Full text: Remote sensing for resources development and environmental management (Volume 1)

27 
sments by birds and 
adjoining farmland 
Logy. 16:349-358. 
Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986 
A GIS-based image processing system for agricultural purposes 
(GIPS/ALP) - A discussion on its concept 
J.Jin King Liu 
Remote Sensing Group, Energy and Mining Research/Service Organization, Industrial Technology Research Institute, Shin 
Chu, Taiwan 
AB^TRAC.. Taree major bottlenecks wnich hinder the development of a complete operational application 
of remote sensing tecnniques to the monitoring and survey in low to medium economic return agricultural 
areas are the data volume, classification accuracy and the geometric accuracy. 
A GIS-based image processing system for agricultural land use and land cover purposes (GIPS/ALP) is 
thus devised to resolve these difficulties. Instead of handling the whole lot of image data, this algorithm 
samples only a small fraction of them for processing and classification based on the polygon files of 
tne agricultural fields in the GIS. And, consequently, this approach dramatically reduces ti.e data volume 
;hich has to be processed even when spatial resolution is greatly improved. In addition, tne classification 
accuracy is considerably improved due to the purity of the pixels chosen and high accessibility of ancillary 
data available in GIS. -he field check strategy modified by this system can contribute the general accuracy, 
too. The geometric accuracy can be as good as that .in the GIS which has been claimed to be considerably 
higher than that can be derived from the most sophisticated corrected images. 
/ 
1. INTRODUCTION 
This paper is intended to discuss the basic concept 
for implementing a GIS-based image processing 
system for agricultural land cover and land use 
purposes (GIPS/ALP). Firstly, the bottlenecks 
in image classification which have to be resolved 
for economical application of remotely sensed 
data are briefly sketched. Secondly, the basic 
concepts of GIPS/ALP are parsed by detailing 
the implementing approaches. Finally, the unique 
advantages of this new approach are concluded. 2 
2. THE BOTTLENECKS IN IMAGE CLASSIFICATION 
Three major bottlenecks which hinder the development 
of a complete operational application of remote 
sensing techniques to the monitoring and survey 
in low to medium economic return agricultural 
areas are the data volume, classification accuracy 
and geometric accuracy. 
It is argued by Allan (1984) that the smaller 
the phenomena of concern and the more frequently 
they need to be monitored the less likely it 
is that the current remote sensing systems will 
provide useful data. This is especially true 
for the situation in Taiwan where the land use 
patterns are complex and the crop fields are 
rather small (usually less than 100 by 100 meters). 
The average accuracy obtained for the first level 
of land use/land cover classification (Anderson 
et al 1976) is 83%>, and it decreases to 58% for 
the second level (Tseng & Sung 1978).It is generally 
recognized that in most geographic information 
systems, remote sensing inputs do not represent 
the primary source of data (Marble & Puequet 
1983) because that remote sensing data themselves 
are not thematic and their derived or classified 
resulcs are generally less accurate in both position 
and class level. It would be delightful if the 
geometric accuracy of remote sensing outputs 
is comparable or equivalent to that in the data 
base of geographic information system. 
The previous discussion means that if both 
the classification and geometric results are 
to be more accurate to meet the needs for the 
management of agricultural lands in the environments 
such as Taiwan, spatial, and spectral resolution 
of the remote sensing systems should be greatly 
improved. Suppose these two conditions are realized, 
another issue will be accordingly raised. It 
is that the volume of data which has to be processed 
in a single project would be too large, in reality, 
the limitation of spatial, spectral and temperal 
resolution of remote sensing data for effective 
operational applications to agricultural purposes 
are basically lied in the data volume. This is 
manifested in the diagram by Allan (1984) which 
shows that at present stage the vr.iurae of remotely 
sensed data is too large to be economically handled 
for the purpose of crop discrimination. 
A GIS—based processing approach is thus devised 
to resolve these difficulties. Instead of handling 
the whole lot of image data, this algorithm samples 
only a small fraction of them on bas^s of the 
polygon file of the agricultural fields in '-he 
GIS. Further discussion is given in the following. 
3. THE BASIC CONCEPT OF GIS BASED IMAGE PROCESSING 
3.1 GIS and TSS data files 
One of the data files in geographic information 
system is the polygon file of which a polygon 
is an ordered set of coordinate points enclosing 
a crop filed. Each polygon can be referred by 
a point called a POLYCENTRE or FIELDCENTRE locatec 
at the geometric centre of the polygon as shown 
in figure la. In the other hand, the data files 
in remote sensing system are digital images. 
An image consists of discrete picture elements 
called PIXELS of which the location in space 
is indicated implicitly by the relative position 
of each pixel in the image as shown in figure lb. 
The GIS polygon file records a relatively 
permanent ground feature, i.e. the crop boundaries 
or cadastral boundaries. However, the content 
or the land use type of each polygon is changeable. 
The land cover types in GIS stand only for the 
condition of a particular time of a particular 
season in the past. Neverthless, what a land 
use planer or manager is interested in is the 
extent of each cover type of present time or 
in the near future as well as the extent of changes 
between between two certain times. This information 
can be obtained, if a sample of eacn crop field
	        
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