Classes Hectare % 
  
  
Dense Forest 734.49 9.37 
Less Dense Forest — 783.90 9,99 
Bushes 1,862.19 23.75 
Shrubs 1,103.58 14.07 
Paddy Fields 621.36 7.92 
Plantation 194.76 2.48 
Other Agricultural — 2,508.30 31.98 
Areas 
Water Bodies 33.12 0.42 
Roads 
Streams 
Settlements 
TOTAL 7,841.70 100.00 
4.2 Land Suitability maps : 
Various land suitability analysis were 
undertaken to output various land 
suitability maps. These maps served as 
input to the strategy formulation phase, 
The processes involved called for land 
suitability requirements for various crops, 
for example, corn and sorghum, pasture, 
paddy, mango, cassava and other root 
crops. The FAO Framework for Land 
Evaluation, 1976 was adopted based on 
crop requirement parameters for each 
ctop, and the four suitability classes were 
defined, namely, *S1", the highly suitable 
"S2", the moderately suitable, *S3", the 
marginally suitable, and “N”, the not 
suitable. Soil suitability maps for each of 
the dominant crops in the area were 
produce for further use as part of strategic 
planning of the main project. 
5. CONCLUSION AND 
RECOMMENDATIONS 
Remote sensing and geographic 
information systems technologies were 
used over a typical forest and agricultural 
area aiming at a sustainable development 
planning of the area under study. An 
interdisciplinary — team effort was 
employed and RS/GIS techniques were 
used as one of the input into strategic 
planning process. The contribution of 
these technologies is indispensable, 
especially when dealing with spatial 
information over a large geographic area. 
And, the manipulation of a large spatial 
613 
database can only be effectively done by 
means of GIS. 
There are a few recommendation points to 
be made regarding the use of RS/GIS in 
this study. Firstly, the classification 
scheme adopted after US Geological 
Survey which attempted to normalize 
airphoto interpretation result and digital 
classification of landsat TM data may not 
be accurate enough to be accepted as final 
result. A more acceptable common means 
should still be sought. The use of FAO 
Framework may be straight forward, but 
the parameters used as input into the 
process should be carefully picked, and 
weighting or scoring of each parameter 
should be carefully adjusted. A 
disciplinary understanding of specific 
processes would be required in this matter. 
6. ACKNOWLEDGMENT 
The authors express their sincere thanks 
for the cooperation and assistance from 
Drs. Apisit Eiumnoh and T.B. Suselo, and 
the “1994 Practicum Team” from the 
Asian Institute of Technology, Bangkok, 
Thailand. And, last but not the least, 
members of staff of Remote Sensing 
Laboratory (RSL) of AIT for the 
assistance rendered in the laboratory and 
related work. 
7. REFERENCES 
Andersson, J.R. et al. 1976, A Land Use 
and Land Cover Classification System for 
Use with Remote Sensor Data, 
Geographical Survey Professional Paper 
964, pp. 13-16, USA. 
Anuta, PE. and R.B. McDonald 1971. 
Crop Survey from Multi-band Satellite 
Photography Using Digital Techniques. 
Remote Sensing of Environment, vol. 2, 
pp. 53-67 
Aronoff, S. 1989. Geographic 
Information Systems : A Management 
Perspective. WDL Publications, Canada 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B4. Vienna 1996