Welcome to GeoAI for Ecology
News
GeoAI
Introducing this blog โ a space for tutorials, research notes, and ideas at the intersection of geospatial AI and ecological science.
Sep 5, 2026
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 taking forest field plots and drone or satellite imagery through to analysis-ready summaries and iLand model inputs, 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. Together they form a single pipeline from field data to simulation.
Featured Tools
iLAND Workbench
A dockable QGIS plugin that brings the iLAND individual-based forest landscape and disturbance model into a GIS workflow โ runtime management, climate and soil data pipelines, and reproducible analysis, all inside QGIS.
QGIS Plugin Forest Modeling Zenodo DOI
forestPulse
An R package that turns forest field plots and drone or satellite imagery into analysis-ready summaries โ canopy structure, basal area, species composition โ and assembles the inputs needed to drive the iLand model.
R Package Field & Imagery Zenodo DOI
2 Open-source tools published
v4.1 Latest iLAND Workbench release
3 Platforms supported: Windows, macOS, Linux
DOI Archived & citable on Zenodo
The CEDAR Lab
Computational Ecology, Data Analytics & Remote Sensing
The 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.
Latest from the Blog
Recent posts on GeoAI methods, R tutorials, and ecology research.
Stay in the loop
New tutorials, tool releases, and research notes โ delivered once or twice a month. Or grab the RSS feed if youโd rather not share an email.
No spam. Unsubscribe with one click. See all subscribe options.