Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows

Abstract

In the last 10 years, development in robotics, computer vision, and sensor technology has provided new spectral remote sensing tools to capture unprecedented ultra-high spatial and high spectral resolution with unmanned aerial vehicles (UAVs). This development has led to a revolution in geospatial data collection in which not only few specialist data providers collect and deliver remotely sensed data, but a whole diverse community is potentially able to gather geospatial data that fit their needs. However, the diversification of sensing systems and user applications challenges the common application of good practice procedures that ensure the quality of the data. This challenge can only be met by establishing and communicating common procedures that have had demonstrated success in scientific experiments and operational demonstrations. In this review, we evaluate the state-of-the-art methods in UAV spectral remote sensing and discuss sensor technology, measurement procedures, geometric processing, and radiometric calibration based on the literature and more than a decade of experimentation. We follow the 'journey' of the reflected energy from the particle in the environment to its representation as a pixel in a 2D or 2.5D map, or 3D spectral point cloud. Additionally, we reflect on the current revolution in remote sensing, and identify trends, potential opportunities, and limitations.

Partitioning plant spectral diversity into alpha and beta components

“…showing changes in spectral composition across the region, similar to our mapping of the first 398 three PCs in Figure 5 Our approach is timely since current technological developments in high-resolution UAV 417 imaging spectroscopy will make this technology more accessible to ecologists in the coming 418 years (Aasen et al 2018 of the spectrum (wavelengths <400 nm and >2400 nm), and applying a Savitzky-Golay filter 574 (order = 3, size = 7) to every pixel in the image to remove high-frequency noise. We masked all 575 pixels with normalized difference vegetation index (NDVI) values <0.8, and brightness-576 normalized all spectra (Feilhauer et al 2010).…”

An Under-Ice Hyperspectral and RGB Imaging System to Capture Fine-Scale Biophysical Properties of Sea Ice

“…Hyperspectral images are required to be orthorectified to enable extraction of meaningful and accurate metric information of the feature of interest (e.g., distances, shapes, and areas). This is ultimately necessary to compute the biochemical properties of the target [28], and to allow for accurate repeat surveys and co-registration with other datasets.…”

“…As the technology becomes more portable and accessible, it has found a wide range of applications. A relevant analogous example is HI cameras equipped onto unmanned aerial systems (UAS) which are filling an essential gap between classical ground, full-size aircraft, and satellite sensing systems allowing more mapping at increased resolutions with ease of repeatability [28][29][30][31].…”

“…Measuring transmitted light rather than reflected light, however, poses the most constraints. Also, pushbroom HI sensors need to be carefully configured so that the integration time and imaging frequency match the required spatial resolution [28]. Acquired images then typically require a series of radiometric and geometric corrections which are far from trivial for dynamic under-water platforms.…”

“…The translucent nature of sea ice would also render the utilization of active light sources, commonly employed in underwater HI applications, as a highly arguable approach. The under-ice realm can be a highly dynamic environment, and where the utilization of common geo-positioning and communication methods employed in typical aerial HI surveys is much more challenging due to the ice cover and viewing geometry [28,37].…”