Flood monitoring and damage assessment in Thailand using multi-temporal HJ-1A/1B and MODIS images

Zhou, Shanshi; Zhang, W C

doi:10.1088/1755-1315/57/1/012016

Cited by 4 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The NDWI highlights open waters from satellite images [17], whereas the mNDWI extracted shallow water bodies and separated built-up structures from water features [55]. SPWI is also capable of extracting water pixels accurately [22]. The EVI is represented as an optimized vegetation index to improve the vegetation signal with better sensitivity in high biomass areas.…”

Section: Flood Extent Mapping During 2001-2020 and Flood Hazard Estim...mentioning

confidence: 99%

“…Other multi-spectral based indices such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Water Ratio Index (WRI), Spectrum Photometric Water Index (SPWI), and Standardized Photometric Water Index (SPWI) are utilized for the delineation of flooded areas and characterizing floods (e.g., estimating turbidity, sediments, etc.) [21][22][23]. Most of these spectral indices (SIs) have utilized near-infrared (NIR) band as floodwater reflects the lowest amount of light energy which helps to identify flooded pixels [24].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Flood Hazard and Risk Zonation in North Bihar Using Satellite-Derived Historical Flood Events and Socio-Economic Data

2022

View full text Add to dashboard Cite

North Bihar is one of the most flood-affected regions of India. Frequent flooding caused significant loss of life and severe economic damages. In this study, hydroclimatic conditions and historical flood events during the period of 2001 to 2020 were coupled over different basins in North Bihar. The main objective of this study is to assess the severity of floods by estimating flood hazards, vulnerability and risk in North Bihar. The uniqueness of this study is to assess flood risk at the village level as no such study was performed earlier. Other thematic data, namely, land-use and drainage networks, were also utilised with flood maps to validate the severity of the event. MOD09A1 satellite data (during 2001–2020) derived indices were used to derive inundation extents and flood frequency. Socio-economic vulnerability (SEV) was derived based on seven census parameters (i.e., population density, house-hold density, literacy rate, agricultural labour, and cultivator, total male, and female) and coupled with flood hazard to derive flood risk over the study region. The study exhibited that a total ~34% of the geographical area of North Bihar was inundated in the last 20 years and the maximum flood extent was seen in 2020. Flood risk map exhibited that ~7%, ~8%, ~13%, ~4%, and ~2% of the geographical area was mapped under Very High, High, Moderate, Low, and Very Low categories, respectively. The 2770 and 3535 number of villages was categorized under Very High and High flood risk zone which are located in north-central and central-western regions. These findings can be applied to distinguish and classify areas of various risk zones to assist in flood mitigation and management activities.

show abstract

Section: Flood Extent Mapping During 2001-2020 and Flood Hazard Estim...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Flood Hazard and Risk Zonation in North Bihar Using Satellite-Derived Historical Flood Events and Socio-Economic Data

2022

View full text Add to dashboard Cite

show abstract

“…From literature, the MNDWI index has been used for the extraction of water on a flat plain with scattered built-up areas using Landsat 8 OLI and SAR data, achieving high accuracy and better performance. Thus, it was also strongly recommended, in comparison with other indexes, to be applied on Landsat images [49][50][51][52].…”

Section: Mndwi Indexmentioning

confidence: 99%

Operational Monitoring and Damage Assessment of Riverine Flood-2014 in the Lower Chenab Plain, Punjab, Pakistan, Using Remote Sensing and GIS Techniques

Sajjad

Chen

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

In flood-prone areas, the delineation of the spatial pattern of historical flood extents, damage assessment, and flood durations allow planners to anticipate potential threats from floods and to formulate strategies to mitigate or abate these events. The Chenab plain in the Punjab region of Pakistan is particularly prone to flooding but is understudied. It experienced its worst riverine flood in recorded history in September 2014. The present study applies Remote Sensing (RS) and Geographical Information System (GIS) techniques to estimate the riverine flood extent and duration and assess the resulting damage using Landsat-8 data. The Landsat-8 images were acquired for the pre-flooding, co-flooding, and post-flooding periods for the comprehensive analysis and delineation of flood extent, damage assessment, and duration. We used supervised classification to determine land use/cover changes, and the satellite-derived modified normalized difference water index (MNDWI) to detect flooded areas and duration. The analysis permitted us to calculate flood inundation, damages to built-up areas, and agriculture, as well as the flood duration and recession. The results also reveal that the floodwaters remained in the study area for almost two months, which further affected cultivation and increased the financial cost. Our study provides an empirical basis for flood response assessment and rehabilitation efforts in future events. Thus, the integrated RS and GIS techniques with supporting datasets make substantial contributions to flood monitoring and damage assessment in Pakistan.

show abstract

“…Las aproximaciones para evaluar las inundaciones incluyen: la evaluación directa a través del trabajo de campo (Hernández-Uribe, Barrios-Piña & Ramírez, 2017;Díaz-Borrego & Rodríguez-Infante, 2016;Rodríguez Gavíria, 2016;Escuder, Matheu & Castillo, 2010); entrevistas, medición de tirantes e inventarios (Moguel, Tejeda & García Pacheco, 2012;Aguilar & Bedoya, 2006); y el enfoque a distancia, mediante la aplicación de sensores remotos satelitales (Singh, Krishna Kant & Singh, 2017;Singh, Akansha & Singh, 2017;Zhou & Zhang, 2017;Acharya et al, 2016;Fisher, Flood & Danaher, 2016;Franci et al, 2016;Twele et al, 2016;Jung et al, 2014). La teledetección es una herramienta que en los últimos años ha tenido gran desarrollo, principalmente en aquellas regiones de difícil acceso, como lo podrían ser las zonas inundadas.…”

Section: Introductionunclassified

Metodología para la estimación de pérdidas económicas de cultivos de maíz siniestrados por inundaciones usando teledetección

Ascención

Mastachi-Loza

Alcántara

et al. 2021

Rev. geogr. Norte Gd.

View full text Add to dashboard Cite

The flooded areas of corn cultivation were determined by means of satellite images in order to know the economic losses due to floods. As a case study, a flood that occurred in 2016 that affected 4 municipalities in the State of Tabasco, Mexico, was analyzed. The methodology included the use of Landsat satellite images (L8), calculation of spectral indices (water, vegetation and humidity), identification of corn growing areas through supervised classification and the estimation of economic losses. The results show that the spectral indices that best managed to identify the flooded areas were: MNDWI and NDWI. To report damages, values in USD (US dollars) were standardized in 2016 prices using exchange rates adjusted for purchasing power parity (PPP). For the case study, the results of damages per hectare were established between 728.57 USD / ha in PPP and 317.25 USD / ha in PPP, which agree with the estimates made in other countries for corn crops.

show abstract

Flood monitoring and damage assessment in Thailand using multi-temporal HJ-1A/1B and MODIS images

Cited by 4 publications

References 9 publications

Flood Hazard and Risk Zonation in North Bihar Using Satellite-Derived Historical Flood Events and Socio-Economic Data

Flood Hazard and Risk Zonation in North Bihar Using Satellite-Derived Historical Flood Events and Socio-Economic Data

Operational Monitoring and Damage Assessment of Riverine Flood-2014 in the Lower Chenab Plain, Punjab, Pakistan, Using Remote Sensing and GIS Techniques

Metodología para la estimación de pérdidas económicas de cultivos de maíz siniestrados por inundaciones usando teledetección

Contact Info

Product

Resources

About