International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004
Figure 5. Histogram depicting the occurrence (special extend)
of each class.
Thematic Class | ID " em ia H a St. dev
Lake 7 10.6 91 26.7
Cultivated 3 5 | 3602 1614 283.3
Cultivated 2 4 | 3706 1597 286.5
Cultivated 1 3 [| 409.1 1601 274.3
Cultivated 4 6 | 425.1 1601 316.2
Barren 2 481.1 1599 389.9
Mixed Forest | 8 | 481.6 1618 413.9
Fir Forest | | 9788 1610 357.9
Table 2. Elevation statistics for the thematic classes (presented
in increasing mean elevation order)
Thematic Class | ID en "co m St. dev
Lake 7 1.52 28 3.68
Cultivated 1 3 14.00 43 7.82
Cultivated 3 5 14.02 44 8.27
Cultivated 2 4 14.13 43 7.70
Cultivated 4 6 15.85 41 10.65
Barren 2 16.06 45 9.88
Mixed Forest 8 17.14 45 9.41
Fir Forest 1 23.20 43 6.95
Table 3. Gradient statistics for thematic classes (presented in
increasing mean gradient order)
The table 2 indicates that fir forest terrain class presents the
greatest height, while mixed forest and barren terrain classes
follow. The cultivated landcover types are developed in lower
elevation. These finding are in accordance with the local
geomorphometric conditions of the study area (high mountain
and limited plains). The most interesting finding of Table | was
the relative high elevation of the barren terrain class.
The table 3 indicates that fir forest and mixed forest present the
higher gradient values, followed by the barren terrain class. The
explanation given is that the forests are developed on the
highest areas which are quite steep in order to be protected by
human activity. Human activity (cultivated lands) is limited to
plain areas or to areas with lower gradient value. The high
gradient of barren terrain class indicates that these polygons are
of high risk relative to flash floods. That is why the relative
496
distribution of barren polygons with high gradient/height values
will be identified and studied in more detail in the following
section.
2.6 Title
A connected components labelling algorithm is applied to the
barren terrain class ( Figure 6).
Figure 6. Barren class
A total of 1314 objects (adjacent pixels of the barren terrain
class) were found with various size dimensions as it can be
observed in figure 7
[~ Graph Type —— r- Mode — 34 j- Summary Statistics ———
| E i idth - ; a
| @ BarGraph H # NonCumdative |] Class width: 1 dí: 23030
Ï © Line Graph i | Displaymin: 1 Actual min : 0
| "io ion I A | Displaymax: 1314 Actual max: 1314
Le || € Cumulative |
| € Area Graph il
| Mean: 572.403 Standard dev: 358.1068
Histogram ot 02avmnoedafos forepits. ccifore
-—1..:.100 7200 300. 400 500 600 700 $800 900 1,000 1,00 1200 1300 |
Figure 7. Object dimension for the barren class
Mean gradient and mean elevation as well as roughness (the
standard deviation of elevation) and local relief (minimum —
maximum elevation) were computed for each object of the
barren terrain class (see Table 4). Elevation is expressed in
meters and gradient in degrees.
Size in Mean Mean
ID i : ;
pixels elevation gradient
309 1327 1373.2 29.51
1 958 978.9 13.71
677 925 432.0 16.98
446 796 896.2 23.30
763 730 581.8 27.13
Inte