journal of forest science

i i i
i
y = c0 + c1 + ... + cnxn l
l
i i
i
y = w y w
⎛
plying an interpolation method for the creation of hydrologically
correct DEM. This method is integrated in the
GRID module called Topogrid (see ESRI 1994).
In the process of skyline yarding distance calculation
we used the LATTICE (ESRI 1994) data structure together
with GRID structure for performing the complicated
operations of line-of-sight analysis.
REUTEBUCH (1988) sees the main problem of the
route-projection routines as being conceptual. Routines
rely on an algorithm rather than on the user’s visual abilities
and experience to guide the direction of the route.
Applying a fuzzy reasoning mechanism which selects
passing points shows that the direction of the route can
be fully controlled in the algorithm.
The fuzzy rule based system which automatically lays
out hauling roads is based on the fuzzy reasoning inference
mechanism which uses the architecture proposed in
TAKAGI et al. (1985, 1986, 1988 in TANAKA 1996).
Their fuzzy reasoning mechanism is classified as the direct
method and was devised using linear functions for
the relevant rules. uzzy reasoning method using linear
functions is based on the following architecture:
Rule i I x is A 1
⎞
⎜∑
⎟ ∑
⎝ ⎠
i1 and ... and x is A n in THEN
i = 1, 2, ...., r
where: i – the superscript of rule,
r – the total number of rules,
A (k = 1, 2, ... ,n) – fuzzy sets,
ik
x – an input variable,
k
yi – the output from the i-th rule,
c – the parameter of the consequence in the i-th rule.
ik
The fuzzy reasoning value is obtained from the weighted
mean:
µ Akx i
( k )
i=
1 i=
1
where: w i – the adaptability of the premise of the i-th rule
and given by the equation:
w i
n
Akx k
i = ∏ µ ( k )
= 1
where: µ – the membership value of the fuzzy
set A . ik
The reasoning rules which are used in our system were
constructed in YOSHIMURA (1997). We changed only the
premise and consequence part of the rules to adjust them
for the hauling road projecting conditions valid in Slovakia.
The fuzzy rule based system was programmed according
to the theoretical foundations described in ULLÉR
(1995).
The gradeline projection routine which connects two
passing points is based on a concept of path distance
model (GAO et al. 1996). The path distance model attempts
to control the grade between two adjacent cells
using a gradient factor. The gradient factor is a directional
one and it considers the effect of the value gradient
from a cell to its neighbour for the cost of travel between
two neighbouring cells. Other factors, such as rockiness,
environmental barriers, slope stability information, can
also be controlled by a path distance model. This control
is carried out by applying an isotropic cost surface which
expresses the costs of movement in terms of distance
equivalents.
CARTOGRAPHIC MODEL O SKYLINE
YARDING DISTANCES
The cartographic model of skyline yarding distances
can be regarded as a map of distances measured from
every cell as a length of the line of sight over the terrain
to the nearest road cell (distance between the centre of
the processed cell and the centre of the nearest road cell)
(TUÈEK, PACOLA 1999). The cartographic model is the
result of data processing methods used on the collection
of maps. Each layer conveys the following information:
digital elevation model – aspect grid – cost allocation
grid (defines for each cell the zone that achieves the minimum
distance in order to reach the cell) – line of sight
(evaluation of intervisibility) – slope distance of line of
sight as a result (ig. 1). Intervisibility between the processed
cells and the deviation of the line of sight from
(A) DEM (cell resolution 50 m); (B) Aspect grid; (C) Cost allocation
grid (controls the homogeneity under the line of sight);
(D) Intervisibility evaluation; (E) Slope distance of line of sight.
Note: NODATA – keyword indicating that the cell is not visible
from the point of observation
Fig. 1. Cartographic model of skyline yarding distances
308 J. FOR. SCI., 47, 2001 (7): 307–313