Full text: Proceedings International Workshop on Mobile Mapping Technology

classes by these satellite data are put in the higher level of 
the hierarchical classification system. For example, 
"Evergreen" and "Deciduous" are in the higher levels than 
"Forest" and "Shrubland" because discrimination of 
"Evergreen" and "Deciduous" is easier than that of "Forest 
" and "Shrubland". 
(4) Forest, Shrubland, and Grassland 
For the purpose of global change studies, the 
discrimination of vegetation into forest, shrubland, and 
grassland is important. Shrubs is small woody plants 
that are branched from the base. The proposed 
classification system uses a threshold value of 3 meters 
height to distinguish shrubland from forest. Though three 
classes of Forest, Shrubland, and Grassland are important, 
Forest and Shrubland are combined in the 2nd level of 
hierarchical system because the discrimination of Forest 
and Shrubland is difficult by low resolution satellite data 
such as AVHRR. 
(5) Harmonization 
The AARS land cover classification system has a 
harmonized characteristics with IGBP-DIS classification 
system because it is the main global land cover 
classification system for global change studies by the use 
of remote sensing. Threshold values of 60% of canopy 
cover for Forest or shrubland and 10% of vegetation cover 
for Non vegetation are selected in order to match the 
IGBP-DIS classification system. However the threshold 
of tree height discriminating shrubland from forest is 
decided as 3 meter because some shrubs are higher than 2 
meters which is the threshold by IGBP-DIS classification 
system. Regarding thresholds for forest, FAO and 
UNESCO have different values: over 40% canopy cover 
for open forest and over 70% for closed(or dense) forest. 
The reason not to select FAO/UNESCO thresholds is that 
two thresholds of 40% and 70% are difficult to 
discriminate by low resolution remote sensing images. 
Ground truth data in this paper means geographically 
specified regions which are identified one of classes in the 
AARS land cover classification system by class code. 
Collection of good ground truth data is a key issue for 
reliable land cover mapping. Ground truth data were 
collected mainly from existing thematic maps by the 
cooperation of the working group members. The used 
maps are listed in the Appendix 1. Some of ground truth 
data were collected by field survey in Central Asia such as 
Kazakhstan, Uzbekistan and Turkmenistan. The 
following three field trips were performed with the 
cooperation of WG member from Kazakhstan. 
(1) From August 23, 19% to September 2, 1996 from 
Almaty to Akmola(Tselinograd) of Kazakhstan 
(2) From July 5, 1997 to July 23, 1997 from 
Akmola(Tselinograd) to Kustanaj of Kazakhstan 
(3) From April 26,1998 to May 8, 1998 from Almaty of 
Kazakhstan, through Uzbekistan, to Ashkhabad of 
Ground truth data of 31 land cover classes were collected 
from 19 types of information sources which are thematic 
maps and field surveys. 
4.1 AVHRR data 
Global Land 1-km AVHRR Data Set was used as the 
source of satellite data. 10-day composite data of AVHRR 
NDVI, channel 4, and channel 5 were used in this study. 
NDVI data from April 1, 1992 to March 31, 1993 and 
channel 4 and channel 5 data from April 1, 1992 to 
October 31,1992 were used. 
4.2 Elevation data 
The Global Land One-kilometer Base Elevation(GLOBE), 
Version 1.0 , was used in this study. GLOBE data is a 
global 30 arc-second grid digital elevation data. 
4.3 Digital Chart of the World(DCW) data 
The DCW is a 1:1,000,000 scale vector base map of the 
world with 17 attribute layers. The seashore lines and 
national boundaries were used in this study for geometric 
registration and product's display. 
Several studies (Janodet 1994, Lambin and Ehrlich 1995) 
demonstrate the advantage of combining NDVI with land 
surface temperature (Ts) data derived from AVHRR 
channel 4 and 5 for single year land cover classification. 
Ts is related, through the surface energy balance equation, 
to surface moisture availability and évapotranspiration, as 
a function of latent flux (Carlson 1981). The combination 
of time series Ts and NDVI allows to characterise surface 
conditions both in terms of fractional vegetation cover, 
surface moisture status and surface resistance to 
évapotranspiration (Goward and Hope 1989, Nemani 
1993). The ratio between Ts and NDVI (referred to here as 
Ts/NDVI) is an adequate measure of the biophysical 
information contained in the Ts-NDVI space because it 
mainly quantifies variations in both Ts and NDVI which 
are characterized by a negative Ts-NDVI relation, i.e., the 
variations which are bio-physically meaningful (Lambin

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