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

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

Sibley book Range map (overall, breeding, wintering) Clip elevations outside observed range GROMS digitize 1119 species 462 species Clip boundary Seabird, terrestrial bird Clip unsuitable habitat types DEM Global migratory bird database Global migratory bird species biodiversity mapping and monitoring GLC 2000 GlobCover 2005 L. Liang and P. Gong, in preparation

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

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

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

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

Four global land cover maps MODIS Land Cover - 500m GlobCover - 300m , 2009 FROM-GLC - 30m FROM-GLC-AGG - 30m

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

Four global land cover maps MODIS Land Cover Cropland Legend CroplandLands covered with temporary crops followed by harvest and a bare soil period. Note that perennial woody crops will be classified as the appropriate forest or shrub land cover type. Cropland/natural vegetation mosaics Lands with a mosaic of croplands, forests, shrubland, and grasslands in which no one component comprises more than 60% of the landscape. MODIS = ∑(cropland+ cropland mosaic) Weighted-MODIS = ∑(cropland*0.6 + cropland mosaic *0.4)

GlobCover CodeCropland Legend 11Post-flooding or irrigated croplands 14Rainfed croplands 20Mosaic Cropland (50-70%) / Vegetation (20-50%) 30Mosaic Vegetation (50-70%) / Cropland (20-50%) GlobCover = ∑( irrigated+ rainfed+ cropMosaic + vegMosaic) Weighted-GlobCover = ∑( irrigated+ rainfed+ cropMosaic*0.6 + vegMosaic*0.35)

FROM-GLC & FROM-GLC-AGG Cropland Legend Rice fieldsLand for rice cultivation. Other croplandsThis category includes arable and tillage land. OrchardsParcels planted with fruit trees or shrubs: single or mixed fruit species, fruit trees associated with permanently grassed surfaces. PasturesGrasslands for grazing. Bare herbaceous croplands Just harvested, fallow land and all other types of land not covered by vegetation such as lake bottoms in dry season. FROM-GLC= ∑(rice+ otherCrop + orchard + pasture + bareCrop) FROM-GLC-AGG = ∑(rice+ otherCrop + orchard + pasture + bareCrop)

County level comparison 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 MODIS-weightedGlobCover-weighted GlobCoverFROM-GLC FROM-GLC-agg

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

State level comparison 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 MODIS-weightedGlobCover-weighted GlobCoverFROM-GLC FROM-GLC-agg

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 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.

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). 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.

Big Five Photo courtesy of Zhiliang Zhu and website

Thank you!