Applications and e valuation of global land cover maps

1. Internationalsymposium on land cover mapping for the African continent Applications and evaluation of global land cover maps Lu Liang, Peng Gong Department of Environmental Science, Policy and Management, University of California, Berkeley And Center for Earth System Science, Tsinghua University 2013.6.27

2. Land cover and land use map applications • Biodiversity monitoring • Health • Food security • Disaster management • Energy potentials • Carbon sciences • Water resources • Forest degradation ……

3. Global migratory bird species biodiversity mapping and monitoring Sibley book GROMS digitize 1119 species 462 species Range map (overall, breeding, wintering) Seabird, terrestrial bird GlobCover2005 Clip boundary Clip elevations outside observed range DEM Clip unsuitable habitat types Global migratory bird database GLC 2000 L. Liang and P. Gong, in preparation

4. 2000 Habitat Overlay (Breeding + Wintering) 2005 Habitat Overlay (Breeding + Wintering)

5. 33% of land area 2000 hotspot area (>40 species) shrink 24% of land area 2005 hotspot area (>40 species)

6. L. Liang and P. Gong. Proceedings of SPIE. 2010

7. Validation and evaluation are important in the application of global LULC data • Taken cropland area estimation as a case study • Important to issues, eg. food security, environmental sustainability • Easily confused with other land cover types • Relative good reference data

8. Four global land cover maps • MODIS Land Cover - 500m - 2001-2011 • GlobCover - 300m - 2006, 2009 • FROM-GLC - 30m - 2010 • FROM-GLC-AGG - 30m - 2010

9. Validation dataset: National Agricultural Statistics Services (NASS) annual agricultural survey Figure. 2010 NASS cropland survey at the county and state level. Blank counties contain no reported value or were excluded from analysis.

10. Four global land cover maps • MODIS Land Cover MODIS = ∑(cropland+ cropland mosaic) Weighted-MODIS = ∑(cropland*0.6 + cropland mosaic *0.4)

11. GlobCover GlobCover= ∑( irrigated+ rainfed+ cropMosaic + vegMosaic) Weighted-GlobCover= ∑( irrigated+ rainfed+ cropMosaic*0.6 + vegMosaic*0.35)

12. FROM-GLC & FROM-GLC-AGG FROM-GLC= ∑(rice+ otherCrop + orchard + pasture + bareCrop) FROM-GLC-AGG = ∑(rice+ otherCrop + orchard + pasture + bareCrop)

13. County level comparison MODIS GlobCover FROM-GLC Figure. Comparison between NASS cropland survey at county level with estimations from six datasets. Dashed line is 1:1 and the solid line is the regression line. MODIS-weighted GlobCover-weighted FROM-GLC-agg

14. Figure. Assessment of six datasets with NASS cropland survey at the county scale. 27.7% 22.4% 24.1%

15. State level comparison MODIS GlobCover FROM-GLC Figure. Comparison between NASS cropland survey at state level with estimation from six datasets. Dashed line is 1:1 and the solid line is the regression line. MODIS-weighted GlobCover-weighted FROM-GLC-agg

16. Figure. Significance test and slope estimation based on linear regression model for each state. Each product was indicated by a fixed direction in one pie chart. Four different colors were used to classify the level of the significant slopes according to the following rules: underestimate (slope<0.8; R2<0.6); overestimate (slope>1.2; R2<0.6); semi-fair estimation (0.8<slope<1.2; R2≤0.6); fair estimation (0.8<slope<1.2; R2≥0.6). States in white did not pass the significant test.

17. Inter-annual and seasonality effects • Full dataset • Inter-annually restricted dataset: contains data from counties that completely overlapped with the 2010 FROM-GLC Landsat scenes • Seasonality restricted dataset: contains survey data from counties that are fully intersected with Landsat images during the growing months (Map-Aug).

18. Inter-annual and seasonality effects • Figure. Bar charts represent R2 and slope of the three type datasets (full, yearly, seasonal) for each product. Two pie charts are number of counties in each dataset.

19. Big Five Photo courtesy of Zhiliang Zhu and website

20. Thank you! luliang@berkeley.edu