Cunliffe, A. M. | Search bioRxiv

bioRxiv2020

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

Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, yet are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely-sensed biomass products and are undersampled by in-situ monitoring. Current global change threats emphasise the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we assess whether canopy height inferred from drone photogrammetry allows the estimation of aboveground biomass (AGB) across low-stature plant species sampled through a global site network. We found mean canopy height is strongly predictive of AGB across species, demonstrating standardised photogrammetric approaches are generalisable across growth forms and environmental settings. Biomass per-unit-of-height was similar within, but different among, plant functional types. We find drone-based photogrammetry allows for monitoring of AGB across large spatial extents and can advance understanding of understudied and vulnerable non-forested ecosystems across the globe.

ecology↗

bioRxiv2018

Vegetation monitoring using multispectral sensors - best practices and lessons learned from high latitudes

Emerging drone technologies have the potential to revolutionise ecological monitoring. The rapid technological advances in recent years have dramatically increased affordability and ease of use of Unmanned Aerial Vehicles (UAVs) and associated sensors. Compact multispectral sensors, such as the Parrot Sequoia (Paris, France) and MicaSense RedEdge (Seattle WA, USA) capture spectrally accurate high-resolution (fine grain) imagery in visible and near-infrared parts of the electromagnetic spectrum, providing supplement to satellite and aircraft-based imagery. Observations of surface reflectance can be used to calculate vegetation indices such as the Normalised Difference Vegetation Index (NDVI) for productivity estimates and vegetation classification. Despite the advances in technology, challenges remain in capturing consistently high-quality data, particularly when operating in extreme environments such as the high latitudes. Here, we summarize three years of ecological monitoring with drone-based multispectral sensors in the remote Canadian Arctic. We discuss challenges, technical aspects and practical considerations, and highlight best practices that emerged from our experience, including: flight planning, factoring in weather conditions, and geolocation and radiometric calibration. We propose a standardised methodology based on established principles from remote sensing and our collective field experiences, using the Parrot Sequoia sensor as an example. With these good practises, multispectral sensors can provide meaningful spatial data that is reproducible and comparable across space and time.

ecology↗