Can We Rely on VIIRS Nightlights to Estimate the Short-Term Impacts of Natural Disasters? Evidence from Five Southeast Asian Countries
Visible Infrared Imaging Radiometer Suite (VIIRS) nightlights are used to model damage caused by earthquakes, floods, and typhoons in five Southeast Asian countries (Indonesia, Myanmar, the Philippines, Thailand, and Vietnam). The data are used to examine the extent to which for each type of hazard there is a difference in nightlight intensity between affected and nonaffected cells based on (i) case studies of specific disasters, and (ii) fixed effect regression models akin to the double difference method to determine any effect that the different natural hazards might have had on the nightlight value. The results show little to no significance regardless of the methodology used, most likely due to noise in the nightlight data and the fact that the tropics have only a few days per month with no cloud cover.
Main Authors: | , , |
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Format: | Working Paper biblioteca |
Language: | English |
Published: |
World Bank, Washington, DC
2019-10
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Subjects: | REMOTE SENSING, NATURAL DISASTER, DAMAGE INDEX, FLOOD, EARTHQUAKE, TYPHOON, VISIBLE INFRARED IMAGING, |
Online Access: | http://documents.worldbank.org/curated/en/757621572539847894/Can-We-Rely-on-VIIRS-Nightlights-to-Estimate-the-Short-Term-Impacts-of-Natural-Disasters https://hdl.handle.net/10986/32661 |
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Summary: | Visible Infrared Imaging Radiometer
Suite (VIIRS) nightlights are used to model damage caused by
earthquakes, floods, and typhoons in five Southeast Asian
countries (Indonesia, Myanmar, the Philippines, Thailand,
and Vietnam). The data are used to examine the extent to
which for each type of hazard there is a difference in
nightlight intensity between affected and nonaffected cells
based on (i) case studies of specific disasters, and (ii)
fixed effect regression models akin to the double difference
method to determine any effect that the different natural
hazards might have had on the nightlight value. The results
show little to no significance regardless of the methodology
used, most likely due to noise in the nightlight data and
the fact that the tropics have only a few days per month
with no cloud cover. |
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