Research Overview

My research focuses on satellite remote sensing, time-series analysis, and geospatial artificial intelligence (GeoAI) to investigate forest disturbance, resilience, and ecosystem dynamics under environmental change. I integrate multi-sensor satellite data fusion, dense time-series modeling, and explainable machine learning to extract reliable and interpretable signals from large-scale Earth observation datasets.

Key Research Themes

Multi-sensor Data Fusion

I developed the Time-series-based Image Fusion (TIF) algorithm to harmonize Landsat and Sentinel-2 imagery into a dense 10 m optical time series. This work has been published in Remote Sensing of Environment and is being integrated into NASA’s Harmonized Landsat and Sentinel-2 (HLS) pipeline.

Forest Resilience and Disturbance

My work evaluates satellite-derived resilience indicators using field observations and time-series analysis, with a focus on understanding forest recovery following drought, insect outbreaks, and other disturbances.

Explainable Machine Learning and GeoAI

I apply machine learning models together with explainability tools (e.g., SHAP) to identify drivers of forest disturbance, resilience change, and infrastructure vulnerability.

Vegetation Risk and Infrastructure Vulnerability

In collaboration with the StormWise program and the Eversource Energy Center, I develop machine learning models that integrate satellite, aerial, LiDAR, and infrastructure data to quantify vegetation-related power outage risk. These tools translate environmental monitoring into actionable insights for utility vegetation management and climate adaptation.