Our proposal "Facilitating Wildfire Insurance Business with Big Data and Machine Learning" (PI: Min Chen; Co-PI: Volker Radeloff) has been selected for funding by the American Family-UW-madison data science initiative program.

Recent wildfires across the western US have caused enormous environmental hazards and economic losses. This project will prototype a machine learning framework, modeled on fires in California, that will improve prediction of wildfire probability and severity at daily, weekly, and monthly scales.

Learn more at https://datascience.wisc.edu/institute/research-amfam/

Our paper "Potential of hotspot solar-induced chlorophyll fluorescence for better tracking terrestrial photosynthesis", led by Dalei Hao, is published at Global Change Biology. This study reveals that leaf physiology, canopy structure, and sun‐sensor geometries all affect the SIF‐GPP relationship. Also it seems a good use of viewing angle (e.g., magical hotspot angle) can help improve SIF-GPP linear relationship.

Check it out at https://doi.org/10.1111/gcb.15554