Discussion and
Recommendations
Biodiversity
Recent years have seen significant advancements in access the free,
moderate to high-resolution multispectral satellite imagery, alongside
the rapid development of UAV technology allowing both multispectral and
hyperspectral data to be captures across a huge range of spatial
resolutions and scales. However, evidence from the papers covered in
this review shows that there are still substantial uncertainties
regarding how best to connect the spectral diversity to species
diversity, with significant variability in the correlations achieved.
This uncertainty appears to exist regardless of whether the measurements
occur with hyperspectral or multispectral instruments, regardless of the
spatial scale, the spatial resolution, location or type of spectral
variation metric tested. This is supported by a 2023 metanalysis that
found an average correlation of just 0.36 between spectral variation and
species diversity in grasslands, with significantly variability
occurring both within and between studies (Thornley et al., 2023).
Furthermore, another systematic review of related papers between 2000
and 2022 suggested that more work needs to be done to identify factors
that influence the SVH (Lyu et al., 2024). Machine learning algorithms
have emerged as new and effective tools for mapping grassland
biodiversity but, like the SVH, needs to begin better accounting for
sources of error and uncertainty. As such, we present some
recommendations regarding the remote sensing of biodiversity:
- Bare soil can weaken the spectral signal and thereby reduce the
correlations between SD metrics and species diversity. It is necessary
to filter out bare soil pixels from remotely sensed imagery where
possible (Kamaraj et al., 2024; Rossi and Gholizadeh, 2023; Xu et al.,
2022).
- In areas of low biomass, dead biomass can also alter the reflected
spectral values, influencing the SD metrics generated. Where possible,
the proportion of live and dead biomass should be measured and
factored into the analysis (Rossi et al., 2022).
- The vegetation phenological stage influences the interaction of plants
with light and so exerts a significant influence on their spectral
signatures. Generating a time-series of spectral diversity can help to
account for these variations (Hall and Laura, 2022; Perrone et al.,
2024; Rossi et al., 2021).
- The vertical complexity of the vegetation structure can reverse the
relationship between SD and species diversity (Conti et al., 2021),
while combing 3D vegetation data with SD has been shown to improve
correlations with species diversity (Hall and Laura., 2022). It is
beneficial to incorporate 3D vegetation data, from SfM or LiDAR, into
the study workflow.
- Several studies suggest spatial resolutions of 1 mm (Wang et al.,
2018) to 1 m (Gholizadeh et al., 2020) tend to work best, but this
does seem site dependent. Therefore, when using UAVs, a range of
survey heights should be tested to ensure the best results.
- Machine learning can be an effective tool for measuring species
diversity, especially random forests regression. However, more work
needs to be done to uncover the sources of error and variability
present in the published literature.
- For a long-term analysis of changing grassland biodiversity,
understanding the local cultural and historical practises that
influence land management styles can provide important context in
understanding current biodiversity and interpreting long term datasets
(Janišová et al. (2024).
Finally, integrated long-term surveys in a range of different grassland
environments, linking ground data collection, low-elevation aerial
observations, high-elevation aerial observations and satellite
observations, should be combined with SD analysis and machine learning.
This can best account for temporal and spatial scale discrepancies,
thereby allowing for the analysis to more effectively identify the most
suitable spectral diversity metrics and regression/machine learning
tools to model biodiversity.
Functional Traits
Leaf Dry Matter
Content
Little progress has been made in the remote sensing of grassland LDM
content, although the work of Polley et al. (2020a) demonstrated that a
significant correlation with LDM content could be established with UAV
based hyperspectral imagery using PLSR. This was backed up by Zhang et
al. (2022) using a similar method but only achieved weaker, though still
significant, relationship with LDM content. PLSR and hyperspectral
remote sensing appear to show promise in mapping LDM content, but this
approach is in its early stages and much more work is required.
• Initial studies by Polley et al. (2020a) and Zhang et al. (2022) show
that UAV based hyperspectral UAV data and PLSR have the potential to
predict LDM content, but more work needs to be done to exploit this
method.
Crude Protein
Of the 11 studies presented on the remote sensing of CP, eight used
primarily multispectral data (from both UAV and satellites) and three
used hyperspectral UAV data. Six of those 11 in total incorporated
machine learning methods. However, barring one exception (Hart et al.,
2020) all studies found strong and significant correlations with CP,
often using just simple band ratios and/or VIs with a mix of different
regression algorithms. Given the ease of use, low costs and
effectiveness of consumer grade multispectral UAVs and free
multispectral satellite imagery, the tools and data for mapping of
grassland CP content from centimetre to decametre scales are becoming
increasingly accessible to a growing range of researchers and land
managers.
Crude protein can be effectively measured at from both multi- and
hyper-spectral data, at large and small spatial scales and with a
range of simple and complex modelling methods. The studies presented
here have exclusively focused on local or regional sites. Therefore,
the feasibility of scaling these surveys to national or global scales
should be assessed in the near future.
Potassium, Phosphorous and
Nitrogen
The two studies dealing with Potassium, Zhang et al. (2023) and
Gholizadeh et al. (2022a), used different observation platforms and
analysis methods but both with strong results. While there has not been
enough research on remote sensing of K in grasslands, the two results
shown suggest that it is feasible, even with two very different
approaches. As such, more work needs to be done to assess the range,
consistency and applicability of these measurement tools.
For the seven studies that provided estimates of P, the four that used
multispectral satellite surveys achieved an average R2value of 0.78, while the three that used hyperspectral imagery (two UAV
based, one satellite-based) averaged just 0.4. It’s difficult to infer
anything significant given the sparse number of studies, but this result
stands in contrast to the review by Van Cleemput et al. (2018), that
found an average R2 of 0.75 for the hyperspectral
remote sensing of Phosphorous in grass- and shrublands This suggests
there are still substantial uncertainties that need to be addressed
regarding the remote sensing of P, especially using hyperspectral
imagery.
The studies measuring Nitrogen produce more consistent results than P,
with an average R2 0.63 and 0.62 for multispectral and
hyperspectral measurements, respectively. This is more in line with
Cleemput et al. (2018), that found an average R2 of
0.74, but that included proximal measurements that are likely to be more
accurate. No significant difference arises from the platform used (UAV,
Aircraft or Satellite), the regression or modelling approach nor the
study location. Some studies did fail establish a significant
relationship, such as Tang et al. (2021) using a standard camera on a
UAV, or Pau et al. (2022) when assessing the NEON aircraft-based
hyperspectral products. Furthermore, no studies made use of
hyperspectral satellite data, with all hyperspectral nitrogen remote
sensing being based on either UAV or aircraft surveys.
- Additional research needs to be done to build on the initial success
of studies mapping grassland potassium and to assess their range and
limits of applicability.
- Recent studies covering the remote sensing of phosphorous show a
greater level of variability in the hyperspectral measurements than
multispectral. No clear explanation for this is offered within the
examined literature. This area requires further study to identify and
mitigate the sources of uncertainty.
- Studies measuring grassland nitrogen content appear more consistent
and robust than K and P. However, a few still fail to establish strong
correlations. This suggests extracting suitable values may still
require fine tuning based on local or regional vegetation
characteristics.
- K, P and N values in grasslands would benefit from greater use of
satellite based hyperspectral data, especially when used in
conjunction with aerial surveys for multi-spatial and -temporal scale
analysis.
Leaf Area Index
Linking high-resolution, hyperspectral data from aircrafts and UAVs to
LAI is a growing area of research. The data coming from these platforms
appear to be capable of modelling LAI with high degrees of accuracy,
with three of the five studies reviewed having R2values of 0.73 or higher. These studies have achieved success using
analysis as basic as linear regression up to RTMs and machine learning,
and over both experimental and natural sites. The NEON LAI product
tested by Pau et al. (2023) are again the worst performing. However, all
the successful studies were performed at just local scales, versus NEON
which is more generalised.
A wide variety of approaches have been used with medium-resolution,
multispectral satellite data. The most successful appears to be those
focused on radiative transfer models (n=5), with an average
R2 of 0.75, and a range from 0.57 to 0.87. Studies
primarily relying on machine learning models (n=8) have generally proven
effective too, with an average R2 of 0.67, ranging
from 0.46 to 0.87. However, those high and low values come from two
studies, Masenyama et al. (2023) and Tsele, Ramoelo and Mcebsi (2023),
and both using Sentinel-2 and both based in mountainous regions of South
Africa. This hints at the possibility of additional uncertainties being
added to surveys in mountainous terrain. Indeed, the work of Peng et al.
(2024) showed that applying a topographic correction to Landsat-8
improved the correlations and reduced the errors from both RTM derived
and machine learning derived LAI data.
Despite three studies performing intercomparisons between global LAI
products since 2018, no product performs consistently better than any
other, and R2 values vary significantly from one
comparison to the next (Li et al., 2018a; Liu et al 2018; Shen et al.,
2023). Even products with greater spatial resolution often require a
broader temporal window for complete daily coverage, meaning that
transient or sharp changes in LAI can be missed (Yin et al., 2020).
Attempts by Munier et al (2020) to extract sub-pixel LAI values failed
to provide an accuracy improvement over grasslands, despite working for
other vegetation types. Recent efforts to fuse finer resolution data
from Landsat and Sentinel-2 with global LAI products such as those from
MODIS, have produced mixed results thus far over grasslands (Li et al.,
2018c; Zhou et al., 2020; Zhou et al., 2023). As such, further
development of these fusion models will be necessary before a reliable,
global LAI model with both high-spatial and -temporal resolution can be
distributed.
• LAI mapping from high-resolution hyperspectral surveys can be
successful using a variety of regression and modelling approaches, but
parameters need to be tuned to specific regions to ensure accuracy.
Research integrating hyperspectral satellite measurements should aid in
this task.
• New research demonstrated that sun induced fluorescence at 687 and 760
nm has a strong association with LAI, potentially opening the door so a
new form of high-resolution LAI mapping.
• With moderate resolution multispectral data, both RTMs, especially
PROSAIL, and machine learning methods, particularly random forests, have
produced consistent and robust estimations of LAI.
• Moderate resolution LAI estimates may also benefit from topographic
corrections in more rugged terrain.
• Comparisons of global LAI products have failed to identify a single
best option and attempts to fuse high- and low-resolution data are still
in development and lack consistency. It is therefore necessary to test a
range of products to find the one most suited to the study area in
question and with the necessary spatial and temporal resolution.