My research examines how urban development and environmental change affect ecosystems, with particular attention to urban forests, surface temperature, and the sustainability of vegetation in cities. I use satellite remote sensing, geospatial analysis, and statistical modeling to investigate environmental patterns and inform climate adaptation.
I am a PhD researcher in Environmental and Ecological Engineering at Purdue University. My work focuses on the relationships among urban development, environmental conditions, and the ability of urban vegetation to remain viable under changing climates. My training in physics, Earth system physics, and atmospheric sciences informs the questions I ask and the quantitative methods I use.
I am interested not only in how much tree canopy a city has, but also in the condition of that vegetation, the pressures it experiences, and how its ecological benefits may change over time. My broader interests include Earth observation, urban climate, environmental disturbance, spatial modeling, and evidence-based environmental management.
Five primary domains connecting physical sciences, geospatial computing, and ecological adaptation.
How built environments, land cover, and environmental stress relate to urban tree greenness, condition, and long-term sustainability.
Urban heat islands, land surface temperature, urbanization, and climate-related exposure in cities.
Satellite observations and GIS for monitoring vegetation, measuring environmental variability, and characterizing landscapes.
Nonlinear statistical modeling, machine learning, spatial analysis, and model validation for environmental research.
How vegetation condition and future vulnerability intersect with neighborhood susceptibility and urban adaptation priorities.
A three-chapter dissertation on urban ecosystem resilience and climate adaptation • Indianapolis, Indiana • Purdue University • Advisor: Brady S. Hardiman
My dissertation asks how urban form and future warming shape the condition of inventoried urban trees and whether the resulting differences in ecological reliability matter for neighborhoods with greater heat-health susceptibility. The three chapters share an Indianapolis environmental data system but answer different questions: Chapter 1 examines current greenness and heat relationships across spatial scales; Chapter 2 models taxon-specific greenness responses under future warming scenarios; Chapter 3 investigates how current condition and future reliability are distributed across neighborhoods.
At what spatial scales are imperviousness, tree greenness, and summer surface heat most strongly associated?
How might different tree taxa respond to projected climate and urbanization warming under surrounding canopy context?
Do susceptible neighborhoods have canopy that is currently green and projected to remain reliable under future warming?
Explore detailed investigations across urban ecology, thermal environments, and remote sensing.
This study investigates whether impervious surfaces relate to tree greenness and urban temperature at the same spatial extent, integrating municipal tree records with 16 PlanetScope observations and Landsat summer LST.
Estimates how observed greenness-temperature relationships differ across common tree taxa and planting contexts, combining models with future urbanization and land surface temperature projections for 2050 and 2100.
Investigating whether the loss and removal of ash street trees associated with emerald ash borer infestation correspond to changes in summer land surface temperature in Chicago using Landsat thermal observations (2007-2017).
Evaluates whether high-resolution satellite observations collected across the growing season can help distinguish urban tree species through phenological patterns using multi-date PlanetScope imagery and Random Forest.
Investigates whether Indianapolis neighborhoods with greater medical susceptibility or social vulnerability have comparable access to urban trees in good current condition and with lower projected future vulnerability.
Documented analytical components and verified computational milestones.
Municipal inventory of 188,476 trees integrated with satellite greenness, canopy, imperviousness, and temperature layers.
Impervious surface representations compared at six window widths from 30m to 330m using generalized additive models.
Main GAM relationships and partial-response curves developed identifying response-specific scale divergence.
Lowest modeled greenness decile mapped across central and heavily developed municipal corridors in Indianapolis.
Multi-date satellite vegetation extraction and machine-learning classification experiments documented.
Preliminary modeling of municipal tree removal and thermal trend relationships in Chicago.
PlanetScope multispectral imagery, Landsat, NDVI time-series, Land Surface Temperature (LST).
ArcGIS Pro, raster extraction, moving window convolution, geospatial integration, spatial mapping.
Random Forest, recursive feature elimination, species-classification experiments.
Generalized additive models (GAMs), nonlinear response functions, repeated subsampling, sensitivity analysis.
Python, pyGAM, R, Google Earth Engine, geospatial data science libraries.
Census-tract aggregation, CDC PLACES, CDC/ATSDR Social Vulnerability Index (SVI), ACS.
I welcome research connections involving urban ecosystems, environmental sustainability, climate resilience, remote sensing, and geospatial analysis.
Get in Touch