Forest Land Coverage Mapping Using Machine Learning Classification

Forests are responsible for approximately 31% of global land area and are crucial to the sustainability of natural resources for life like water, carbon, and air. Understanding how forest biomass is developing helps us better understand the management and preservation of these vital ecosystems. One factor contributing to the health of forest ecosystems is the total amount of land that forests occupy. In the Larsen Lab, my mentor Brian Lee and I have combined remote sensing technology and machine learning to map out how forest land coverage is changing. We used a random forest machine learning approach to classify the data to identify different types of land from satellite imagery. By combining this gathered data and analysis methods, we can determine the composition of the global land area and its allocation.

Project Mentor: Brian Lee

Faculty Advisor: Ashley Larsen