Geoscientist (GIS Analyst/Spatial Data Scientist/Environmental Consultant)

About Me My name is Heather Nicholson and I have a Ph.D. in Geosciences from Florida Atlantic University. My specialty is the use of spatial data science techniques to study the environmental around us in order to better predict future changes. I have over four years of experience with spatial data science techniques, and over six+ years in the geospatial field. I have proficient knowledge in Python and the SKLearn and Python API for ArcGIS libraries. To learn more about my skill set and experience, please see below.

ArcGIS Pro, QGIS, Python, HTML, Anaconda

The following are projects that I have worked on.

Salt Marsh Species Classification and Soil Property Modeling Using Multiple Remote Sensors

Abstract: Salt marshes are highly dynamic ecosystems that rely on multiple environmental and physical drivers that determine species distribution and soil property distribution. However, climate change and human interference are threatening the delicate ecosystem. One of the easiest ways to monitor marsh dynamics is through remote sensing. Traditional methods may not handle the large, non-parametric datasets well and often do not spatially determine areas of uncertainty. This dissertation research developed a framework to map marsh species and predict ground soil properties using multiple remote sensing data sources by integrating modern Object-based Image Analysis (OBIA), machine learning, data fusion, and band indices techniques. It also sought to determine areas of uncertainty in the final outputs and differences between different spectral resolutions. Five machine learning classifiers were examined including Support Vector Machine (SVM) and Random Forest (RF) to map marsh species. Overall results illustrated that RF and SVM typically performed best, especially when using hyperspectral data combined with DEM information. Seven regressors were assessed to map three different soil properties. Again, RF and SVM performed the best no matter the dataset used, or soil property mapped. Soil salinity had r as high as 0.93, soil moisture had r as high as 0.91, and soil organic an r as high as 0.74 when using hyperspectral data.