Applying Google earth engine for flood mapping and monitoring in the downstream provinces of Mekong river

Bui, Nghia; Pal, Indrajit; Chollacoop, Nuwong; Mukhopadhyay, Anirban

doi:10.1016/j.pdisas.2022.100235

Cited by 35 publications

(13 citation statements)

References 31 publications

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“…It can help different urban areas to quickly and accurately calculate the level and severity of flood disasters and carry out flood disaster assessment. Especially for disaster and emergency departments, the model method in this paper provides data and theoretical support for personnel evacuation and subregional flood control and disaster relief according to the severity of flood disaster [1,13,15]. It provides a theoretical method and calculation basis for the government's post-disaster reconstruction and the compilation of flood loss reports in various cities.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, researchers have combined real-time dynamic meteorological data, such as weather forecasts, to estimate flood damage and provide theoretical guidance for flood disaster management. Nghia et al [13] applied Google Earth engine to flood mapping and monitoring in lower Mekong provinces. Karthikeyan et al [14], based on MODIS 8-day reflectance data, used K-means clustering and hot spot analysis methods to measure the spatiotemporal distribution of flood areas and estimate flood disaster losses suffered by farmers.…”

Section: Introductionmentioning

confidence: 99%

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Study on the Remote Sensing Spectral Method for Disaster Loss Inversion in Urban Flood Areas

Duan

Zheng

Jin

et al. 2022

Water

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To address the problems of traditional hydrological and hydraulic methods of estimating disasters in urban flood areas, such as small scale, poor timeliness, and difficulty of obtaining data, an inversion method of estimating urban flood disaster area based on remote sensing spectroscopy is proposed. In this paper, the spatial distribution of urban flood disasters is first inverted based on large-scale multidimensional remote sensing spectral orthography. Then, spatial coupling inversion of the remote sensing spectrum-urban economy-flood disaster is performed by simulating the urban economic density through single spectral remote sensing at night. Finally, losses at the urban flood area are estimated. The results show that (1) the heavy rain in Henan Province on 20 July is centered in Zhengzhou, and the spatial distribution of urban flood disasters accords with Zipf’s law; (2) the estimated damage to the urban flood area in Henan Province is 132,256 billion yuan, and Zhengzhou has the most serious losses at 43,147 billion yuan, accounting for 32.6% of the entire province’s losses. These results are consistent with the official data (accuracy ≥ 90%, R2 ≥ 0.95). This study can provide a new approach for accurately and efficiently estimating urban flood damage at a large scale.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Study on the Remote Sensing Spectral Method for Disaster Loss Inversion in Urban Flood Areas

Duan

Zheng

Jin

et al. 2022

Water

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“…Floodwaters can cover between 30% and 50% of the VMD (Dinh et al, 2019;Kuenzer, Guo, et al, 2013). However, recent studies have highlighted the annual peak flooded area and depth is decreasing (Nghia et al, 2022;Vu et al, 2022) (Figure 3). During the dry season, droughts are increasingly common, with saline intrusion transgressing upstream in coastal provinces and causing crop and freshwater-based aquaculture damages (Binh, 2015;Kang et al, 2021;Loc, Lixian, et al, 2021;Pham et al, 2022;Phan et al, 2020).…”

Section: Climate and Watermentioning

confidence: 99%

Progress toward resilient and sustainable water management in the Vietnamese Mekong Delta

Trí

Yarina²,

Nguyen

et al. 2023

WIREs Water

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Sinking and shrinking, the Vietnamese Mekong Delta is a materialization of dynamic river flows, sediment flows, and coastline processes. Past policy aspirations and extensive water infrastructures have shaped the delta into one the most significant food producing landscapes in Southeast Asia. Yet, these changes have also created new environmental risks by transforming the hydrological system. Research has produced a growing and increasingly diverse empirical literature on the delta's environmental context, without necessarily providing water resource managers, policymakers and practitioners with the information needed to galvanize more resilient development. This focus review presents a detailed overview of the recent scientific findings, exploring how the management of water resources is changing, as well as their inter‐relationship with land use, policy, socio‐economic transitions, and global environmental crises. Compound and systemic risks to the delta include climate change, hydrometeorological hazards, upstream developments and an unsustainable development trajectory. We outline scientific knowledge gaps, as well as the pressing need for sharable analysis‐ready data and innovations. Finally, we provide recommended future research avenues for multiscale actions toward a sustainable and resilient delta future.This article is categorized under: Human Water > Water Governance Science of Water > Water Extremes Science of Water > Water and Environmental Change

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“…The economy is a crucial component for measuring the capacity of communities to deal with issues such as coastal floods. There is a correlation between human activities and the economy, where higher activity levels in an area indicate a growing economy [16,22,31].To measure this relationship, the researcher utilized spatial data that captures community activities, namely night-time satellite imagery. The researcher developed an economic capacity model using VIIRS/Night-time V/1 imagery, which can be accessed openly through the GEE platform.…”

Section: Capacity Seen From the Level Of The Economymentioning

confidence: 99%

Combining Landsat, VIIRS Night-time Light, and Sentinel-1 SAR for Spatial Flood Risk Assessment in Coastal Area: an Earth Engine Cloud Computing Process

Siddiq,

Sejati

2023

IOP Conf. Ser.: Earth Environ. Sci.

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The intensity of development in coastal areas stimulates various potential issues such as flood disasters. This study aims to demonstrate the importance of latest methods and geospatial data as inputs for coastal spatial planning policies in efforts to reduce flood disaster risks. Leveraging spatial analysis with cloud computing through Google Earth Engine (GEE), this research assesses flood risk components—hazards, vulnerability, and capacity. The method involves processing SAR Sentinel-1 data to map flood inundation as a representation of hazards, analyzing Landsat and WorldPop data to evaluate vulnerability, and assessing capacity by utilizing VIIRS nighttime light level imagery to determine economic activities. The chosen research study location is the coastal area of Pekalongan due to frequent flood disasters throughout the year. The results demonstrate that cloud computing is capable of assessing flood risks. The flood inundation model using SAR data covers an area of 2,780 hectares with an accuracy of 96.75%. The analysis also reveals the highest vulnerability level, reaching 15.7% (946.32 hectares) of the total area. The capacity analysis indicates a medium to high level of 15% (913.6 hectares). The assessment of flood risks in the coastal area is dominated by the medium to very high-risk class, covering 43% (2,631.84 hectares) of the area. In conclusion, integrating cloud-based flood risk modeling into spatial planning is crucial, considering disaster resilience for sustainable human habitats.

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Applying Google earth engine for flood mapping and monitoring in the downstream provinces of Mekong river

Cited by 35 publications

References 31 publications

Study on the Remote Sensing Spectral Method for Disaster Loss Inversion in Urban Flood Areas

Study on the Remote Sensing Spectral Method for Disaster Loss Inversion in Urban Flood Areas

Progress toward resilient and sustainable water management in the Vietnamese Mekong Delta

Combining Landsat, VIIRS Night-time Light, and Sentinel-1 SAR for Spatial Flood Risk Assessment in Coastal Area: an Earth Engine Cloud Computing Process

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