Innovations to expand drone data collection and analysis for rangeland monitoring

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 days, while a workflow without these innovations was estimated to require 141 days 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.

Remote sensing from unoccupied aerial systems: Opportunities to enhance Arctic plant ecology in a changing climate

“…200 × 300 m) using a standard RGB camera can generate data in excess of tens of gigabytes (Wyngaard et al, 2019). UAS flights are often conducted as successive overlapping missions to cover a study area (Gillan et al, 2021), and in some cases will include data collected from multiple instruments simultaneously (e.g. Yang et al, 2020).…”

“…Long-term data preservation is also an important and often digital object identifiers (DOIs) for data and uses an API for data search, discovery and retrieval (Gillan et al, 2019(Gillan et al, , 2021Swetnam et al, 2017). Platforms like CyVerse also provide cloud computation and analysis support, facilitating a fully cloud-based data processing, storage and publishing workflow (e.g.…”

“…Platforms like CyVerse also provide cloud computation and analysis support, facilitating a fully cloud-based data processing, storage and publishing workflow (e.g. Gillan et al, 2021).…”

“…New or expanded investments in open‐source, cloud‐based, secure and version‐controlled data storage platforms that have hierarchical storage capabilities should facilitate ease of discovery and use of UAS data. A notable example is the CyVerse Open Science Workspace (https://cyverse.org/), which supports hierarchical data storage, includes file and archive version control, provides digital object identifiers (DOIs) for data and uses an API for data search, discovery and retrieval (Gillan et al, 2019, 2021; Swetnam et al, 2017). Platforms like CyVerse also provide cloud computation and analysis support, facilitating a fully cloud‐based data processing, storage and publishing workflow (e.g.…”

Democratizing macroecology: Integrating unoccupied aerial systems with the National Ecological Observatory Network

“…This can result in massive time savings, particularly when surveying large areas. For instance, Gillan et al (2021) was able to survey and process data covering over 190 ha of rangeland in approximately 30 days versus an estimated 141 days using a conventional UAS workflow, with an estimated 47 days saved just from using an RTK system versus GCPs. Even with RTK and PPK corrections, it is still considered good practice to lay out some GCPs at precisely known locations, then quantify geolocation error in the final SfM products by measuring the difference between the field‐ and UAS‐measured GCP locations.…”