Innovations to expand drone data collection and analysis for rangeland monitoring

Gillan, Jeffrey K.; Ponce‐Campos, Guillermo E.; Swetnam, Tyson L.; Gorlier, Alessandra; Heilman, Philip; McClaran, Mitchel P.

doi:10.1002/ecs2.3649

Cited by 14 publications

(9 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Vegetation-induced elevation error has been studied using convolutional neural networks and drone-based high-resolution orthophotos (Jiang et al 2022). A data file of 900 images takes 24 hours to process on a computer (PC), but a high-performance computer (HPC) can complete the analysis in 1.6 hours (Gillan et al 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Using a 2D Profilometer to Determine Volume and Thickness of Stockpiles and Ground Layers of Roads

Niskanen

Immonen

Hallman

et al. 2023

J. Transp. Eng., Part B: Pavements

View full text Add to dashboard Cite

Construction materials and related management, handling, and storage provisions account for a large part of road construction expenses. For that reason, improved material flow monitoring techniques can achieve significant cost and time savings, as well as better quality control. The study assessed the performance of a solid-state pulsed time-of-flight laser lidar profilometer in measuring the volume of soil stockpiles and road layer thicknesses. The 3D (X, Y, Z, and intensity) image calculation was based on the analysis of multiple combined point clouds measured with an excavator-integrated profilometer. Error analysis confirmed the accuracy of road layer thickness estimation within one centimetre and an error level of approximately 3% when measuring soil stockpile volumes. In combination with a theoretical model of the superstructure, this 3D measurement technique can help contractors and supervisors to ensure road quality.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Using a 2D Profilometer to Determine Volume and Thickness of Stockpiles and Ground Layers of Roads

Niskanen

Immonen

Hallman

et al. 2023

J. Transp. Eng., Part B: Pavements

View full text Add to dashboard Cite

show abstract

“…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.…”

Section: Challenges and Next Steps For Uas Data Processing And Sharingmentioning

confidence: 99%

“…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).…”

Section: Challenges and Next Steps For Uas Data Processing And Sharingmentioning

confidence: 99%

“…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).…”

Section: Challenges and Next Steps For Uas Data Processing And Sharingmentioning

confidence: 99%

“…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.…”

Section: Perspectives Challenges and Future Directionsmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

The Arctic is warming at a faster rate than any other biome on Earth, resulting in widespread changes in vegetation composition, structure and function that have important feedbacks to the global climate system. The heterogeneous nature of arctic landscapes creates challenges for monitoring and improving understanding of these ecosystems, as current efforts typically rely on ground, airborne or satellite‐based observations that are limited in space, time or pixel resolution. The use of remote sensing instruments on small unoccupied aerial systems (UASs) has emerged as an important tool to bridge the gap between detailed, but spatially limited ground‐level measurements, and lower resolution, but spatially extensive high‐altitude airborne and satellite observations. UASs allow researchers to view, describe and quantify vegetation dynamics at fine spatial scales (1–10 cm) over areas much larger than typical field plots. UASs can be deployed with a high degree of temporal flexibility, enabling observation across diurnal, seasonal and annual time‐scales. Here we review how established and emerging UAS remote sensing technologies can enhance arctic plant ecological research by quantifying fine‐scale vegetation patterns and processes, and by enhancing the ability to link ground‐based measurements with broader‐scale information obtained from airborne and satellite platforms. Synthesis. Improved ecological understanding and model representation of arctic vegetation is needed to forecast the fate of the Arctic in a rapidly changing climate. Observations from UASs provide an approach to address this need, however, the use of this technology in the Arctic currently remains limited. Here we share recommendations to better enable and encourage the use of UASs to improve the description, scaling and model representation of arctic vegetation.

show abstract

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

et al. 2022

View full text Add to dashboard Cite

Macroecology research seeks to understand ecological phenomena with causes and consequences that accumulate, interact, and emerge across scales spanning several orders of magnitude. Broad-extent, fine-grain information (i.e., high spatial resolution data over large areas) is needed to adequately capture these cross-scale phenomena, but these data have historically been costly to acquire and process.Unoccupied aerial systems (UAS or drones carrying a sensor payload) and the National Ecological Observatory Network (NEON) make the broad-extent, fine-grain observational domain more accessible to researchers by lowering costs and reducing the need for highly specialized equipment. Integration of these tools can further democratize macroecological research, as their strengths and weaknesses are complementary. However, using these tools for macroecology can be challenging because mental models are lacking, thus requiring large up-front investments in time, energy, and creativity to become proficient. This challenge inspired a working group of UAS-using academic ecologists, NEON professionals, imaging scientists, remote sensing specialists, and aeronautical engineers at the 2019 NEON Science Summit in Boulder, Colorado, to synthesize current knowledge on how to use UAS with NEON in a mental model for an intended audience of ecologists new to these tools. Specifically, we provide (1) a collection of core principles for collecting high-quality UAS data for NEON integration and (2) a case study illustrating a sample workflow for processing UAS data into meaningful ecological information and integrating it with NEON data collected on the groundwith the Terrestrial Observation System-and remotely-from the Airborne Observation Platform. With this mental model, we advance the democratization of macroecology by making a key observational domain-the broad-extent, fine-grain domain-more accessible via NEON/UAS integration.

show abstract

Innovations to expand drone data collection and analysis for rangeland monitoring

Abstract: Innovations to expand drone data collection and analysis for rangeland monitoring.

Cited by 14 publications

References 34 publications

Using a 2D Profilometer to Determine Volume and Thickness of Stockpiles and Ground Layers of Roads

Using a 2D Profilometer to Determine Volume and Thickness of Stockpiles and Ground Layers of Roads

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

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

Contact Info

Product

Resources

About