Using Bi-Seasonal WorldView-2 Multi-Spectral Data and Supervised Random Forest Classification to Map Coastal Plant Communities in Everglades National Park

A study using bi-seasonal WorldView-2 satellite data and supervised random forest classification successfully mapped coastal plant communities in Everglades National Park with 86% accuracy, revealing that black and red mangroves dominated the landscape, covering half of the study site. The approach outperformed single-season data, providing fine-scale resolution to detect changes in rare and endangered species habitats, including the buttonwood/glycophyte community harboring the federally-endangered Chromolaena frustrata. This method offers a robust tool for monitoring vegetation shifts due to sea level rise and land-use change, with implications for other threatened ecosystems.

Wendelberger, K., Gann, D., & Richards, J. (2018). Using Bi-Seasonal WorldView-2 Multi-Spectral Data and Supervised Random Forest Classification to Map Coastal Plant Communities in Everglades National Park. Sensors, 18(3), 829. https://doi.org/10.3390/s18030829