Swarm Information Foraging to Develop Forest Inventories

This study addresses the challenge of accurate wildfire behavior predictions by developing comprehensive forest attribute databases. Diameter at Breast Height (DBH) values of trees are important to predict fire behavior across forest stands. Diameter at Breast Height (DBH) metrics for are difficult to obtain at a large spatial scale. This paper proposes a multi-agent based methodology for developing a trend function relating area density and tree height to DBH values. In the multi-agent system, an effective tree selection algorithm is essential in developing an accurate trend function while minimizing the number of trees that need to be measured. Gaussian regression functions as a decision-making algorithm by motivating the agent to sample trees that minimize the uncertainty of the developing trend function. Comparing tree selection value algorithms, we find Gaussian regression as a UAV decision making algorithm excels in selecting trees to measure that develop a trend function yielding the most accurate DBH values compared to a threshold-based approach and random tree selection. Gaussian regression is also utilized for the computation of the final trend function due to its effectiveness in capturing non-linear relationships between variables. This research showcases the effectiveness of Gaussian regression in dynamic information foraging problems as well as the effectiveness of utilizing multi-agent systems for DBH collection and predictions.