Lessons from Haiti: applying innovative, affordable, and replicable solutions using drones and spatial data

In 2016, Category 4 Hurricane Matthew made landfall in the south of Haiti, causing destruction unseen in more than a decade during the hurricane season, with winds as high as 230 kilometers per hour and 600 millimeters of rain in less than 24 hours. The toll was very high, with a total of 546 victims, 128 missing, 439 injured, and 2.1 million people affected.

Following the disaster, the Government of Haiti (GoH) was confronted with the challenge of determining the overall economic impact in order to gauge the magnitude of the event, identify priority sectors for reconstruction, understand different geographic impacts, and comprehend relative public versus private damages. Therefore, the Government performed a rapid damage and loss assessment with technical support from the World Bank and the Inter-American Development Bank (IDB) to help put in place a relief, recovery, reconstruction, and development plan. In a context marked by extreme urgency, it was imperative to swiftly estimate the damages in order to assign the appropriate financial resources for the recovery efforts.

There was an absence of information on the actual impacts of the hurricane, and reaching the affected areas was complicated by road conditions, which were in a poor state after the passage of the hurricane. In addition, roads were vacated as much as possible to facilitate the job of emergency services. This raised a challenge – how could the damages and losses be properly assessed?

This was done through the GRADE assessment, an innovative methodology developed by the World Bank with the support of the Global Facility for Disaster Reduction and Recovery (GFDRR). Using disaster risk modeling techniques, in combination with historical damage data, census and socioeconomic survey data, satellite imagery, drone footage, and other media, the GRADE assessment rapidly quantifies damages, providing support to governments in the estimation of losses. 

Figure 1 - Tracking Hurricane Matthew – Source: HaitiData.org

To implement the GRADE assessment, remote sensing is critical. In this case, the National Center for Geospatial Information in Haiti (Centre National de l'Information Géo-Spatiale – CNIGS) provided geographic information system (GIS) and spatial data support, and a first attempt was carried out through satellite imagery. Unfortunately, it was prevented by the state of Haiti’s telecommunications infrastructure (i.e., low internet bandwidth) after the disaster, the presence of clouds obstructing the view, and the relatively low resolution of the available imageries.

Upon realizing that it was not going to be possible to work with satellite data, but that it was still necessary to analyze the situation in the affected areas, a second attempt at remote sensing was done using Unmanned Aerial Vehicles (UAVs), more commonly known as drones, which successfully mapped key communes in areas where the force of Matthew had been especially disruptive, such as the Grand’Anse and the Sud departments.