The heavily populated highlands of Ethiopia are currently at low risk for malaria transmission, but global warming may change the risk level significantly. The inhabitants of the Ethiopian Highlands are highly vulnerable to this potential hazard due to their lack of immunity. Here, we identify hotspots within the Highlands where projected warming towards the end of the 21st century will increase the risk of malaria transmission significantly. Based on projected temperature changes, we conclude that about a third of the region’s population and roughly 14% of its land area will become at high risk for malaria transmission within a century under the high-emissions-no-mitigation baseline scenario for future climate change. Our analysis combines dynamically down-scaled regional climate projections, high resolution satellite observations of temperature, and a village-scale malaria transmission model that was developed based on climatic, environmental, entomological, and medical data collected by our group in comprehensive multi-year field surveys of villages in this region. The projected impacts of global warming on malaria transmission in Africa have been controversial. We propose a framework that reconciles seemingly contradictory conclusions, and informs strategies for climate adaptation not only over the Ethiopian Highlands but broadly over Africa, where more than 90% of malaria deaths occur every year.
Satellite-based Hurricane Risk Assessment for Roadways via Vegetation 3D Modeling and Building Detection
Infrastructures such as roadways, power lines, and communications networks play a critical role in our society. However, they are also susceptible to failures, especially after natural events, easily affecting large geographical areas. Predicting where and when these failures will occur with high confidence is very difficult due to the stochastic nature of such events. Nevertheless, it is possible to know which areas are more vulnerable in advance and plan accordingly. This paper aims to use just remote sensing techniques based on satellite images to detect roadways vulnerabilities to hurricanes. The framework exhibits a modular architecture that enables detecting and mapping in 3D vegetation and detecting buildings. We propose a risk function based on the information retrieved from the satellite image which can be used to create a risk map of the area. The study area has been selected in Tallahassee, Florida where a high-resolution satellite image has been acquired in September 2018, before Hurricane Michael main hit. The findings of this work can help the management teams and city responders to identify the most vulnerable regions which are under the risk of disruption and to organize the resources prior to the event. The advantages of our approach are that the entire framework can be use as an end-to-end standalone solution for risk analysis at city level and can be easily expanded with other source of data.
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- NSF-PAR ID:
- 10358642
- Date Published:
- Journal Name:
- Publications Transportation Research Board
- ISSN:
- 0276-945X
- Page Range / eLocation ID:
- 1-12
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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