If the
bute-
ts of
ed to
ploit
class
tored
their
V, O,
,V,0)
) and
CROP CLASSES:
AG: START WITH AGRICULTURAL LANDS.
AG1: NON PRODUCTIVE LANDS.
AG211: TIMBERLAND.
AG2121: BRUSHLAND.
AG2122:GRASSLANDS.
AG221: FALLOW LANDS.
AG22211 : ORCHARDS.
AG22212:VINE AND BUSH CROPS.
AG22221:ROW CROPS.
AG222221:IRRIGATED PASTURE LANDS.
AG AG2 s AG212
AG22
AG222^ AG2221
AG2121
AG2122
AG2222
AG22222
AG222221
AG222222
AG222222:CONTINUOS COVER CROPS, SMALL GRAINS, HAY ETC.
CLUES FOR INTERPRETATION:
AG1:VEGETATION & SOIL ABSENT OR OBSCURED BY ROCK ETC.
AG2:VEGETATION CLEARLY DISCERNIBLE ON PHOTOGRAPH.
AG21:CULTIVATION PATTERN ABSENT; FIELD BOUNDARIES IRREGULARLY SHAPED.
AG22:CULTIVATION PATTERN ABSENT; FIELD BOUNDARIES REGULARLY SHAPED.
AG211: TREES PRESENT, COVERING MOST OF THE GROUND.
AG212: TREES ABSENT OR WIDELY SCATTERED, GROUND COVERED BY LOW LYING
VEGETATION.
AG2121:CROWNS OF INDIVIDUAL PLANTS DISCERNIBLE, TEXTURE COARSE AND
MOTTLED.
«ETC.
Figure 6: A Decision Tree Representation of the Dichotomous Photo Interpretation
Key for Crop Classification (Source, E.G. Mtalo, 1990, page 188).
S VO
(s, V, o) ————M (V, O) (s.v,0) — (S)
V SO
(s, v, o) ——— Bs (S, O) (s,v,0) — (V)
O sv
(v0 — y (S, V) (svo — pp (0)
SVO
(s, v, o) — (True, False)
Figure 7: Basic Operators of the LEARN Subsystem.
A typical query is processed as illustrated below:
Choose Option:
Enter Subject: BAND I
BAND 1 has spectral wavelength 0.45 to 0.52
BAND 1 has blue nominal spectral location.
BAND 1 application is sensing in the chlorophyll
absorption region.
BAND 1 has spectral wavelength 0.45 to 0.52 or
blue nominal spectral location.
Complex queries involving class relationships and limited
inheritance of class attributes by class members can also be
processed by the LEARN sub-system. During knowledge
compilation multiple assignments of attributes(on the same
relationship) to the same object are generalized into a single fact
203
, that is the statements, "plot A56 has area-50 ha"; "plot A56
has average slope-15 degrees"; "plot A56 has curve
number-80" ; are replaced by "plot A56 has area-50 ha or
average slope-15 degrees or curve number-80". Conversely,
occurrences of multiple facts bearing the same relationship to a
single attribute are replaced by a single expression in which the
"subject" consists of individual subjects concatenated by the
"or" operator.
3.3 Processing and Manipulation of Fuzzy
Knowledge.
It is an established fact that human beings often express
knowledge in fuzzy terms(Zadeh ,1989). In certain fields of
application experts rely to a large extent on fuzzy, non-precise
facts and information during the solution of complex problems.
This is also true in the soil erosion domain where crucial inputs,
such as, soil characteristics, plant cover, topography, erosion
state etc. are often expressed in fuzzy non-precise terms.
Processing of fuzzy knowledge by computer systems poses
three important problems, specifically, the representation and
storage of non-precise (fuzzy) information and data,
representation of the uncertainty inherent in fuzzy information
and data, and retrieval and analysis of fuzzy information and
data. Conventional databases cannot store or query fuzzy
information since they impose a strict format for data entry and
query. Partial solution to the problem of fuzzy queries has
however been achieved by the extension of the relational data
model as in Kandel (1986). Although such solutions enable
processing of fuzzy queries they still do not permit the storage
of fuzzy data such as for example "terrain slope-slightly less
than 15 degrees".