Uses of Spectroscopy for Tundra
Studies
4.1. Long-term Vegetation Changes
with NDVI
Long-term satellite data has revealed “greening” of Arctic tundra
since the 1970s based on increases to NDVI derived from Landsat and
AVHRR time-series data (Myers-Smith et al., 2020; Wang & Friedl, 2019).
While tundra greening remains the most common trend across the Arctic,
“browning”, represented by a decreasing trend in NDVI values, has
occurred in various regions and scales across the tundra (Myers-Smith et
al., 2020). Greening and browning trends were one of the first
indications that the Arctic tundra was being significantly impacted by
climate change. Variations in greening/browning over different years
have most commonly been attributed to climate warming (e.g., (Berner et
al., 2020; Bhatt et al., 2021; Cooper, 2014)), herbivory by small
mammals (Olofsson et al., 2012), and vegetation disturbance and
subsequent recovery after extreme warming events (Bokhorst et al.,
2012). However, there are significant limitations of the sensitivity of
NDVI to high latitude ecosystem change (Huemmrich et al., 2021). For
example, recent evidence suggests that some of these changes’ impacts
are fine-scale in nature (i.e., < 5-30 m), making many common
remote sensing platforms impractical for studying these dynamics
(Myers-Smith et al., 2020; Niittynen et al., 2020). Moving beyond
greening and browning into the shifting landscape of numerous other
metrics unlocked by IS, such as changes in land cover type and
biophysical traits, will provide key insights into the magnitude and
nature of high latitude ecosystem change.
From the outset, advanced IS data collections, such as from SBG, should
be organized and calibrated to allow for future analysis of multi-year
trends. In addition, improved land cover descriptions from SBG will
enhance the interpretation of the existing NDVI trend analyses by
establishing the capacity of different land cover types to respond to
environmental change and for that change to be reflected by observable
changes in NDVI. Ground measurements collected over extended time series
will improve our understanding of the nature of spectral reflectance
change associated with measured land cover change and inform remote
sensing needs.
4.2. Land Cover and Vegetation
Classification
Surface reflectance data have long been used to classify and map
vegetation types from landscape to global scales. Accurate data
identifying the distribution of and changes to land cover types provide
a significant opportunity for understanding Arctic environmental change.
Improved mapping and classification of circumpolar land cover and its
changes will be key to understanding the effects of global environmental
change on Arctic ecosystems (Sections 4.2-4.4). Overcoming the
challenges associated with mapping land cover at appropriate levels of
thematic, spatial, and temporal detail will ultimately provide a
significant advancement in our understanding of Arctic ecosystems.
Mapping Arctic vegetation types at high spatial resolution and with
sufficient thematic detail has been challenging in part due to a
relative sparsity of spectroscopic data. Global-scale land cover maps,
such as the MODIS land cover product (Sulla-Menashe et al., 2019), are
typically produced at a level of thematic detail that cannot distinguish
between functionally distinct landforms (e.g., low- versus high-centered
polygons) and vegetation types (e.g., low versus tall shrublands)
present in Arctic tundra. Different arctic vegetation types are often
combined into simpler, but less effective classes, or are represented by
inappropriate classes (e.g., “grassland”) which do not reflect tundra
ecosystem composition. The utility of land cover maps for tracking
Arctic environmental change hinges on improving land cover
classification, as subtle changes in vegetation properties, such as
increased shrub abundance, do not necessarily involve a transition from
one class to another within a mapped pixel.
Moving beyond land cover types and into the mapping of plant functional
types or finer taxonomic groups from spectra may be possible at
continental scales if IS data with large spatial coverage (e.g., ABoVE
airborne campaigns (Section 2) and SBG) are harnessed and developed.
Acquiring and applying more detailed spectroscopic data for Arctic
vegetation types will enable mapping with improved thematic detail,
particularly if they are analyzed in tandem with ancillary high spatial
resolution datasets that capture important environmental covariates such
as topography (e.g., ArcticDEM) and edaphic characteristics (e.g.,
seasonal inundation, snow depth and hardness, active layer thickness,
depth to water). Few studies have yet applied detailed IS data to map
Arctic vegetation types (Smith et al., 2021; Thomson et al., 2021), but
an increase in available imagery may enable future work in this area.
Land cover maps with classifications designed for Arctic vegetation
types are typically limited in spatial or temporal range (Chasmer et
al., 2014; Greaves et al., 2019), precluding comprehensive study of
Arctic vegetation dynamics, or are coarse in spatial or temporal
resolution (e.g., gridded 1 km CAVM) (Raynolds et al., 2019), precluding
accurate characterization of the high level of spatial heterogeneity and
temporal variability in Arctic vegetation. (Bartsch et al., 2016)
suggested that a 30 m spatial grain, which is the proposed spatial
resolution for SBG, is sufficient for capturing many of the dynamics of
Arctic land cover. However, depending on whether species-level or
functional type-level maps are being generated, even higher spatial
resolution (e.g., 3 m from Planet) may be insufficient to distinguish
Arctic vegetation except at broad thematic levels (e.g., trees vs. shrub
vs. water). Therefore, the use and further development of advanced
subpixel mixture analysis will enable high accuracy vegetation
classifications with reasonable instrument spatial resolution and broad
spatial coverage (Thomson et al., 2021). Tapping the information content
of higher spatial resolution data (e.g., Section 2) will be essential to
preparing the algorithms and analysis pipelines to utilize a spaceborne
imaging spectrometer such as SBG that has a finer spectral resolution
occurring at an intermediate spatial resolution to map Arctic vegetation
(Section 5).
Another key limitation to mapping vegetation in Arctic tundra is the
lack of high-quality, georeferenced training data. Existing observations
are scattered across numerous countries, land management agencies, and
historical datasets. Disparate datasets often do not capture similar
levels of detail, and thus can be challenging to integrate. Land cover
maps, and the algorithms and data that go into producing them, are only
as credible as the underlying training data. Typically, land cover maps
are trained on datasets of land cover type that are produced by visual
interpretation of very high spatial resolution imagery (e.g., using
Google Earth), but the availability of suitable (midsummer) imagery is
extremely limited in the Arctic tundra (Section 1). Field data provide
the most reliable source of georeferenced Arctic ground verification,
but they are inherently limited in scope and are spatially biased
towards areas with a long history of research (e.g., northern Alaska’s
Dalton Highway corridor). Airborne data (including UAS observations) can
bridge the scaling from field data to spatially extensive gridded
datasets (Assmann et al., 2020). This scaling will ultimately enable
training of machine learning algorithms to effectively map Arctic
vegetation at continental scales.
Finally, the unique seasonal characteristics of the Arctic impose
additional challenges on mapping tundra vegetation at scale.
Phenological differences can help to separate co-occurring and
spectrally similar plant functional types (Macander et al., 2017), but
the phenology itself is highly variable through space and time since it
is sensitive to moisture status and interannual variability in
meteorologic conditions (Sections 4.4 and 4.5). Land cover mapping
algorithms may misinterpret spectral changes caused by interannual
variation as real land cover change. The brief snow-free season in the
Arctic tundra may inhibit sufficient characterization of
phenology-driven spectral changes, which further reduces our ability to
identify spurious change detection. A sufficiently large and
representative training dataset, as described above, will help prevent
vegetation mapping algorithms from misclassifying changes in moisture
status and phenology with changes in land cover in the Arctic Tundra.
4.3. Retrieval of Biophysical
Properties and Plant
Traits
The strong connection between IS and the biophysical properties of plant
leaves and canopies makes it possible to retrieve a host of important
vegetation properties with spectroscopy
(Serbin & Townsend, 2020).
Particularly, the mapping of plant functional traits, i.e., the
morphological, biochemical, phenological, and physiological attributes
of leaves and canopies (Violle et al., 2007), has been a priority and
key focal area of study (Asner et al., 2015; Asner & Martin, 2008;
Cawse-Nicholson et al., 2021; Singh et al., 2015; Z. Wang et al., 2019,
2020) . These functional traits, which are closely related to vegetation
establishment, growth, and functioning, are key to understanding
vegetation responses to climate change, as well as process modeling of
terrestrial ecosystems (Gamon et al., 2019; Myers-Smith et al., 2019; Xu
& Trugman, 2021; Zakharova et al., 2019). For example, traits that
describe leaf photosynthetic capacity (e.g., foliar pigments, nitrogen,
and Vcmax), biogeochemistry (e.g., ligno-cellulose,
carbon, and macronutrients), and water cycling (e.g., stomatal
conductance) are important to characterize ecosystem carbon, water, and
energy cycling and response to climate change (Chapin, 2003; Myers-Smith
et al., 2019; Ollinger & Smith, 2005; Rogers et al., 2017; Tang et al.,
2018; Woodward & Diament, 1991) . Similarly, traits related to
vegetation structure, such as leaf area and canopy height, are important
for determining ecosystem energy partitioning (e.g., through surface
albedo and temperature), as well as surface-atmosphere interactions
(Aalto et al., 2018) that feedback to the global climate system (Zhang
et al., 2018).
In the Arctic, plant functional traits vary remarkably within and across
plant species and over space and time, controlled by the complex,
fine-scale gradients in climate, topography, water, and nutrients
(Andresen & Lougheed, 2021; Bjorkman et al., 2018; Black et al., 2021;
Chen et al., 2020; Thomas et al., 2020). In particular, traits that
confer differing competitive advantages, such as those related to plant
size and resource economics (e.g., leaf area, seed mass, height, LMA, N,
LDMC) (Thomas et al., 2020), are highly sensitive to changes in
micro-environments, making them difficult to characterize with
traditional field surveys (Metcalfe et al., 2018; Schimel et al., 2015).
In addition, the photosynthetic capacity (V cmaxand J max) and response to environmental
conditions of Arctic plants are significantly different from the current
assumptions in the process models used to forecast Arctic change (Rogers
et al., 2017).
Non-vascular plants which dominate large areas of the Arctic, have very
different biochemical attributes and possess morphologies that are not
yet easily measured (Sections 3.2-3.3) (Holt & Nelson, 2021). Water
content varies in non-vascular plants based almost entirely on
environmental conditions since they do not actively conduct water, which
greatly influences their spectral signatures (Figure 4). Variable water
content in the non-vascular ground layer visible to remote sensing
instruments presents a primary challenge and significant opportunity to
understand ecosystem function where they dominate. Methods using a
combination of VNIR, SWIR and MIR show promise for addressing water
content in non-vascular plants (Granlund et al., 2018; Neta et al.,
2010). Testing these estimations of water content at large spatial
scales remains a challenge. Most traits in non-vascular plants exhibit
different spectral responses from those of vascular plants (Cornelissen
et al., 2007), precluding direct use of existing trait retrieval
approaches developed for vascular plants. Recent work by (Thomson et
al., 2021) shows that chemometric estimation in non-vascular plants
using remote sensing is possible but there are only a few species
studied over a small area. Collectively these challenges have created
significant uncertainties in our understanding and modeling of Arctic
ecosystems (Fisher et al., 2018; Metcalfe et al., 2018; Myers-Smith et
al., 2019). Developing algorithms to estimate properties of non-vascular
plants using spectra and remote sensing will enable more accurate
quantification of plant functional traits.
IS can provide a tool to spatially map a variety of plant functional
traits across scales (e.g., from watershed to biome) which has been
demonstrated in many other biomes (e.g., Asner & Martin, 2008; Martin
et al., 2008; Singh et al., 2015; Z. Wang et al., 2019, 2020). The
launch of SBG and other IS missions (e.g., EnMAP) will provide important
data to further enable spatiotemporal mapping of traits across the
broader Arctic tundra biome
(Cawse-Nicholson et al.,
2021) . Simultaneously, spectral data from aircraft (e.g., Miller et
al., 2019) and low-altitude, near-surface platforms, including automated
trams (John A. Gamon et al., 2006; Goswami et al., 2011; Healey et al.,
2014), tower-mounted instruments (e.g., Drolet et al., 2014; Hilker et
al., 2011), and unoccupied aerial systems (Assmann et al., 2020;
Cunliffe et al., 2021; Shiklomanov et al., 2019; Yang et al., 2020),
have increased in northern high latitudes. These diverse spectral
platforms are likely to revolutionize our means for collecting trait
information, which could usher a new paradigm in our understanding and
modeling of Arctic vegetation dynamics. For example, using traits
derived at watershed and larger scales, the spatial variation in traits
across plant species, plant functional types (PFTs), communities, and
even ecosystems can be easily characterized (Figure 6). The drivers of
spatial variation in plant traits can also be investigated in
combination with other core remote sensing datasets, such as topography,
climate, soil properties, and vegetation maps, which is a key to
understanding plant responses to climate change (Durán et al., 2019) In
addition, as a critical uncertainty in process models (Rogers et al.,
2017), the spatial information on plant traits and biophysical
properties inferred from IS could be directly integrated with models to
inform and improve predictions (Fer et al., 2018; Shiklomanov et al.,
2021), thereby reducing current predictive uncertainties (Dietze et al.,
2014).
The high spatial heterogeneity in vegetation composition, structure, and
abiotic environments (Section 3) pose a unique challenge to retrieve
plant traits using spectroscopy in the Arctic, as compared to other
biomes (Thomson et al., 2021; Yang et al., 2021). Traditional radiative
transfer model-based retrieval assumes the underlying vegetation layer
to be homogeneous (Jacquemoud et al., 2009), which is not met in tundra
landscapes. Empirical modeling that builds statistical relationships
between field trait observations and remote sensing spectra using
machining learning or latent variable techniques is a powerful
alternative (Curran et al., 1997; Singh et al., 2015; Z. Wang et al.,
2020; Wold et al., 2001). However, to construct an empirical model, a
plot-to-pixel connection is required. This requirement can be easily met
in forest or managed ecosystems where a single tree can occupy one or
multiple image pixels or a vegetation layer is homogenous across
relatively large areas. The Arctic poses challenges to plot-to-pixel
connections given the high level of species mixing in imagery pixels of
> 5 m resolution, which, combined with the remote and
meteorologically harsh environment, restricts the collection of
quantitative plot observations to develop trait models.
Unoccupied Aircraft Systems (UAS) remote sensing collects spectral data
at a very high spatial resolution and has shown great promise to serve
as an intermediate data source to connect ground and high-altitude
platforms (Thomson et al., 2021). In addition to the high spatial
heterogeneity, the common presence of non-vegetated surfaces (e.g.,
water, soil, rocks, and litter) and their highly variable spectral
characteristics (Section 3.5), present additional challenges to the
mapping of traits. Typically, non-vegetated surfaces can be excluded
over the course of trait model development and application in
low-latitude ecosystems (e.g., Wang et al., 2019), but non-vegetated
surfaces are highly mixed with vegetation surfaces in the Arctic, which
must be accounted for in trait model development. Lastly, the short
growing season and harsh environment means that vegetation spectra and
traits can change rapidly during the growing season (Section 4.4).
Therefore, trait models built from data collected at a certain time of
year may only be applicable to a small time window (e.g., < 1
month), as compared to low-latitude ecosystems where vegetation growth
peaks can persist for several months. SBG and other time-series spectral
platforms (e.g., PACE, CHIME, DESIS, EnMAP) hold great potential to
address this issue by facilitating the development of time-series models
that capture seasonal trait dynamics.