Dimensionality Analysis

2.1. Intrinsic Dimensionality and Relevance to Arctic Optical Diversity and Ecosystems

Intrinsic dimensionality, the number of independent degrees of freedom in a dataset, has been used to measure the information content of spectral catalogues (Cawse-Nicholson et al., 2021; Thompson et al., 2017). Roughly speaking, the dimensionality of the upwelling light field indicates different physical and chemical properties apparent in the terrain that are revealed in the measured radiance spectrum. Here we characterize the differences in intrinsic dimensionality among different areas of the Arctic, as represented in the airborne ABoVE dataset acquired by AVIRIS-NG over Alaska and northwestern Canada. This dimensionality analysis demonstrates the high degree of spectral diversity of the Arctic tundra, highlighting the advantage of a large-scale experiment such as ABoVE and the increased information content provided by imaging spectrometers, as opposed to multispectral sensors.

2.2. Dimensionality Analysis Approach

We analyzed the AVIRIS-NG dataset acquired in 2017, consisting of over 200 different flightlines, segmented at ~3 km intervals (600 pixels at 5 m). The measured spectrum is calibrated to units of absolute radiance as in (Chapman et al., 2019). We estimated surface reflectance spectra using the approach of (Thompson et al., 2018). Finally, we calculated the intrinsic dimensionality of each segment independently using the strategy of (Thompson et al., 2017). Within each segment, the intrinsic dimensionality was calculated from the image stack, cloud fraction and the mean and standard deviation of Normalized Difference of Vegetation Index (NDVI) were summarized from the imagery, and the central latitude and longitude were extracted. We plotted the frequency distribution of dimensionality for the cloud-free segments, summarized by latitude and NDVI, to examine trends and patterns in spectral dimensionality (Figure 2).

2.3. Dimensionality Analysis Results and Implications

Dimensionality was calculated for a total of 14,519 segments, of which 12,626 were cloud-free and used in subsequent analysis. Dimensionality values were positively skewed with a long tail of high values. Generally, a broad range of dimensionality was observed across the gradient of latitude and greenness. Above 62° N, segments with moderate NDVI values (0.25-0.75) consistently had higher dimensionality than those with either low (< 0.25) or high (> 0.75) NDVI. The lowest dimensionality values, < 20, were found mostly in the low NDVI category corresponding to non-vegetated terrain and open water. These systems were optically less diverse than the vegetated areas. Inconsistent observing conditions, such as solar angle and the amount of atmospheric haze, affect the sensor’s ability to resolve the subtlest features and probably play some role in the broad spread of dimensionality values. Even excluding the largest values, the modes of the distributions lie between 20 and 40, similar to previous studies of midlatitude regions (Thompson et al., 2017). This demonstrates that Arctic tundra exhibits considerable spectral heterogeneity across the surveyed region. Unlocking the large amount of information available in these dimensions can provide new insights into tundra characteristics and function and will be the focus of future studies. Considering that this analysis was restricted to one segment size, it is quite likely that there is even more information embedded in these spectra. Dimensionality analyses like this but conducted across a range of segment sizes and with coincident finer-grained data provide an important opportunity to inspect the spatial resolution of vegetation or surface substrate patches in Arctic landscapes. Such analyses may be necessary to understand the properties of interest and heterogeneity within the mixture of non-vegetated and vegetated surfaces.