TY - CHAP
T1 - From Cloud to Refugee Camp
T2 - A Satellite-Based Flood Analytics Case-Study in Congo-Brazzaville
AU - Ho, Jeff C.
AU - Vu, William
AU - Tellman, Beth
AU - Dinga, Jean Bienvenu
AU - N’diaye, Patrick Impeti
AU - Weber, Sam
AU - Bauer, Jean Martin
AU - Schwarz, Bessie
AU - Doyle, Colin
AU - Demuzere, Matthias
AU - Anderson, Tyler
AU - Glinskis, Emmalina
N1 - Publisher Copyright:
© 2021 Elsevier Ltd.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - In November 2017, floods in Impfondo, Congo forced evacuations and damaged crops, homes, and roads. The World Food Programme (WFP) supported the government’s response by providing food aid but was delayed by one month due to inadequate information. To enable faster flood response, WFP partnered with Cloud to Street to develop a near real-time Congo flood monitoring system in collaboration with the government. The system used precipitation information (GSMaP), and satellites (MODIS, Landsat, Sentinel-2, PlanetScope, and Worldview-3) to estimate flood damage and alert stakeholders via WhatsApp Messenger and an online platform. The system was used to assess flood risk of 16, 000 refugees, resulting in the recommendation to move refugees from one high risk site (Makotipoko), reducing the flood exposure of up to 7, 000 people. Despite limitations of the flood monitoring system (cloud cover, inaccurate rainfall forecasting, and population data), it provides evidence that satellite-based flood analytics can inform local decision making.
AB - In November 2017, floods in Impfondo, Congo forced evacuations and damaged crops, homes, and roads. The World Food Programme (WFP) supported the government’s response by providing food aid but was delayed by one month due to inadequate information. To enable faster flood response, WFP partnered with Cloud to Street to develop a near real-time Congo flood monitoring system in collaboration with the government. The system used precipitation information (GSMaP), and satellites (MODIS, Landsat, Sentinel-2, PlanetScope, and Worldview-3) to estimate flood damage and alert stakeholders via WhatsApp Messenger and an online platform. The system was used to assess flood risk of 16, 000 refugees, resulting in the recommendation to move refugees from one high risk site (Makotipoko), reducing the flood exposure of up to 7, 000 people. Despite limitations of the flood monitoring system (cloud cover, inaccurate rainfall forecasting, and population data), it provides evidence that satellite-based flood analytics can inform local decision making.
KW - Emergency response
KW - Near real-time flood monitoring
KW - Operational system
KW - Refugees
KW - Relocation
KW - Remote sensing
KW - Republic of the congo
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U2 - 10.1016/B978-0-12-819412-6.00006-7
DO - 10.1016/B978-0-12-819412-6.00006-7
M3 - Chapter
AN - SCOPUS:85127675771
SP - 131
EP - 146
BT - Earth Observation for Flood Applications
PB - Elsevier
ER -