Satellite remote sensing and integrated modeling

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Classify permanent water using Sentinel-1 SAR in the present, then apply change detection algorithm on Landsat data to determine when areas became permanently inundated

Utilize Sentinel-2 and Random Forest machine learning algorithm to classify tree cover in the present, then examine tree cover and above ground biomass trends between different forest areas

Integrate satellite remote sensing forest fire and smoke density data with infrastructure data to gain insights for risk reduction

Compute mean anomaly of vegetation index to estimate sustained mangrove change, then examine specific areas of interest

Consulting for Planet Labs on application of Planet satellite data to climate risk disclosures under proposed Federal SEC climate disclosure rule

Processed aerial LiDAR and ground plot data for use in global mangrove biomass mapping effort