Abstract
In recent times, the use of Unmanned Aerial Vehicles (UAVs) as tools for environmental remote sensing has become more commonplace. Compared to traditional airborne remote sensing, UAVs can provide finer spatial resolution data (up to 1 cm/pixel) and higher temporal resolution data. For the purposes of vegetation monitoring, the use of multiple sensors such as near infrared and thermal infrared cameras are of benefit. Collecting data with multiple sensors, however, requires an accurate spatial co-registration of the various UAV image datasets. In this study, we used an Oktokopter UAV to investigate the physiological state of Antarctic moss ecosystems using three sensors: (i) a visible camera (1 cm/pixel), (ii) a 6 band multispectral camera (3 cm/pixel), and (iii) a thermal infrared camera (10 cm/pixel). Imagery from each sensor was geo-referenced and mosaicked with a combination of commercially available software and our own algorithms based on the Scale Invariant Feature Transform (SIFT). The validation of the mosaic’s spatial co-registration revealed a mean root mean squared error (RMSE) of 1.78 pixels. A thematic map of moss health, derived from the multispectral mosaic using a Modified Triangular Vegetation Index (MTVI2), and an indicative map of moss surface temperature were then combined to demonstrate sufficient accuracy of our co-registration methodology for UAV-based monitoring of Antarctic moss beds.
Highlights
In recent times, the increased development and availability of micro and small-sized UnmannedAerial Vehicle (UAV) platforms in combination with lightweight and low-cost Inertial MeasurementUnits (IMUs), GPS receivers, and scientific imaging sensors has driven a proliferation in the civilian use of Unmanned Aerial Vehicles (UAVs)
Test Sites In Turner et al [26] we introduced a technique to georeference and mosaic multiple visible images collected by a Micro Unmanned Aerial Vehicle (MUAV)
Despite flying at the same height Above Ground Level (AGL) during each UAV mission, the resulting image mosaics have different spatial extents and different spatial resolutions caused by differences in the technical parameters of each sensor
Summary
Units (IMUs), GPS receivers, and scientific imaging sensors has driven a proliferation in the civilian use of UAVs. Small fixed wings, helicopters, and multi-rotor UAVs with a total weight of 5 kg or less (typically known as Micro-UAVs or MUAVs) are increasingly being used for scientific purposes, in areas such as photogrammetry and environmental remote sensing [1,2]. UAVs have been proven to be useful for mapping agricultural crops, for example, mapping of vineyards [5], monitoring of wheat trials [6], and quantitative remote sensing of orchards and vineyards [7,8]. Research on the use of multiple sensors, which are expanding the remote sensing capabilities of UAV platforms, is rather limited. The use of multiple sensors presents unique challenges related, in particular, to the co-registration of the different image sensors
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