Efficient Drone-Based Rare Plant Monitoring Using a Species Distribution Model and AI-Based Object Detection

William Reckling,Gary Kauffman,Karl Wegmann,Helena Mitasova,Rebekah Reid

doi:10.3390/drones5040110

William Reckling, Gary Kauffman + Show 3 more

Open Access

https://doi.org/10.3390/drones5040110

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Abstract

Monitoring rare plant species is used to confirm presence, assess health, and verify population trends. Unmanned aerial systems (UAS) are ideal tools for monitoring rare plants because they can efficiently collect data without impacting the plant or endangering personnel. However, UAS flight planning can be subjective, resulting in ineffective use of flight time and overcollection of imagery. This study used a Maxent machine-learning predictive model to create targeted flight areas to monitor Geum radiatum, an endangered plant endemic to the Blue Ridge Mountains in North Carolina. The Maxent model was developed with ten environmental layers as predictors and known plant locations as training data. UAS flight areas were derived from the resulting probability raster as isolines delineated from a probability threshold based on flight parameters. Visual analysis of UAS imagery verified the locations of 33 known plants and discovered four previously undocumented occurrences. Semi-automated detection of plant species was explored using a neural network object detector. Although the approach was successful in detecting plants in on-ground images, no plants were identified in the UAS aerial imagery, indicating that further improvements are needed in both data acquisition and computer vision techniques. Despite this limitation, the presented research provides a data-driven approach to plan targeted UAS flight areas from predictive modeling, improving UAS data collection for rare plant monitoring.

Highlights

Monitoring rare plant populations is critical to identifying threats to occurrences and establishing long-term population trends
To target Unmanned aerial systems (UAS) monitoring of rare plants in challenging-to-inaccessible locations and acquire UAS data with a high probability of finding new plant locations, we propose a general approach/workflow that combines machine learning with UAS flight planning designed explicitly for a studied plant (Figure 1)
Targeted Flight Plan and UAS Data Isolines generated from the predictive model for the 230,955 km2 Blue Ridge ecoregion resulted in 173 polygons totaling 1840 km2 with a 95% probability that environmental conditions are suitable for Geum radiatum (Figure 11)

Summary

Introduction

Monitoring rare plant populations is critical to identifying threats to occurrences and establishing long-term population trends. Monitoring is the repeated process of collecting and analyzing data about a species to evaluate progress towards a management objective [1,2]. In the United States, monitoring is mandated by the U.S Congress, the federal Bureau of Land Management, and the individual States. Federal regulations such as The Endangered Species Act, National Environmental Policy Act, and Federal Land Policy Act outline the steps to protect and recover endangered species. The methods include resource management activities such as research, census, habitat protection, and species restoration to unoccupied parts of the historic range [3]

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