood Event and Its Impact

Cited by 23 publications

References 169 publications

OmbriaNet—Supervised Flood Mapping via Convolutional Neural Networks Using Multitemporal Sentinel-1 and Sentinel-2 Data Fusion

OmbriaNet—Supervised Flood Mapping via Convolutional Neural Networks Using Multitemporal Sentinel-1 and Sentinel-2 Data Fusion

Data-based wildfire risk model for Mediterranean ecosystems – case study of the Concepción metropolitan area in central Chile

Research challenges and opportunities for using big data in global change biology

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