Implementing the Sendai Framework in developing countries using remote sensing techniques for the evaluation of natural hazards

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Abstract

The UNISDR Sendai Framework for Disaster Risk Reduction has
the stated outcome for “The substantial reduction of disaster risk
and losses in lives, livelihoods and health and in the economic,
physical, social, cultural and environmental assets of persons,
businesses, communities and countries”. Priority 1 of the
Framework, understanding disaster risk, requires policies and
practices for disaster risk management to be based on an
understanding of disaster risk in all its dimensions of
vulnerability, capacity, exposure of persons and assets, hazard
characteristics and the environment. In addition, complementary
schemes such as the Millennium Challenge Corporation, 100
Resilient Cities and the UNISDR Disaster Resilient Scorecard
also have an essential requirement to identify, understand, and
use current and future risk scenarios. As natural hazards are a
common catalyst for disaster risk, understanding current and
future risk scenarios requires detailed preliminary appraisals of
natural hazard and risk scenarios, both at local and national
levels, as detailed in the Sendai Framework. However, due to the
data-intense nature of such appraisals, undertaking them can be
expensive and time consuming, and thus hinder progress at
meeting the aims of Priority 1. As such, we here evaluate the
potential of available “Open Source” data, such as
ASTER/SRTM Digital Elevation Models (DEMs) and
Landsat/Sentinel satellite imagery, coupled with a range of
processing techniques, for the cost and time effective screening
and preliminary assessment of a range of natural hazards.
Despite the spatial resolution of these data being between 30 –
50 m, the outputs provide an important preliminary assessment
of natural hazards, thus enabling policies and practices for
disaster risk management to be focused on areas of high
susceptibility and vulnerability. These methods are applicable to
communities across the globe, but particularly to those within
developing countries that may be lacking alternative data
sources