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.

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.

Abstract
Accurate and up‐to‐date land cover maps are vital for underpinning evidence‐based landscape management decision‐making. However, the technical skills required to extract tailored information about land cover dynamics from these open‐access geospatial data often limit their use by those making landscape management decisions.
Using Dartmoor National Park as an example, we demonstrate an open‐source toolkit which uses open‐source software (QGIS and RStudio) to process freely available Sentinel‐2 and public LiDAR data sets to produce fine scale (10 m2 grain size) land cover maps.
The toolbox has been designed for use by staff within the national park, for example, enabling land cover maps to be updated as required in the future.
An area of 945 km2 was mapped using a trained random forest classifier following a classification scheme tailored to the needs of the national park.
A 2019 land cover map had an overall user's accuracy of 79%, with 13 out of 17 land cover classes achieving greater than 70% accuracy.
Spatially, accuracy was related via logistical regression to blue band surface reflectance in the spring and topographic slope derived from LiDAR (1 m resolution), with greater accuracy in steeper terrain and areas exhibiting higher blue reflectance.
Between an earlier (2017–2019) and later (2019–2021) time frame, 8% of pixels changed, most of the change by area occurred in the most common classes. However, the largest proportional increase occurred in Upland Meadows, Lowland Meadows and Blanket Bog, all habitats subject to restoration efforts. Identifying areas of change enables future field work to be better targeted.
We discuss the application of this mapping to land management within the Dartmoor national park and of the potential of tailored land cover and land cover change mapping, via this toolbox, to evidence‐based environmental decision‐making more widely.

Beavers can profoundly alter riparian environments, most conspicuously by creating dams and wetlands. Eurasian beaver (Castor fiber) populations are increasing and it has been suggested they could play a role in the provision of multiple ecosystem services, including natural flood management. Research at different scales, in contrasting ecosystems is required to establish to what extent beavers can impact on flood regimes. Therefore, this study determines whether flow regimes and flow responses to storm events were altered following the building of beaver dams and whether a flow attenuation effect could be significantly attributed to beaver activity. Four sites were monitored where beavers have been reintroduced in England. Continuous monitoring of hydrology, before and after beaver impacts, was undertaken on streams where beavers built sequences of dams. Stream orders ranged from 2nd to 4th, in both agricultural and forest-dominated catchments. Analysis of >1000 storm events, across four sites showed an overall trend of reduced total stormflow, increased peak rainfall to peak flow lag times and reduced peak flows, all suggesting flow attenuation, following beaver impacts. Additionally, reduced high flow to low flow ratios indicated that flow regimes were overall becoming less “flashy” following beaver reintroduction. Statistical analysis, showed the effect of beaver to be statistically significant in reducing peak flows with estimated overall reductions in peak flows from −0.359 to −0.065 m3 s−1 across sites. Analysis showed spatial and temporal variability in the hydrological response to beaver between sites, depending on the level of impact and seasonality. Critically, the effect of beavers in reducing peak flows persists for the largest storms monitored, showing that even in wet conditions, beaver dams can attenuate average flood flows by up to ca. 60%. This research indicates that beavers could play a role in delivering natural flood management.

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.

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.

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.

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.