Innovations to expand drone data collection and analysis for rangeland monitoring
Jeffrey K. Gillan, Guillermo E. Ponce‐Campos, Tyson L. Swetnam, Alessandra Gorlier, Philip Heilman, Mitchel P. McClaran
Abstract
Abstract In adaptive management of rangelands, monitoring is the vital link that connects management actions with on‐the‐ground changes. Traditional field monitoring methods can provide detailed information for assessing the health of rangelands, but cost often limits monitoring locations to a few key areas or random plots. Remotely sensed imagery, and drone‐based imagery in particular, can observe larger areas than field methods while retaining high enough spatial resolution to estimate many rangeland indicators of interest. However, the geographic extent of drone imagery products is often limited to a few hectares (for resolution ≤1 cm) due to image collection and processing constraints. Overcoming these limitations would allow for more extensive observations and more frequent monitoring. We developed a workflow to increase the extent and speed of acquiring, processing, and analyzing drone imagery for repeated monitoring of two common indicators of interest to rangeland managers: vegetation cover and vegetation heights. By incorporating a suite of existing technologies in drones (real‐time kinematic GPS), data processing (automation with Python scripts, high performance computing), and cloud‐based analysis (Google Earth Engine), we greatly increased the efficiency of collecting, analyzing, and interpreting high volumes of drone imagery for rangeland monitoring. End‐to‐end, our workflow took 30 d, while a workflow without these innovations was estimated to require 141 d to complete. The technology around drones and image analysis is rapidly advancing which is making high volume workflows easier to implement. Larger quantities of monitoring data will significantly improve our understanding of the impact management actions have on land processes and ecosystem traits.