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Revisiting the Ecosystem Extent Account: Lessons Learned from Germany

Marius Bellingen, Simon Felgendreher, Johannes Oehrlein, Jonathan Reith, Simon Schürz

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Objectives of the Ecosystem Extent Account

National Statistical Office Germany (Destatis)

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  • Common spatial unit of analysis for all stakeholders

- Nationally and internationally compatible

- Detailed, yet simple and user-friendly

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  • Monitoring of changes in ecosystem area and composition across time and space

- Automated high-resolution tracking of ecosystem areas

- Consistent methods to generate time-series

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  • Basis for condition and service accounts

- Set of condition variables and services are measured

- Spatial unit of analysis

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Are there general guidelines and rules that can help to achieve these goals?

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Implementation of the Ecosystem Extent Account

National Statistical Office Germany (Destatis)

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Source: Guidance on Biophysical Modelling for Ecosystem Accounting (n.d.)

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Implementation of the Ecosystem Extent Account

National Statistical Office Germany (Destatis)

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Classification

Pre-process

Apply the classification Classify

Post-process

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Input data

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Extent Account Aggregate

& Ecosystem Map & Map

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Classification

National Statistical Office Germany (Destatis)

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Bogaart et al. (2019):

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based on ecological principles

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mappable

collectively exhaustive

mutually exclusive

practical

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linkable

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current or future relevance at MMU

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specific data requirements

ecosystem provide unique bundle of ES

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sufficient conditions

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crosswalks

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necessary conditions

  • Each class has certain criteria based on a total of 26 variables (land cover, elevation…)

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  • Matrix that can be applied to geometry structure

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Classification

National Statistical Office Germany (Destatis)

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+ crosswalks to

MAES, IUCN, CLC, EUNIS

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Input Data

National Statistical Office Germany (Destatis)

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Requirements:

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  • Time-series: frequency and consistency requirements for dynamic and stable variables

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  • Geo-referenced data: respect the MMU, exhaustiveness

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  • Quality: governmental, scientifc or international reputable sources, minimally processed

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Input Data

National Statistical Office Germany (Destatis)

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+ project and harmonize data sources if necessary

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Applying the classification

National Statistical Office Germany (Destatis)

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1. Pre-processing

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  • Many data sources can be pre-processed and combined without any reference to priorities and orderings of the classification

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  • Lower time and computational capacity requirements

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  • Pre-processing variables must not depend on final area or neighbors of geometry

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Examples:

- New geometries: build up areas from linear features (roads, railroads, hedgerows), areas outside of land cover data (open sea)

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- Intersection of land cover data and other sources (e.g. riparian forests)

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- Add data to existing geometries (e.g. water body morphology)

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Pre-process examples (riparian forests, rivers)

National Statistical Office Germany (Destatis)

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Sources: LBM-DE, WFD, Basis DLM, own calculation

Sources: LBM-DE, Copernicus Riparian Zones High Resolution Layer, Basis-DLM; own calculation

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Applying the classification

National Statistical Office Germany (Destatis)

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2. Classifying

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  • Iterative classification procedure

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Classifying examples (marine waters, settlements)

National Statistical Office Germany (Destatis)

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Sources: LBM-DE, BfN, BfG, National Park Wattenmeer, NLWKN; own calculation

Sources: LBM-DE, Schug et al. (2021), LoD1-DE (BKG), own calculation

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Applying the classification

National Statistical Office Germany (Destatis)

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3. Post-processing

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  • Quality control, repair geometries, clean up splitter polygons

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  • Log classification data sources

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  • Aggregation to higher administrative and classification levels

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Extent Account & Ecosystem Map

Sources: Destatis, GDI-TH, Esri, HERE, Garmin, FAO,NOAA, USGS

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Discussion

National Statistical Office Germany (Destatis)

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  • Different ways to create extent account, but some general guidelines apply

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  • Semi-automated implementation procedure can facilitate generation of time-series and revisions with moderate cost and effort

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  • New compilers can benefit from implementation guide with practical details

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  • Some aspects of the extent account are not well-specified yet (conversion, ownership)

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Thank you!

Simon Schürz�simon.schuerz@destatis.de�

www.destatis.de

www.destatis.de/kontakt

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