The Digital Land Cover Model of Germany (LBM-DE) under the umbrella of Copernicus Natural Resource Management using Copernicus Services and Data

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1 The Digital Land Cover Model of Germany (LBM-DE) under the umbrella of Copernicus Natural Resource Management using Copernicus Services and Data Munich, 12. November 2015

2 Digital Land Cover Model Germany (LBM-DE) Area-wide information on land cover and land use Based on the Basis-DLM (Topics settlements, transportation, vegetation and water) Update under usage of satellite imagery of the relevant reference year M. Hovenbitzer Copernicus in der Praxis am Beispiel des LBM-DE München Seite 2

3 Motivation Governmental requirements Objectives of the digital land cover model (LBM-DE) Capture information on land cover (LC) and land use (LU) with high resolution based on national topographic reference data for meeting the governmental requirements e.g. in the fields of environment, transportation, agriculture, forestry, water pollution control, regional planning Derivation of CORINE Land Cover (CLC) on the basis of the LC/LU information of the LBM-DE M. Hovenbitzer Copernicus in der Praxis am Beispiel des LBM-DE München Seite 3

4 CORINE Land Cover CORINE = Coordination of Information on the Environment (CLC) Starting point (1980s): land cover datasets in EU-MS scarcely existing or not comparable Target: dataset with environmental information all over Europe with unified rules concerning consistent actuality / mapping periods consistent nomenclature (40 CLC-Classes) equal minimum mapping units First production of CLC by interpretation of satellite imagery for the reference year 1990 Further updates for the reference years 2000, 2006 and 2012 (2018 planned!) M. Hovenbitzer Landbedeckung/-nutzung Möglichkeiten durch Copernicus InterGeo2015 Stuttgart Seite 4

5 CORINE Land Cover 2006 Overview M. Hovenbitzer Landbedeckung/-nutzung Möglichkeiten durch Copernicus InterGeo2015 Stuttgart Seite 5

6 Involved institutions LBM-DE / CLC Resolution of to the project LBM-DE influences i.a. the following actors: EC + EEA Reporting duty (incl. CLC) BMU Project structure plan: Derivation of CLC from LBM-DE Main data input for LBM-DE: ATKIS Basis-DLM M. Hovenbitzer Copernicus in der Praxis am Beispiel des LBM-DE München Seite 6

7 LBM-DE Concept Input data for LBM-DE2012 was the ATKIS Basis-DLM, frozen for the reference year 2012 Update only for polygon feature types of the topics settlement, transportation, vegetation, water Preparation of Basis-DLM for the update in simplified shape with resolved overlapping features Preliminary LC/LU-codes based on semantic transformation Primary data source were multi-temporal satellite imagery of the sensors RapidEye (5m) and DMC (22m) with reference year 2012 (+2011) Minimum mapping unit (MMU) = 1 ha M. Hovenbitzer Landbedeckung/-nutzung Möglichkeiten durch Copernicus InterGeo2015 Stuttgart Seite 7

8 Overview imagery RapidEye Spring 2011 (IMAGE2012) RapidEye Summer 2012 DMC 2 Summer IMAGE2012:SPOT, IRS - DOP, LBM-DE 2012 M. Hovenbitzer Landbedeckung/-nutzung Möglichkeiten durch Copernicus InterGeo2015 Stuttgart Seite 8

9 LBM-DE Concept New: Separation of Land Cover (LC) and Land Use (LU) CLC: no consistent separation between land cover and land use CLC 142: Sport & Freizeit LBM-DE: separation into LC and LU to completely describe the environment M. Hovenbitzer Landbedeckung/-nutzung Möglichkeiten durch Copernicus InterGeo2015 Stuttgart Seite 9

10 LBM-DE Concept Advantages of the distinction between land cover (LB) and land use (LN) during the update of the LBM-DE2012: Using satellite imagery the land cover is easy to map with good quality No need to weigh up land use against land cover during mapping Combination of LB- and LN-codes to unique value pairs allows the derivation of CLC-codes based on a cross table Partly it is possible to return the results into ATKIS Basis-DLM (mainly agriculture, forest,...) Higher flexibility in general: Possibility of user-oriented dataset generation M. Hovenbitzer Landbedeckung/-nutzung Möglichkeiten durch Copernicus InterGeo2015 Stuttgart Seite 10

11 Usage of the Copernicus Program to update the German LBM-DE 2012 Copernicus Contributing Missions Core01 cov.1 Core01 cov. 2 Core03 IMAGE2012 (RapidEye, IRS, Spot-5 from 2011 till 2013) In the future usage of the Sentinel sensors aimed Esp. Sentinel-2 as secondary data source 11

12 Produktionsprozess Multi-sensoral, multi-temporal and multispectral analysis Primary data source: RapidEye 2012 Secondary data sources: Copernicus and DMC Semi-automated update process using objectbased image analysis Result Land cover and land use dataset for whole Germany with a minimum mapping unit of 1ha Further aggregation levels possible Derivation of CLC2012 (MMU 25ha) based on the LBM-DE 2012 incl. CLC Change Layer from 2006 to 2012 (MMU 5ha)

13 Object-oriented image analysis inside LBM-DE project Development of tailored rule sets (interactive controlling) Segmentiation based on polygon borders Data load per category after optimized semantic pre-assessment Segmentation according to spectral information Classification based on pixel values Generalisation process Matching with suggestions Integration into manual production process 13

14 Added values of the LBM-DE towards the ATKIS Basis-DLM Overall higher actuality of forest, arable land and pastures towards the ATKIS stock data Example I: Separation of arable land and pastures RapidEye

15 Separation of arable land and pastures, example I DMC SPOT

16 Separation of arable land and pastures, example I RapidEye RapidEye Ergebnis: rote Linien = ATKIS -Geometrie, gelbe Linien = zusätzliche LBM-DE 2012 Geometrie 16

17 Separation of arable land and pastures, example I 17

18 Added value of the LBM-DE towards the ATKIS Basis-DLM More detailed differentiation of forests (broad-leaved, coniferous, mixed, rejuvenation) Differentiation Rejuvenation Result: red lines = ATKIS -geometry, yellow lines = additional LBM-DE 2012 geometry 18

19 Added value of the LBM-DE towards the ATKIS Basis-DLM Detailed differentiation of forests (broad-leaved, coniferous,mixed, rejuvenation) AX_Wald Rejuvenation to B324 Result: red lines = ATKIS -geometry, yellow lines = additional LBM-DE 2012 geometry 19

20 Added value of the LBM-DE towards the ATKIS Basis-DLM Nation-wide homogenous LU/LC-dataset of Germany Uniform methodological approach based on consistent guidelines Separation of land cover and land use Diverse application fields Statistic Change-Monitoring Input parameter for models (e.g. climate) Significant optimisation potential for special classes of the ATKIS Basis-DLM 20

21 Perspective Integration of Sentinel-2 data and/or services Free of charge secondary data source Analysis of time series inside the vegetation period More robust separation of arable land and pastures More precise localisiation of semi-natural graslands Sentinel-1 data sets Estimation and assessment of the potential added value 21

22 Your Contact Bundesamt für Kartographie und Geodäsie Dr. Michael Hovenbitzer Referat GI7 Richard-Strauss-Allee Frankfurt Germany Phone IABG mbh Jörg Ullrich Geodata Factory Hermann-Reichelt-Str Dresden Germany Phone

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