At this year’s meeting of the Savanna Science Network in Skukuza, located in Kruger National Park, PhD researcher Luisa Pflumm shared new insights into savanna ecosystem monitoring using drone-based remote sensing.
In her presentation titled “Multitemporal UAS-Based Monitoring of Savanna Phenology and Drought Stress in Kruger National Park,” Luisa introduced results from an extensive two-year multispectral drone time series collected across study sites in the park. The dataset provides a detailed view of seasonal vegetation dynamics and early indicators of drought stress in savanna ecosystems, demonstrating the potential of high-resolution UAS observations for ecological monitoring.
The work is the result of an international collaboration. Co-authors from the Earth Observation Research Cluster (EORC) include Mirjana Bevanda, Antonio Castaneda, Anna Bischof, and Tobias Ullmann. The project is conducted together with partners from South African National Parks (SANParks), including Corli Coetsee and Ben Wigley, as well as collaborators from University of Bayreuth, Steve Higgins and Nita Palett.
During the talk, Luisa highlighted how repeated multispectral UAS surveys allow researchers to track fine-scale phenological changes across the heterogeneous savanna landscape. The time series captures variations in vegetation greenness and productivity across seasons and between years, providing new perspectives on how savanna vegetation responds to climatic variability.
Looking ahead, the project will expand its methodological framework by integrating drone-based LiDAR and thermal observations. These additional data streams are expected to enable a more comprehensive analysis of savanna structure, plant stress, and energy balance, supporting improved understanding of spatio-temporal dynamics in one of the world’s most iconic ecosystems.
The presentation underscored the growing role of UAS technology in ecological research and demonstrated how long-term, high-resolution datasets can contribute to advancing savanna science and ecosystem monitoring.
