The world of late seems oversaturated with stories about drones. These suddenly pervasive machines straddle a divide in geography, being simultaneously an important tool for proximal sensing in physical geography and technology with military origins that human geographers have critically engaged. This paper, a collaboration between a physical and a human geographer, is an exploration of the epistemological nexus that a critical drone methodology offers the discipline, and which we suggest provides a new opportunity for collaborative human/physical geography. Drawing on our own research with drones and that of others, we demonstrate how recent scholarship on vertical geographies and longstanding remote-sensing frameworks are challenged by drone methodologies where social, environmental and technological concerns are entangled with the politics of access to proximal airspace and, in doing so, define a new conceptual atmospheric zone within the Earth's atmospheric boundary layer – the “Nephosphere” – where drone experimentation occurs. We argue that engagement with non-military uses of drones is crucial for the discipline, now that we are entering an uncertain aerial future that will be replete with flying robots, and suggest drones are reconfiguring geographic imaginations. In short, we call on geographers to participate actively in the shaping of new drone methodologies where the values and perils of the technology can be critically debated from the starting point of the experiential, rather than the speculative.

Covering 40% of the terrestrial surface, dryland ecosystems characteristically have distinct vegetation structures that are strongly linked to their function. Existing survey approaches cannot provide sufficiently fine-resolution data at landscape-level extents to quantify this structure appropriately. Using a small, unpiloted aerial system (UAS) to acquire aerial photographs and processing theses using structure-from-motion (SfM) photogrammetry, three-dimensional models were produced describing the vegetation structure of semi-arid ecosystems at seven sites across a grass–to shrub transition zone. This approach yielded ultra-fine (< 1 cm2) spatial resolution canopy height models over landscape-levels (10 ha), which resolved individual grass tussocks just a few cm3 in volume. Canopy height cumulative distributions for each site illustrated ecologically-significant differences in ecosystem structure. Strong coefficients of determination (r2 from 0.64 to 0.95) supported prediction of above-ground biomass from canopy volume. Canopy volumes, above-ground biomass and carbon stocks were shown to be sensitive to spatial changes in the structure of vegetation communities. The grain of data produced and sensitivity of this approach is invaluable to capture even subtle differences in the structure (and therefore function) of these heterogeneous ecosystems subject to rapid environmental change. The results demonstrate how products from inexpensive UAS coupled with SfM photogrammetry can produce ultra-fine grain biophysical data products, which have the potential to revolutionise scientific understanding of ecology in ecosystems with either spatially or temporally discontinuous canopy cover.

AbstractNon-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, yet are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely-sensed biomass products and are undersampled by in-situ monitoring. Current global change threats emphasise the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we assess whether canopy height inferred from drone photogrammetry allows the estimation of aboveground biomass (AGB) across low-stature plant species sampled through a global site network. We found mean canopy height is strongly predictive of AGB across species, demonstrating standardised photogrammetric approaches are generalisable across growth forms and environmental settings. Biomass per-unit-of-height was similar within, but different among, plant functional types. We find drone-based photogrammetry allows for monitoring of AGB across large spatial extents and can advance understanding of understudied and vulnerable non-forested ecosystems across the globe.

Beavers influence hydrology by constructing woody dams. Using a before
after control impact experimental design, we quantified the effects of a
beaver dam sequence on the flow regime of a stream in SW England.
Building upon our previous research (Puttock et al. 2021), we consider
the mechanisms that underpin flow attenuation in beaver wetlands.
Rainfall-driven hydrological events were extracted between 2009 and
2020, for the impacted (n=612) and control (n=634) catchments, capturing
events seven years before and three years after beaver occupancy, at the
impacted site. General additive models were used to describe average
hydrograph geometry across all events. After beaver occupancy, Lag times
increased by 55.9% and declined by 17.5% in impacted and control
catchments, respectively. Flow duration curve analysis showed a larger
reduction in frequency of high flows, following beaver dam construction,
with declines of Q5 exceedance levels of 33% and 15% for impact and
control catchments, respectively. Using event total rainfall to predict
peak flow, five generalised linear models were fitted to test the
hypothesis that beaver dams attenuate flow, to a greater degree, with
larger storm magnitude. The best performing model showed we can have
high confidence that beaver dams attenuated peak flows, with increasing
magnitude, up to between 0.5-2.5 m. s
for the 94. percentile of event
total rainfall; but we cannot confidently detect attenuation beyond the
97. percentile. Increasing flow attenuation, with
event magnitude, is attributed to transient floodplain storage in low
gradient/profile floodplain valleys. These findings support the
assertion that beaver dams restore attenuated flows. However, with
long-term datasets of extreme hydrological events lacking, it is
challenging to predict the effect of beaver dams during extreme events
with high precision. Beaver dams will have spatially variable impacts on
hydrological processes, requiring further investigation to quantify
responses to dams across differing landscapes and scales.

The distribution of vegetation within urban zones is well understood to be important for delivery of a range of ecosystem services. While urban planners and human geographers are conversant with methodologies
for describing and exploring the volumetric nature of built spaces there is less research that has developed imaginative ways of visualising the complex spatial and volumetric structure of urban vegetation from the treetops to the ground. Using waveform LiDAR data to measure the three-dimensional nature of the urban greenspace, we explore different ways of virtually, and tangibly engaging with volumetric models describing the 3D
distribution of urban vegetation. Using waveform LiDAR data processed into voxels (volumetric pixels) and experimenting with a variety of creative approaches to visualise the volumetric nature of the data, we describe the
development of new methods for mapping the urban green volume, using a combination of Geographic Information Systems (GIS), Minecraft, 3D printing and Computer Numerical Control (CNC) milling processes. We
demonstrate how such methodologies can be used to reveal and explore the complex nature of the urban green volume. We also describe the outcome of using these models to engage diverse audiences with the volumetric data. We explain how the products could be used readily by a range of urban researchers and stakeholders: from town and city councils, to architects and ecologists.

We outline the principles of the natural capital approach to decision making and apply these to the contemporary challenge of very significantly expanding woodlands as contribution to attaining net zero emissions of greenhouse gases. Drawing on the case of the UK, we argue that a single focus upon carbon storage alone is likely to overlook the other ‘net zero plus’ benefits which woodlands can deliver. A review of the literature considers the wide variety of potential benefits which woodlands can provide, together with costs such as foregone alternative land uses. We argue that decision making must consider all of these potential benefits and costs for the right locations to be planted with the right trees. The paper closes by reviewing the decision support systems necessary to incorporate this information into policy and decision making. Read the free Plain Language Summary for this article on the Journal blog.

For much of their existence, the environmental benefits of artificial satellites, particularly through provision of remotely sensed data, seem likely to have greatly exceeded their environmental costs. With dramatic current and projected growth in the number of Earth-observation and other satellites in low Earth orbit, this trade-off now needs to be considered more carefully. Here we highlight the range of environmental impacts of satellite technology, taking a life-cycle approach to evaluate impacts from manufacture, through launch, to burn-up during de-orbiting. These include the use of renewable and nonrenewable resources (including those associated with the transmission, long-term storage, and distribution of data), atmospheric consequences of rocket launches and satellite de-orbiting, and impacts of a changing nighttime sky on humans and other organisms. Initial estimations of the scale of some impacts are sufficient to underscore the need for more detailed investigations and to identify potential means by which impacts can be reduced and mitigated.

In the UK, tree, hedgerow, and woodland (THaW) habitats are key havens for biodiversity and support many related ecosystem services. The UK is entering a period of agricultural policy realignment with respect to natural capital and climate change, meaning that now is a critical time to evaluate the distribution, resilience, and dynamics of THaW habitats. The fine-grained nature of habitats like hedgerows necessitates mapping of these features at relatively fine spatial resolution-and freely available public archives of airborne laser scanning (LiDAR) data at 90%. It was also possible to combine LiDAR mapping data and Sentinel-1 SAR data to rapidly track canopy change through time (i.e. every 3 months) using, cloud-based processing via Google Earth Engine. The resultant toolkit is also provided as an open-access web app. The results highlight that whilst nearly 90% of the tallest trees (above 15 m) are captured within the National Forest Inventory (NFI) database only 50% of THaW with a canopy height range of 3-15 m are recorded. Current estimates of tree distribution neglect these finer-grained features (i.e. smaller or less contiguous THaW canopies), which we argue will account for a significant proportion of landscape THaW cover.

Drylands cover ca. 40% of the land surface and are hypothesised to play a major role in the global carbon cycle, controlling both long-term trends and interannual variation. These insights originate from land surface models (LSMs) that have not been extensively calibrated and evaluated for water-limited ecosystems. We need to learn more about dryland carbon dynamics, particularly as the transitory response and rapid turnover rates of semi-arid systems may limit their function as a carbon sink over multi-decadal scales. We quantified aboveground biomass carbon (AGC; inferred from SMOS L-band vegetation optical depth) and gross primary productivity (GPP; from PML-v2 inferred from MODIS observations) and tested their spatial and temporal correspondence with estimates from the TRENDY ensemble of LSMs. We found strong correspondence in GPP between LSMs and PML-v2 both in spatial patterns (Pearson’s r = 0.9 for TRENDY-mean) and in inter-annual variability, but not in trends. Conversely, for AGC we found lesser correspondence in space (Pearson’s r = 0.75 for TRENDY-mean, strong biases for individual models) and in the magnitude of inter-annual variability compared to satellite retrievals. These disagreements likely arise from limited representation of ecosystem responses to plant water availability, fire, and photodegradation that drive dryland carbon dynamics. We assessed inter-model agreement and drivers of long-term change in carbon stocks over centennial timescales. This analysis suggested that the simulated trend of increasing carbon stocks in drylands is in soils and primarily driven by increased productivity due to CO2 enrichment. However, there is limited empirical evidence of this 50-year sink in dryland soils. Our findings highlight important uncertainties in simulations of dryland ecosystems by current LSMs, suggesting a need for continued model refinements and for greater caution when interpreting LSM estimates with regards to current and future carbon dynamics in drylands and by extension the global carbon cycle.

Insect pollinators are affected by the spatio-temporal distribution of floral resources, which are dynamic across time and space, and also influenced heavily by anthropogenic activities. There is a need for spatial data describing the time-varying spatial distribution of flowers, which can be used within behavioral and ecological studies. However, this information is challenging to obtain. Traditional field techniques for mapping flowers are often laborious and limited to relatively small areas, making it difficult to assess how floral resources are perceived by pollinators to guide their behaviors. Conversely, remote sensing of plant traits is a relatively mature technique now, and such technologies have delivered valuable data for identifying and measuring non-floral dynamics in plant systems, particularly leaves, stems and woody biomass in a wide range of ecosystems from local to global scales. However, monitoring the spatial and temporal dynamics of plant floral resources has been notably scarce in remote sensing studies. Recently, lightweight drone technology has been adopted by the ecological community, offering a capability for flexible deployment in the field, and delivery of centimetric resolution data, providing a clear opportunity for capturing fine-grained information on floral resources at key times of the flowering season. In this review, we answer three key questions of relevance to pollination science – can remote sensing deliver information on (a) how isolated are floral resources? (b) What resources are available within a flower patch? and (c) how do floral patches change over time? We explain how such information has potential to deepen ecological understanding of the distribution of floral resources that feed pollinators and the parameters that determine their navigational and foraging choices based on the sensory information they extract at different spatial scales. We provide examples of how such data can be used to generate new insights into pollinator behaviors in distinct landscape types and their resilience to environmental change.

Changes in plant cover and productivity are important in driving Arctic soil carbon dynamics and sequestration, especially in peatlands. Warming trends in the Arctic are known to have resulted in changes in plant productivity, extent and community composition, but more data are still needed to improve understanding of the complex controls and processes involved. Here we assess plant productivity response to climate variability between 1985 and 2020 by comparing peak growing season NDVI (Normalised Difference Vegetation Index data from Landsat 5 and 7), to seasonal-average weather data (temperature, precipitation and snow-melt timing) in nine locations containing peatlands in high- and low-Arctic regions in Europe and Canada. We find that spring (correlation 0.36 for peat dominant and 0.39 for mosaic; MLR coefficient 0.20 for peat, 0.29 for mosaic), summer (0.47, 0.42; 0.18, 0.17) and preceding-autumn (0.35, 0.25; 0.33, 0.27) temperature are linked to peak growing season NDVI at our sites between 1985 and 2020, whilst spring snow melt timing (0.42, 0.45; 0.25, 0.32) is also important, and growing season water availability is likely site-specific. According to regression trees, a warm preceding autumn (September-October-November) is more important than a warm summer (June-July-August) in predicting the highest peak season productivity in the peat-dominated areas. Mechanisms linked to soil processes may explain the importance of previous-Autumn conditions on productivity. We further find that peak productivity increases in these Arctic peatlands are comparable to those in the surrounding non-peatland-dominant vegetation. Increased productivity in and around Arctic peatlands suggests a potential to increased soil carbon sequestration with future warming, but further work is needed to test whether this is evident in observations of recent peat accumulation and extent.

Abstract. We first validate the performance of the Portable Optical Particle
Spectrometer (POPS), a small light-weight and high sensitivity optical
particle counter, against a reference scanning mobility particle sizer
(SMPS) for a month-long deployment in an environment dominated by biomass
burning aerosols. Subsequently, we examine any biases introduced by
operating the POPS on a quadcopter drone, a DJI Matrice 200 V2. We report
the root mean square difference (RMSD) and mean absolute difference (MAD) in
particle number concentrations (PNCs) when mounted on the UAV and operating
on the ground and when hovering at 10 m. When wind speeds are low (less than 2.6 m s−1), we find only modest differences in the RMSDs and MADs of 5 % and
3 % when operating at 10 m altitude. When wind speeds are between 2.6 and 7.7 m s−1 the RMSDs and MADs increase to 26.2 % and 19.1 %, respectively,
when operating at 10 m altitude. No statistical difference in PNCs was
detected when operating on the UAV in either ascent or descent. We also find
size distributions of aerosols in the accumulation mode (defined by
diameter, d, where 0.1 ≤ d ≤ 1 µm) are relatively consistent
between measurements at the surface and measurements at 10 m altitude, while
differences in the coarse mode (here defined by d &gt;  1 µm)
are universally larger. Our results suggest that the impact of the UAV
rotors on the POPS PNCs are small at low wind speeds, but when operating
under a higher wind speed of up to 7.6 m s−1, larger discrepancies occur. In
addition, it appears that the POPS measures sub-micron aerosol particles
more accurately than super-micron aerosol particles when airborne on the
UAV. These measurements lay the foundations for determining the magnitude of
potential errors that might be introduced into measured aerosol particle
size distributions and concentrations owing to the turbulence created by the
rotors on the UAV.
.

Abstract
. Context
. Microclimate (fine-scale temperature variability within metres of Earth’s surface) is highly influential on terrestrial organisms’ ability to survive and function. Understanding how such local climatic conditions vary is challenging to measure at adequate spatio-temporal resolution. Microclimate models provide the means to address this limitation, but require as inputs, measurements, or estimations of multiple environmental variables that describe vegetation and terrain variation.
.
. Objectives
. To describe the key components of microclimate models and their associated environmental parameters. To explore the potential of drones to provide scale relevant data to measure such environmental parameters.
.
. Methods
. We explain how drone-mounted sensors can provide relevant data in the context of alternative remote sensing products. We provide examples of how direct micro-meteorological measurements can be made with drones. We show how drone-derived data can be incorporated into 3-dimensional radiative transfer models, by providing a realistic representation of the landscape with which to model the interaction of solar energy with vegetation.
.
. Results
. We found that for some environmental parameters (i.e. topography and canopy height), data capture and processing techniques are already established, enabling the production of suitable data for microclimate models. For other parameters such as leaf size, techniques are still novel but show promise. For most parameters, combining spatial landscape characterization from drone data and ancillary data from lab and field studies will be a productive way to create inputs at relevant spatio-temporal scales.
.
. Conclusions
. Drones provide an exciting opportunity to quantify landscape structure and heterogeneity at fine resolution which are in turn scale-appropriate to deliver new microclimate insights.
.

Natural flood management (NFM), or working with natural processes, is a growing flood risk management method in the UK, Europe and worldwide. However, unlike the current dominant technical flood management, it lacks an established evidence base of flood risk parameters. This lack of evidence base can limit the uptake of NFM as a flood management method. This paper critically evaluates examples of NFM and wider relevant literature in order to identify NFM knowledge gaps and suggest how to overcome these. The UK is used as a microcosm of different environments for diverse examples. The sections include: land cover, land management, landscape interactions and trade-offs, evaluating the wider benefits of NFM and, finally, scaling from plot to catchment. This concludes in a suggested framework for a new approach to NFM research, which encompasses spatial scales, interactions and trade-offs of NFM and consistency of reporting results. Widening the NFM empirical evidence base should be seen as an opportunity for a new approach to flood research through exploring new habitats and new flood resilience methods.

Striving to achieve a diverse and inclusive workplace has become a major goal for many organisations around the world [. ]

Structure from motion and multi-view Stereo (SfM+MVS) is a technique for creating land surface models from optical remote sensing images. SfM+MVS is often coupled with aerial drones and has advanced considerably within spatial ecology for assessing: landslide progression, hydrological pathways, coastal erosion, arable crop yields and woody vegetation. However, there are few examples of its application to understanding grassland functions and services.

Grasslands are important as they deliver a range of functions and services, including: biodiversity provision, regulation of hydrology and food and fibre provision. This thesis advances understanding of the spatial and temporal uncertainty of drone and SfM+MVS-based workflows in grassland ecology. It details new solutions which address key limitations of SfM+MVS-based workflows, and explores novel applications within conservation management in grassy habitats. A combination of traditional agronomic techniques, airborne Light Detection and Ranging (LiDAR) and drone and SfM+MVS-coupled approaches were used to develop a greater understanding of the capabilities of current techniques and technologies. This thesis developed and evaluated novel methods to advance quantitative understanding of spatial variation in vegetation canopy characteristics and biodiversity provision across temperate grasslands. The research presented herein clearly details how traditional agronomic techniques alongside a drone, SfM+MVS and LiDAR-coupled workflow can support grassland conservation monitoring schemes through resource efficient measurements of key sward canopy characteristics.

This thesis extends the understanding of the capabilities, limitations and confidence in drone-based SfM+MVS for understanding grassland functions and services, aiding sound and timely management of protected features. This was achieved through answering four research questions:

i) How does the choice of SfM+MVS software (including settings and user experience/time) impact spatial and temporal uncertainty of sward height measurements?

ii) can drone data capture sward height variability, and are drone and SfM+MVS-based measurements reproducible across replicate image datasets?

iii) can airborne LiDAR-derived data products be used to address the lack of sufficiently accurate and fine-grain bare-Earth elevation reference data required for generating sward height measurements from a drone and SfM+MVS-based workflow?

iv) can a drone and SfM+MVS-based workflow be used to measure key habitat quality indicators for observed patterns in nectar feeding insects in high nature value grassland habitats?
Summaries of how these four research questions were addressed:
I Chapter 3 details the need to consider choice of software in SfM+MVS studies. Up until now, most studies carried out employing an SfM+MVS workflow were not statistically reproducible. When designing a drone and SfM+MVS-based study it is crucial to consider differences between software and how robust the workflow, including software, are by considering the variation in the SfM+MVS-derived vegetation canopy height measurements between replicate image datasets. To address the latter point I proposed that an SfM+MVS workflow for time series analyses should capture at least one replicate image dataset. This would, at a small cost, improve the reproducibility of the results, which is crucial when monitoring fine-grained indicators of environmental change over time. The findings presented in this part of the thesis have important implications for the application of SfM+MVS in ecology as well as in other fields of Earth and environmental science.

II the extent to which aerial photographs taken from a drone could deliver new insights into the spatial heterogeneity of an intensively managed grassland field were determined in Chapter 4. It is argued that fine-grained monitoring of temperate grasslands at management relevant extents is either technically or practically not possible with traditional manual approaches. Thus, an easily applied workflow which can support decision-making of grassland farmers and conservation managers, allowing the optimisation of sward management for production and/or biodiversity aims is demonstrated. The need for this workflow is evidenced in scientific literature on the conservation of grassland invertebrates and bird communities which reveals the intrinsic challenge of accurately and precisely quantifying grassland habitats, let alone at the temporal resolution required to capture the complex nature of population dynamics.

III the extent to which the quality of information derived from LiDAR-based data products could be improved, and the extent to which LiDAR-based data products can be used alongside drone-based aerial photographic data to deliver new insights into the spatial heterogeneity of short-sward grassland habitats were determined in Chapter 5. It is argued that not only can a workflow centred around LiDAR-derived data products deliver accurate ground height measurements, it also addresses concerns pertaining to the lack of sufficiently accurate and fine-grain bare-Earth elevation reference data. The proposed workflow extends previous attempts at calibrating LiDAR-derived height measurements into pastures and meadows.

IV Chapter 6 is set on the backdrop of the rapid decline in key grassland habitats experienced around large parts of the globe since the early 1900s which stress the importance of assessing the wider implications and often complex dynamics of change. While in-situ species population counts have and will continue to be an important asset for conservation management schemes and policy making, they are time consuming. Instead, spatial and temporal assessment of habitat quality offers resource efficient means of tracking patterns and trends in key habitat quality indicators. However, current approaches are time consuming and often fail to capture patterns of important features. Hence, in Chapter 6 a novel, resource efficient image- and LiDAR and machine learning-coupled workflow capable of delivering structural measurements at the grain size and over the extent required in conservation management was developed. Thus, there is arguably now a strong case for re-evaluating existing conservation monitoring schemes in the light of new technologies and techniques.

The leaf economics spectrum1,2 and the global spectrum of plant forms and functions3 revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species2. Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities4. However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability4,5. Here we derive a set of ecosystem functions6 from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems7,8.

This thesis explores the use of drone-based data acquisitions for deriving structural plant traits and spectral reflectance of vegetation which are variables of interest for carbon stock estimations and for understanding vegetation functioning. Previous work demonstrated that fine-grained spatio-temporal insights gained from drone-acquired data are critical for understanding local processes but also for interpreting dynamics in coarse scale representations of landscapes from Earth observing satellite data. This work builds on this by assessing uncertainties in data acquisition and processing workflows and demonstrating novel applications of drone acquired data. The findings from the individual chapters presented herein allow conclusions on which metrics can be used with confidence to assess the status and track changes in vegetation structure over time. An example of how the finely resolved spatial information on structural plant traits can be used for simulations of radiation propagation, towards functional landscape representations is demonstrated.
The presented work includes experiments conducted in the United Kingdom, Italy and Malaysia arising from international collaborations and addresses three critical questions in the proximal sensing of vegetation:
1. To what extent can drone Structure-from-Motion (SfM) photogrammetry-derived products deliver accurate information about vegetation height parameters? Using SfM photogrammetry for deriving robust vegetation height parameters is of particular interest when seeking to derive growing stock volume and biomass for plantation and forest management purposes and for the assessment of carbon stocks. The associated study focused on an oil palm plantation in Sarawak, Malaysia and examined the quality of SfM-based estimates of palm height and inferred stem height, both metrics which are commonly used for allometric estimates of biomass. Further, the impact of acquisition methodology on point cloud precision was investigated. Results showed that SfM could provide palm height metrics at the individual tree level with mean relative errors between 11.7% and 18.9% dependant on palm age and that for mature palms (>10 years) flight plans favouring coverage over spatial resolution and overlap did not decrease the accuracy.
2. How accurate and consistent are surface reflectance and vegetation index products acquired from drone-based sensors over vegetation canopies? Quantifying the spatial and temporal consistency of surface reflectance and vegetation index data acquired by lightweight sensors mounted on drone platforms is essential for applications in precision agriculture, for species classification and for studying vegetation functioning. This topic was addressed through two studies. The first study compared drone acquired spectral data over a maize field in Grosseto, Italy against reference datasets from near simultaneous airborne and satellite based image acquisitions. While uncertainties in drone acquired surface reflectance were found to be greater than anticipated (5-28% relative errors over the maize field), VIs were highly correlated and comparable across scales. The second study investigated the use of vegetation index data to track phenology related changes over time for mostly deciduous tree species in Cornwall, UK. VIs proved to be sufficiently consistent for both, acquisitions under overcast and cloud free skies to resolve phenological changes with illumination based uncertainties an order of magnitude smaller than the total increase in index values across Spring green-up.
3. Can drone-based data be used to constrain and drive models of radiative transfer for understanding photon-plant interactions in complex heterogeneous canopies? Combining drone acquired canopy height models and vegetation index information to represent vegetation in a 3D radiative transfer model represents a new opportunity for simulating the interaction of light with vegetation at fine spatial scales. Previously, the information required for modelling heterogeneous vegetation canopies could only be acquired through laborious measurements in-situ or financially costly laser-scanning methods. This topic was explored by creating a representation of a local wildlife conservation site in Cornwall, UK within the Discrete Anisotropic Radiative Transfer (DART) model. The model was used for a case study focused on simulating the photosynthetically active radiation (PAR) reaching the understory as hourly fractions and spatially explicit (1 m spatial resolution) daily light
integrals across Spring green-up. Results showed that while the drone-data parameterised model could represent the variability across discontinuous vegetation cover, PAR reaching the understory was considerably overestimated at start-of-peak greenness due to uncertainties in modelled plant area density and leaf angular distributions.
The primary data acquisitions of all the presented studies were performed exclusively with lightweight multi-rotor drones, trialling relatively low-cost consumer grade and multi-spectral cameras which have since been widely adopted by research groups globally. The presented results therefore represent a timely contribution with relevant insights from appropriate acquisition methodologies to novel applications of drone acquired data for representing vegetation in a radiative transfer modelling context.

Across the semi-arid ecosystems of the southwestern USA, there has been widespread encroachment of woody shrubs and trees including Juniperus species into former grasslands. Quantifying vegetation biomass in such ecosystems is important because semi-arid ecosystems are thought to play an important role in the global land carbon (C) sink, and changes in plant biomass also have implications for primary consumers and potential bioenergy feedstock. Oneseed Juniper (J. monosperma) is common in desert grasslands and pinyon-juniper rangelands across the intermountain region of southwestern North America; however, there is limited information about the aboveground biomass (AGB) and sapwood area (SWA) for this species, causing uncertainties in estimates of C stock and transpiration fluxes. In this study, we report on canopy area, stem diameter, maximum height and biomass measurements from J. monosperma trees sampled from central New Mexico. Dry biomass ranged between 0.4 kg and 625 kg, and cross-sectional sapwood area was measured on n=200 stems using image analysis. We found a strong linear relationship between canopy area and AGB (r2 = 0.96), with a similar slope to that observed in other juniper species, suggesting that this readily measured attribute is well suited for upscaling studies. There was a 9% bias between different approaches to measuring canopy area, indicating care should be taken to account for these differences to avoid systematic biases. We found equivalent stem diameter (ESD) was a strong predictor of biomass, but that existing allometric models under-predicted biomass in larger trees. We found sapwood area could be predicted from individual stem diameter with a power relationship, and that tree-level SWA should be estimated by summing the SWA predictions from individual stems rather than ESD. Our improved allometric models for J. monosperma support more accurate and robust measurements of C storage and transpiration fluxes in Juniperus-dominated ecosystems.

AbstractEurasian beaver (Castor fiber) populations are expanding across Europe. Depending on location, beaver dams bring multiple benefits and/or require management. Using nationally available data, we developed: a Beaver Forage Index (BFI), identifying beaver foraging habitat, and a Beaver Dam Capacity (BDC) model, classifying suitability of river reaches for dam construction, to estimate location and number of dams at catchment scales. Models were executed across three catchments, in Great Britain (GB), containing beaver. An area of 6747 km2 was analysed for BFI and 16,739 km of stream for BDC. Field surveys identified 258 km of channel containing beaver activity and 89 dams, providing data to test predictions. Models were evaluated using a categorical binomial Bayesian framework to calculate probability of foraging and dam construction. BFI and BDC models successfully categorised the use of reaches for foraging and damming, with higher scoring reaches being preferred. Highest scoring categories were ca. 31 and 79 times more likely to be used than the lowest for foraging and damming respectively. Zero-inflated negative binomial regression showed that modelled dam capacity was significantly related (p = 0.01) to observed damming and was used to predict numbers of dams that may occur. Estimated densities of dams, averaged across each catchment, ranged from 0.4 to 1.6 dams/km, though local densities may be up to 30 dams/km. These models provide fundamental information describing the distribution of beaver foraging habitat, where dams may be constructed and how many may occur. This supports the development of policy and management concerning the reintroduction and recolonisation of beaver.

Compact multi-spectral sensors that can be mounted on lightweight drones are now widely available and applied within the geo- and environmental sciences. However; the spatial consistency and radiometric quality of data from such sensors is relatively poorly explored beyond the lab; in operational settings and against other sensors. This study explores the extent to which accurate hemispherical-conical reflectance factors (HCRF) and vegetation indices (specifically: normalised difference vegetation index (NDVI) and chlorophyll red-edge index (CHL)) can be derived from a low-cost multispectral drone-mounted sensor (Parrot Sequoia). The drone datasets were assessed using reference panels and a high quality 1 m resolution reference dataset collected near-simultaneously by an airborne imaging spectrometer (HyPlant). Relative errors relating to the radiometric calibration to HCRF values were in the 4 to 15% range whereas deviations assessed for a maize field case study were larger (5 to 28%). Drone-derived vegetation indices showed relatively good agreement for NDVI with both HyPlant and Sentinel 2 products (R2 = 0.91). The HCRF; NDVI and CHL products from the Sequoia showed bias for high and low reflective surfaces. The spatial consistency of the products was high with minimal view angle effects in visible bands. In summary; compact multi-spectral sensors such as the Parrot Sequoia show good potential for use in index-based vegetation monitoring studies across scales but care must be taken when assuming derived HCRF to represent the true optical properties of the imaged surface.

Accelerated soil erosion can result in substantial declines in soil fertility and has devastating environmental impacts. Consequently, understanding if rates of soil erosion are acceptable is of local and global importance. Herein we use empirical soil erosion observations collated into an open access geodatabase to identify the extent to which existing data and methodological approaches can be used to develop an empirically-derived understanding of soil erosion in the UK (by way of an example). The findings indicate that whilst mean erosion rates in the UK are low, relative to the rest of Europe for example, 16% of observations on arable land were greater than the supposedly tolerable rate of 1 t ha−1 yr−1 and maximum erosion rates were as high as 91.7 t ha−1 yr−1. However, the analysis highlights a skew in existing studies towards locations with a known erosion likelihood and methods that are biased towards single erosion pathways, rather than an all-inclusive study of erosion rates and processes. Accordingly, we suggest that future soil erosion research and policy must address these issues if an accurate assessment of soil erosion rates at the national-scale are to be established. The interactive geodatabase published alongside this paper offers a platform for the simultaneous development of soil erosion research, formulation of effective policy and better protection of soil resources.

The high-mountain cryosphere forms water towers that are important for ecosystem services provision, supplying large populations living in mountains and the surrounding lowlands and producing potable water resources, and water for agriculture, industry and hydropower generation. However, continued glacier recession and mass loss is projected throughout the twenty-first century, and this raises major concerns regarding the future sustainability of cryospheric water resources. While glacier meltwater represents an essential drought-resilient freshwater resource in vulnerable drought-prone regions, little research has focused on the contribution made by runoff from rock glaciers. These are located widely throughout the high-mountain cryosphere and estimates of rock glacier water volume equivalent (WVEQ) vs glaciers suggests that the former may constitute increasingly important long-term water stores. Owing to the insulating effects of thick supraglacial debris cover, rock glaciers are climatically more resilient than glaciers; therefore, their relative importance versus glaciers may increase under future climate warming. Yet, while the hydrological role of debris-free glaciers and debris-covered glaciers has been the subject of much research, that of rock glaciers has received comparatively little attention. Given the need for strong climate adaptation in many of the world’s mountain regions, it is clear that a more comprehensive understanding of all components of the hydrological cycle in the high-mountain cryosphere is required.

In this thesis, I develop the scientific understanding of rock glacier significance in deglacierizing mountains across a range of spatial scales (local, national, regional and global), with a specific focus on High Mountain Asia (HMA). The review chapter critically assesses the state of current scientific knowledge regarding the hydrological role of rock glaciers in high mountain systems and serves to form the context for the empirical chapters. The thesis has three key themes to which the empirical chapters are aligned: (1) the distribution and hydrological significance of rock glaciers at global scales, (2) the distribution and hydrological significance of rock glaciers at regional and national spatial scales (Himalaya and Nepalese Himalaya), and (3) advancing rock glacier evolutionary theory.

(1) the thesis created a meta-analysis of existing systematic rock glacier inventories and compiled the first near-global rock glacier database (RGDB). The RGDB presented here includes >73,000 rock glaciers (intact = ~39,500, relict = ~33,500), which contain a WVEQ of 83.7 ± 16.7 Gt [~69–102 trillion litres]. Furthermore, the estimated ratio of rock glacier: glacier WVEQ is 1:456 globally.

(2) the results of the meta-analysis described in (1) show that only ~9% of studies included in the RGDB cover the Hindu Kush Himalaya (HKH); therefore, I produced the first systematic rock glacier inventory for the (i) Nepalese Himalaya (national-scale), and (ii) Himalaya (regional-scale). In the former (i) I inventoried >6,000 rock glaciers, and these are estimated to contain a WVEQ of 20.90 ± 4.18 km³ (19.16 ± 3.83 Gt). For the Nepalese Himalaya estimated rock glacier: glacier WVEQ ratio is 1:9. In the latter (ii) ~25,000 rock glaciers have been inventoried. The total WVEQ is 51.80 ± 10.36 km³ (47.48 ± 9.50 Gt) with an estimated rock glacier: glacier WVEQ ratio of 1:24. The results of Theme 1 and 2 indicate that rock glaciers form considerable long-term water stores, which may become increasingly important as climatically-driven glacier recession and mass loss continues throughout the twenty-first century and beyond.

(3) in order to understand debris-free glacier transition to rock glaciers I use in situ sedimentological data and kite aerial photography (KAP) data and develop a conceptual hypothesis to explain the key drivers of this process. The thesis suggests that sediment connectivity (i.e. the strength of the link between sediment sources and downslope landforms) is one such driver of these transition processes. As a consequence, I hypothesise that the presence of well-developed lateral moraines along glacier margins serves to reduce this connectivity, and thus reduce the likelihood of glacier-to-rock glacier transition occurring. The corelationships between rock glaciers and glacial, periglacial and paraglacial processes are also evaluated in the context of rock glacier origin and the changing influence these processes have upon rock glacier evolution through their lifecycle.

Collectively, this research has shaped the understanding of the current and potential future role of rock glaciers in mountain hydrology and is the first to comprehend the distribution and hydrological significance of rock glaciers globally and in the Himalaya.

Peatlands are recognised as an important but vulnerable ecological resource. Understanding the effects of existing damage, in this case erosion, enables more informed land management decisions to be made. Over the growing seasons of 2013 and 2014 photosynthesis and ecosystem respiration were measured using closed chamber techniques within vegetated haggs and erosional peat pans in Dartmoor National Park, southwest England. Below-ground total and heterotrophic respiration were measured and autotrophic respiration estimated from the vegetated haggs.
The mean water table was significantly higher in the peat pans than in the vegetated haggs; because of this, and the switching from submerged to dry peat, there were differences in vegetation composition, photosynthesis and ecosystem respiration. In the peat pans photosynthetic CO2 uptake and ecosystem respiration were greater than in the vegetated haggs and strongly dependent on the depth to water table (r2>0.78, p

Monitoring coastal environments is a challenging task. This is because of both the logistical demands involved with in-situ data collection and the dynamic nature of the coastal zone, where multiple processes operate over varying spatial and temporal scales. Remote sensing products derived from spaceborne and airborne platforms have proven highly useful in the monitoring of coastal ecosystems, but often they fail to capture fine scale processes and there remains a lack of cost-effective and flexible methods for coastal monitoring at these scales. Proximal sensing technology such as lightweight drones and kites has greatly improved the ability to capture fine spatial resolution data at user-dictated visit times. These approaches are democratising, allowing researchers and managers to collect data in locations and at defined times themselves. In this thesis I develop our scientific understanding of the application of proximal sensing within coastal environments. The two critical review pieces consolidate disparate information on the application of kites as a proximal sensing platform, and the often overlooked hurdles of conducting drone operations in challenging environments. The empirical work presented then tests the use of this technology in three different coastal environments spanning the land-sea interface. Firstly, I use kite aerial photography and uncertainty-assessed structure-from-motion multi-view stereo (SfM-MVS) processing to track changes in coastal dunes over time. I report that sub-decimetre changes (both erosion and accretion) can be detected with this methodology. Secondly, I used lightweight drones to capture fine spatial resolution optical data of intertidal seagrass meadows. I found that estimations of plant cover were more similar to in-situ measures in sparsely populated than densely populated meadows. Lastly, I developed a novel technique utilising lightweight drones and SfM-MVS to measure benthic structural complexity in tropical coral reefs. I found that structural complexity measures were obtainable from SfM-MVS derived point clouds, but that the technique was influenced by glint type artefacts in the image data. Collectively, this work advances the knowledge of proximal sensing in the coastal zone, identifying both the strengths and weaknesses of its application across several ecosystems.

In many of the world's high mountain systems, glacier recession in response to climate change is accompanied by a paraglacial response whereby glaciers are undergoing a transition to rock glaciers. We hypothesise that this transition has important implications for hydrological resources in high mountain systems and the surrounding lowlands given the insulating effects that debris cover can have on glacier ice. Despite this, however, little is known about how this transition occurs nor how quickly, which glaciers are liable to transition, the factors driving this process and the water supply implications that follow. This paper assesses the role of glacier and rock glacier textural properties from a deglaciating region of the Himalayas to begin to address some of these issues. We investigated six landsystems on the spectrum from glaciers-to-rock glaciers in the Khumbu Himal, Nepal, and sampled for clast shape and roundness during 2016 and 2017. Kite aerial photography was additionally used to capture aerial images of an ongoing glacier-to-rock glacier transitional landform (Chola Glacier) to elucidate the surface geomorphic features of a fully transitioned landform. This image data, processed using a structure-from-motion multi-view stereo photogrammetry approach, revealed the presence of a spatially coherent ridge-and-furrow surface morphology in the lower reaches of Chola Glacier, which is potentially indicative of an ongoing glacier-to-rock glacier transition. We show that glacier-derived and slope-derived clast roundness significantly statistically different (Kolmogorov–Smirnov two-sample test: Dmax = 0.62, two-tail p <. 001; n = 1650) and suggest that sediment connectivity (i.e. linkage between sediment sources and downslope landforms) is one of the drivers of the transition process. Consequently, we hypothesise that the presence of well-developed lateral moraines along glacier margins serves to reduce this connectivity and thus the likelihood of glacier-to-rock glacier transition occurring. Understanding such processes has implications for predicting the geomorphological evolution of deglacierizing mountains under future climate warming and the water supply consequences that follow.

Recent climate change has had a major impact on biodiversity and has altered the geographical distribution of vascular plant species. This trend is visible globally; however, more local and regional scale research is needed to improve understanding of the patterns of change and to develop appropriate conservation strategies that can minimise cultural, health, and economic losses at finer scales. Here we describe a method to manually geo-reference botanical records from a historical herbarium to track changes in the geographical distributions of plant species in West Cornwall (South West England) using both historical (pre-1900) and contemporary (post-1900) distribution records. We also assess the use of Ellenberg and climate indicator values as markers of responses to climate and environmental change. Using these techniques we detect a loss in 19 plant species, with 6 species losing more than 50% of their previous range. Statistical analysis showed that Ellenberg (light, moisture, nitrogen) and climate indicator values (mean January temperature, mean July temperature and mean precipitation) could be used as environmental change indicators. Significantly higher percentages of area lost were detected in species with lower January temperatures, July temperatures, light, and nitrogen values, as well as higher annual precipitation and moisture values. This study highlights the importance of historical records in examining the changes in plant species' geographical distributions. We present a method for manual geo-referencing of such records, and demonstrate how using Ellenberg and climate indicator values as environmental and climate change indicators can contribute towards directing appropriate conservation strategies.

The spatial distributions of biodiversity and people vary across landscapes and are critical to the delivery of ecosystem services and disservices. The high densities of people and often of birds in urban areas lead to frequent human–avian interactions, which can be positive or negative for people's well-being. The identities of the bird species providing these services or disservices tend to be quite different; however, it is unclear how their abundance and richness covary with human population density, and hence with potential recipients of these services and disservices. We surveyed bird populations in 106 tiles (500 × 500 m) across the 174 km2 of an extended urban area in southern England. From the literature, we identified two groups of species: those associated with positive interactions for human well-being and those that display behaviours that are negative for human well-being. We estimated the abundance (adjusted for detection probability) and richness of each group and modelled how they covary with human population density. Aggregation of population estimates for the 35 service and nine disservice species observed revealed 593,128 (95% confidence interval: 541,817–657,046) and 225,491 (200,134–235,066) birds respectively. Across the surveyed tiles, there were 1.09 service and 0.42 disservice birds per person. There was a peaking quadratic relationship between service abundance and human population density, but a negative linear relationship between richness and human density. Conversely, there were positive linear relationships for both abundance and richness of disservice species with human density. The ratio of service to disservice birds shifted from 3.5–1 at intermediate human densities to 1–1 in more densely populated areas. Synthesis and applications. Differences in the distributions of service and disservice species, and the extremely low ratios of birds to people particularly in socioeconomically deprived areas, mean that people there have few opportunities for contact with birds, and the contact they do have is equally likely to be negative as positive for human well-being. We recommend spatial targeting of improvements in green infrastructure, combined with the targeted provisioning of food and nesting places for service species, to promote positive interactions between birds and people.

The world of late seems oversaturated with stories about drones. These suddenly pervasive machines straddle a divide in geography, being simultaneously an important tool for proximal sensing in physical geography and technology with military origins that human geographers have critically engaged. This paper, a collaboration between a physical and a human geographer, is an exploration of the epistemological nexus that a critical drone methodology offers the discipline, and which we suggest provides a new opportunity for collaborative human/physical geography. Drawing on our own research with drones and that of others, we demonstrate how recent scholarship on vertical geographies and longstanding remote-sensing frameworks are challenged by drone methodologies where social, environmental and technological concerns are entangled with the politics of access to proximal airspace and, in doing so, define a new conceptual atmospheric zone within the Earth's atmospheric boundary layer – the “Nephosphere” – where drone experimentation occurs. We argue that engagement with non-military uses of drones is crucial for the discipline, now that we are entering an uncertain aerial future that will be replete with flying robots, and suggest drones are reconfiguring geographic imaginations. In short, we call on geographers to participate actively in the shaping of new drone methodologies where the values and perils of the technology can be critically debated from the starting point of the experiential, rather than the speculative.

Grasslands deliver a range of ecosystem services, including the provision of food and biodiversity, and regulation of soil carbon storage and hydrology. Monitoring schemes are needed to quantify spatial changes in these multiple functions alongside ecosystem degradation. Sward height is widely recognised as a key spatial variable in the provision of these services. Current manual monitoring approaches are labour intensive, and often fail to capture spatial patterns of important features, including sward height. Proximal sensing from small aerial drones carrying lightweight cameras can be transformed into surface height models using image-based structure-from-motion and Multi-View Stereo-based approaches; this presents a new opportunity for monitoring the spatial structure of grassland sward height. We combined aerial photographs with field survey data and an open-source image-based modelling-processing workflow to generate sward height measurements for a field comprising mainly Lolium perenne (perennial ryegrass) and Trifolium pratense (red clover). We compared the derived measurements with in situ data captured on the same day using traditional agronomic sward height techniques to determine the quality of the drone-derived surface model product for sward characterisation. The SfM and Multi-View Stereo-based surface model had a mean absolute sward height measurement error of between 3.7 and 4.2 cm. To produce field observations with equivalent quality would require up to 550 sward height measurements for the study site (area: 8,059 m2), which is not feasible over larger extents required for conservation of key species or agronomic purposes. Synthesis and applications. We demonstrate how the collection of precise and detailed information on the spatial structure of grasslands can be made over management-relevant extents. Aerial digital photographs can be transformed into surface models using an image-based modelling approach: structure-from-motion and Multi-View Stereo techniques. Image-based measurements of sward heights were compared with manual sward height data captured on the same day. This novel source of vegetation spatial information could improve sward management for conservation and agronomy applications. The approach supports frequent surveys, at user-controlled revisit times, and delivers data for spatial monitoring of key grassland functions and services.

A method to estimate peat depth and extent is vital for accurate estimation of carbon stocks and to facilitate appropriate peatland management. Current methods for direct measurement (e.g. ground penetrating radar, probing) are labour intensive making them unfeasible for capturing spatial information at landscape extents. Attempts to model peat depths using remotely sensed data such as elevation and slope have shown promise but assume a functional relationship between current conditions and gradually accrued peat depth. Herein we combine LiDAR-derived metrics known to influence peat accumulation (elevation, slope, topographic wetness index (TWI)) with passive gamma-ray spectrometric survey data, shown to correlate with peat occurrence to develop a novel peat depth model for Dartmoor.
Total air absorbed dose rates of Thorium, Uranium and Potassium were calculated, referred to as radiometric dose. Relationships between peat depth, radiometric dose, elevation, slope and TWI were trained using 1334 peat depth measurements, a further 445 measurements were used for testing. All variables showed significant relationships with peat depth. Linear stepwise regression of natural log-transformed variables indicated that a radiometric dose and slope model had an r2 = 0.72/0.73 and RMSE 0.31/0.31 m for training/testing respectively. This model estimated an area of 158 ±101 km2 of peaty soil >0.4 m deep across the study area. Much of this area (60 km2) is overlain by grassland and therefore may have been missed if vegetation cover was used to map peat extent. Using published bulk density and carbon content values we estimated 13.1 Mt C (8.1-21.9 Mt C) are stored in the peaty soils within the study area. This is an increase on previous estimates due to greater modelled peat depth. The combined use of airborne gamma-ray spectrometric survey and LiDAR data provide a novel, practical and repeatable means to estimate peat depth with no a priori knowledge, at an appropriate resolution (10 m) and extent (406 km2) to facilitate management of entire peatland complexes.

Glacier- and snowpack-derived meltwaters are threatened by climate change. Features such as rock glaciers (RGs) are climatically more resilient than glaciers and potentially contain hydrologically valuable ice volumes. However, while the distribution and hydrological significance of glaciers is well studied, RGs have received comparatively little attention. Here, we present the first near-global RG database (RGDB) through an analysis of current inventories and this contains >73,000 RGs. Using the RGDB, we identify key data-deficient regions as research priorities (e.g. Central Asia). We provide the first approximation of near-global RG water volume equivalent and this is 83.72 ± 16.74 Gt. Excluding the Antarctic and Subantarctic, Greenland Periphery, and regions lacking data, we estimate a near-global RG to glacier water volume equivalent ratio of 1:456. Significant RG water stores occur in arid and semi-arid regions (e.g. South Asia East, 1:57). These results represent a first-order approximation. Uncertainty in the water storage estimates includes errors within the RGDB, inherent flaws in the meta-analysis methodology, and RG thickness estimation. Here, only errors associated with the assumption of RG ice content are quantified and overall uncertainty is likely larger than that quantified. We suggest that RG water stores will become increasingly important under future climate warming.

Coastal dunes are globally-distributed dynamic ecosystems that occur at the land-sea interface. They are sensitive to disturbance both from natural forces and anthropogenic stressors, and therefore require regular monitoring to track changes in their form and function ultimately informing management decisions. Existing techniques employing satellite or airborne data lack the temporal or spatial resolution to resolve fine-scale changes in these environments, both temporally and spatially whilst fine-scale in-situ monitoring (e.g. terrestrial laser scanning) can be costly and is therefore confined to relatively small areas. The rise of proximal sensing-based Structure-from-Motion Multi-View Stereo (SfM-MVS) photogrammetric techniques for land surface surveying offers an alternative, scale-appropriate method for spatially distributed surveying of dune systems. Here we present the results of an inter- and intra-annual experiment which utilised a low-cost and highly portable kite aerial photography (KAP) and SfM-MVS workflow to track sub-decimeter spatial scale changes in dune morphology over timescales of between 3 and 12 months. We also compare KAP and drone surveys undertaken at near-coincident times of the same dune system to test the KAP reproducibility. Using a Monte Carlo based change detection approach (Multiscale Model to Model Cloud Comparison (M3C2)) which quantifies and accounts for survey uncertainty, we show that the KAP-based survey technique, whilst exhibiting higher x,y,z uncertainties than the equivalent drone methodology, is capable of delivering data describing dune system topographical change. Significant change (according to M3C2); both positive (accretion) and negative (erosion) was detected across 3, 6 and 12 month timescales with the majority of change detected below 500 mm. Significant topographic changes as small as ~20 mm were detected between surveys. We demonstrate that portable, low-cost consumer-grade KAP survey techniques, which have been employed for decades for hobbyist aerial photography can now deliver science-grade data, and we argue that kites are well-suited to coastal survey where winds and sediment might otherwise impede surveys by other proximal sensing platforms, such as drones.

Exposure to nature provides a wide range of health benefits. A significant proportion of these are delivered close to home, because this offers an immediate and easily accessible opportunity for people to experience nature. However, there is limited information to guide recommendations on its management and appropriate use. We apply a nature dose-response framework to quantify the simultaneous association between exposure to nearby nature and multiple health benefits. We surveyed ca. 1000 respondents in Southern England, UK, to determine relationships between (a) nature dose type, that is the frequency and duration (time spent in private green space) and intensity (quantity of neighbourhood vegetation cover) of nature exposure and (b) health outcomes, including mental, physical and social health, physical behaviour and nature orientation. We then modelled dose-response relationships between dose type and self-reported depression. We demonstrate positive relationships between nature dose and mental and social health, increased physical activity and nature orientation. Dose-response analysis showed that lower levels of depression were associated with minimum thresholds of weekly nature dose. Nearby nature is associated with quantifiable health benefits, with potential for lowering the human and financial costs of ill health. Dose-response analysis has the potential to guide minimum and optimum recommendations on the management and use of nearby nature for preventative healthcare.

The movements of organisms and the resultant flows of ecosystem services are strongly shaped by landscape connectivity. Studies of urban ecosystems have relied on two-dimensional (2D) measures of greenspace structure to calculate connectivity. It is now possible to explore three-dimensional (3D) connectivity in urban vegetation using waveform lidar technology that measures the full 3D structure of the canopy. Making use of this technology, here we evaluate urban greenspace 3D connectivity, taking into account the full vertical stratification of the vegetation. Using three towns in southern England, UK, all with varying greenspace structures, we describe and compare the structural and functional connectivity using both traditional 2D greenspace models and waveform lidar-generated vegetation strata (namely, grass, shrubs and trees). Measures of connectivity derived from 3D greenspace are lower than those derived from 2D models, as the latter assumes that all vertical vegetation strata are connected, which is rarely true. Fragmented landscapes that have more complex 3D vegetation showed greater functional connectivity and we found highest 2D to 3D functional connectivity biases for short dispersal capacities of organisms (6 m to 16 m). These findings are particularly pertinent in urban systems where the distribution of greenspace is critical for delivery of ecosystem services.

In peatlands plant growth and senescence affect annual ecosystem carbon dioxide (CO2) exchange, and CO2 fluxes show considerable inter-annual variability. Phenology is a fundamental indicator of ecosystem carbon dynamics and can be measured from remote sensing systems, but the extent to which satellite products provide useful proxies of peatland vegetation phenology is not well known. Using MODIS vegetation products coupled with field observations of phenology from a basic camera system and measurements of Gross Primary Productivity (GPP) measured using a closed chamber method, we sought to establish the extent to which satellite observations capture phenological processes at a UK peatland site. Daily, true-colour digital images were captured with a time-lapse camera (Brinno TLC100) between 23-Apr-2013 and 29-Oct-2013 and converted into a Green-Red Vegetation Index (GRVI). These were compared with a range of MODIS vegetation products at various spatial resolutions. We found that vegetation products with finer spatial resolution (

Urban greenspace has a major impact on human health and quality of life, and thus the way in which such green infrastructure is constructed, managed and maintained is of critical importance. A range of studies have demonstrated the relationship between the areal coverage and distribution of vegetation and the provision of multiple urban ecosystem services. It is not known how sensitive findings are to the spatial resolution of the underlying data relative to the grain size of urban land cover heterogeneity. Moreover, little is known about the three-dimensional (3D) structure of urban vegetation and delivery of services, and addressing such questions is limited by the availability of data describing canopy structure from the tree tops to the ground. Waveform airborne laser scanning (lidar) offers a new way of capturing 3D data describing vegetation structure. We generated voxels (volumetric pixels) from waveform lidar (1·5 m resolution), differentiated vegetation layers using height as a determinant, and computed statistics on surface cover, volume and volume density per stratum. We then used a range of widely available remote sensing products with varying spatial resolution (1 to 100 m) to map the same greenspace, and compared results to those from the waveform lidar survey. We focused on data from three urban zones in the UK with distinct patterns of vegetation cover. We found −3%, +7·5% and +26·1% differences in green surface cover compared with, respectively, town planning maps (

Quantifying the extent of soil erosion at a fine spatial resolution can be time consuming and costly; however, proximal remote sensing approaches to collect topographic data present an emerging alternative for quantifying soil volumes lost via erosion. Herein we compare terrestrial laser scanning (TLS), and both unmanned aerial vehicle (UAV) and ground photography (GP) structure-from-motion (SfM) derived topography. We compare the cost-effectiveness and accuracy of both SfM techniques to TLS for erosion gully surveying in upland landscapes, treating TLS as a benchmark. Further, we quantify volumetric soil loss estimates from upland gullies using digital surface models derived by each technique and subtracted from an interpolated pre-erosion surface. Soil loss estimates from UAV and GP SfM reconstructions were comparable to those from TLS, whereby the slopes of the relationship between all three techniques were not significantly different from 1:1 line. Only for the TLS to GP comparison was the intercept significantly different from zero, showing that GP is more capable of measuring the volumes of very small erosion features. In terms of cost-effectiveness in data collection and processing time, both UAV and GP were comparable with the TLS on a per-site basis (13.4 and 8.2 person-hours versus 13.4 for TLS); however, GP was less suitable for surveying larger areas (127 person-hours per ha−1 versus 4.5 for UAV and 3.9 for TLS). Annual repeat surveys using GP were capable of detecting mean vertical erosion change on peaty soils. These first published estimates of whole gully erosion rates (0.077 m a−1) suggest that combined erosion rates on gully floors and walls are around three times the value of previous estimates, which largely characterize wind and rainsplash erosion of gully walls. Copyright © 2017 John Wiley & Sons, Ltd.

Urban environments are expanding globally, and by 2050 nearly 70% of the world's population will live in towns and cities, where opportunities to experience nature are more limited than in rural areas. This transition could have important implications for health and wellbeing given the diversity of benefits that nature delivers. Despite these issues, there is a lack of information on whether or how the experience of nature changes as green space becomes less available. We explore this question for residents of two case study cities of varying urban designs, sprawling (Brisbane, Australia) and compact (three English towns, U.K). Second, we examine how people's feelings of connection to nature (measured using the Nature Relatedness scale) vary across this same gradient of nature availability. Despite climatic and cultural differences we found substantial similarities between the two locations. Lower levels of neighbourhood tree cover were associated with a reduced frequency of visits to private and public green spaces, and a similar pattern was found for the duration of time spent in private and public green spaces for Brisbane. Residents of both urban areas showed similar levels of nature relatedness, and there was a weak but positive association between tree cover and Nature Relatedness. These results suggest that regardless of the style of urban design, maintaining the availability of nature close to home is a critical step to protect people's experiences of nature and their desire to seek out those experiences.

This manuscript describes the development of an android-based smartphone application for capturing aerial photographs and spatial metadata automatically, for use in grassroots mapping applications. The aim of the project was to exploit the plethora of on-board sensors within modern smartphones (accelerometer, GPS, compass, camera) to generate ready-to-use spatial data from lightweight aerial platforms such as drones or kites. A visual coding 'scheme blocks' framework was used to build the application ('app'), so that users could customise their own data capture tools in the field. The paper reports on the coding framework, then shows the results of test flights from kites and lightweight drones and finally shows how open-source geospatial toolkits were used to generate geographical information system (GIS)-ready GeoTIFF images from the metadata stored by the app. Two Android smartphones were used in testing-a high specification OnePlus One handset and a lower cost Acer Liquid Z3 handset, to test the operational limits of the app on phones with different sensor sets. We demonstrate that best results were obtained when the phone was attached to a stable single line kite or to a gliding drone. Results show that engine or motor vibrations from powered aircraft required dampening to ensure capture of high quality images. We demonstrate how the products generated from the open-source processing workflow are easily used in GIS. The app can be downloaded freely from the Google store by searching for 'UAV toolkit' (UAV toolkit 2016), and used wherever an Android smartphone and aerial platform are available to deliver rapid spatial data (e.g. in supporting decision-making in humanitarian disaster-relief zones, in teaching or for grassroots remote sensing and democratic mapping).

Water resources in many of the world’s arid mountain ranges are threatened by climate change, and in parts of the South American Andes this is exacerbated by glacier recession and population growth. Alternative sources of water, such as more resilient permafrost features (e.g. rock glaciers), are expected to become increasingly important as current warming continues. Assessments of current and future permafrost extent under climate change are not available for the Southern Hemisphere, yet are required to inform decision making over future water supply and climate change adaptation strategies. Here, downscaled model outputs were used to calculate the projected changes in permafrost extent for a first-order assessment of an example region, the Bolivian Andes. Using the 0 °C mean annual air temperature as a proxy for permafrost extent, these projections show that permafrost areas will shrink from present day extent by up to 95 % under warming projected for the 2050s and by 99 % for the 2080s (under the IPCC A1B scenario, given equilibrium conditions). Using active rock glaciers as a proxy for the lower limit of permafrost extent, we also estimate that projected temperature changes would drive a near total loss of currently active rock glaciers in this region by the end of the century. In conjunction with glacier recession, a loss of permafrost extent of this magnitude represents a water security problem for the latter part of the 21st century, and it is likely that this will have negative effects on one of South America’s fastest growing cities (La Paz), with similar implications for other arid mountain regions.

Shallow, degraded peatlands differ in both their structure and function from deeper, peatland ecosystems. Previous work has shown that shallow, drained peatlands demonstrate rapid storm runoff that is only minimally controlled by antecedent hydrological conditions. However, such peatlands are also known to exhibit significant variation in ecohydrological organisation and structure across different spatial scales. In addition, predictions of hydrological response using spatially distributed numerical models of rainfall-runoff may be flawed unless they are evaluated with datasets describing the spatial variability of hydrological responses. This paper evaluates to what extent, flow generation and water storage within shallow, degraded peatland catchments may be controlled by the spatial attributes of the contributing area of the peatland, the drainage ditch size, morphology and geometry. Results from an experiment conducted over multiple spatial scales and multi-annual timescales highlights that subtle variations in the local slope and topography account for the long-term spatial patterns of water table depth. Neither the local scale of the drainage feature or the topographic contributing area is shown to be a definitive predictor of runoff in the studied catchments. Results also highlight the importance of using spatially distributed observations to ensure that estimates of water storage and runoff are representative of the fine scale spatial variability that occurs in such damaged and shallow peatlands.

Private gardens provide vital opportunities for people to interact with nature. The most popular form of interaction is through garden bird feeding. Understanding how landscape features and seasons determine patterns of movement of feeder-using songbirds is key to maximising the well-being benefits they provide. To determine these patterns we established three networks of automated data loggers along a gradient of greenspace fragmentation. Over a 12-month period we tracked 452 tagged blue tits Cyantistes caeruleus and great tits Parus major moving between feeder pairs 9,848 times, to address two questions: (i) Do urban features within different forms, and season, influence structural (presence-absence of connections between feeders by birds) and functional (frequency of these connections) connectivity? (ii) Are there general patterns of structural and functional connectivity across forms? Vegetation cover increased connectivity in all three networks, whereas the presence of road gaps negatively affected functional but not structural connectivity. Across networks structural connectivity was lowest in the summer when birds maintain breeding territories, however patterns of functional connectivity appeared to vary with habitat fragmentation. Using empirical data this study shows how key urban features and season influence movement of feeder-using songbirds, and we provide evidence that this is related to greenspace fragmentation.

Peatlands are recognized as important carbon stores; despite this, many have been drained for agricultural improvement. Drainage has been shown to lower water tables and alter vegetation composition, modifying primary productivity and decomposition, potentially initiating peat loss. To quantify CO2 fluxes across whole landscapes, it is vital to understand how vegetation composition and CO2 fluxes vary spatially in response to the pattern of drainage features. However, Molinia caerulea-dominated peatlands are poorly understood despite their widespread extent. Photosynthesis (PG600) and ecosystem respiration (REco) were modelled (12°C, 600μmol photons m-2s-1, greenness excess index of 60) using empirically derived parameters based on closed-chamber measurements collected over a growing season. Partitioned below-ground fluxes were also collected. Plots were arranged 1/8, 1/4 and 1/2 the distance between adjacent ditches in two catchments located in Exmoor National Park, southwest England. Water table depths were deepest closest to the ditch and non-significantly (p=0·197) shallower further away. Non-Molinia species coverage and the Simpson diversity index significantly decreased with water table depth (p

Charcoal production for urban energy consumption is a main driver of forest degradation in sub Saharan Africa. Urban growth projections for the continent suggest that the relevance of this process will increase in the coming decades. Forest degradation associated to charcoal production is difficult to monitor and commonly overlooked and underrepresented in forest cover change and carbon emission estimates. We use a multitemporal dataset of very high-resolution remote sensing images to map kiln locations in a representative study area of tropical woodlands in central Mozambique. The resulting maps provided a characterization of the spatial extent and temporal dynamics of charcoal production. Using an indirect approach we combine kiln maps and field information on charcoal making to describe the magnitude and intensity of forest degradation linked to charcoal production, including aboveground biomass and carbon emissions. Our findings reveal that forest degradation associated to charcoal production in the study area is largely independent from deforestation driven by agricultural expansion and that its impact on forest cover change is in the same order of magnitude as deforestation. Our work illustrates the feasibility of using estimates of urban charcoal consumption to establish a link between urban energy demands and forest degradation. This kind of approach has potential to reduce uncertainties in forest cover change and carbon emission assessments in sub-Saharan Africa.

Covering 40% of the terrestrial surface, dryland ecosystems characteristically have distinct vegetation structures that are strongly linked to their function. Existing survey approaches cannot provide sufficiently fine-resolution data at landscape-level extents to quantify this structure appropriately. Using a small, unpiloted aerial system (UAS) to acquire aerial photographs and processing theses using structure-from-motion (SfM) photogrammetry, three-dimensional models were produced describing the vegetation structure of semi-arid ecosystems at seven sites across a grass–to shrub transition zone. This approach yielded ultra-fine (< 1 cm2) spatial resolution canopy height models over landscape-levels (10 ha), which resolved individual grass tussocks just a few cm3 in volume. Canopy height cumulative distributions for each site illustrated ecologically-significant differences in ecosystem structure. Strong coefficients of determination (r2 from 0.64 to 0.95) supported prediction of above-ground biomass from canopy volume. Canopy volumes, above-ground biomass and carbon stocks were shown to be sensitive to spatial changes in the structure of vegetation communities. The grain of data produced and sensitivity of this approach is invaluable to capture even subtle differences in the structure (and therefore function) of these heterogeneous ecosystems subject to rapid environmental change. The results demonstrate how products from inexpensive UAS coupled with SfM photogrammetry can produce ultra-fine grain biophysical data products, which have the potential to revolutionise scientific understanding of ecology in ecosystems with either spatially or temporally discontinuous canopy cover.

Globally, the historic and recent exploitation of peatlands through management practices such as agricultural reclamation, peat harvesting or forestry, have caused extensive damage to these ecosystems. Their value is now increasingly recognised, and restoration and rehabilitation programmes are underway to improve some of the ecosystem services provided by peatlands: blocking drainage ditches in deep peat has been shown to improve the storage of water, decrease carbon losses in the long-term, and improve biodiversity. However, whilst the restoration process has benefitted from experience and technical advice gained from restoration of deep peatlands, shallow peatlands have received less attention in the literature, despite being extensive in both uplands and lowlands. Using the experience gained from the restoration of the shallow peatlands of Exmoor National Park (UK), and two test catchments in particular, this paper provides technical guidance which can be applied to the restoration of other shallow peatlands worldwide. Experience showed that integrating knowledge of the historical environment at the planning stage of restoration was essential, as it enabled the effective mitigation of any threat to archaeological features and sites. The use of bales, commonly employed in other upland ecosystems, was found to be problematic. Instead, 'leaky dams' or wood and peat combination dams were used, which are both more efficient at reducing and diverting the flow, and longer lasting than bale dams. Finally, an average restoration cost (306 ha-1) for Exmoor, below the median national value across the whole of the UK, demonstrates the cost-effectiveness of these techniques. However, local differences in peat depth and ditch characteristics (i.e. length, depth and width) between sites affect both the feasibility and the cost of restoration. Overall, the restoration of shallow peatlands is shown to be technically viable; this paper provides a template for such process over analogous landscapes.

This study tracks local vegetation change in West Cornwall (South West England) within regional context, using historic herbarium (pre-1900) and recent vegetation records (post-1900). The focus centres on species lost from the region over the past century. For this study we used a collection of herbarium records published in 1909 (Davey's "Flora of Cornwall") and contemporary records from the "New Atlas of British and Irish Flora" downloaded from the National Biodiversity Network (NBN), online database. Both data sets were spatially analysed using ArcGIS in order to detect local scale species loss. Our results showed that species loss was highest in the south (11 plant species), compared to the loss from middle areas (6 plant species) and in the northern area (8 plant species) of West Cornwall. Results on species change at the local scale were different to the changes that are happening at the national scale. Loss from West Cornwall was detected for two plant species, Mountain Melick (. Melica nutans) and Field Eryngo (. Eryngium campestare). These key results amplify the importance of local scale research and conservation in order to protect ecosystems functioning, genetic diversity, ecosystem services and regional identity.

Water scarcity is a growing issue for high altitude arid countries like Bolivia, where serious water resource concerns exist because of climate change and population growth. In this study we use a recent Bolivian rock glacier inventory (Rangecroft et al. 2014) to estimate the water equivalent storage of these understudied cryospheric reserves. This paper shows that Bolivian rock glaciers currently store between 11.7 and 137 million cubic meters of water. Rock glacier water equivalents are compared to corresponding ice glacier water equivalent to allow an assessment of the hydrological importance of rock glaciers as water stores in this water scarce region. It can be seen that in the densely glaciated Cordillera Real (15°-16°S) rock glaciers form a small component of mountain water stores; however, along the Cordillera Occidental (17°-22°S), where ice glaciers are absent, rock glaciers are a more important part of the cryospheric water store, suggesting that they could be important for local water management. This is the first time that the water equivalence of the Bolivian rock glacier store has been quantified and is a first step toward assessing the contribution and importance of alternative high altitude water sources.

Upland ecosystems are recognized for their importance in providing valuable ecosystem services including water storage, water supply and flood attenuation alongside carbon storage and biodiversity. The UK contains 10-15% of the global resource of upland blanket peatlands, the hydrology and ecology of which are highly sensitive to external anthropogenic and climatic forcing. In particular, drainage of these landscapes for agricultural intensification and peat extraction has resulted in often unquantified damage to the peatland hydrology, and little is understood about the spatially distributed impacts of these practices on near-surface wetness. This paper develops new techniques to extract spatial data describing the near-surface wetness and hydrological behaviour of drained blanket peatlands using airborne thermal imaging data and airborne Light Detection and Ranging (LiDAR) data. The relative thermal emissivity (E{open}r) of the ground surface is mapped and used as a proxy for near-surface wetness. The results show how moorland drainage and land surface structure have an impact on airborne measurements of thermal emissivity. Specifically, we show that information on land surface structure derived from LiDAR can help normalize signals in thermal emissivity data to improve description of hydrological condition across a test catchment in Exmoor, UK. An in situ field hydrological survey was used to validate these findings. We discuss how such data could be used to describe the spatially distributed nature of near-surface water resources, to optimize catchment management schemes and to deliver improved understanding of the drivers of hydrological change in analogous ecosystems.

Lightweight, portable unmanned aerial vehicles (UAVs) or ‘drones’ are set to become a key component of a water resource management (WRM) toolkit, but are currently not widely used in this context. In practical WRM there is a growing need for fine-scale responsive data, which cannot be delivered from satellites or aircraft in a cost-effective way. Such a capability is needed where water supplies are located in spatially heterogeneous dynamic catchments. In this review, we demonstrate the step change in hydrological process understanding that could be delivered if WRM employed UAVs. The paper discusses a range of pragmatic concepts in UAV science for cost-effective and practical WRM, from choosing the right sensor and platform combination through to practical deployment and data processing challenges. The paper highlights that multi-sensor approaches, such as combining thermal imaging with fine-scale structure-from-motion topographic models are currently best placed to assist WRM decisions because they provide a means of monitoring the spatio-temporal distribution of sources, sinks and flows of water through landscapes. The manuscript highlights areas where research is needed to support the integration of UAVs into practical WRM – e.g. in improving positional accuracy through integration of differential global positioning system sensors, and developing intelligent control of UAV platforms to optimize the accuracy of spatial data capture.

Full waveform lidar has a unique capability to characterise vegetation in more detail than any other practical method. The reflectance, calculated from the energy of lidar returns, is a key parameter for a wide range of applications and so it is vital to extract it accurately. Fifteen separate methods have been proposed to extract return energy (the amount of light backscattered from a target), ranging from simple to mathematically complex, but the relative accuracies have not yet been assessed. This paper uses a simulator to compare all methods over a wide range of targets and lidar system parameters. For hard targets the simplest methods (windowed sum, peak and quadratic) gave the most consistent estimates. They did not have high accuracies, but low standard deviations show that they could be calibrated to give accurate energy. This may be why some commercial lidar developers use them, where the primary interest is in surveying solid objects. However, simulations showed that these methods are not appropriate over vegetation. The widely used Gaussian fitting performed well over hard targets (0.24% root mean square error, RMSE), as did the sum and spline methods (0.30% RMSE). Over vegetation, for large footprint (15 m) systems, Gaussian fitting performed the best (12.2% RMSE) followed closely by the sum and spline (both 12.7% RMSE). For smaller footprints (33. cm and 1. cm) over vegetation, the relative accuracies were reversed (0.56% RMSE for the sum and spline and 1.37% for Gaussian fitting). Gaussian fitting required heavy smoothing (convolution with an 8 m Gaussian) whereas none was needed for the sum and spline. These simpler methods were also more robust to noise and far less computationally expensive than Gaussian fitting. Therefore it was concluded that the sum and spline were the most accurate for extracting return energy from waveform lidar over vegetation, except for large footprint (15 m), where Gaussian fitting was slightly more accurate. These results suggest that small footprint (≪ 15 m) lidar systems that use Gaussian fitting or proprietary algorithms may report inaccurate energies, and thus reflectances, over vegetation. In addition the effect of system pulse length, sampling interval and noise on accuracy for different targets was assessed, which has implications for sensor design.

Rock glaciers in the arid Bolivian Andes are potentially important water sources, but little is known about their spatial distribution and characteristics. We provide the first rock glacier inventory for the region (15-22°S), based on mapping using remote sensing data in Google Earth, supported by field validation. of the 94 rock glaciers identified, 57 per cent were classified as active (containing ice) and the remaining as relict (not containing ice). The majority (87%) have a southerly aspect (SE, S and SW), and the rock glacier length and area averages were 500m and 0.12km2, respectively. We approximate the lower limit of permafrost to be at 4700m in the Bolivian Andes, with the mean minimum altitude of rock glacier fronts estimated to be 4980m for active rock glaciers, and about 100m lower for relict rock glaciers. The inventory provides an important first step towards assessing the spatial distribution of regional permafrost as well as information to allow permafrost-based water resources in the Bolivian Andes to be understood against a backdrop of severe glacier recession. Copyright

Losses of dissolved organic carbon (DOC) from drained peatlands are of concern, due to the effects this has on the delivery of ecosystem services, and especially on the long-term store of carbon and the provision of drinking water. Most studies have looked at the effect of drainage in deep peat; comparatively, little is known about the behaviour of shallow, climatically marginal peatlands. This study examines water quality (DOC, Abs400, pH, E4/E6 and C/C) during rainfall events from such environments in the south west UK, in order to both quantify DOC losses, and understand their potential for restoration. Water samples were taken over a 19month period from a range of drains within two different experimental catchments in Exmoor National Park; data were analysed on an event basis. DOC concentrations ranging between 4 and 21mgL-1 are substantially lower than measurements in deep peat, but remain problematic for the water treatment process. Dryness plays a critical role in controlling DOC concentrations and water quality, as observed through spatial and seasonal differences. Long-term changes in depth to water table (30days before the event) are likely to impact on DOC production, whereas discharge becomes the main control over DOC transport at the time scale of the rainfall/runoff event. The role of temperature during events is attributed to an increase in the diffusion of DOC, and therefore its transport. Humification ratios (E4/E6) consistently below 5 indicate a predominance of complex humic acids, but increased decomposition during warmer summer months leads to a comparatively higher losses of fulvic acids. This work represents a significant contribution to the scientific understanding of the behaviour and functioning of shallow damaged peatlands in climatically marginal locations. The findings also provide a sound baseline knowledge to support research into the effects of landscape restoration in the future.

We demonstrate the potential of using freely available satellite data from the Landsat ETM+ sensor for generating carbon balance estimates for lowland peatlands. We used a lowland ombrotrophic peatland in the UK as our test site representing a range of peatland conditions. A literature survey was undertaken to identify the simplest classification schema that could be used to distinguish ecohydrological classes for carbon sequestration on the peatland surface. These were defined as: active raised bog, Eriophorum-dominated bog, milled unvegetated peat and drained or degraded bog, with bracken and Carr woodland to define the bog edges. A maximum likelihood classifier (MLC) was used to map the spatial distribution of the six classes on the peatland surface. A Landsat ETM+ band-5 derived brightness-texture layer created using geostatistical methods greatly improved classification accuracies. The results showed the best accuracy of the MLC, when compared to finer scale methods, with Landsat ETM+ bands alone was 74%, which increased to 93% when including the brightness-texture layer. An estimate of carbon sequestration status of the site was performed that showed good agreement with the results of a finer-scale-based estimate. The coarse-scale map estimating -12000kg carbon and fine scale map estimating +23000kg carbon per annum. We conclude that with further development of our tool, if textural measures are used alongside optical data in MLC, it is possible to achieve good quality estimates of carbon balance status for peatland landscapes. This represents a potentially powerful operational toolkit for land managers and policy makers who require spatially distributed information on carbon storage and release for carbon pricing and effective land management. © 2014 John Wiley & Sons, Ltd.

Summary: Fine-scale information on soil surface roughness (SSR) is needed for calculating heat budgets, monitoring soil degradation and parameterizing surface runoff and sediment transfer models. Previous work has demonstrated the potential of using hyperspectral, hemispherical conical reflectance factors (HCRFs) to retrieve the SSR of different soil crusting states. However, this was achieved by using dry soil surfaces, generated in controlled laboratory conditions. The primary aim of this study was therefore to test the impact that in situ variations in surface soil moisture (SSM) content had on the ability of directional reflectance factors to characterize SSR conditions. Five soil plots (20 cm × 20 cm in area) representing different agricultural conditions were subjected to different durations of natural rainfall to produce a range of different levels of SSR. The values of SSM varied from 8.7 to 20.1% across all soil plots. Point laser data (4-mm sample spacing) were geostatistically analysed to give a spatially-distributed measure of SSR, giving sill variance values from 3.2 to 23.0. The HCRFs from each soil state were measured using a ground-based hyperspectral spectroradiometer for a range of viewing zenith angles from extreme forward-scatter (θr = -60°) to extreme back-scatter (θr = +60°) at a 10° sampling resolution in the solar principal plane. The results showed that despite a large range of SSM values, forward-scattered reflectance factors exhibited a very strong relationship with SSR (R2 = 0.84 at θr = -60°). Our findings demonstrate the operational potential of HCRFs for providing spatially-distributed SSR measurements, across spatial extents containing spatio-temporal variations in SSM content. © 2014 British Society of Soil Science.

West Cornwall is the most south westerly part of mainland United Kingdom with a strong maritime climate. This paper analyses the earliest archived instrumental meteorological records collected in West Cornwall (SW England). Observations were obtained from the Met Office archive (Camborne 1957-2010; Culdrose 1985-2011), Trengwainton Garden (1940-2010), and from the Royal Cornwall Polytechnic Society, (data for Falmouth (1880-1952) and Helston (1843-1888)). Homogeneity tests were used (Levene and Brown-Forsythe tests) to exclude any trends not related to climate variability. The data exhibit trends in annual mean and maximum temperatures over the timescales analysed, and show a general temperature increase in the 20th and 21st century. Annual and seasonal temperature change was found to vary locally with strongly positive trends in autumn, spring and summer seasons. Trends in precipitation are positive only for the 19th century and only for one station. Correlation with the North Atlantic Oscillation (NAO) index gives negative results for precipitation data. However correlation with the NAO index is positive with temperature, especially in the winter season. Return period analysis shows a decrease in intensity and frequency of extreme precipitation events in the post-1975 period (Camborne and Trengwainton Garden stations). Climate change in the 20th century and future continued warming is likely to have major implications on biodiversity in this region. © 2014 Springer Science+Business Media Dordrecht.

Airborne laser scanning systems (Light Detection and Ranging, LiDAR) are very well suited to the study of landscape and vegetation structure over large extents. Spatially distributed measurements describing the three-dimensional character of landscape surfaces and vegetation architecture can be used to understand eco-geomorphic and ecohydrological processes, and this is particularly pertinent in peatlands given the increasing recognition that these landscapes provide a variety of ecosystem services (water provision, flood mitigation and carbon sequestration). In using LiDAR data for monitoring peatlands, it is important to understand how well peatland surface structures (with fine length scales) can be described. Our approach integrates two laser scanning technologies, namely terrestrial laser scanning (TLS) and airborne LiDAR surveys, to assess how effective airborne LiDAR is at measuring these fine-scale microtopographic ecohydrological structures. By combining airborne and TLS, we demonstrate an improved spatial understanding of the signal measured by the airborne LiDAR. Critically, results demonstrate that LiDAR digital surface models are subject to specific errors related to short-sward ecosystem structure, causing the vegetation canopy height and surface-drainage network depth to be underestimated. TLS is shown to be effective at describing these structures over small extents, allowing the information content and accuracy of airborne LiDAR to be understood and quantified more appropriately. These findings have important implications for the appropriate degree of confidence ecohydrologists can apply to such data when using them as a surrogate for field measurements. They also illustrate the need to couple LiDAR data with ground validation data in order to improve assessment of ecohydrological function in such landscapes. © 2014 John Wiley & Sons, Ltd.

Climate change is projected to have a strongly negative effect on water supplies in the arid mountains of South America, significantly impacting millions of people. As one of the poorest countries in the region, Bolivia is particularly vulnerable to such changes due to its limited capacity to adapt. Water security is threatened further by glacial recession with Bolivian glaciers losing nearly half their ice mass over the past 50 years raising serious water management concerns. This review examines current trends in water availability and glacier melt in the Bolivian Andes, assesses the driving factors of reduced water availability and identifies key gaps in our knowledge of the Andean cryosphere. The lack of research regarding permafrost water sources in the Bolivian Andes is addressed, with focus on the potential contribution to mountain water supplies provided by rock glaciers.

We describe the results of an experiment designed to compare the radiometric performance of four different spectroradiometers in ideal field conditions. A carefully designed experiment where instruments were simultaneously triggered was used to measure the Hemispherical Conical Reflectance Factors (HCRF) of four targets of varying reflectance. The experiment was in two parts. Stage 1 covered a 2 hour period finishing at solar noon, where 50 measurements of the targets were collected in sequence. Stage 2 comprised 10 rapid sequential measurements over each target. We applied a method for normalising full width half maximum (FWHM) differences between the instruments, which was a source of variability in the raw data. The work allowed us to determine data reproducibility, and we found that lower-cost instruments (Ocean Optics and PP Systems) produced data of similar radiometric quality to those manufactured by Analytical Spectral Devices (ASD –here we used the ASD FieldSpec Pro) in the spectral range 400-850 nm, which is the most significant region for research communities interested in measuring vegetation dynamics. Over the longer time-series there were changes in HCRF caused by the structural and spectral characteristics of some targets.

Ecologists require spatially explicit data to relate structure to function. To date, heavy reliance has been placed on obtaining such data from remote-sensing instruments mounted on spacecraft or manned aircraft, although the spatial and temporal resolutions of the data are often not suited to local-scale ecological investigations. Recent technological innovations have led to an upsurge in the availability of unmanned aerial vehicles (UAVs) - aircraft remotely operated from the ground - and there are now many lightweight UAVs on offer at reasonable costs. Flying low and slow, UAVs offer ecologists new opportunities for scale-appropriate measurements of ecological phenomena. Equipped with capable sensors, UAVs can deliver fine spatial resolution data at temporal resolutions defined by the end user. Recent innovations in UAV platform design have been accompanied by improvements in navigation and the miniaturization of measurement technologies, allowing the study of individual organisms and their spatiotemporal dynamics at close range. © the Ecological Society of America.

There is widespread recognition that spatially distributed information on soil surface roughness (SSR) is required for hydrological and geomorphological applications. Such information is necessary to describe variability in soil structure, which is highly heterogeneous in time and space, to parameterize hydrology and erosion models and to understand the temporal evolution of the soil surface in response to rainfall. This paper demonstrates how results from semivariogram analysis can quantify key elements of SSR for such applications. Three soil types (silt, silt loam, and silty clay) were used to show how different types of structural variance in SSR evolve during simulated rainfall events. All three soil types were progressively degraded using artificial rainfall to produce a series of roughness states. A calibrated laser profiling instrument was used to measure SSR over a 10 cm × 10 cm spatial extent, at a 2 mm resolution. These data were geostatistically analyzed in the context of aggregate breakdown and soil crusting. The results show that such processes are represented by a quantifiable decrease in sill variance, from 7.81 (control) to 0.94 (after 60 min of rainfall). Soil surface features such as soil cracks, tillage lines and erosional areas were quantified by local maxima in semivariance at a given length scale. This research demonstrates that semivariogram analysis can retrieve spatiotemporal variations in soil surface condition; in order to provide information on hydrological pathways. Consequently, geostatistically derived SSR shows strong potential for inclusion as spatial information in hydrology and erosion models to represent complex surface processes at different soil structural scales. © 2013. American Geophysical Union. All Rights Reserved.

This paper describes a research project that aimed to translate complex spatial and scientific data about coastal change into accessible digital formats for general audiences. The project used fine-scale remote sensing techniques including airborne and terrestrial laser scanning to produce spatially accurate and realistic 3D digital visualisations of projected sea level rise at Cotehele Quay, a site on the River Tamar in Cornwall owned and managed by the National Trust. Area residents and stakeholders were involved in a series of focus groups which provided guidance on the integration of the spatial models into a short film. The paper focuses on how the participatory, iterative process adopted in the project shaped the content and design of the film. The paper concludes with a discussion of how this process enhanced the viability of the film as a communication tool for use in wider engagement activities.

Keywords: : Terrestrial laser scanning , heritage , sea-level rise , community engagement , multi-media

Information on soil surface roughness at the centimetre scale is needed for inclusion in a
range of physical and functional algorithms including heat budgets, runoff and sediment transfer models, and can also be used to understand soil degradation processes. Previous work has shown that such information can be obtained from multiple view angle measurements of hyperspectral Hemispherical Conical Reflectance Factors (HCRFs), but the issue of whether this technique works on soils of different biochemical composition has not yet been demonstrated. The objective of this work
was therefore to determine the capability of these approaches for discriminating soil surface
roughness conditions when different soil types are considered. Five soil types with varying
biochemical properties were subjected to artificial rainfall, producing a sequence of soil states of progressively declining soil surface roughness. Point laser data (2 mm sample spacing) were geostatistically analysed to give a spatially-distributed measure of surface roughness. HCRFs from the soil states were measured using a ground-based hyperspectral spectroradiometer for a range of
viewing zenith angles in the solar principal plane from the extreme forwardscatter (-60°) to the extreme backscatter (+60°) at 10° sampling resolution in the solar principal plane. A directional index (Anisotropy Measure; AM) was determined, using a ratio between extreme forward-scattered and backscattered HCRFs. Regression analysis of AM against a geostatistically-derived value of soil surface
roughness (sill variance) was used to test the ability of the AM for description of surface roughness for all soil types. The results show that use of a directional AM index dramatically improved the relationship with sill variance compared to the use of a single viewing angle (R2 = 0.68 at θr = 40°; R2 = 0.88 (AM)), demonstrating the great potential of this approach for compensating for spectral differences between different soil types. The results provide an empirical and theoretical basis for the future retrieval of spatially-distributed assessments of soil surface structure across larger spatial
extents.

This study presents a new interpretation of the evidence for Holocene lake-level changes from Hawes Water in NW England constrained by detailed stratigraphic data, radiocarbon chronology and palaeo-environmental evidence. Lake levels are seen to decline gradually from the start of the Holocene through to 9960 cal. yr BP, in response to lake infilling and the prevailing dry climatic conditions. Low lake levels then persist until 6000 cal. yr BP, punctuated by two transgressive phases. Rising sea levels during the Holocene high-level sea stand are thought to be responsible for a major rise in lake level at 6000 cal. yr BP driven by changes in the local water-table. The rise in lake level is coincident with a rise in anthropogenic activity across the site, possibly reflecting the migration of coastal Mesolithic communities inland in response to rising sea levels. © the Author(s) 2011.

The melting of mountain glaciers and ice caps is expected to contribute significantly to sea-level rise in the twenty-first century although the magnitude of this contribution is not fully constrained. Glaciers in the Patagonian Icefields of South America are thought to have contributed about 10% of the total sea-level rise attributable to mountain glaciers in the past 50 years. However, it is unclear whether recent rates of glacier recession in Patagonia are unusual relative to the past few centuries. Here we reconstruct the recession of these glaciers using remote sensing and field determinations of trimline and terminal moraine location. We estimate that the North Patagonian Icefield has lost 103±20.7 km3 of ice since its late Holocene peak extent in AD 1870 and that the South Patagonian Icefield has lost 503±101.1 km3 since its peak in AD 1650. This equates to a sea-level contribution of 0.0018±0.0004 mm yr−1 since 1870 from the north and 0.0034±0.0007 mm yr−1 since 1650 from the south. The centennial rates of sea-level contribution we derive are one order of magnitude lower than estimates of melting over the past 50 years3, even when we account for possible thinning above the trimline. We conclude that the melt rate and sea-level contribution of the Patagonian Icefields increased markedly in the twentieth century.

This paper reviews the currently available optical sensors, their limitations and opportunities for deployment at Eddy Covariance (EC) sites in Europe. This review is based on the results obtained from an online survey designed and disseminated by the Co-cooperation in Science and Technology (COST) Action ESO903—“Spectral Sampling Tools for Vegetation Biophysical Parameters and Flux Measurements in Europe” that provided a complete view on spectral sampling activities carried out within the different research teams in European countries. The results have highlighted that a wide variety of optical sensors are in use at flux sites across Europe, and responses further demonstrated that users were not always fully aware of the key issues underpinning repeatability and the reproducibility of their spectral measurements. The key findings of this survey point towards the need for greater awareness of the need for standardisation and development of a common protocol of optical sampling at the European EC sites.

Biodiversity is an inherently spatial phenomenon. It is determined by environmental heterogeneity and by spatially structured ecological processes such as disturbance, competition and dispersal. However, conventional biodiversity indices are based on discrete samples or pooled sets of samples without sufficient consideration of the spatial relationships between the samples. Here we describe a new method for spatial analysis of species diversity, based on a paired-sample version of the widely used Gini-Simpson diversity index and its numbers equivalent. The index and its numbers equivalent are plotted as a function of lag distance between two samples along spatial and/or environmental gradients. We demonstrate the potential of this approach by applying it to two transects of fine-scale (5×5cm quadrat) vegetation data from sites with contrasting hydrology within a raised bog, where the location of each quadrat is accurately recorded and the height of the bog surface above the water table is measured using a terrestrial laser scanner. Both transects have similar alpha-diversity as measured using the Gini-Simpson index, and the transition between alpha- and gamma-diversity occurs at similar length scales, suggesting that species aggregate at similar scales along both transects. However, the transect from the central bog dome has higher gamma-diversity than that from the bog margin, and shows more marked significant spatial structure at a length scale of 135-140cm, corresponding to the typical hummock-hollow microtopography at the site. We show that beta-diversity at both transects can be attributable to both species clustering along the hydrological gradient, consistent with niche partitioning, as well as independent spatial aggregation of species that is not explained by hydrology. Synthesis. The paired-sample diversity index described here is a potentially useful tool in detecting and attributing patterns of beta-diversity along both spatial and environmental gradients. © 2010 the Authors. Journal of Ecology © 2010 British Ecological Society.

1. Biodiversity is an inherently spatial phenomenon. It is determined by environmental heterogeneity and by spatially structured ecological processes such as disturbance, competition and dispersal. However, conventional biodiversity indices are based on discrete samples or pooled sets of samples without sufficient consideration of the spatial relationships between the samples.

2. Here we describe a new method for spatial analysis of species diversity, based on a paired-sample version of the widely used Gini–Simpson diversity index and its numbers equivalent. The index and its numbers equivalent are plotted as a function of lag distance between two samples along spatial and/or environmental gradients.

3.  We demonstrate the potential of this approach by applying it to two transects of fine-scale (5 × 5 cm quadrat) vegetation data from sites with contrasting hydrology within a raised bog, where the location of each quadrat is accurately recorded and the height of the bog surface above the water table is measured using a terrestrial laser scanner.

4.  Both transects have similar alpha-diversity as measured using the Gini–Simpson index, and the transition between alpha- and gamma-diversity occurs at similar length scales, suggesting that species aggregate at similar scales along both transects. However, the transect from the central bog dome has higher gamma-diversity than that from the bog margin, and shows more marked significant spatial structure at a length scale of 135–140 cm, corresponding to the typical hummock–hollow microtopography at the site. We show that beta-diversity at both transects can be attributable to both species clustering along the hydrological gradient, consistent with niche partitioning, as well as independent spatial aggregation of species that is not explained by hydrology.

5. Synthesis. The paired-sample diversity index described here is a potentially useful tool in detecting and attributing patterns of beta-diversity along both spatial and environmental gradients.

This paper describes a study aimed at quantifying uncertainty in field measurements of vegetation canopy hemispherical conical reflectance factors (HCRF). The use of field spectroradiometers is common for this purpose, but the reliability of such measurements is still in question. In this paper we demonstrate the impact of various measurement uncertainties on vegetation canopy HCRF, using a combined laboratory and field experiment employing three spectroradiometers of the same broad specification (GER 1500). The results show that all three instruments performed similarly in the laboratory when a stable radiance source was measured (NEΔL < 1 mW m−2 sr−1 nm−1 in the range of 400–1000 nm). In contrast, field-derived standard uncertainties (u = SD of 10 consecutive measurements of the same surface measured in ideal atmospheric conditions) significantly differed from the lab-based uncertainty characterisation for two targets: a control (75% Spectralon panel) and a cropped grassland surface. Results indicated that field measurements made by a single instrument of the vegetation surface were reproducible to within ± 0.015 HCRF and of the control surface to within ± 0.006 HCRF (400–1000 nm (± 1σ)). Field measurements made by all instruments of the vegetation surface were reproducible to within ± 0.019 HCRF and of the control surface to within ± 0.008 HCRF (400–1000 nm (± 1σ)). Statistical analysis revealed that even though the field conditions were carefully controlled and the absolute values of u were small, different instruments yielded significantly different reflectance values for the same target. The results also show that laboratory-derived uncertainty quantities do not present a useful means of quantifying all uncertainties in the field. The paper demonstrates a simple method for u characterisation, using internationally accepted terms, in field scenarios. This provides an experiment-specific measure of u that helps to put measurements in context and forms the basis for comparison with other studies.

Coccolithophores are the largest source of calcium carbonate in the oceans and are considered to play an important role in oceanic carbon cycles. Current methods to detect the presence of coccolithophore blooms from Earth observation data often produce high numbers of false positives in shelf seas and coastal zones due to the spectral similarity between coccolithophores and other suspended particulates. Current methods are therefore unable to characterise the bloom events in shelf seas and coastal zones, despite the importance of these phytoplankton in the global carbon cycle. A novel approach to detect the presence of coccolithophore blooms from Earth observation data is presented. The method builds upon previous optical work and uses a statistical framework to combine spectral, spatial and temporal information to produce maps of coccolithophore bloom extent. Validation and verification results for an area of the north east Atlantic are presented using an in situ database (N = 432) and all available SeaWiFS data for 2003 and 2004. Verification results show that the approach produces a temporal seasonal signal consistent with biological studies of these phytoplankton. Validation using the in situ coccolithophore cell count database shows a high correct recognition rate of 80% and a low false-positive rate of 0.14 (in comparison to 63% and 0.34 respectively for the established, purely spectral approach). To guide its broader use, a full sensitivity analysis for the algorithm parameters is presented.

The ability to quantitatively and spatially assess soil surface roughness is important in geomorphology and land degradation studies. This paper describes the results of an experiment designed to investigate whether
hyperspectral directional reflectance factors can describe fine-scale variations in soil surface roughness. A Canadian silt loam soil was sieved to an aggregate size range of 1–4.75 mm and exposed to five different artificial rainfall durations to produce soils displaying progressively decreasing levels of surface roughness. Each soil state was measured using a point laser profiling instrument at 2 mm spatial resolution, in order to
provide information on the structure and spatial arrangement of soil particles. Hyperspectral directional reflectance factors were measured using an Analytical Spectral Devices FieldSpec Pro Spectroradiometer
(range 350–2500 nm), at a range of measurement angles (\theta_{r} = +60° to -60°) and illumination angle conditions (\theta_{i}= 28°–74°). Directional reflectance factors varied with illumination and view angles, and
with soil structure. Geostatistically-derived indicators of soil surface roughness (sill variance) were
regressed with directional reflectance factors. The results showed a strong relationship between directional
reflectance and surface roughness (R2 = 0.94 where \theta_{r}= 60°, \theta_{i} = 67°–74°). This fine-scale quasi-natural experiment allowed the control of slope, initial aggregate size and rainfall exposure, permitting
an investigation into factors affecting a soil’s bidirectional reflectance response. This has highlighted the relationship between fine-scale variations in surface roughness, illumination angle and reflectance response. The results show how the technique could provide a quantitative measure of surface
roughness at fine spatial scales.

Remote sensing techniques have potential for peatland monitoring, but most previous work has focused on spectral approaches that often result in poor discrimination of cover
types and neglect structural information. Peatlands contain structural “microtopes” (e.g. hummocks and hollows) which
are linked to hydrology, biodiversity and carbon sequestration, and information on surface structure is thus a useful proxy
for peatland condition. The objective of this work was to develop and test a new eco-hydrological mapping technique for ombrotrophic (rain-fed) peatlands using a combined spectral-structural remote sensing approach. The study site was Wedholme Flow, Cumbria, UK. Airborne light dectection
and ranging (LiDAR) data were used with IKONOS data in a combined multispectral-structural approach for mapping peatland condition classes. LiDAR data were preprocessed so
that spatial estimates of minimum and maximum land surface height, variance and semi-variance (from semi-variogram analysis) were extracted. Th ese were assimilated alongside IKONOS data into a maximum likelihood classification procedure, and thematic outputs were compared. Ecological survey data were used to validate the results. Considerable improvements in thematic separation of peatland classes were
achieved when spatially-distributed measurements of LiDAR variance or semi-variance were included. Specifically, the classification accuracy improved from 71.8% (IKONOS data
only) to 88.0% when a LiDAR semi-variance product was used. of note was the improved delineation of management classes (including Eriophorum bog, active raised bog and degraded
raised bog). The application of a combined textural-optical approach can improve land cover mapping in areas where reliance on purely spectral discrimination approaches would
otherwise result in considerable thematic uncertainty.

Remote sensing techniques provide a means by which meaningful spatial information
on soil properties can be obtained. In the last 10 years, advancements in remote sensing techniques
and technologies have given rise to exciting research within soil science. This paper provides a
critical insight into the role played by remote sensing in this fi eld, with a specifi c focus on soil
surface monitoring. Two key soil properties are considered in this review, soil surface roughness
and moisture, because these two variables have benefi ted most from recent cutting-edge advances
in remote sensing. of note is the fact that the major recent advancements in spatial assessment of
soil structure have emerged from optical remote sensing, while the soil moisture community has
benefi ted from advancements in microwave systems, justifying the focus of this paper in these
specifi c directions. The paper considers the newest techniques within active, passive, optical
and microwave remote sensing disciplines and concludes by considering future challenges, multisensor
approaches and the issue of scale – which is a key cross-disciplinary research question of
relevance to soil scientists and remote sensing scientists alike.

The results of an experiment to show variations in the directional reflectance
factor of a Luvisol during a controlled crusting experiment are described. Soil
sampled in the field after tillage was sieved into free-draining trays, and
exposed to artificial rainfall for differing periods of time, ranging from 5 to
60 min. The resulting samples demonstrated different stages in the development
of the soil’s structural crust. The topography of each dried sample was
characterized over a 565 cm area using a laser profilometer, and digital surface
models (DSMs) were subsequently analysed using variogram models. DSMs
were also used to generate statistical measures of random roughness.
Directional reflectance factors of each sample were characterized in the solar
principal plane under clean skies using an ASD FieldSpec Pro spectroradiometer,
using an 8 degree foreoptic attached to an A-frame device. Directional reflectance factors were analysed in relation to spatial statistical measures obtained from the laser profilometer data. The results demonstrate that changes in the sill variance of soil samples following crusting, and hence changes in soil structure, were best described by backscattered radiation measured at + 30 degrees in the visible and near-infrared (e.g. R2 = 0.947 (658 nm)), and at + 15 degrees in the short-wave infrared (e.g. R2 = 0.992 (1700 nm)). View zeniths are expressed from the nadir, and were relative to the solar zenith angle, which ranged from 80.76 degrees to 74.55 degrees during the measurement sequences. The results from these tests show great promise for broader-scale monitoring of soil condition, particularly when considered in the context of the new pointable remote sensing systems in operation, coupled with new-generation sensors with in-built directional capabilities.

An experiment to determine the most accurate and repeatable method for generating instrument inter-calibration functions (ICFs) is described, based upon data collected with a dual-beam GER1500 spectroradiometer system. The quality of reflectance data collected using a dual-beam spectroradiometer system is reliant upon accurate inter-calibration of the sensor pairs to take into account differences in their radiant sensitivity and spectral characteristics. A cos-conical field-based method for inter-calibrating dual-beam spectroradiometers was tested alongside laboratory inter-calibration procedures. The field-based method produced the most accurate results when a field-derived ICF collected close in time was used to correct the spectral scan. A regression model to predict the ICF at a range of wavelengths was tested, using inputs of solar zenith angle, cosine of solar zenith angle and broadband diffuse-to-global irradiance ratios. The linear multiple regression model described up to 78% of the variability in ICF, the remainder of the variability was most likely due to complexities of instrumental behaviour in response to warm-up time, ambient temperature, and environmental conditions at the time of measurement. Collection of ICFs using a stable laboratory source was shown to provide unsatisfactory results due to differences between lamp outputs and the field-measured solar spectrum. Consequently, the most practical and accurate method of deriving inter-calibration functions is to use field-derived ICFs, collected close in time and space to the data requiring correction.

Ground calibration targets (GCT) fulfil an essential role in vicarious calibration
and atmospheric correction methodologies. However, assumptions are often made about the
temporal stability of GCT reflectance. This letter presents results from a multi-year study
aimed at testing the temporal stability of a typical weathered concrete GCT in southern
England. Very accurate measurements of hemispherical-directional reflectance factors in the 400-1000 nm range, were collected using a mobile dual-beam spectroradiometer. Results
demonstrated that the calibration surface was subject to seasonal growth of a biological
material, which caused the reflectance factor to vary by a factor of two during the year
(range = 16.4% reflectance at 670nm). The spectral effect of this was most noticeable in
field spectra collected in April. As environmental conditions became drier throughout the summer, concrete reflectance factors increased. Over multiple seasons the same patterns in reflectance factors repeated, indicating the predictable nature of the biological signature. The research also suggested that the biological material was affected to a small but measurable extent on a daily basis by changes in relative humidity occurring after onset of a local sea breeze. The research highlights the dynamic nature of weathered GCTs, and has wider implications for those using similar sites for vicarious calibration or atmospheric correction purposes.

Reflectance spectra of coral colonies and associated sand and rubble were obtained over the fringing reefs of Eilat, Israel using two GER 1500 radiometers. The overall spectral response curve of Red Sea corals displayed the same three inflection points reported for Pacific corals, with notable differences between in vitro and in situ measurements. Fourth derivative analysis of relatively pure spectra (filling the sensor's field of view) allowed for differentiation of coral and non-coral targets with 95-99% accuracy. The characteristic peaks revealed by the fourth derivative match those obtained on Hawaiian corals.

A field experiment investigated the hypothesis that the nadir reflectance of calibration surface substrates (asphalt and concrete) remains stable over a range of time-scales. Measurable differences in spectral reflectance factors were found over periods as short as 30 minutes. Multi-date reflectance measurements were compared using ANOVA and found to differ significantly (p = 0.001). Surface reflectance showed a relationship with the relative proportion of diffuse irradiance, over periods when solar zenith changes were minimal. These findings illustrate the anisotropic nature of calibration surfaces, and place emphasis on the need for collection of diffuse and global irradiance measurements at the time of remotely-sensed data acquisition.