Lessons for Remote Post-earthquake Reconnaissance from the 14 August 2021 Haiti Earthquake

On 14th August 2021, a magnitude 7.2 earthquake struck the Tiburon Peninsula in the Caribbean nation of Haiti, approximately 150 km west of the capital Port-au-Prince. Aftershocks up to moment magnitude 5.7 followed and over 1,000 landslides were triggered. These events led to over 2,000 fatalities, 15,000 injuries and more than 137,000 structural failures. The economic impact is of the order of US$1.6 billion. The on-going Covid pandemic and a complex political and security situation in Haiti meant that deploying earthquake engineers from the UK to assess structural damage and identify lessons for future building construction was impractical. Instead, the Earthquake Engineering Field Investigation Team (EEFIT) carried out a hybrid mission, modelled on the previous EEFIT Aegean Mission of 2020. The objectives were: to use open-source information, particularly remote sensing data such as InSAR and Optical/Multispectral imagery, to characterise the earthquake and associated hazards; to understand the observed strong ground motions and compare these to existing seismic codes; to undertake remote structural damage assessments, and to evaluate the applicability of the techniques used for future post-disaster assessments. Remote structural damage assessments were conducted in collaboration with the Structural Extreme Events Reconnaissance (StEER) team, who mobilised a group of local non-experts to rapidly record building damage. The EEFIT team undertook damage assessment for over 2,000 buildings comprising schools, hospitals, churches and housing to investigate the impact of the earthquake on building typologies in Haiti. This paper summarises the mission setup and findings, and discusses the benefits, and difficulties, encountered during this hybrid reconnaissance mission

Lessons for Remote Post-earthquake Reconnaissance from the 14 August 2021 Haiti Earthquake

On 14th August 2021, a magnitude 7.2 earthquake struck the Tiburon Peninsula in the Caribbean nation of Haiti, approximately 150 km west of the capital Port-au-Prince. Aftershocks up to moment magnitude 5.7 followed and over 1,000 landslides were triggered. These events led to over 2,000 fatalities, 15,000 injuries and more than 137,000 structural failures. The economic impact is of the order of US$1.6 billion. The on-going Covid pandemic and a complex political and security situation in Haiti meant that deploying earthquake engineers from the UK to assess structural damage and identify lessons for future building construction was impractical. Instead, the Earthquake Engineering Field Investigation Team (EEFIT) carried out a hybrid mission, modelled on the previous EEFIT Aegean Mission of 2020. The objectives were: to use open-source information, particularly remote sensing data such as InSAR and Optical/Multispectral imagery, to characterise the earthquake and associated hazards; to understand the observed strong ground motions and compare these to existing seismic codes; to undertake remote structural damage assessments, and to evaluate the applicability of the techniques used for future post-disaster assessments. Remote structural damage assessments were conducted in collaboration with the Structural Extreme Events Reconnaissance (StEER) team, who mobilised a group of local non-experts to rapidly record building damage. The EEFIT team undertook damage assessment for over 2,000 buildings comprising schools, hospitals, churches and housing to investigate the impact of the earthquake on building typologies in Haiti. This paper summarises the mission setup and findings, and discusses the benefits, and difficulties, encountered during this hybrid reconnaissance mission.</jats:p

Evaluation of SAR and Optical Data for Flood Delineation Using Supervised and Unsupervised Classification

Precise and accurate delineation of flooding areas with synthetic aperture radar (SAR) and multi-spectral (MS) data is challenging because flooded areas are inherently heterogeneous as emergent vegetation (EV) and turbid water (TW) are common. We addressed these challenges by developing and applying a new stepwise sequence of unsupervised and supervised classification methods using both SAR and MS data. The MS and SAR signatures of land and water targets in the study area were evaluated prior to the classification to identify the land and water classes that could be delineated. The delineation based on a simple thresholding method provided a satisfactory estimate of the total flooded area but did not perform well on heterogeneous surface water. To deal with the heterogeneity and fragmentation of water patches, a new unsupervised classification approach based on a combination of thresholding and segmentation (CThS) was developed. Since sandy areas and emergent vegetation could not be classified by the SAR-based unsupervised methods, supervised random forest (RF) classification was applied to a time series of SAR and co-event MS data, both combined and separated. The new stepwise approach was tested for determining the flood extent of two events in Italy. The results showed that all the classification methods applied to MS data outperformed the ones applied to SAR data. Although the supervised RF classification may lead to better accuracies, the CThS (unsupervised) method achieved precision and accuracy comparable to the RF, making it more appropriate for rapid flood mapping due to its ease of implementation

Evaluation of SAR and Optical Data for Flood Delineation Using Supervised and Unsupervised Classification

Precise and accurate delineation of flooding areas with synthetic aperture radar (SAR) and multi-spectral (MS) data is challenging because flooded areas are inherently heterogeneous as emergent vegetation (EV) and turbid water (TW) are common. We addressed these challenges by developing and applying a new stepwise sequence of unsupervised and supervised classification methods using both SAR and MS data. The MS and SAR signatures of land and water targets in the study area were evaluated prior to the classification to identify the land and water classes that could be delineated. The delineation based on a simple thresholding method provided a satisfactory estimate of the total flooded area but did not perform well on heterogeneous surface water. To deal with the heterogeneity and fragmentation of water patches, a new unsupervised classification approach based on a combination of thresholding and segmentation (CThS) was developed. Since sandy areas and emergent vegetation could not be classified by the SAR-based unsupervised methods, supervised random forest (RF) classification was applied to a time series of SAR and co-event MS data, both combined and separated. The new stepwise approach was tested for determining the flood extent of two events in Italy. The results showed that all the classification methods applied to MS data outperformed the ones applied to SAR data. Although the supervised RF classification may lead to better accuracies, the CThS (unsupervised) method achieved precision and accuracy comparable to the RF, making it more appropriate for rapid flood mapping due to its ease of implementation.Geo-engineeringOptical and Laser Remote Sensin

Supporting co-development phase of Nature Based Solution by combined use of Earth Observation and modeling

A protected natural area in the Emilia Romagna region, Northern Italy is threatened by hydro-meteorological hazards, particularly sea storms. In the last 50 years the northern part of the Bellocchio Park (Sacca Bellocchio II Nature Reserve, Site code EUAPP0072 - Ferrara, Italy) was interested by an intensive urbanization (Lido di Spina) with the realization of infrastructures, e.g. roads and residential settlements. This land use change led to the construction of embankments and to the conversion of wetlands. These modifications, in combination to even more frequent storm surge events increased coastal erosion. In addition, inland flooding caused by storm surges acts with the reduction of the lagoon and the increase of soil salinity. As an example, the last event occurred in December 2020 eroded a large portion of the Bellocchio beach. Co-design, co-development and deployment of NBS solutions to reduce storm surge risk in the Bellocchio Park is one of the objectives of the H2020 project OPEn-air laboRAtories for Nature baseD solUtions to Manage environmental risks (OPERANDUM). BellocchioBellochio park is in fact one of the 10 Open Air Laboratories (OAL) where the evidence of mitigation of hydro-meteorological risk by NBS will be demonstrated by the combination of different models, approaches and data.During the co-design process in the Bellocchio park, potential deployment locations of sand dunes have been identified in collaboration with local authorities devoted to the management of the natural area and to the coast defense (CB and ARSTePC-RER) and an environmental engineering consultant assisting Arpae (IRIS sas). Field visits were devoted to the analysis of the environmental features, strengths and weaknesses of candidate sites.This work aims to explore the usefulness of the combined use of multisource remote sensing and modeling in decision making during the co-design process of a NBS. The impacts of the most intense extreme storm surge events in the last 30 years have been documented by delineating flooded areas along the coast using Synthetic Aperture Radar and Multispectral image data. Coastal erosion has been also described by means of change detection analysis and very high resolution multispectral EO data. This screening has given a picture of areas at the risk, i.e. the area most likely to be affected by storm-surge events. Auxiliary data like Digital Terrain Models has been assimilated in a dedicated model to produce flood maps under different scenarios, i.e. different locations and size of NBS and different intensities of storm surge. The integrated analysis was helpful in defining the priority sites, among the ones defined by the stakeholders and engineers, in term of effectiveness for storm surge risk reduction.Optical and Laser Remote SensingGeo-engineerin

Documenting Impacts of Hydro-Meteorological Events Using Earth Observation

The ambition of H2020 OPERANDUM project is to develop and document Nature Based Solutions (NBS) to mitigate risks associated with hydro-meteorological (HM) hazards. NBS mitigate risks by reducing the vulnerability of a particular system. The aim of this work is to demonstrate the use of multisource remote sensing data in documenting the impact of extreme HM events to advance knowledge on vulnerability and exposure. In particular the focus is to document past impacts due to extreme events selected from a characterization of recent (3 0 years) HM events in 11 Open Air Laboratories (OALs) where co-design, co-development and deployment of NBS are taking place. The impacts were documented by applying a wide spectrum of satellite image data and other, close - range, remote sensing techniques. A better understanding of the consequences due to extreme HM events in a particular area (OALs) is essential to identify elements at risk and expected to provide a reference to evaluate the reduction of vulnerability and mitigation of risks past the completion of NBS.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Optical and Laser Remote SensingGeo-engineerin

Lessons for Remote Post-earthquake Reconnaissance from the 14 August 2021 Haiti Earthquake

On 14th August 2021, a magnitude 7.2 earthquake struck the Tiburon Peninsula in the Caribbean nation of Haiti, approximately 150 km west of the capital Port-au-Prince. Aftershocks up to moment magnitude 5.7 followed and over 1,000 landslides were triggered. These events led to over 2,000 fatalities, 15,000 injuries and more than 137,000 structural failures. The economic impact is of the order of US$1.6 billion. The on-going Covid pandemic and a complex political and security situation in Haiti meant that deploying earthquake engineers from the UK to assess structural damage and identify lessons for future building construction was impractical. Instead, the Earthquake Engineering Field Investigation Team (EEFIT) carried out a hybrid mission, modelled on the previous EEFIT Aegean Mission of 2020. The objectives were: to use open-source information, particularly remote sensing data such as InSAR and Optical/Multispectral imagery, to characterise the earthquake and associated hazards; to understand the observed strong ground motions and compare these to existing seismic codes; to undertake remote structural damage assessments, and to evaluate the applicability of the techniques used for future post-disaster assessments. Remote structural damage assessments were conducted in collaboration with the Structural Extreme Events Reconnaissance (StEER) team, who mobilised a group of local non-experts to rapidly record building damage. The EEFIT team undertook damage assessment for over 2,000 buildings comprising schools, hospitals, churches and housing to investigate the impact of the earthquake on building typologies in Haiti. This paper summarises the mission setup and findings, and discusses the benefits, and difficulties, encountered during this hybrid reconnaissance mission.</jats:p