Improving Flood Detection and Monitoring through Remote Sensing

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

doi:10.3390/w14030364

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…Level-1 GRDH in IW mode. The σ o VH backscatter intensity image is more sensitive to water bodies than the σ o VV image, which is more sensitive to stable objects [23], [25]. Data was collected per twelve temporal visit cycles.…”

Section: Methodsmentioning

confidence: 99%

Inventory of River Basin Flood Events for Drainage System Examination Using Multi-Temporal Composite Remote Sensing Data

Hamim,

Gumano,

Sondang

et al. 2023

TEM Journal

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Palembang City is divided into two regions by the Musi river, and there are 114 tributaries streamed within Palembang City, with 31.58% located in the Seberang Ilir region and 68.42% in the Seberang Ulu region. In the lowland topography of Palembang City, with most of the region below 5 m above mean sea level, flooding becomes repetitive. This paper examines the flooded area within the Bendung river basin using multi-temporal composite remote sensing radar data from Sentinel 1. Flood events in November 2018 and December 2021 were selected. The flood extent and affected populations will be calculated using Google Earth Engine (GEE) and Geographic Information System (GIS) application. In this study, flood extent was delineated and compared for different digital elevation models. It is found that the threshold value, the DEM, and the availability of radar data in conjunction with the flood event will better delineate the affected area to the flood. The DEM contributed a 37.5% to 83% difference in the delineated area calculation. The script in the GEE is customizable for different areas of interest and can be applied to different areas with adjustments to the threshold value that should be a minimum of 1.0 or higher.

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Section: Methodsmentioning

confidence: 99%

Inventory of River Basin Flood Events for Drainage System Examination Using Multi-Temporal Composite Remote Sensing Data

Hamim,

Gumano,

Sondang

et al. 2023

TEM Journal

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“…Examples of significant events with a declared state of emergency that occurred in the Basilicata Region in the last ten years are listed in Table 1. These events led the scientific community to develop relevant research for landslide characterization [20,22,25,26] and flood monitoring, vulnerability and risk mapping [24,[27][28][29][30][31][32][33]. In terms of national and regional hazard data, Figure 1 shows the spatial distribution of the landslide and flood hazard in the Basilicata Region, derived from Italian Institute for Environmental Research and Protection (ISPRA) 2020-2021 mosaic layers.…”

Section: Multi-hazard Context In the Basilicata Regionmentioning

confidence: 99%

Mapping Social Vulnerability to Multi-Hazard Scenarios: A GIS-Based Approach at the Census Tract Level

Lapietra,

Colacicco,

Rizzo

et al. 2024

Applied Sciences

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Floods and landslides cause continuous damage to ecosystems, infrastructures, and populations. Particularly, the occurrence and the existence of different natural hazards in the same territory highlight the need to improve risk mitigation strategies for local authorities and community resilience solutions for inhabitants. Analyzing and mapping social vulnerability provides information about the main features of a specific community to deal with natural events. Specifically, the interaction between multi-hazards and the socio-economic environment suggests multidisciplinary assessments that merge the physical and the socio-economic features of the affected territories, providing a useful approach to support multi-risk reduction planning. In this context, the article focuses on integrating landslide and flood hazard scenarios with social vulnerability in the Basilicata Region (southern Italy) at the census tract level. Thirteen municipalities were chosen as multi-hazard hot spots, while open-source platforms were selected for hazard and social vulnerability data collection and analyses. A geographic information system (GIS)-based approach was applied to combine different hazard scenarios with social vulnerability distribution among 1331 census tracts to detect the most vulnerable sub-municipality areas that need special attention in multi-risk reduction strategies. The results are presented in the form of maps, which provide a relevant suitable tool in local emergency planning.

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“…Over the past decades, remote sensing has been applied in flood analyses and achieved great success. Relevant studies include flood detection, flood forecasting, and disaster assessment [2][3][4][5][6][7][8]. Taking flood detection as an example, various methods have been used to detect flood areas with multi-spectral images and Synthetic Aperture Radar (SAR) images.…”

Section: Introductionmentioning

confidence: 99%

Flood Analysis Using Multi-Scale Remote Sensing Observations in Laos

Hansana,

Guo,

Zhang

et al. 2023

Remote Sensing

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Heavy rains usually hit Laos countrywide and cause serious floods, influencing local agriculture, households, and the economy. Therefore, it is crucial to monitor the flooding in Laos to better understand the flood patterns and characteristics. This paper aims to analyze the influence of the flooding in Laos with multi-source data, e.g., Synthetic Aperture Radar (SAR), optical multi-spectral images, and geographic information system data. First, the flood areas in Laos from 2018 to 2022 are detected using a decision fusion method. Based on the flood areas and the global Land Use/Land Cover (LULC) product, the macro scale global impact of the flood is analyzed. Second, taking the Vientiane Capital as a case study area, a flood forecasting method is applied to estimate the risk of flooding. Finally, optical images before and after the flood event are extracted for a close-up comparison at the micro scale. Based on the above multi-scale analysis, floods in Laos are found to be predominantly concentrated in the flat areas near the Mekong River, with a decreasing trend over time, which could be helpful for flood management and mitigation strategies in Laos. The validation results exhibited notable average indices across a five-year period, with mIoU (0.7782), F1 score (0.7255), and overall accuracy (0.9854), respectively.

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Improving Flood Detection and Monitoring through Remote Sensing

Abstract: Floods are among the most threatening and impacting environmental hazards [...]

Cited by 8 publications

References 13 publications

Inventory of River Basin Flood Events for Drainage System Examination Using Multi-Temporal Composite Remote Sensing Data

Inventory of River Basin Flood Events for Drainage System Examination Using Multi-Temporal Composite Remote Sensing Data

Mapping Social Vulnerability to Multi-Hazard Scenarios: A GIS-Based Approach at the Census Tract Level

Flood Analysis Using Multi-Scale Remote Sensing Observations in Laos

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