Michael C. Dietze

This study integrates GEDI LiDAR with an existing data assimilation system to improve terrestrial carbon monitoring, reducing woody biomass uncertainty by 63.7% and achieving RMSE reduction of 38.3 kg/m2.

This study uses a hybrid state data assimilation framework to generate accurate carbon budget maps of North America, reducing uncertainties in carbon pools and identifying regional trends.

This study develops an iterative Bayesian forecast model for Ixodes scapularis population dynamics, showing improved accuracy with weather and host data, particularly for nymph survival and disease risk prediction.

The paper evaluates how plant invasions affect tick exposure risk through microclimate and host abundance pathways in native and invaded plant communities.

This study reduces carbon cycle uncertainties using model-data fusion at NEON sites by integrating multiple data sources and identifying indirect constraints.

This paper compares pre-settlement and modern forests in the Upper Midwest using historical and current data, finding significant changes in composition, biomass, and spatial patterns.