JDJCSET | Earth and Environmental Sciences Poster Session
Post-Hurricane Helene Landslide Detection and Slope Analysis in Western North Carolina Using Google Earth Engine
Academic Level at Time of Presentation
Graduate
Major
Geoinformatics
List all Project Mentors & Advisor(s)
Dr. Haluk Cetin
Presentation Format
Poster Presentation
Abstract/Description
As climate-induced disasters become more frequent, rapid identification of at-risk areas is crucial to protecting communities and fostering resilience. This project demonstrates the rapid capabilities of Google Earth Engine (GEE) for assessing environmental disasters by identifying landslide-prone areas in Western North Carolina following Hurricane Helene. The findings aim to support disaster response and inform strategies for resilient and sustainable community development. Using Sentinel-2 imagery processed in GEE, this project applied cloud masking, normalized difference vegetation index (NDVI), and normalized burn ratio (NBR) to quantify significant land cover changes. Key thresholds and slope data were used to isolate landslide areas and assess terrain characteristics in affected regions. Preliminary findings indicate that landslide detection was highly effective, accurately classifying most landslide-prone areas validated through visual interpretation of true color imagery. The Normalized Burn Ratio (NBR) identified landslide areas to a broader extent, while the Normalized Difference Vegetation Index (NDVI) proved more effective for detecting changes near streams and turbid water bodies. An analysis of slope data is ongoing to explore potential correlations between slope characteristics and landslide occurrences. These results underscore the value of Google Earth Engine for swift environmental disaster assessments. The insights gained from this study can inform disaster mitigation and urban planning efforts, aiding in the development of resilient, sustainable communities in landslide-prone areas.
Fall Scholars Week 2024 Event
Earth and Environmental Sciences Poster Session
Post-Hurricane Helene Landslide Detection and Slope Analysis in Western North Carolina Using Google Earth Engine
As climate-induced disasters become more frequent, rapid identification of at-risk areas is crucial to protecting communities and fostering resilience. This project demonstrates the rapid capabilities of Google Earth Engine (GEE) for assessing environmental disasters by identifying landslide-prone areas in Western North Carolina following Hurricane Helene. The findings aim to support disaster response and inform strategies for resilient and sustainable community development. Using Sentinel-2 imagery processed in GEE, this project applied cloud masking, normalized difference vegetation index (NDVI), and normalized burn ratio (NBR) to quantify significant land cover changes. Key thresholds and slope data were used to isolate landslide areas and assess terrain characteristics in affected regions. Preliminary findings indicate that landslide detection was highly effective, accurately classifying most landslide-prone areas validated through visual interpretation of true color imagery. The Normalized Burn Ratio (NBR) identified landslide areas to a broader extent, while the Normalized Difference Vegetation Index (NDVI) proved more effective for detecting changes near streams and turbid water bodies. An analysis of slope data is ongoing to explore potential correlations between slope characteristics and landslide occurrences. These results underscore the value of Google Earth Engine for swift environmental disaster assessments. The insights gained from this study can inform disaster mitigation and urban planning efforts, aiding in the development of resilient, sustainable communities in landslide-prone areas.