A SEMI-AUTOMATIC AND MULTISCALE APPROACH FOR ASSESSING THE AGREEMENT OF LARGE SCALE FOREST MAPS

Transcription

1 10 Sept. 10 D5L1 Forestry SAR Part II Chris Schmullius 1 A SEMI-AUTOMATIC AND MULTISCALE APPROACH FOR ASSESSING THE AGREEMENT OF LARGE SCALE FOREST MAPS R. Leiterer 1, J. Reiche 1, O. Cartus 1, M. Santoro 2, C. Thiel 1, M. Herold 3, C. Schmullius 1 1) Friedrich-Schiller-University Jena, Institute of Geography, Department of Earth Observation, Grietgasse 6, Jena, Germany, reik.leiterer@uni-jena.de (2) GAMMA Remote Sensing, Worbstrasse 225, CH-3073 Gümligen, Switzerland, santoro@gamma-rs.ch (3) Wageningen University and Research Centre, Laboratory of Geo-Information Science and Remote Sensing, Droevendaalsesteeg 3, 6708 PB Wageningen, Netherlands, Martin.Herold@wur.nl

2 10 Sept. 10 D5L1 Forestry SAR Part II Chris Schmullius 2

3 10 Sept. 10 D5L1 Forestry SAR Part II Chris Schmullius 3 Overview FOREST DRAGON Project Study Area and Reference Datasets Cross-Comparison Method Results & Conclusion

4 10 Sept. 10 D5L1 Forestry SAR Part II Chris Schmullius 4 Overview Study Area & Data Method Results & Conclusion FOREST DRAGON Project creation of large scale forest growing stock volume (GSV) maps for China validation of the created GSV maps change analyses between forest maps of different time steps (Session Dragon 14:10) creation of a disturbance identification database

5 10 Sept. 10 D5L1 Forestry SAR Part II Chris Schmullius 5 Overview Study Area & Data Method Results & Conclusion Study Area about 4.5 Million km² about 630 ERS-1/2 tandem pairs pixel size: 50 m x 50 m Figure: Location of the test regions Daxinganling and Xiaoxinganling and corresponding GSV maps

6 10 Sept. 10 D5L1 Forestry SAR Part II Chris Schmullius 6 Overview Study Area & Data Method Results & Conclusion Reference Datasets Product Sensor Resolution Year UMD AVHRR 1 x 1 km GLC2000 Spot 1 x 1 km 2000 VCF-MOD44 MODIS 0.5 x 0.5 km 2000 /05 GlobCover MERIS 0.3 x 0.3 km NLCD Landsat/ CBERS 0.05 x 0.05 km 1999

7 10 Sept. 10 D5L1 Forestry SAR Part II Chris Schmullius 7 Cross-Comparison Method various spatial scales of land cover products > scale independent intersection method different methods for the class assignation > semi-automatic legend harmonisation different amount of thematic classes > re-classification

8 10 Sept. 10 D5L1 Forestry SAR Part II Chris Schmullius 8 (1) Intersection Method Class A Class B Class A Class B

9 10 Sept. 10 D5L1 Forestry SAR Part II Chris Schmullius 9 (2) Legend Harmonisation GlobCover: class 110 (Mosaic forest or shrubland (50-70%) / grassland (20-50%) NLCD: class 3 (mixed forest > 30 %) Figure: Land cover cross comparison in Xiaoxingaling for the GlobCover product and the NLCD dataset considered as reference

10 10 Sept. 10 D5L1 Forestry SAR Part II Chris Schmullius 10 (3) Re-Classification Non-Forest [Classes] Forest [Classes] Class Description GSV map 1, 2, 5 3, 4 [1] GlobCover 11-40, [2] GLC [3] UMD 0, [4] VCF2000 <15 % Canopy Cover 15 % Canopy Cover NLCD 1, 2, , 4, 5, 6 [6] C. Schmullius, J. Reiche, R. Leiterer, O. Cartus, M. Santoro, U. Wegmüller, Z. Li, X. Tian & F. Ling (2010): FOREST DRAGON 2: Mid- Term Results of the European Partners. Proceedings of the 2010 Dragon 2 Symposium, May 2010, Guilin, China. O. Cartus, J. Reiche, R. Leiterer, M. Santoro, C. Schmullius & L. Zengyuan (2009): Generation and Cross-vlidation of Large-area Forest Stem Volume Maps for Northeast and Southeast China,Using ERS-1/2 Tandem Coherence. Proceedings CD of the 2009 ISDE 6 Symposium, September 2009, Beijing, China. [5]

11 10 Sept. 10 D5L1 Forestry SAR Part II Chris Schmullius 11 Sample Design Figure: Example of Sample Plots with confluence centre points for Northeast China. The red squares on the picture on the left indicate sample plots. The picture on the right shows a blow-up of a sample plot

12 10 Sept. 10 D5L1 Forestry SAR Part II Chris Schmullius 12 Results (1) Figure: Correlation between the GSV classes and the continuous MODIS tree cover classes Figure: Correlation between the aggregated GSV class and the continuous MODIS tree cover classes

13 10 Sept. 10 D5L1 Forestry SAR Part II Chris Schmullius 13 Results (2) OA Kappa Coefficient GSV vs. NLCD GSV vs. GlobCover GSV vs. VCF (>15% CC) GSV vs. GLC GSV vs. AVHRR LCC Table: Overall agreement between the GSV map and the LC products for the test area Xiaoxinganling based on aggregated forest/ non-forest classes

14 10 Sept. 10 D5L1 Forestry SAR Part II Chris Schmullius 14 Results (2) GSV Map GLC2000 MODIS LC IGBP, UMD Figure: Overall agreement between the GSV map and the LC products for Northeast and Southeast China based on aggregated forest/ non-forest classes Ran, Y., Li, X. & Lu, L. (2010). Evaluation of four remote sensing based land cover products over China, International Journal of Remote Sensing, 31(1-2), pp

15 10 Sept. 10 D5L1 Forestry SAR Part II Chris Schmullius 15 Conclusion development of a scale independent intersection method development of an enhanced legend harmonisation process > assessment of the quality of large scale forest maps (overall agreement > 70 %) > completely transferable, scale and thematic independent accuracy assessment approach

16 Thanks for your attention! 10 Sept. 10 D5L1 Forestry SAR Part II Chris Schmullius 16

17 10 Sept. 10 D5L1 Forestry SAR Part II Chris Schmullius 17 References 1. Cartus, O., Santoro, M., Schmullius, C., Yong, P., Erxue, C. & Zengyuan, L. (2008). Creation of Large Area Forest Biomass Maps for Northeast China using ERS-1/2 Tandem Coherence, Proceedings of the Dragon 1 Programme Final Results , April 2008, Beijing, ESA SP-655 (CD-Rom). 2. Bicheron, P., Defourny, P., Brockmann, C., Schouten, L., Vancutsem, C., Huc, M., Bontemps, S., Leroy, M., Achard, F., Herold, M., Ranera, F. & Arino, O. (2008). GlobCover: products description and validation report, ESA GlobCover project, ftp://uranus.esrin.esa.int/ pub/globcover _v2/. 3. Bartholmé, E. & Belward, A. (2005). GLC2000: a new approach to global land cover mapping from Earth observation data, International Journal of Remote Sensing, 26(9), pp Hansen, M., DeFries, R., Townshend, J.R.G. & Sohlberg, R. (2000). Global land cover classification at 1km resolution using a decision tree classifier, International Journal of Remote Sensing, 21, pp Jung, M., Henkel, K., Herold, M. & Churkina, G. (2006). Exploiting synergies of global land cover products for carbon cycle modelling, Remote Sensing of Environment, 101, pp Liu, J., Liu, M., Deng, X., Zhuang, D., Zhang, Z. & Luo, D. (2002). The land use and land cover database and its relative studies in China, Journal of Geographical Sciences, 12, pp