Current Practices in UAS-based Environmental Monitoring

Abstract

With the increasing role that unmanned aerial systems (UAS) are playing in data collection for environmental studies, two key challenges relate to harmonizing and providing standardized guidance for data collection, and also establishing protocols that are applicable across a broad range of environments and conditions. In this context, a network of scientists are cooperating within the framework of the Harmonious Project to develop and promote harmonized mapping strategies and disseminate operational guidance to ensure best practice for data collection and interpretation. The culmination of these efforts is summarized in the present manuscript. Through this synthesis study, we identify the many interdependencies of each step in the collection and processing chain, and outline approaches to formalize and ensure a successful workflow and product development. Given the number of environmental conditions, constraints, and variables that could possibly be explored from UAS platforms, it is impractical to provide protocols that can be applied universally under all scenarios. However, it is possible to collate and systematically order the fragmented knowledge on UAS collection and analysis to identify the best practices that can best ensure the streamlined and rigorous development of scientific products.

Drone-derived canopy height predicts biomass across non-forest ecosystems globally

“…Our findings demonstrate that data will be most comparable when wind speeds are similar, but also that, where differences are unavoidable, it will be possible to derive corrections for how wind influences canopy reconstructions from drone photogrammetry. We call for the continued development of harmonised and community-based protocols to maximise knowledge gains and support cross-biome syntheses 31,33,34,48 .…”

“…Such in-camera processing is a problem for JPG-format image files from cameras like the widely used DJI Phantom 4 Advanced/Pro FC6310 camera, and so capturing RAW-format images can help avoid this error source 38,45 . We anticipate ongoing improvements to camera geolocation and orientation information from drone systems will continue to improve the accuracy and reliability of the camera parameter estimation, particularly in densely vegetated and thus texturally complex settings (Supplementary Note 1) 34,38,45,57,58 .…”

“…Fully realising the potential of drone photogrammetry in plant science requires reproducible workflows, which minimise biases 30,33,34 . Over the past few years, thousands of hectares of low-stature ecosystems have been surveyed with drones across the globe, yielding information-rich datasets.…”

“…Over the past few years, thousands of hectares of low-stature ecosystems have been surveyed with drones across the globe, yielding information-rich datasets. However, drone-photogrammetry products are sensitive to the ways in which data are (i) collected (e.g., ground sampling distance, image overlap, viewing geometry, spatial control, illumination conditions) 27,30,[34][35][36][37][38] , (ii) processed (e.g., software, lens model, specification of control accuracy, selection of processing quality, depth filtering) 27,[36][37][38] , and (iii) analysed (e.g., canopy height metrics, spatial grain and interpolation, statistical treatment) 20,[27][28][29]31 . These sensitivities are more pronounced for subjects with complex texture, such as vegetation, and hinder comparisons between products obtained from different workflows.…”

Multispectral Mapping on 3D Models and Multi-Temporal Monitoring for Individual Characterization of Olive Trees

“…Among the available aerial remote sensing platforms, Unmanned Aerial Vehicles (UAVs) are considered to be cost-effective and they can capture high-resolution imagery from multiple viewpoints [11,12]. In contrast to satellite images, UAV-based cameras provide a higher spatial resolution and a more detailed observation of individual plants [13]. These solutions are an increasingly used trend for several monitoring tasks using various types of sensors [14].…”