Season, Classifier, and Spatial Resolution Impact Honey Mesquite and Yellow Bluestem Detection using an Unmanned Aerial System

Abstract

In Texas, mesquite and yellow-bluestem invasions are widespread. Identifying and monitoring juvenile and adult plants using high-resolution imagery from airborne sensors while they colonize new areas across the landscape can help land managers prioritize locations for treatment and eradication. In this study, we evaluated how data collection design using an unmanned aerial system (UAS) can affect plant detection and mapping. We used a Phantom 3 Professional unmanned aerial vehicle with a Parrot Sequoia multispectral camera for detecting and mapping native honey mesquite (Prosopis glandulosa) and non-native yellow bluestem (Bothriochloa ischaemum) at a rangeland site in northwest Texas. Flights were conducted seasonally during the period from summer 2017 to fall 2018 to test the seasonal impact of detecting plant species. Flights were conducted at altitudes of 30, 60, and 100 m, and four image classification techniques were tested to determine their viability of detecting distinct plant species. Results suggest that flights at 100-m aircraft altitude during the spring season are more effective (>80% user accuracies) for mapping mesquite canopies based on reflectance values and image segmentation information. Yellow bluestem mapping accuracies were low (< 20% user accuracies). Lower spatial resolution (100-m altitude flights, 12-cm pixel resolution) provided less noise and more generalization capabilities for the image classification methods. Overall, random forests and Support Vector Machine classification algorithms outperformed probability-based image classifiers. Land owners and rangeland ecologists using their own UAS in rangeland management can use this information to plan their data collection campaigns before the application of chemical treatments or manual eradication.