I build geospatial analytical systems for understanding how complex landscapes behave through time.
My work focuses on Earth observation, remote sensing, and spatial modeling to diagnose ecological dynamics in disturbance-prone landscapes.
Rather than treating landscapes as static maps or collections of indices, I approach them as relational systems whose internal structure evolves through time. Using multispectral satellite time-series and terrain-conditioned environmental analysis, I build diagnostic frameworks that preserve the signals and constraints embedded in environmental data while producing spatial products that can meaningfully inform planning, risk assessment, and land-use decisions.
My current work centers on the Relational Landscapes framework and the RLX Covariance Kinematics monitoring system. These systems analyze relationships among vegetation productivity, moisture expression, terrain structure, and disturbance signals to detect structural ecological change across heterogeneous landscapes.
Across desktop and cloud geospatial environments, I develop reproducible workflows that integrate remote sensing, spatial modeling, and heterogeneous environmental datasets, much of it implemented using Google Earth Engine and satellite time-series processing pipelines.
I am particularly interested in pre-disturbance landscape conditions, wildfire-prone ecosystems, and the development of spatial diagnostics that translate complex environmental systems into decision-ready geospatial intelligence without flattening the ecological nuance those systems contain.