Full text: Proceedings International Workshop on Mobile Mapping Technology

and Ehrlich 1995). Since Ts displays the opposite trend 
to NDV1 when moving from sparse to dense vegetation 
landscapes, the use of the ratio Ts/NDVI increases the 
capability of discrimination of vegetation classes. The 
ratio of Ts/NDVI has been interpreted biophysically as 
regional surface resistance to evapotranspiration (Nemani 
and Running, 1989). This concept provides theoretical 
support for using this ratio in land cover analysis. 
6.1 Extraction of land surface temperature 
(Ts) data 
The land surface temperature, Ts, was derived from 
channel 4 and channel 5 using split window algorithm 
(Price 1984). The formula to derive Ts (in centigrade) is 
as follows. 
Ts =T4 + 3.33 (T4-T5) - 160 
Where T4 and T5 are brightness temperature of AVHRR 
channel 4 and channel 5. The formula assumes a constant 
surface emissivity of 0.96. 
6.2 Monthly composite 
The maximum value composite method (Holben, 1986) 
was applied to AVHRR NDVI and Ts 10-day composite 
data to get monthly composite data. The maximum value 
of NDVI and Ts were selected independently for every 
month. Ts responds both to short-term variations in 
energy balance related to rainfall events and changes in 
soil moisture, and to seasonal changes. The monthly 
composite of Ts data artificially removes the short time 
scale variations in 10-days composite Ts, leaving only the 
seasonal trend. It mainly includes lower frequency 
information, which is related to land cover types (Lambin 
and Ehrlich, 1995). The ratio of the maximum Ts and 
maximum NDVI ratio Ts/NDVI were then calculated for 
every month. In the actual calculation, Ts+273.15 was 
used for Ts, and NDVI+1 was used for NDVI in order to 
avoid zero value. 
6.3 Transformation of map projection and 
geometric registration 
The map projection of NOAA AVHRR 1-km Land Data 
Set is based on the Interrupted Goode Homolosine 
projection. Monthly NDVI data and the derived Ts/NDVI 
data based on this projection were transformed to 
latitude/longitude projection (Plate Carree Projection) 
with 30 second grid. The Digital Chart of the World 
(DCW) was used as reference of geometric registration. 
The vector data of seashore lines in the DCW was 
transformed to raster data with 30 second grid. By 
comparing NDVI and Ts/NDVI data with the 30-second 
DCW seashore line data, there are one pixel difference at 
most parts of Asia and about three or four pixels 
difference at some regions in high latitude. 
Then geometric registration was applied using 250 GCPs 
in the DCW. The positional accuracy after geometric 
registration was 0.5 pixel(one pixel: 30-second grid) in 
these 250 GCPs. After transformation of map projection 
and geometric registration, the following rectangular 
region were extracted to cover the whole Asia. 
Location of the center of the upper left pixel: 
25deg Omin 15sec east in longitude, 89deg 59min 45sec 
north in latitude 
Location of the center of the lower left pixel: 
25deg0min 15sec east in longitude, 14deg 59min 45sec 
south in latitude 
Location of the center of the upper right pixel: 
164deg 59min 45sec west in longitude, 89deg 59min 
45sec north in latitude 
Location of the center of the lower right pixel: 
164deg 59min 45sec west in longitude, 14deg 59min 
45sec south in latitude 
The pixel numbers of the extracted region is 20,400 by 
6.4 Dataset preparation 
By the data preprocessing described above, the following 
data were prepared for the classification. 
- Ts/NDVI: seven monthly data from April to October 
- Maximum NDVI: the maximum monthly data from 
April 1992 to March 1993 
- Minimum NDVI: the minimum monthly data from 
April 1992 to March 1993 
- Digital elevation data 
All these data are coregistered in 30-second grid in 
Land cover classification was done by the following steps. 
(1) Clustering of monthly Ts/NDVI from April to 
(2) Finding classification rules for decision tree method 
(3) Classification by decision tree method 
(4) Post-classification modification 
Clustering by ISODATA was applied to monthly 
Ts/NDVI from April to October 1992. One hundred 
clusters were obtained as a result of clustering. Rules for 
decision tree method were found by comparing cluster

Note to user

Dear user,

In response to current developments in the web technology used by the Goobi viewer, the software no longer supports your browser.

Please use one of the following browsers to display this page correctly.

Thank you.