Remote Sensing of Grassland
Biodiversity
Hyperspectral Remote Sensing of Grassland
Biodiversity
Hyperspectral instruments are most typically mounted on crewed aircraft
for most grassland studies. Some satellites, such as Hyperion, have
carried hyperspectral sensors, while in recent years a growing number of
studies have made use of UAVs for carrying hyperspectral instruments.
Each approach has drawbacks in terms of spatial resolution, spatial
coverage, positioning and accuracy.
A good agreement was found between airborne (crewed aircraft)
hyperspectral data, spatial resolution and species turnover
(β-diversity) in a highland savanna (Rocchini et al., 2010). However,
spectral diversity in the smaller sampling areas (100
m2) produced a less robust correlation with
β-diversity than in the larger sampling area, 1,000
m2, probably due to increased noise when using smaller
grain sizes.
Focusing on α-diversity (richness and Shannon’s Diversity index), Wang
et al. (2016) found a strong positive relationship between biodiversity
and productivity, and between optical diversity and species diversity
for a Canadian prairie using airborne hyperspectral data, ground
sampling and eddy covariance measurements. Comparing spectral diversity,
recorded via a tram system, with a range of biodiversity metrics at an
experimental prairie test site indicated that a resolution of 1 to 10 cm
is best, and spectral diversity correlated differently with different
biodiversity metrics (Wang et al., 2018). Spatial resolution also
affects the confounding impact of bare soil on the correlation between
remote sensing measures of species richness, and, depending on the
resolution, different methods to account for bare soil need to be
applied (Gholizadeh et al., 2018).
Gholizadeh et al. (2019) assessed α-diversity in restored grassland
plots in Nebraska that had been seeded with native prairie grasses, some
of which were old and contained invasive species while others were
younger and mainly contained grasses of the original study design.
Ground based (quadrats) and airborne hyperspectral surveys were used. In
young plots, spectral diversity was strongly related to α-diversity, but
in old plots the relationship was not significant. The relationship
between hyperspectral measures of biodiversity and ground surveys varied
from one year to the next and weakened over the growing season –
emphasising the need for multitemporal measures of grassland
biodiversity (Gholizadeh et al., 2020).
Lyu et al. (2020) combined a handheld spectrometer, Hyperion and Landsat
data for species mapping to assess grassland degradation in Mongolia.
This was achieved by comparing the relative proportions of different
grass species, extracted from Hyperion data, with ground surveys and
metrics derived from Landsat imagery, such as above ground biomass
(AGB), net primary productivity and vegetation coverage. By focusing on
the identification of typical and indicator species only, classification
accuracies of over 70% were achieved.
Utilising a UAV mounted hyperspectral sensor, Yang and Du (2021)
classified plant species in a desert steppe ecosystem in inner Mongolia.
They used a large variety of vegetation indices and decision tree
classification to detect plant species with 87% accuracy. In an Alpine
steppe nature reserve, Xu et al. (2022) compared four spectral metrics
from a UAV mounted spectrometer with two species diversity indices
(species richness and the Shannon–Wiener index). The authors found that
the relationships between spectral diversity and species diversity were
significantly strengthened when bare soil was filtered from the survey
data.
Finally, a meta-analysis of grassland biodiversity predictions from
spectral diversity metrics found an overall correlation coefficient of r
= 0.36 across studies (Thornley et al., 2023). The authors noted high
levels of variability both within and between studies, with leaf spectra
producing a stronger relationship than overall canopy spectra. Surveys
of arid, tropical and southern hemisphere sites were lacking, and more
scalable and multitemporal studies are required to reduce the
uncertainty in the SV/biodiversity correlations.
Multispectral Remote Sensing of Grassland
Biodiversity
RGB cameras capture light across the visible wavelengths, while
multispectral instruments typically capture a few additional discrete
wavelengths of light in the near and shortwave infrared range, meaning
that less information can be derived regarding the surface under
observation when compared with hyperspectral instruments. However, they
are in use on many more satellites resulting in a multi-decadal record
of observations on platforms such as the Landsat series. Furthermore,
they are cheaper and smaller than hyperspectral cameras, making them
more accessible and suitable to consumer grade UAV use.
While examining the degradation of grasslands in South Africa, Mansour
et al. (2015) employed field sampling, SPOT 5 data and random forest
machine learning classification. Indicator species were identified and
used to assess the level of degradation. The identification of indicator
species was improved from 75 % to 89% when ground-based edaphic
measurements were integrated with SPOT 5 data. Lu and He (2017) used a
UAV with a near infrared (NIR)-GB camera to map tall grassland species
in southern Canada at 5 cm resolution. The authors achieved an accuracy
of 85% overall (averaged across all dates and species surveyed) using
object-based classification, but suggest that this accuracy can be
improved with more precise instruments and a greater number of spectral
bands. Across 200 sites in southwest France, Lopes et al. (2017) used a
timeseries of SPOT 5 derived NDVI over 18 dates, and a combination of
the eight Sentinel 2 bands across eight dates to predict biodiversity.
Results, compared to the Shannon and Simpson indices were poor,
suggesting that high temporal resolution, moderate-high spatial
resolution and multispectral data are not suited to mapping biodiversity
at the grassland scale.
In Alpine meadows on the Tibetan Plateau, Sun et al. (2018) flew a UAV
at just 2 m elevation for highly detailed imagery. Plant species in each
image were manually identified and compared to ground-based quadrat
surveys and with a range of species composition indices. The UAV surveys
proved highly effective compared with indices derived from traditional
methods (r2 values between 0.726 and 0.872), while
also covering a larger, more representative area and measuring a greater
number of species. Shoko et al. (2020) attempted to differentiate
between C3 and C4 species (Festuca
costata and Themeda triandra ) in South Africa using multidate Sentinel
2 data. The authors achieved greater accuracy in winter (between 91.8%
and 95.3%), than summer (between 81.4% and 90.3%).
Using Sentinel 1 (Synthetic Aperture Radar) and Sentinel 2, in
combination with ground surveys, Fauvel et al. (2020) measured and
predicted plant diversity metrics in terms of richness indices,
diversity indices and some functional indices in grasslands in southwest
France. The methods used worked better for Simpson and Shannon indices
than richness indices, and moderately well for functional indices.
Incorporating Sentinel 1 data did not significantly improve predictions.
In Conti et al. (2021) the authors assessed the links between spectral
and taxonomic diversity, and vertical complexity, using a UAV mounted
multispectral sensor in a mesic meadow in South Bohemia, Czech Republic.
It was found that the relationship between spectral and taxonomic
diversity was mediated by grassland vertical complexity - the more
pronounced the vertical complexity, the more negative the relationship
between taxonomic and spectral diversity. On Alpine grasslands, Rossi et
al. (2022) flew a UAV with a consumer grade camera and combined the
imagery with airborne hyperspectral surveys. The fused data set tested
the effects of spatial resolution and a variety of spectral metrics on
measuring species diversity. The authors found the fused dataset worked
well but also produced a surprising finding – that spectral metrics
centred on spectral complexity was negatively correlated with species
richness. The authors, note that the presence of live and dead biomass
acted as significant confounding variables in their correlations.
On a semi-natural meadow in Saxony, Germany, Pöttker et al. (2023)
achieved accuracies of up to 88% by using ground surveys and a
multispectral UAV in combination with convoluted neural networks to map
plant communities. Yang et al. (2023) combined ground sampling,
environmental data and MODIS imagery to predict biodiversity and AGB in
the Qinghai–Tibet Plateau grasslands using a random forest model,
achieving and r2 of 0.60 for their plant species
diversity model.