Section 1 Introduction

1.1 Tracking land-use change

The tracking of land use and land-use change is fundamental to producing accurate and consistent greenhouse gas inventories (GHGI) for the Land Use, Land-Use Change and Forestry (LULUCF) sector. This is necessary to meet the international requirements of the Kyoto Protocol to the UN Framework Convention on Climate Change (UNFCCC) and the Paris Agreement and the national requirements of the UK’s Climate Change Act and related legislation within the UK’s Devolved Administrations.

The estimation of land-use change in the current UK GHGI is based on a combination of infrequent CEH Countryside Surveys and afforestation/deforestation statistics from the GB Forestry Commission. It uses Approach 2 (non-spatial land-use change matrices) as described in the KP Guidance. However, this matrix-based approach, and its implementation in the UK, have some important limitations. Firstly, the non-spatial matrix-based approach is insufficient for tracking annual land-use change: the matrices have no time dimension, and are defined independently each year. There is therefore no possibility of representing a sequence of land-use on the same parcel of land (such as afforestation followed by deforestation, or crop-pasture rotations). Secondly, the data used to estimate these matrices in the UK are rather limited. The CEH Countryside Surveys were only carried out approximately once per decade, and whilst the geographical extent was very wide, the actual ground area surveyed was small as a fraction of the total UK area. The afforestation/deforestation statistics from the Forestry Commission have good national coverage (excluding Northern Ireland) but do not contain any information on the spatial location or land use prior to afforestation or following deforestation.

In October 2019, the UNFCCC Expert Review of the UK 1990-2017 GHG inventory raised concerns in relation to the reporting requirements of the second commitment period of the Kyoto protocol. They questioned whether the current approach is appropriate for the identification and tracking of lands where the elected Article 3.4 activities occur (i.e. Cropland Management, Grazing Land Management and Wetland Drainage and Rewetting). They recommended that the UK explore how to make the best possible use of available data and prepare and implement a work-plan to enable the use of these data. The UK has already explored several approaches to land use tracking, including a data assimilation approach to integrate available land-use data into land-use vectors, which was successfully piloted in Scotland (Levy et al. 2018). This project builds on that approach to assess gross land-use change, and land-use history for the whole of the UK from 1990 to 2019.

As well as improving accuracy of the GHGI, a time series of spatially explicit land-use change would enable better tracking of mitigation activities and improve baseline data for scenario modelling. These baseline data are needed for understanding the potential of land-based mitigation and adaptation options. The government’s ambitions for Net Zero by 2050 or sooner means that the LULUCF sector will have an increasingly critical role in the UK’s overall GHG balance. This kind of scenario modelling will become very important to inform the setting of future carbon budgets and monitor progress towards the UK’s legal obligations to GHG emissions reductions. An accurate spatio-temporal land-use change data set would be useful to other stakeholders and UK government departments. For example, from the perspectives of biodiversity conservation, air quality, or ecosystem services, there are clear applications of these data for understanding and tracking the effects of land use.

1.2 Approach

If we had reliable maps of land use each year, we could infer land-use change by difference. However, even with advances in satellite sensors, GIS and spatial data handling, the accuracy of change detection from EO-based products is generally too poor to do this; the different EO products are inconsistent (with each other, and with themselves over time), irregular, and become more infrequent as we go back in time. Change is more reliably detected by repeat gound-based surveys, but these have other short-comings. For example, the annual June Agricultural Census gives a long record of areas in different land uses, but does not provide spatial data, or any information on gross change (i.e. what land uses have changed to which other land uses). The CEH Countryside Survey did provide spatial data with gross change, but without complete coverage, and only at infrequent intervals.

In light of the above, some data assimilation method, which combines the spatio-temporal data with non-spatial repeat survey data, would appear to provide a solution. To this end, we previously developed a methodology using a Bayesian data assimilation approach, and this has been applied successfully to Scotland (Levy et al. 2018). This method allowed for the use of a wider range of data types, including high-resolution spatial data, and combined them in a mathematically coherent way. Importantly, the method produced the appropriate data structure needed for modelling the effects of land-use change on GHG emissions - the set of unique land-use vectors (i.e. unique sequences of land use, or land-use histories) and their associated areas. An important feature is that the uncertainty in land-use change can be easily propagated to provide the uncertainty in GHG emissions, because the procedure explicitly handles the distribution of plausible vectors of land-use change. The approach provides a general framework for combining multiple disparate data sources with a simple model which describes how these data sources inter-relate. This allows us to constrain estimates of gross land-use change with reliable national-scale census data, whilst retaining the detailed spatial information available from several other sources. Here, we apply this methodology to improve and update the tracking of land-use change for the UK. Our aim was to apply a Bayesian approach to make spatially- and temporally-explicit estimates of land-use change in the UK, using multiple sources of data.

The tasks required to achieve this aim were:

  • A.1 updating and obtaining new data, and the necessary processing;
  • A.2 running the data assimilation algorithm to produce a time series of maps (deliverable A.1);
  • A.3 summarising the output in vector format and characterising the uncertainty with respect to random, systematic, and correlated error terms (deliverables A.1 & A.2);
  • A.4 documenting the code and data processing workflow (deliverable A.3);

The remainder of this report describes these four tasks. The Methods section describes the basic methodology for the data assimilation. The Data Sources section and subsequent sections describes the data sources assessed for inclusion. The Data Assimilation section describes the data assimilation procedure in some more detail, with results in subsequent sections. Finally, we discuss the results compared with the previous method, and consider areas where further work is needed.

All the code is written in R using the “literate programming” paradigm implemented with Rmarkdown, which combines the source code, text/graphical output, documentation, and report text within the same document. This ensures integrity of documentation, code and corresponding outputs. All the Rmarkdown files are held in a GitHub repository, for version control and wider accessibility. The documentation is rendered using bookdown and made publicly available as a web site via GitHub Pages. This documentation describes the data processing workflow so as to make it reproducible.

References

Levy, P., M. van Oijen, G. Buys, and S. Tomlinson. 2018. “Estimation of Gross Land-Use Change and Its Uncertainty Using a Bayesian Data Assimilation Approach.” Biogeosciences 15 (5): 1497–1513. https://doi.org/10.5194/bg-15-1497-2018.