TY - GEN
T1 - Monitoring and Analyzing (CO2) Concentrations and Vegetation Health in Lima, Peru Using Satellite Images
AU - Benjamin, Arriaga
AU - Miguel, Munoz
AU - Jose Ignacio, Armas
AU - Victor Andres, Ayma
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Climate change, driven significantly by carbon diox-ide (CO2) emissions, poses a major threat globally, contributing to extreme weather events and health impacts. This study focuses on Lima, Peru, a city facing severe air pollution, to monitor and analyze CO2 concentrations using satellite imagery. By leveraging Sentinel-2 and OCO-3 data, we examine the relationship between CO2 levels and vegetation health, using vegetation indices such as NDVI. Our findings reveal temporal variability in these relationships, underscoring the complex interactions between atmospheric CO2 and possible vegetation influenced by seasonal and environmental factors. The study recommends increasing the frequency of satellite observations and integrating these findings with climate models to improve urban green space management and climate change mitigation strategies. This approach enhances spatial and temporal resolution of CO2 data, facilitating informed decision-making for environmental and public health interventions.
AB - Climate change, driven significantly by carbon diox-ide (CO2) emissions, poses a major threat globally, contributing to extreme weather events and health impacts. This study focuses on Lima, Peru, a city facing severe air pollution, to monitor and analyze CO2 concentrations using satellite imagery. By leveraging Sentinel-2 and OCO-3 data, we examine the relationship between CO2 levels and vegetation health, using vegetation indices such as NDVI. Our findings reveal temporal variability in these relationships, underscoring the complex interactions between atmospheric CO2 and possible vegetation influenced by seasonal and environmental factors. The study recommends increasing the frequency of satellite observations and integrating these findings with climate models to improve urban green space management and climate change mitigation strategies. This approach enhances spatial and temporal resolution of CO2 data, facilitating informed decision-making for environmental and public health interventions.
KW - CO
KW - Images
KW - Processing
KW - Satellite
KW - Vegetation index
UR - https://www.scopus.com/pages/publications/85217238096
U2 - 10.1109/INTERCON63140.2024.10833497
DO - 10.1109/INTERCON63140.2024.10833497
M3 - Conference contribution
AN - SCOPUS:85217238096
T3 - Proceedings of the 2024 IEEE 31st International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2024
BT - Proceedings of the 2024 IEEE 31st International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 31st IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2024
Y2 - 6 November 2024 through 8 November 2024
ER -