The Pan-European Forest Type Map 2006 Methodology & results

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1 The Pan-European Forest Type Map 2006 Methodology & results Pieter Kempeneers, JRC Lucia Seebach, JRC Fernando Sedano, JRC Peter Strobl, JRC Jesus San-Miguel-Ayanz, JRC EC - Joint Research Centre, IES Land Management and Natural Hazard Unit FOREST Action Joint NASA LCLUC Science Team Meeting and GOFC-GOLD/NERIN, NEESPI Workshop Tartu, ESTONIA, August

2 Mission: Provide customer-driven scientific and technical support for the conception, development, implementation and monitoring of European Union policies. Land Management & Natural Hazard Unit

3 Aim of forest map 2006 Joint Research Centre JRC high resolution Forest Cover Map 2006: Consistent over Europe Independent of national boundaries Higher level of spatial detail than currently available High level of geometric (<25m) and thematic accuracy Compatible with INSPIRE standards Multi-purpose for European decision making due to comparable forest information Transboundary studies related to forestry Input data for large area modelling with high spatial detail Basis for value-added products (forest species, forest fragmentation, etc) 3

4 Forest/Non Forest Map 2000 Year based on ca. 400 Landsat ETM + scenes (GLCF+Image2000) Coverage: EU27 + CH, Balkan States, (NO+TR) Pekkarinen, A. et al., (2009). Pan-European forest/non-forest mapping with Landsat ETM+ and CORINE Land Cover 2000 data ISPRS Journal of Photogrammetry and Remote Sensing, 64 (2), pp

5 Input data Joint Research Centre Satellite Data 2006 Corine Land Cover 2000/ MODIS Bossard et al CORINE land cover technical guide Addendum. EEA Technical Report No

6 Output products Forest/Non-Forest map Forest type map Non-Forest Forest Non-Forest Broadleaved Forest Coniferous Forest Water 6

7 Forest map method High Resolution (HR) spectral information HR training data (non forested land, forest, water) CLC classify (ANN) Forest mask 7

8 Forest type map method High Resolution (HR) spectral information Medium Resolution (MR) temporal information HR MR High+Medium Resolution (H+MR) spectral + temporal information upsample to HR grid training data (non forested land, forest, water) H+MR CLC classify (ANN) Forest mask classify (ANN) training data (forest types) CLC Forest Type map 8

9 Test scenes Boreal region Fragmented landscape Mountaneous area (Alps) Mountaneous area (Med) Humid Mediterranean area Dry Mediterranean area

10 Validation Visual interpretation of Google earth image data National Forest Inventory of Spain 10

11 Visual interpretation Point id : Land cover: Broadleaf Crown cover: % Open/dense forest Scene Non Forested land Broadleaved Forest Coniferous Forest Water Total Mediterranean (humid) Mediterranean (dry) Fragmented landscape Mountaneous (Alps) Mountaneous (Med) Boreal region Total

12 National Forest Inventory data Available for Spain only Two test scenes: humid and dry Mediterranean area Focus on Forested land only: validate forest types 12

13 Number of points in NFI 1669 NFI Forest points (1011 Broadleaved Coniferous) 1369 points visual interpreted (Forest and non-forest) 13

14 Visual interpretation Non-Forest (NF) Broadleaved Forest (BF) Coniferous Forest (CF) Water (W) Results All scenes NF BF CF W Total NF BF CF W User Acc Prod. Acc Overall Accuracy Forest/Non-Forest: 88% Overall Accuracy Forest types: 82% NFI (Spain) Broadleaved Forest (BF) Coniferous Forest (CF) Med Humid + Dry region BF CF Total BF CF User Acc. Prod. Acc Overall Accuracy: 88% 14

15 Conlusions Forest maps available Forest/Non-Forest: 1990, 2000, 2006 (soon) Forest types: 2006 (BETA VERSION) Validation using visual interpretation (6 test scenes) Forest/Non-Forest map: overall accuracy 88% Forest Type map: overall accuracy 82% Validation using NFI data (2 test scenes) Forest Types within forest: overall accuracy: 88% Further validation underway 15

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