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Geospatial data is any information about an object, event, or phenomenon relative to its location on the Earth’s surface. In the context of LULUCF, monitoring with geospatial data can enhance the LULUCF policy implementation through the use of high resolution and detailed data on landscape elements which can support assessments of land cover, land-use change, carbon stocks and land management.

This section explains why geospatial monitoring is increasingly important for LULUCF reporting, how Member States can use it in practice, and which datasets can serve as a basis for geographically explicit monitoring under the LULUCF Regulation.

Table of contents

Why does geospatial monitoring matter for the LULUCF Regulation?

Implementing geographically explicit monitoring

How to move to geographically-explicit monitoring?

Monitoring approaches

Ensuring time-series consistency when moving to higher Tiers

Related case studies

Why does geospatial monitoring matter for the LULUCF Regulation?

The aim of the LULUCF Regulation is to assess greenhouse gas (GHG) emissions and removals from land use, land use change and forestry in the 2030 climate and energy framework. The revised Regulation encourages improved monitoring and reporting through a shift towards higher IPCC Tiers, where an increased use of geographically-explicit monitoring is essential.

In this context, geographically-explicit monitoring refers to the use of geospatial datasets to identify and track land use and land-use change to ensure that estimates of GHG emissions and removals are calculated consistently with the observed changes. When countries use wall‑to‑wall mapping or dense point‑sample systems, this spatial detail enables verification, analysis and spatially targeted policy measures that would not be possible otherwise.

To support this, the Governance Regulation (Reg. 2018/1999 Annex V, Part 3) introduced the methodological requirement for the 2021-2030 period that “Member States shall use geographically-explicit land-use conversion data” for the LULUCF sector.

Implementing geographically explicit monitoring

Within the framework of the Regulation, moving towards geographically-explicit monitoring implies the use of cartographic datasets, or repeated measurements of high density sampling plots for land-use change monitoring and for estimating carbon fluxes related to land use and land use change.

The reporting country should select the most suitable dataset or apply a multi-source approach using several datasets. The requirement to use geographically-explicit land use datasets does not impose a specific data format, coverage, or continued type of the sampling. What is intended is that such data remains interoperable with other datasets to facilitate carbon calculations at high spatial resolution. 

How to move to geographically-explicit monitoring?

The first step towards a geographically-explicit monitoring approach is the compilation of a strategy for using geospatial datasets and to ensure consistency across time series. The second step is the selection and gathering of spatial datasets as well as a fit-for-purpose analysis to determine that resolution, method, and quality are appropriate for the monitoring approach.

Each Member State can develop its own approach based on relevant national and/or European datasets, provided that geographically-explicit data is used. In the sections below we explain examples of monitoring approaches as well as selected geographically-explicit datasets.

Monitoring approaches

Some countries rely on national datasets to track land use over time, i.e. from cadastral systems or a national land use product. These data types should meet the following conditions:

    • Have a sufficient spatial resolution that can allow to accurately track land-use and changes and that is compatible with the national definition (i.e. forest land).
    • Have a well-described and understood nomenclature (number of classes and their definitions) that can to be translated into IPCC categories or national subcategories and reflect carbon stock changes.
    • Have consistent time coverage to facilitate evolution of the dynamics of land-use changes.
    • Have a time series data topologically consistent to geometric artefacts and uncertainties in land use change products to avoid the detection of ‘false changes’. Such products that are specifically designed to track changes consistently over time are to be prioritized.

Another monitoring approach used by countries is the application of a systematic grid across the country, using a grid points to collect information. This approach relies on sampling available data either in raster or vector format to collect the land use information through fusion of multiple datasets with different spatial or thematic resolution. 

Available geospatial datasets through the Data Catalogue

The Data Catalogue on the GHG Knowledge Hub provides access to geospatial datasets that support analysis relevant to LULUCF. Here, users can explore and access datasets related to land-use, land-use change and forestry filtered by land-use category, key parameter, and target pool. The list of geospatial datasets on the data catalogue is not exhaustive and is subject to updates.

Pan-European datasets

National and regional data

Statistical and other non-spatial data

Other datasets can provide useful information for LULUCF activity data and calculation parameters, such as agricultural practices, even if they are not spatial products. When combined with geographically-explicit monitoring, these datasets can support high-resolution estimated of carbon emissions and removals, particularly when available at subnational levels.

Ensuring time-series consistency when moving to higher Tiers

In the IPCC Guidelines, Tiers refers to different levels of methodological complexity used to estimate greenhouse gas emissions and removals. As countries are encouraged to move to higher Tiers, the IPCC Guidelines provide recommendations to ensure that methodological improvements do not introduce breaks in the time series. Chapter 5 of Volume 1 describes several practical techniques for maintaining time-series consistency when data sourced or methods change, including the use of overlapping datasets, surrogate data, interpolation, trend extrapolation, and non-linear trend analysis. These approaches allow inventories to benefit from improved monitoring without compromising the continuity of the reported estimates.

One approach towards time series consistency is described in the case study Combination of data and a simple model to increase time resolution in the French inventory. Here, inventory compilers combined data with high accuracy but low temporal resolution (NFI) and data with high temporal resolution but lower accuracy (harvest statistics). The case studies The vector maps of Portugal and The combination of multiple geographically-explicit data on a grid for France also provide examples of how approaches to ensure time-series consistency have been implemented in two Member States.

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