Figure 3: The top panel shows aerial views depicting beta diversity in a
diverse landscape (A) and a low-diversity landscape (B), and alpha
diversity in and a highly diverse grassland (C) and a low-diversity
grassland (D). The second panel shows the spectral signatures associated
with the coloured boxes on the top panel. The bottom panel shows the
corresponding spectral diversity in the green, red, red-edge and
near-infrared wavelengths, from left to right, respectively.
Spectral Variation
Hypothesis
The SVH has been employed across a broad range of spatial scales, yet
there appears to be little consistency regarding the ideal spatial
resolution at which it best operates. At an experimental prairie site,
Wang et al. (2018) found the idea pixel size to be between 1 mm and 10
cm to establish a strong relationship between species richness and
spectral diversity, with the relationship fading after 10 cm. Similarly,
Polley et al. (2019) suggests that the sensitivity of SD to species
diversity is scale dependent and that the necessary spectral details may
be lost with greater spatial scales. Gholizadeh et al. (2019) found a
strong relationship between SD and species richness at both 0.5 m and 1
m resolution, but not at 5 m. However, Gholizadeh et al. (2022a) failed
to connect SD to species richness at both 1 m and 30 m, only achieving
significant correlations with the Simpsons diversity index. Similarly,
no strong correlations were found between species richness and a large
range of standard SD metrics at 2 cm and 5 cm pixel sizes from
multispectral UAV surveys (Perrone et al., 2024). The authors suggest
that phenological stage plays a key role, and finer resolutions can be
more noisy, due to factors like shadowing. Jackson et al., (2022) used a
multispectral UAV to estimate biodiversity at 0.1-0.5 cm resolution.
They could predict the Shannon–Weiner and Simpson’s biodiversity
indices well, but not species richness. They note that the measure of SD
decreased by 30% with every additional 1 m in elevation that the UAV
was flown.
Confounding factors that influence how well SD is related to
biodiversity have been explored on a variety of grasslands in recent
years. Yang et al. (2023b) noted how an open-pit coal mine influenced
α-, β-, and γ- diversity in an Inner Mongolian steppe, and that grazing
increased the area that was most strongly affected. Using Landsat data
for Himalayan grasslands, Chitale et al. (2019) explained that variance
in species richness measured using vegetation indices increased from 54
to 85 % after the inclusion of physiographic indices. In more general
terms, it has been found that accounting for the effects of bare soil on
the spectral readings can significantly improve remote sensing
estimations of both α- and β- diversity (Kamaraj et al., 2024; Xu et
al., 2022). In contrast to many other studies, Conti et al. (2021) found
a negative relationship between SD and taxonomic diversity in mesic
meadows in Czechia, with the vertical complexity driving the
relationship – the more vertically complex the grass structure, the
more negative the relationships between SD and taxonomic diversity. In
addition, the timing of flowering plants (Perrone et al., 2024), the
presence of non-native species (Van Cleemput et al., 2023) and the
proportion of live and dead biomass (Rossi et al., 2022), have all now
been identified as confounding factors.
Several studies have explored the use of time series analysis to
overcome uncertainties in the SD/biodiversity relationship. Rossi et al.
(2021) introduced a spatio-temporal version of Rao’s quadratic entropy
index (RaoQ) to examine changes in β-diversity over time with Sentinel-2
imagery and account for variations in grassland management and
phenology. They suggest that, with higher resolution data, that this
method can be applied to α-diversity too. In exploring how α- and
β-diversity vary over two years in prairie grassland, Gholizadeh et al.
(2020) found significant differences in species richness, due to factors
such as fires and weather, and recommend multi-temporal surveys to
account for these changes. When assessing grasslands in the USA and
Europe, Rossi et al. (2024), with a Sentinel-2 timeseries, found a
stronger and more consistent relationship with species diversity from
temporal SD than spatial SD, suggesting that an analysis based on a
single snapshot in time can be misleading.
Some new approaches have also attempted to tackle these uncertainties.
Zhao et al. (2021b) used cluster analysis of hyperspectral data to
identify distinct spectral species, allowing them to accurately predict
plant species diversity (R2 of 0.73). Developing this
idea further, Rossi and Gholizadeh (2023) used spectral unmixing. They
determine number the distinct spectral entities, called endmember,
within each image. Then calculate the number of endmembers and their
abundance within each pixel and use that information to create endmember
spectral diversity metrics. The authors claims that this approach is
less sensitive to soil and can also be applied to multi-temporal
datasets, which may help to overcome some of the previously identified
confounding factors.
Machine Learning
To assess plant species diversity over part of the Tibetan Plateau, Zhao
et al. (2022) used high-accuracy surface modelling (HASM), Landsat-8
data and a range of machine learning models, namely least absolute
shrinkage and selection operator, ridge regression, eXtreme Gradient
Boosting and Random Forest (RF). The authors found that the models
combined with HASM performed better than the machine learning models
alone, with the best combination being eXtreme Gradient Boosting and
HASM, followed closely by RF and HASM. Fauvel et al. (2020) experimented
with combining the multispectral data of Sentinel-2 with the radar from
Sentinel-1 to map biodiversity in grasslands in southern France with
multiple regressions methods - Linear regression, K-Nearest Neighbours,
Kernel Ridge Regression, RF and Gaussian Process. They found that RF
worked best overall, with R2 values above 0.4 for the
Simpson and Shannon indices, and the addition of Sentinel-1 data
provided no significant improvements. Another attempt to combine
Sentinel-1 and -2 came from Muro et al. (2022) with RF and deep neural
networks employed to predict biodiversity. The deep neural networks
model performed slightly better than RF, though both performed poorly
under cross validation, and the addition of Sentinel-1 again provided
little benefit. Several other studies found success with mapping plant
species diversity using different forms of neural networks. In semi
natural meadows in Germany, convoluted neural networks were used to
classify multispectral UAV data, mapping vegetation units with
accuracies of up to 88% (Pöttker et al., 2023). In three distinct
German grasslands, a residual neural network model was used with a time
series of Sentinel-2 data to map a range of plant biodiversity metrics,
achieving R2 values of up to 0.68 and showed
significant improvements in accuracy compared other machine learning
methods assessed (Dieste et al., 2024). Employing convoluted neural
networks in a different way, Gallman et al. (2022) managed to identify
and count individual flowers from images taken by drone mounted standard
high-resolution camera, performing as well or better than manual
counting for most flower species.
However, RF tended to produce the most accurate results for the majority
of studies. Using weather data and MODIS based Normalised Difference
Vegetation Index (NDVI) over Tibet, Tian and Fu (2022) found RF to
produce the most accurate measures of plant species diversity compared
to numerous other machine learning methods. Again, over the Tibetan
Plateau and using MODIS data (and weather, soil and topographic
variables) Yang et al. (2023a) achieved an R2 of 0.6
for plant species diversity with RF after using stepwise regression for
variable selection. In mountainous grasslands in South Africa, Mashiane,
Ramoelo and Adelabu (2024) used vegetation indices (VIs) from Sentinel-2
and Landsat-8 and RFs to model species richness and the Shannon–Wiener
index, achieving r2 values above 0.85 for both. RF
modelling was also most accurate compared to other ML methods in the
Three Rivers Headwater Region of China, where Yang et al. (2024) used
stepwise regression to select among variables from Landsat, climate,
soil and topographic data. Indeed, using VIs, canopy height and textural
data derived from multispectral UAV surveys over two summers in a wet
grassland near Berlin, Bazzo et al. (2024) also found RF modelling to
produce the most accurate and consistent measures of species richness.
Finally, in an assessment of plant diversity in numerous ecosystems
across the world (including 315 grassland plots), Xin et al (2024) again
found that RF models produced the most consistent and accurate results
compared to other regression and machine learning models.
Other methods
Some researchers have taken slightly different approaches to remotely
mapping grassland biodiversity. Löfgren et al. (2018) attempted to use
both satellite and UAV based NDVI values to map the richness of
specialist species in grasslands on Baltic island in southern Sweden,
but achieved only weak, negative correlations. In the alpine grasslands
of Tibert, Qin et al. (2020) achieved significant correlations between
richness, Shannon, Simpson and Pielou’s indices derived from the manual
counting of species from UAV imagery versus traditional quadrat surveys,
and identified more species (71) from UAV imagery than from quadrat
surveys (63). Another study on the Tibetan Plateau found a significant
relationship between UAV measured bare patches in grasslands with
decreases in richness and increased species turnover (Hua et al., 2023).
In mountainous grasslands of northern Portugal, Monteiro et al. (2021)
found that the NIR/Green ratio values from Sentinel-2 and their seasonal
amplitude correlated well with species richness, producing an
R2 of 0.44. In tallgrass prairies in the USA, Hall and
Lara (2022) compared combinations of hyperspectral UAV and LiDAR, then
multispectral UAV, phenometric data and Structure from Motion (SfM), and
finally RGB-SfM for the mapping of 10 different species, achieving
accuracies of 78%, 52%, 45%, respectively. Using a uniquely cross
disciplinary approach, Janišová et al. (2024) combined a time series of
satellite based NDVI going back to 1984 with ground surveys, history and
ethnology for land use change and cultural practises in two villages in
the Serbian Carpathian grasslands. By gaining an understanding of the
history and culture of the regions, the authors were better informed
regarding the historical land management practises, how they’ve changed
and the influence this has had on current biodiversity levels. This
further enhanced their enhanced their interpretation of the historical
NDVI record and allowed authors to make specific recommendation on land
use, such as a partial return to historical land management practises to
at least partially restore some of the lost species richness.