Café
WeCLISH Climate Café
Welcome!
We are glad you are interested in conversing with the Café Guest and other participants invested in climate justice, research, policy, advocacy, and actions.
We meet on Zoom | Every 4th Friday of the Month, 9–10 am EST
REGISTER NOW for the next free café event.
Don’t forget to visit About the Café to learn:
What the Climate Café is and is not, its values, ground rules, and more…
Next Climate Café
Friday, November 29, 2024, 9–10 AM (EST), Zoom, Passcode: 329436 | Register here
Topic: Leveraging Geographic Information System, Remote Sensing and Machine Learning in Climate Change Research
Collaborators: WeCLISH and PARTAKE Africa
Café Guests
Dr Jinfei Wang
ORCID: https://orcid.org/0000-0002-8404-0530
Dr Jinfei Wang is a Professor in the Department of Geography and Environment at Western University.
As an expert in remote sensing and Geographic Information Systems (GIS), Dr Wang leads remote sensing research in Sub-Saharan African countries, including Malawi. She is also one of PARTAKE Africa’s leading researchers who will examine the spatiotemporal landscape diversity in Malawi and Rwanda.
Dr Wang is passionate about applying diverse methods and data to examine the impact of climate change on the environment. Her research interests include (a) algorithms for automatic linear and other human-made feature detection from images; (b) methods for GIS feature extraction and land use/cover change detection in urban environments using multispectral and hyperspectral data; (c) methods for object-oriented information extraction from high resolution remotely sensed imagery; and (d) applications of radar/optical remote sensing and GIS for environmental change analysis near large rivers/mountains and in marsh and mangrove wetlands.
Kamaldeen Mohammed
ORCID: https://orcid.org/0000-0003-0231-3142
Kamaldeen Mohammed is a WeCLISH Fellow and a PhD candidate in the Department of Geography and Environment at the Faculty of Social Sciences, Western University.
Mohammed is interested in researching the vulnerability of smallholder farmers to climate change and pathways for climate change adaptation, resilience, and mitigation in sub–Saharan Africa.
He is keen to understand the implications of climate change on agriculture, food security, forests, health and livelihoods in rural Africa and ways of harnessing nature to improve climate change resilience and mitigation.
To this end, he has used GIS, remote sensing, and machine learning in his research in Sub-Saharan African countries, including Ghana, Malawi, and Tanzania.
Co-supervised by Drs Luginaah and Wang, Mohammed is pursuing participatory community mapping of forest ecosystem services, carbon stock and food security in Africa for his dissertation research.
Description
Let’s drink in the WeCLISH Climate Café’s Zoom room and connect over an insightful conversation with this month’s Café Guests: Dr Jinfei Wang and Kamaldeen Mohammed.
The integration of GIS, remote sensing, and machine learning is transforming climate change research by providing powerful tools to monitor and analyze environmental changes. Professor Wang, an expert in remote sensing and GIS and a PARTAKE Africa’s leading academic researcher, will join Kamaldeen Mohammed, a WeCLISH Fellow to demonstrate how they have been using multisource remote sensing and machine learning in their research. Specifically, they will present a framework that combines local knowledge, multi-source remote sensing, and advanced machine learning, such as super learning to improve forest carbon assessment in data-poor regions.
Please mark your calendar and join us to listen, get inspired, and share your thoughts for an engaging conversation!
Readings
- Singh, C., Karan, S.K., Sardar, P., & Samaddar, S. R. (2022). "Remote sensing-based biomass estimation of dry deciduous tropical forest using machine learning and ensemble analysis." Journal of Environmental Management,308, 114639. https://doi.org/10.1016/j.jenvman.2022.114639
- Mukama, K., Mustalahti, I., & Zahabu, E. (2012). Participatory forest carbon assessment and REDD+: Learning from Tanzania. International Journal of Forestry Research, 1, 126454.