What I Work On
I develop computational methods that combine machine learning, remote sensing, and spatial analysis to study how forests and landscapes change over time โ and what that means for biodiversity, carbon storage, and ecosystem health.
### Remote Sensing & GIS Leveraging satellite imagery, LiDAR, and geospatial tools to map and monitor forests at scale โ from individual tree crowns to continental patterns.
Remote Sensing GIS
### GeoAI & Machine Learning Applying deep learning, computer vision, and spatial statistics to extract ecological insights from complex geospatial datasets.
Deep Learning Spatial ML
### Forest & Landscape Ecology Studying how forest structure, composition, and disturbance regimes shape ecosystems across spatial and temporal scales.
Forest Ecology Landscape
Research Themes
Developing automated approaches to detect forest disturbances โ including deforestation, degradation, and recovery โ using time series of satellite observations. This work combines change detection algorithms with machine learning classifiers to produce timely, accurate maps of forest dynamics.
Using remote sensing and AI to predict species distributions, map biodiversity patterns, and understand how landscape structure influences ecological communities. Multi-sensor data fusion (optical, radar, LiDAR) enables habitat characterization at scales relevant to conservation planning.
Quantifying forest carbon stocks and fluxes through integration of field measurements, allometric models, and remotely sensed canopy structure. These methods support national forest inventories, REDD+ monitoring, and climate change mitigation strategies.
Building open-source software that makes geospatial ecology research more reproducible and accessible. Current projects include forestPulse, an R package for forest change analysis, and iLAND Workbench, a QGIS plugin for individual-based forest landscape and disturbance modeling with full data-preparation workflows, runtime management, and reproducible analysis pipelines.
CEDAR Lab
Computational Ecology, Data Analytics & Remote Sensing
CEDAR Lab focuses on developing and applying geospatial AI methods to pressing questions in forest and landscape ecology. We build open-source tools, publish reproducible research, and share knowledge through tutorials and educational content.
๐ฒ Forest Ecology
๐ฐ๏ธ Remote Sensing
๐ฆ Open Source Tools
๐ Tutorials & Teaching
Latest from the Blog
Check out recent posts on GeoAI methods, R tutorials, and ecology research.