Remote Sensing of Grassland Variables Across Seasons and Using Multiple Spectral Devices
Creators: name: Pearce, Craig
Publisher: name: Lancaster University
Year: 2024

description: In the UK, the regeneration and conservation of semi-natural grasslands is
 important, especially for grasslands protected by legislation such as UK
 Biodiversity Action Plan (BAP) priority habitats or Sites of Special
 Scientific Interest (SSSIs). As such, monitoring grassland condition is
 necessary, but conventional methods of measuring grassland condition are
 time consuming and limited in their spatial coverage. This thesis tested
 if remote sensing can provide a more cost- and time-effective solution to
 measuring grassland condition as defined by the Common Standards
 Monitoring (CSM). 
 
 
 A field spectroscopy experiment was designed to explore the potential link
 between grassland spectral reflectance, plus a metric representing a
 traditional measure of grassland condition referred to as CSM-condition.
 Partial least squares regressions were used to evaluate the relationship
 between grassland multi-spectral reflectance and a range of
 condition-related grassland variables; between the condition related
 grassland variables and CSM-condition and between the grassland
 multi-spectral reflectance and CSM-condition. The evaluation tested the
 relationships across grassland types, seasons and spectral devices used;
 and between grassland variable observations made in terms of mass or %
 cover. 
 
 
 When analysing data collected at patch level during the summer; the mass
 of 
 
 bryophytes, dead material and graminoids plus the % cover of forbs can be
 predicted to a moderate level of accuracy (R2 values of &gt;0.5 and
 nRMSE &lt;100) when analysing data from all seven grasslands. When
 analysing data from all Parsonage Down NNR grasslands; the mass of
 bryophytes, the % cover of live material, % cover-based live:dead ratio
 and CSM-condition could be predicted to a high level of accuracy (R2values
 of &gt;0.7 and nRMSE &lt;100). Moisture content plus the % cover
 of dead material, forbs and gram:forb ratio were all predicted to a
 moderate level of accuracy as well as CSM-condition predicted by grassland
 variable values. When using data from all Ingleborough NNR grasslands; the
 % cover of forbs and biomass plus the mass of bryophytes, dead material
 and live material could be predicted to a moderate level of accuracy. When
 using patch level data collected across three seasons; the % cover of dead
 material, live material and live:dead ratio plus the mass of graminoids
 could be predicted when using three seasons of data collected on one
 grassland, or for all three Parsonage grasslands, to at least a moderate
 level of accuracy although some models trained with % cover data had a
 high accuracy. Forbs (mass and % 
 
 cover) plus the mass of gram:forb ratio, live material and live:dead ratio
 could be 
 
 predicted to at least a moderate level of accuracy for some grasslands.
 
 
 When using data from all grasslands collected in one season the mass of a
 range of grassland variables could be predicted to a moderate level of
 accuracy for the spring and autumn months but not when using % cover data.
 Using CROPSCAN and SVC data produced similar results, with slightly
 stronger results from the CROPSCAN, but using data from the Rikola camera
 produced weaker results. When the results of trained PLSR models were
 extrapolated to field level, the projected predicted grassland variable
 values from models trained with CROPSCAN MSR 16R data looked promising but
 the results have not been externally validated using a separate data set.
 The most important spectral bands for predicting grassland variables and
 CSM-condition were NIR and SWIR with the red edge (647nm) and 470nm also
 having some importance. The most important grassland variables for
 predicting CSMcondition were depended on whether the grassland variable
 was mass-based or % cover-based. When using mass data; graminoid:forb
 ratio mass and live:dead ratio mass were consistently important. When
 using % cover data; forbs cover, graminoids cover and live:dead ratio
 cover were consistently important.
 
 
 Overall, the results suggest that some of the condition-related variables
 considered in this thesis are predicted with reasonable accuracy and
 precision at patch level (i.e. R2 values of &gt;0.5 and nRMSE
 &lt;100), but producing reliable results requires a sufficient
 quantity of data to train the statistical models (at least 30 quadrats of
 samples), particularly if the results are to be extrapolated to field
 level as additional data are required for the external validation of the
 results. Grassland variable prediction success varied with number of sites
 considered and with season with no clear consistent pattern. Also, none of
 the grassland variables could be consistently predicted strongly across
 all the different grasslands or seasons. 
 
 
 This has implications for any land manager who wishes to emulate the
 methods in this thesis. The results suggest that this thesis provides a
 more cost- and timeeffective solution for capturing grassland condition;
 but anyone emulating these methods would have to carefully choose the
 variables, grassland types and seasons to collect data and would have to
 collect a sufficient quantity of data for model training, testing and
 evaluation. A consequence of adopting, or refining, the approach in this
 thesis could be more effective monitoring (and therefore timely
 intervention where necessary) of UK Biodiversity Action Plan (BAP)
 priority habitats and Sites of Special Scientific Interest (SSSIs).
 Refining this approach could include testing different modelling
 approaches and focusing further work on the successful aspects of this
 research such as key grassland and wavelength variables.