The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Voi. XXXVII. Part B7. Beijing 2008
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University(BULC website),GLCC(GLCC website), were used
mainly to decide candidates of training data.
3.2.3 Thematic data: Some thematic data such as population
density data, World mangrove atlas, snow data derived from
MODIS, and shoreline vector data were used for individual land
cover class mapping. Population density data used is LandScan
Dataset which comprises a worldwide population database
compiled on a 30” by 30” latitude/ longitude grid (Population
website). World mangrove atlas is initially published by a hard
copy (Spaldings 1997), and updating digital version is available
through internet (UNEP-WCMC Interactive Maps website). A
Global Self-consistent, Hierarchical, High-resolution Shoreline
Database (GSHHS) is continuously updating vector shoreline
data available through internet (GSHHS website).
3.3 Regional land cover maps
Approximately two hundred existing regional maps were used
as reference to check intermediate classification result and to
find suitable locations of training data. Table 1 shows only a
part of them as examples. 4
The Atlas of Canada
National Land Cover Database 2001 (USA)
Forests and Protected Areas of Brazil
Forests and Protected Areas of Argentina
Corine land cover 2000 (CLC2000) 100 m - version
8/2005
Vegetation Atlas of China(2001)
Forest cover map of Insular Southeast Asia
Agriculture and Forest Land in China,Mongolia and the
States of the FSU
Afghanistan Land Cover 2002
Land Cover / Land Use of South Africa
2001/02 Land Use of Australia Version 3 (non-
agricultural and interpretive agricultural land use)
Australia's Major Vegetation Groups
Circumpolar Arctic Vegetation Map
Global Distribution of Current Forests
Table 1 Examples of regional maps used as reference
4. LEGEND
Twenty land cover types including water were defined using
Land Cover Classification System versions (LCCS2)
developed by FAO(LCCS website). In the actual mapping
process, initially defined land cover legend was slightly
modified through the collection of training data. In other words,
definition of legend and collection of training data are
interactive process. Table 2 shows the finally defined land
cover legend for GLCNMO.
code
GLCNMO land cover class
1
Broadleaf Evergreen Forest
2
Broadleaf Deciduous Forest
3
Needleleaf Evergreen Forest
4
Needleleaf Deciduous Forest
5
Mixed Forest
6
Tree Open
7
Shrub
8
Herbaceous
9
Herbaceous with Sparse Tree / Shrub
10
Sparse Vegetation
11
Cropland
12
Paddy field
13
Cropland /Other Vegetation Mosaic
14
Mangrove
15
Wetland
16
Bare area, consolidated (gravel, rock)
17
Bare area, unconsolidated (sand)
18
Urban
19
Snow / Ice
20
Water Bodies
Table 2. Land cover legend for GLCNMO
5. TRAINING DATA
Collection of training data is the most important part of land
cover mapping. The following steps were performed in this
study for fourteen land cover classes: 1 Broadleaf Evergreen
Forest, 2 Broadleaf Deciduous Forest, 3 Needleleaf Evergreen
Forest, 4 Needleleaf Deciduous Forest, 5 Mixed Forest, 7 Shrub,
8 Herbaceous, 9 Herbaceous with Sparse Tree / Shrub, 10
Sparse Vegetation, 11 Cropland, 12 Paddy field, 13 Cropland
/Other Vegetation Mosaic, 16 Bare area, consolidated (gravel,
rock), and 17 Bare area, unconsolidated (sand).
(1) GLC2000 land cover map was used to select candidates of
training regions
(2) Landsat data observed around 2000 were used as a
background image when training sites are selected by shape file.
(3) MODIS NDVI seasonal patterns are drawn for each training
polygon. If different training polygons of the same land cover
class, for example cropland, have different NDVI patterns,
these training data are assigned as different subclasses, for
example cropland A and cropland B.
(4) Trial of classification by supervised method
(5) Compare intermediate classified result with regional land
cover maps. It was also checked by National Mapping
Organizations participating in Global Mapping project.
(6) If no good agreement with regional land cover maps,
unsuitable training data are deleted with the reference of
Landsat, NDVI pattern, and Google Earth/Virtual Earth.
Necessary additional training data are selected with reference
of regional land cover maps and Google Earth/Virtual Earth.
(7) Modification of training data are repeated until the
classified result has no serious difference from regional land
cover maps.
Final numbers of training polygons are 824 for 105 subclass in
Eurasia, 492 for 47 subclass in North America, 136 for 30
subclass in South America, 124 for 39 subclass in Africa, and
31 for 16 subclass in Oceania. Globally 1497 training polygons
were collected.
6. CLASSIFICATION
6.1 Main classification
Subclasses of fourteen land cover classes of Table 2 were
classified by supervised method of MODIS data for each
continent. Maximum likelihood method by ENVI software was
applied for MODIS seven bands and NDVI of eight 16-day