Air pollution presents a major risk to human health, resulting in premature deaths and reduced quality of life. Quantifying the role of vegetation in reducing air pollution concentrations is an important contribution to urban natural capital accounting. However, most current methods to calculate pollution removal are static, and do not represent atmospheric transport of pollutants, or interactions among pollutants and meteorology. An additional challenge is defining urban extent in a way that captures the green and blue infrastructure providing the service in a consistent way. We developed a refined urban morphology layer which incorporates urban green and blue space. We then applied an atmospheric chemistry transport model (EMEP4UK) to calculate pollutant removal by urban natural capital for pollutants including PM 2.5 , NO 2 , SO 2 , O 3. We calculated health benefits directly from the change in pollutant concentrations (i.e. exposure) rather than from tonnes of pollutant removed. Urban natural capital across Britain removes 28,700 tonnes of PM 2.5 , NO 2 , SO 2 , O 3. The economic value of the health benefits are substantial: £136 million in 2015, resulting from 900 fewer respiratory hospital admissions, 220 fewer cardiovascular hospital admissions, 240 fewer deaths and 3600 fewer Life Years Lost.

Peatlands are important reserves of terrestrial carbon and biodiversity, and given that many peatlands across the UK and Europe exist in a degraded state, their conservation is a major area of concern and a focus of considerable research. Aerial surveys are valuable tools for habitat mapping and conservation and provide useful insights into their condition. We investigate how SfM photogrammetry-derived topography and habitat classes may be used to construct an estimate of carbon loss from erosion features in a remote blanket bog habitat. An autonomous, unmanned, aerial, fixed-wing remote sensing platform (Quest UAV 300™) collected imagery over Moor House, in the Upper Teesdale National Nature Reserve, a site with a high degree of peatland erosion. The images were used to generate point clouds into orthomosaics and digital surface models using SfM photogrammetry techniques, georeferenced and subsequently used to classify vegetation and peatland features. A classification of peatbog feature types was developed using a random forest classification model trained on field survey data and applied to UAV-captured products including the orthomosaic, digital surface model and derived surfaces such as topographic index, slope and aspect maps. Using the area classified as eroded peat and the derived digital surface model, we estimated a loss of 438 tonnes of carbon from a single gully. The UAV system was relatively straightforward to deploy in such a remote and unimproved area. SfM photogrammetry, imagery and random forest modelling obtained classification accuracies of between 42% and 100%, and was able to discern between bare peat, saturated bog and sphagnum habitats. This paper shows what can be achieved with low-cost UAVs equipped with consumer grade camera equipment and relatively straightforward ground control, and demonstrates their potential for the carbon and peatland conservation research community.

Hedges and lines of trees (woody linear features) are important boundaries that connect and enclose habitats, buffer the effects of land management, and enhance biodiversity in increasingly impoverished landscapes. Despite their acknowledged importance in the wider countryside, they are usually not considered in models of landscape function due to their linear nature and the difficulties of acquiring relevant data about their character, extent, and location. We present a model which uses national datasets to describe the distribution of woody linear features along boundaries in Great Britain. The method can be applied for other boundary types and in other locations around the world across a range of spatial scales where different types of linear feature can be separated using characteristics such as height or width. Satellite‐derived Land Cover Map 2007 (LCM2007) provided the spatial framework for locating linear features and was used to screen out areas unsuitable for their occurrence, that is, offshore, urban, and forest areas. Similarly, Ordnance Survey Land‐Form PANORAMA®, a digital terrain model, was used to screen out where they do not occur. The presence of woody linear features on boundaries was modelled using attributes from a canopy height dataset obtained by subtracting a digital terrain map (DTM) from a digital surface model (DSM). The performance of the model was evaluated against existing woody linear feature data in Countryside Survey across a range of scales. The results indicate that, despite some underestimation, this simple approach may provide valuable information on the extents and locations of woody linear features in the countryside at both local and national scales.

Earth Observation (EO) data is seen as a major source of information to characterise the Earth's surface, but is conventionally analysed using pixel-based approaches that do not incorporate the concept of landscape features or realworld objects. The UK land cover maps to date have been developed in an attempt to exploit landscape features to improve the quality and accuracy of their derived products. For Land Cover Map 2007 (LCM2007) landscape features will be derived from a generalised version of OS MasterMap to capture the required real-world objects. This paper describes the generalisation process that aligns the scale of the landscape features with the information content of high spatial resolution EO data as the first step in the production of LCM2007.

No abstract