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Optical satellite data products (e.g., Sentinel-2, PlanetScope, Landsat) require proper validation across diverse ecosystems. This has conventionally been achieved using airborne and more recently unmanned aerial vehicle (UAV) based hyperspectral sensors which constrain operations by both their cost and complexity of use. The MicaSense Altum is an accessible multispectral sensor that integrates a radiometric thermal camera with 5 bands (475 nm–840 nm). In this work we assess the spectral reflectance accuracy of a UAV-mounted MicaSense Altum at 25, 50, 75, and 100 m AGL flight altitudes using the manufacturer provided panel-based reflectance conversion technique for atmospheric correction at the Mer Bleue peatland supersite near Ottawa, Canada. Altum derived spectral reflectance was evaluated through comparison of measurements of six known nominal reflectance calibration panels to in situ spectroradiometer and hyperspectral UAV reflectance products. We found that the Altum sensor saturates in the 475 nm band viewing the 18% reflectance panel, and for all brighter panels for the 475, 560, and 668 nm bands. The Altum was assessed against pre-classified hummock-hollow-lawn microtopographic features using band level pair-wise comparisons and common vegetation indices to investigate the sensor's viability as a validation tool of PlanetScope Dove 8 band and Sentinel-2A satellite products. We conclude that the use of the Altum needs careful consideration, and its field deployment and reflectance output does not meet the necessary cal/val requirements in the peatland site. [ABSTRACT FROM AUTHOR]

Substrate complexity is strongly related to biodiversity in aquatic habitats. We illustrate a novel framework, based on Structure-from-Motion photogrammetry (SfM) and Multi-View Stereo (MVS) photogrammetry, to quantify habitat complexity in freshwater ecosystems from Unmanned Aerial Vehicle (UAV) and underwater photography. We analysed sites in the Xingu river basin, Brazil, to reconstruct the 3D structure of the substrate and identify and map habitat classes important for maintaining fish assemblage biodiversity. From the digital models we calculated habitat complexity metrics including rugosity, slope and 3D fractal dimension. The UAV based SfM-MVS products were generated at a ground sampling distance (GSD) of 1.20–2.38 cm while the underwater photography produced a GSD of 1 mm. Our results show how these products provide spatially explicit complexity metrics, which are more comprehensive than conventional arbitrary cross sections. Shallow neural network classification of SfM-MVS products of substrate exposed in the dry season resulted in high accuracies across classes. UAV and underwater SfM-MVS is robust for quantifying freshwater habitat classes and complexity and should be chosen whenever possible over conventional methods (e.g., chain-and-tape) because of the repeatability, scalability and multi-dimensional nature of the products. The SfM-MVS products can be used to identify high priority freshwater sectors for conservation, species occurrences and diversity studies to provide a broader indication for overall fish species diversity and provide repeatability for monitoring change over time. [ABSTRACT FROM AUTHOR]