Multi-temporal assessment of diversity and condition in UK semi-natural grasslands using optical reflectance

With 40% of the world’s plants estimated to be under threat of extinction and ever lowering levels of ecological intactness of biological systems, the requirement to effectively monitor plant species and diversity has never been more pressing. Globally, natural, and semi-natural grassland ecosystems are at particular risk of degradation and conversion. Semi-natural grasslands in the UK currently make up about 1-2% of the permanent lowland grassland cover. Once degraded due to agricultural additions or inappropriate management, they can be difficult and costly to restore. As these systems display high levels of plant and invertebrate diversity, there is a need to safeguard their decline. However, there are currently significant challenges to providing the data needed to assess the condition of these systems. Remote sensing could contribute by providing information on herbaceous plant diversity and vegetation state across a wide range of spatial scales and time. Optical traits are a subset of plant traits that are detectable using reflectance data from leaf to canopy scales, dependent on the configuration of the sensor employed and can be linked to taxonomic diversity and condition of vegetation. Very high spatial resolution hyperspectral imaging technologies are, for the first time, enabling in-situ grassland plant phenotyping at the leaf, individual and high-resolution canopy scale. Analyses of these spectra have demonstrated promising results in application of mapping of taxonomic units and diversity metrics. However there is little evidence of the temporal stability of these observations. At the landscape scale, openly available, higher spatial resolution satellite data is also enabling examination of smaller field parcels, which are typical of UK fragmented landscapes. In this context, spectral time-series have the potential to be used to predict the condition of vegetation communities of conservation interest. In this thesis, the use of optical remote sensing data to further our understanding of semi-natural grasslands and to safeguard their decline, is examined, with a particular focus on the exploitation of multi-temporal sampling. Firstly, spectral variation in space, as a surrogate measure for species or community type diversity (also known as the spectral variation hypothesis), is assessed via a meta-analysis of existing studies. The results of the synthesis reveal some promise for the approach, but a large amount of variation between study outcomes is observed, suggesting that methodological approaches are important in the effectiveness of the proxy. Secondly, spectral data is collected alongside botanical and phenological diversity data at high spatial resolution over a growing season to test the stability of the spectral variation hypothesis over time. The results of these experiments show that the ability to detect biodiversity using this method is seasonally, and possibly, site dependent. Next, the suitability of hyperspectral leaf reflectance for distinguishing 17 herbaceous species growing within a calcareous grassland is examined. The application of machine learning classification models to multi-temporal leaf spectra show that although species are distinguishable at most sampling times within the year, the transferability of these models is very limited between sampling dates. Finally satellite time-series of vegetation indices are used to predict favourable or unfavourable vegetation condition criteria in calcareous fields across two years. A number of indices were successful in distinguishing between the different condition criteria but there was variation in results found between the two years sampled, due to differences in intra-annual vegetation phenology. Overall the results of this thesis, show promise for remote sensing of grassland biodiversity and condition. Both high spatial resolution hyperspectral data, as well as coarser resolution multi-spectral data sets, can be useful in evaluation of these systems. However, the dynamic nature of leaves and canopies over time, will require a multi-temporal approach to model building, which should be an integral part of developing these methods in the future

Intra-annual taxonomic and phenological drivers of spectral variance in grasslands

According to the Spectral Variation Hypothesis (SVH), spectral variance has the potential to predict taxonomic composition in grasslands over time. However, in previous studies the relationship has been found to be unstable. We hypothesise that the diversity of phenological stages is also a driver of spectral variance and could act to confound the species signal. To test this concept, intra-annual repeat spectral and botanical sampling was performed at the quadrat scale at two grassland sites, one displaying high species diversity and the other low species diversity. Six botanical metrics were used, three taxonomy based and three phenology based. Using uni-temporal linear permutation models, we found that the SVH only held at the high diversity site and only for certain metrics and at particular time points. We also tested the seasonal influence of phenological stage dominance, alongside the taxonomic and phenological diversity metrics on spectral variance using linear mixed models. A term of percentage mature leaves, alongside an interaction term of percentage mature leaves and species diversity, explained 15-25% of the model variances, depending on the spectral region used. These results indicate that the dominant canopy phenology stage is a confounding variable when examining the spectral variance-species diversity relationship. We emphasise the challenges that exist in tracking species or phenology-based metrics in grasslands using spectral variance but encourage further research that contextualises spectral variance data within seasonal plant development alongside other canopy structural and leaf traits

The feasibility of leaf reflectance-based taxonomic inventories and diversity assessments of species-rich grasslands: a cross-seasonal evaluation using waveband selection

Hyperspectral leaf-level reflectance data may enable the creation of taxonomic inventories and diversity assessments of grasslands, but little is known about the stability of species-specific spectral classes and discrimination models over the course of a growing season. Here, we present a cross-seasonal dataset of seventeen species that are common to a temperate, dry and nutrient-poor calcareous grassland, which spans thirteen sampling dates, a week apart, during the spring and summer months. By using a classification model that incorporated waveband selection (a sparse partial least squares discriminant analysis), most species could be classified, irrespective of the sampling date. However, between 42 and 95% of the available spectral information was required to obtain these results, depending on the date and model run. Feature selection was consistent across time for 70 out of 720 wavebands and reflectance around 1410 nm, representing water features, contributed the most to the discrimination. Model transferability was higher between neighbouring sampling dates and improved after the “green-up” period. Some species were consistently easy to classify, irrespective of time point, when using up to six latent variables, which represented about 99% of the total spectral variance, whereas other species required many latent variables, which represented very small spectral differences. We concluded that it did seem possible to create reliable taxonomic inventories for combinations of certain grassland species, irrespective of sampling date, and that the reason for this could lie in their distinctive morphological and/or biochemical leaf traits. Model transferability, however, was limited across dates and cross-seasonal sampling that captures leaf development would probably be necessary to create a predictive framework for the taxonomic monitoring of grasslands. In addition, most variance in the leaf reflectance within this system was driven by a subset of species and this finding implies challenges for the application of spectral variance in the estimation of biodiversity

Prediction of grassland biodiversity using measures of spectral variance: a meta-analytical review

Over the last 20 years, there has been a surge of interest in the use of reflectance data collected using satellites and aerial vehicles to monitor vegetation diversity. One methodological option to monitor these systems involves developing empirical relationships between spectral heterogeneity in space (spectral variation) and plant or habitat diversity. This approach is commonly termed the ‘Spectral Variation Hypothesis’. Although increasingly used, it is controversial and can be unreliable in some contexts. Here, we review the literature and apply three-level meta-analytical models to assess test results of the hypothesis across studies using several moderating variables, relating to the botanical and spectral sampling strategies, and the types of sites evaluated. We focus on the literature relating to grasslands, which are less well studied compared to forests and are likely to require separate treatment due to their dynamic phenology and the taxonomic complexity of their canopies over small scales. Across studies, results suggest an overall positive relationship between spectral variation and species diversity (mean correlation co-efficient = 0.36). However, high levels of both within study and between study heterogeneity was found. Whether data was collected at the leaf or canopy level had the most impact on the mean effect size, with leaf level studies displaying a stronger relationship compared to canopy level studies. We highlight the challenges facing synthesis of these kinds of experiments, the lack of studies carried out in arid or tropical systems and the need for scalable, multi-temporal assessments to resolve controversy in the field

Intra-annual taxonomic and phenological drivers of spectral variance in grasslands

According to the Spectral Variation Hypothesis (SVH), spectral variance has the potential to predict taxonomic composition in grasslands over time. However, in previous studies the relationship has been found to be unstable. We hypothesise that the diversity of phenological stages is also a driver of spectral variance and could act to confound the species signal. To test this concept, intra-annual repeat spectral and botanical sampling was performed at the quadrat scale at two grassland sites, one displaying high species diversity and the other low species diversity. Six botanical metrics were used, three taxonomy based and three phenology based. Using uni-temporal linear permutation models, we found that the SVH only held at the high diversity site and only for certain metrics and at particular time points. We tested the seasonal influence of the taxonomic and phenological metrics on spectral variance using linear mixed models. A significant interaction term of percent mature leaves and species diversity was found, with the most parsimonious model explaining 43% of the intra-annual change. These results indicate that the dominant canopy phenology stage is a confounding variable when examining the spectral variance -species diversity relationship. We emphasise the challenges that exist in tracking species or phenology-based metrics in grasslands using spectral variance but encourage further research that contextualises spectral variance data within seasonal plant development alongside other canopy structural and leaf traits

Second-hand smoke in four English prisons: an air quality monitoring study

BACKGROUND: To measure levels of indoor pollution in relation to smoking in four English prisons. METHODS: TSI SidePak AM510 Personal Aerosol Monitors were used to measure concentrations of particulate matter less than 2.5 mum in diameter (PM2.5) for periods of up to 9 h in selected smoking and non-smoking areas, and personal exposure monitoring of prison staff during a work shift, in four prisons. RESULTS: PM2.5 data were collected for average periods of 6.5 h from 48 locations on 25 wing landings where smoking was permitted in cells, on 5 non-smoking wings, 13 prisoner cells, and personal monitoring of 22 staff members. Arithmetic mean PM2.5 concentrations were significantly higher on smoking than non-smoking wing landings (43.9 mug/m(3) and 5.9 mug/m(3) respectively, p < 0.001) and in smoking than non-smoking cells (226.2 mug/m(3) and 17.0 mug/m(3) respectively, p < 0.001). Staff members wore monitors for an average of 4.18 h, during which they were exposed to arithmetic mean PM2.5 concentration of 23.5 mug/m(3). CONCLUSIONS: The concentration of PM2.5 pollution in smoking areas of prisons are extremely high. Smoking in prisons therefore represents a significant health hazard to prisoners and staff members