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Birds are useful indicators of overall biodiversity, which continues to decline globally, despite targets to reduce its loss. The aim of this paper is to understand the importance of different spatial drivers for modelling bird distributions. Specifically, it assesses the importance of satellite‐derived measures of habitat productivity, heterogeneity and landscape structure for modelling bird diversity across Great Britain. Random forest (RF) regression is used to assess the extent to which a combination of satellite‐derived covariates explain woodland and farmland bird diversity and richness. Feature contribution analysis is then applied to assess the relationships between the response variable and the covariates in the final RF models. We show that much of the variation in farmland and woodland bird distributions is explained (R2 0.64–0.77) using monthly habitat‐specific productivity values and landscape structure (FRAGSTATS) metrics. The analysis highlights important spatial drivers of bird species richness and diversity, including high productivity grassland during spring for farmland birds and woodland patch edge length for woodland birds. The feature contribution provides insight into the form of the relationship between the spatial drivers and bird richness and diversity, including when a particular spatial driver affects bird richness positively or negatively. For example, for woodland bird diversity, the May 80th percentile Normalized Difference Vegetation Index (NDVI) for broadleaved woodland has a strong positive effect on bird richness when NDVI is >0.7 and a strong negative effect below. If relationships such as these are stable over time, they offer a useful analytical tool for understanding and comparing the influence of different spatial drivers.

Land cover is a key environmental variable, underpinning widespread environmental research and decision-making. The UK Centre for Ecology and Hydrology (UKCEH) have provided reliable land cover information since the early 1990’s; this supports multiple scientific, government and commercial objectives. Recent advances in computation and satellite data availability have enabled annual UKCEH land cover maps since 2017. Here we introduce the latest, annual UK Land Cover Map, representing 2021 (LCM2021) and describe its production and validation. LCM2021 methods replicate those for LCM2017 to LCM2020 with minor deviations to enhance accuracy. LCM2021 is based on the classification of satellite and spatial context data into 21 land cover/habitat classes, from which a product suite is derived. The production of LCM2021 involved three highly automated key stages: pre-processing of input data, image classification and production of the final data products. Google Earth Engine scripts were used to create an input data stack of satellite and context data. A set of training areas was created, based on data harvested from historic UKCEH land cover maps. The training data were used to construct a Random Forest classifier, which yielded classified images. Compiled results were validated against 35,182 reference samples, with correspondence tables indicating variable class accuracy and an overall accuracy of 82.6 % for the 21-class data and 86.5 % at a 10 aggregated-class level. •The UK Land Cover Map product suite includes a set of raster products in various projections, thematic and spatial resolutions (10 m, 25 m and 1 km), and land–parcel or vector products. The data are provided in 21-class (all configurations) and aggregated 10-class (1 km raster products only) versions. All raster products are freely available for academic and non-commercial research. The data for Great Britain (GB) are provided in the British National Grid projection (EPSG: 27700) and the Northern Ireland (

Geospatial techniques for quantifying, modelling, and mapping natural capital and ecosystem services have the potential to improve our understanding of the benefits provided by natural assets and identify changes in land use that could increase these benefits. However, questions remain around how such an approach could be implemented in practice. In this thesis, analyses are undertaken across multiple scales to explore how geospatial techniques can be applied to help solve current challenges in land management and planning. At the local scale, a land cover and benefit transfer methodology is developed and applied for the first time to value current natural capital assets within individual farms in the UK. This work highlights how the land cover product used in the methodology can have a substantial impact on valuations, with differences of up to 58% found at the five farms studied. The magnitude of these differences varies according to the landscape structure of the farm, with higher resolution land cover products incorporating larger amounts of woodland, primarily through inclusion of smaller patches, leading to overall higher valuations. At the national scale, the creation of new natural capital assets is explored by investigating proposed large-scale afforestation targets in the UK. In the initial part of the study, the feasibility of meeting these targets is investigated in the first national assessment of land available for afforestation, considering a range of physical, environmental, and policy constraints in three hypothetical planting scenarios. This found that while there is sufficient space to meet the afforestation targets in all three scenarios, this would require planting on a large proportion of unconstrained land, which could limit opportunities for spatially targeting woodland creation. The implications of this transformational change in British land cover, and policies that would be required to support this transition, are highlighted. In the secon

To explore the feasibility of meeting recently proposed large-scale tree planting targets, a UK wide assessment of land available for afforestation was carried out, considering a range of physical, environmental and policy constraints in three hypothetical planting scenarios. Results show there is sufficient space to meet these targets in all three scenarios, even if planting is prevented on good to moderate quality agricultural land and within protected areas. However, this would require planting on a large proportion of unconstrained land, especially for the more ambitious targets, which is unevenly distributed across the UK. This would limit opportunities for spatially targeting woodland creation, which may restrict the provision of additional ecosystem services such as air pollution control and recreation, and induce widespread negative impacts on landscapes and communities. In order to overcome these limitations, relaxing constraints, such as permitting afforestation of higher quality agricultural land, will need to be considered. Meeting many of the proposed afforestation targets would result in a transformational change in British land cover, which could replace or significantly impact the business models of tens of thousands of farms, and see the replacement of hundreds of thousands to millions of hectares of grassland, arable and horticultural land and other land covers. This would require rates of planting that far exceed those seen historically. Policies and mechanisms that could be used to encourage this planting, both by the state and private sectors, are discussed.

To explore the feasibility of meeting recently proposed large-scale tree planting targets, a UK wide assessment of land available for afforestation was carried out, considering a range of physical, environmental and policy constraints in three hypothetical planting scenarios. Results show there is sufficient space to meet these targets in all three scenarios, even if planting is prevented on good to moderate quality agricultural land and within protected areas. However, this would require planting on a large proportion of unconstrained land, especially for the more ambitious targets, which is unevenly distributed across the UK. This would limit opportunities for spatially targeting woodland creation, which may restrict the provision of additional ecosystem services such as air pollution control and recreation, and induce widespread negative impacts on landscapes and communities. In order to overcome these limitations, relaxing constraints, such as permitting afforestation of higher quality agricultural land, will need to be considered. Meeting many of the proposed afforestation targets would result in a transformational change in British land cover, which could replace or significantly impact the business models of tens of thousands of farms, and see the replacement of hundreds of thousands to millions of hectares of grassland, arable and horticultural land and other land covers. This would require rates of planting that far exceed those seen historically. Policies and mechanisms that could be used to encourage this planting, both by the state and private sectors, are discussed.

By 2050, global food production must increase by 70% to meet the demands of a growing population with shifting food consumption patterns. Sustainable intensification has been suggested as a possible mechanism to meet this demand without significant detrimental impact to the environment. Appropriate monitoring techniques are required to ensure that attempts to sustainably intensify arable agriculture are successful. Current assessments rely on datasets with limited spatial and temporal resolution and coverage such as field data and farm surveys. Earth Observation (EO) data overcome limitations of resolution and coverage, and have the potential to make a significant contribution to sustainable intensification assessments. Despite the variety of established EO-based methods to assess multiple indicators of agricultural intensity (e.g. yield) and environmental quality (e.g. vegetation and ecosystem health), to date no one has attempted to combine these methods to provide an assessment of sustainable intensification. The aim of this thesis, therefore, is to demonstrate the feasibility of using EO to assess the sustainability of agricultural intensification. This is achieved by constructing two novel EO-based indicators of agricultural intensity and environmental quality, namely wheat yield and farmland bird richness. By combining these indicators, a novel performance feature space is created that can be used to assess the relative performance of arable areas. This thesis demonstrates that integrating EO data with in situ data allows assessments of agricultural performance to be made across broad spatial scales unobtainable with field data alone. This feature space can provide an assessment of the relative performance of individual arable areas, providing valuable information to identify best management practices in different areas and inform future management and policy decisions. The demonstration of this agricultural performance assessment method represents an importan

The valuation of natural capital within individual farms could inform environmentally beneficial land use change and form the basis of agricultural subsidy schemes based on the provision of ecosystem services. Land cover extents can be used in a benefit transfer approach to produce monetary valuations of natural capital rapidly and at low cost. However, the methodology has not before been used within individual farms, and the impact of land cover data characteristics on the accuracy of valuations is uncertain. Here, we apply the approach to five UK farms of contrasting size, configuration and farming style, using three widely available land cover products. Results show that the land cover product used has a substantial impact on valuations, with differences of up to 58%, and the magnitude of this effect varies considerably according to the landscape structure of the farm. At most sites, valuation differences are driven by the extent of woodland recorded in the landscape, with higher resolution land cover products incorporating larger amounts of woodland through inclusion of smaller patches, leading to higher overall valuations. Integrating more accurate land cover data and accounting for the condition, configuration and location of natural capital has potential to improve the accuracy of valuations.

Context: Studies evaluating biodiversity loss and altered ecosystem services have tended to examine changes over the last few decades, despite the fact that land use change and its negative impacts have been occurring over a much longer period. Examining past land use change, particularly over the long-term and multiple time periods, is essential for understanding how rates and drivers of change have varied historically. Objectives: To quantify and assess patterns of change in semi-natural habitats across a rural landscape at five time points between 1930 and 2015. Methods: We determined the habitat cover at over 3700 sites across the county of Dorset, southern England in 1930, 1950, 1980, 1990 and 2015, using historical vegetation surveys, re-surveys, historical maps and other contemporary spatial data. Results: Considerable declines in semi-natural habitats occurred across the Dorset landscape between 1930 and 2015. This trend was non-linear for the majority of semi-natural habitats, with the greatest losses occurring between 1950 and 1980. This period coincides with the largest gains to arable and improved grassland, reflecting agricultural expansion after the Second World War. Although the loss of semi-natural habitats declined after this period, largely because there were very few sites left to convert, there were still a number of habitats lost within the last 25 years. Conclusions: The findings illustrate a long history of habitat loss in the UK, and are important for planning landscape management and ameliorative actions, such as restoration. Our analysis also highlights the role of statutory protection in retaining semi-natural habitats, suggesting the need for continued protection of important habitats.

Land cover mapping of large areas is challenging due to the significant volume of satellite data to acquire and process, as well as the lack of spatial continuity due to cloud cover. Temporal aggregation—the use of metrics (i.e., mean or median) derived from satellite data over a period of time—is an approach that benefits from recent increases in the frequency of free satellite data acquisition and cloud-computing power. This enables the efficient use of multi-temporal data and the exploitation of cloud-gap filling techniques for land cover mapping. Here, we provide the first formal comparison of the accuracy between land cover maps created with temporal aggregation of Sentinel-1 (S1), Sentinel-2 (S2), and Landsat-8 (L8) data from one-year and test whether this method matches the accuracy of traditional approaches. hirty-two datasets were created for Wales by applying automated cloud-masking and temporally aggregating data over different time intervals, using Google Earth Engine. Manually processed S2 data was used for comparison using a traditional two-date composite approach. Supervised classifications were created, and their accuracy was assessed using field-based data. Temporal aggregation only matched the accuracy of the traditional two-date composite approach (77.9%) when an optimal combination of optical and radar data was used (76.5%). Combined datasets (S1, S2 or S1, S2, and L8) outperformed single-sensor datasets, while datasets based on spectral indices obtained the lowest levels of accuracy. The analysis of cloud cover showed that to ensure at least one cloud-free pixel per time interval, a maximum of two intervals per year for temporal aggregation were possible with L8, while three or four intervals could be used for S2. This study demonstrates that temporal aggregation is a promising tool for integrating large amounts of data in an efficient way and that it can compensate for the lower quality of automatic image selection and cloud masking. It also sh

Accurate crop yield estimates are important for governments, farmers, scientists and agribusiness. This paper provides a novel demonstration of the use of freely available Sentinel-2 data to estimate within-field wheat yield variability in a single year. The impact of data resolution and availability on yield estimation is explored using different combinations of input data. This was achieved by combining Sentinel-2 with environmental data (e.g. meteorological, topographical, soil moisture) for different periods throughout the growing season. Yield was estimated using Random Forest (RF) regression models. They were trained and validated using a dataset containing over 8000 points collected by combine harvester yield monitors from 39 wheat fields in the UK. The results demonstrate that it is possible to produce accurate maps of within-field yield variation at 10 m resolution using Sentinel-2 data (RMSE 0.66 t/ha). When combined with environmental data further improvements in accuracy can be obtained (RMSE 0.61 t/ha). We demonstrate that with knowledge of crop-type distribution it is possible to use these models, trained with data from a few fields, to estimate within-field yield variability on a landscape scale. Applying this method gives us a range of crop yield across the landscape of 4.09 to 12.22 t/ha, with a total crop production of approx. 289,000 t.

Sustainable intensification (SI) has been proposed as a possible solution to the conflicting problems of meeting projected increases in food demand and preserving environmental quality. SI would provide necessary production increases while simultaneously reducing or eliminating environmental degradation, without taking land from competing demands. An important component of achieving these aims is the development of suitable methods for assessing the temporal variability of both the intensification and sustainability of agriculture. Current assessments rely on traditional data collection methods that produce data of limited spatial and temporal resolution. Earth Observation (EO) provides a readily accessible, long-term dataset with global coverage at various spatial and temporal resolutions. In this paper we demonstrate how EO could significantly contribute to SI assessments, providing opportunities to quantify agricultural intensity and environmental sustainability. We review an extensive body of research on EO-based methods to assess multiple indicators of both agricultural intensity and environmental sustainability. To date these techniques have not been combined to assess SI; here we identify the opportunities and initial steps required to achieve this. In this context, we propose the development of a set of essential sustainable intensification variables (ESIVs) that could be derived from EO data.

Increasingly, the application of models in urban hydrology has undergone a shift toward integrated structures that recognize the interconnected nature of the urban landscape and both the natural and engineered water cycles. Improvements in computational processing during the past few decades have enabled the application of multiple, connected model structures that link previously disparate systems together, incorporating feedbacks and connections. Many applications of integrated models look to assess the impacts of environmental change on physical dynamics and quality of landscapes. Whilst these integrated structures provide a more robust representation of natural dynamics, they often place considerable data requirements on the user, whereby data are required at contrasting spatial and temporal scales which can often transcend multiple disciplines. Concomitantly, our ability to observe complex, natural phenomena at contrasting scales has improved considerably with the advent of increasingly novel monitoring technologies. This has provided a pathway for reducing model uncertainty and improving our confidence in modeled outputs by implementing suitable monitoring regimes. This commentary assesses how component models of an exemplar integrated model have advanced over the past few decades, with a critical focus on the role of monitoring technologies that have enabled better identification of the key physical process. This reduces the uncertainty of processes at contrasting spatial and temporal scales, through a better characterization of feedbacks which then enhances the utility of integrated model applications.

This paper presents the current status of UK land cover mapping and considers the requirements for future land cover monitoring systems, including accurate change detection. The paper then presents preliminary results from two different change detection methodologies. Plans for future work are also described.

A range of monitoring techniques including digital cameras, radiometers and satellites have the potential to provide new sources of data on vegetation dynamics, including consistent data on the spatial and temporal variability of phenological events and vegetation productivity. This paper compares four data sets collected in 2012 that capture the above-canopy and below-canopy phenology of a deciduous woodland. The longest time-series is provided by weekly below-canopy photos recorded weekly from 1998 onwards. The weekly photos are used to assess the inter-annual variability from 2004 onwards and suggest that green-up varies between 8th April and 3rd May.

Agroforestry plantations and urban habitats contribute importantly to atmospheric volatile compound fluxes in densely populated areas. Simulation of emissions from such habitats is associated with several key challenges, including high spatial heterogeneity due to habitat fragmentation and high diversity of planted tree species. On the other hand, plants in urban habitats and in agroforestry plantations commonly receive more nutrients and water than species in natural communities, resulting in higher production and potentially greater capacity for volatile production per unit of land area. This chapter reviews the strategies for simulation of biogenic volatile organic compound (BVOC) fluxes from urban habitats and agroforestry plantations and provides an outline for parameterization of volatile emission models for densely populated areas with high vegetation fragmentation and large number of gardened, often exotic, tree species.

Forests play an important role in the global climate system because they take up and store large amounts of carbon in the form of biomass. This paper examines techniques of retrieving structural forest information using the remote sensing techniques of LiDAR and SAR. Both sensing methods can provide information on the vertical structure of forests. Certain LiDAR instruments can record a vertical waveform of reflected radiation from the forest which can be related to vertical biomass distribution. Forest height is a biophysical parameter that can be used to estimate aboveground forest biomass using allometric equations. Interferometric and polarimetric SAR instruments at different wavelengths can deliver information on terrain elevation under forest, tree canopy height and biomass. Examples of airborne imaging LiDAR (Optech), spaceborne profiling LiDAR (ICESAT-GLAS), airborne interferometric SAR (ESAR) and spaceborne interferometric SAR (ALOS-PALSAR) are presented. In particular, L-band and X-band SAR have been investigated for use to generate a forest height and biomass map without the need for an external Digital Terrain Model. For denser forests a longer wavelength such as P-band would be required.