Rokos, Demetrius
san 2 ain of city | e Application of photo-interpretation methods and techniques for the recognition of
x: plans linear (private roads, fences, etc.) and/or surface (buildings, building plots, etc.)
T AT. | patterns on air-photos.
e Application of edge detection methods on satellite imagery for the recognition of
E linear (private roads, fences, etc.) patterns.
| * Application of classification methods on satellite imagery for the recognition of
surface (buildings, building plots, etc.) patterns.
Femoic * Application of texture analysis on satellite imagery for the recognition of surface
(KVR, ; : (buildings, building plots, etc.) patterns.
photos 3 | Fencing of private | e Application of photo-interpretation methods and techniques on air-photos for the
forests and forest recognition of linear patterns (fences).
areas e Application of edge detection methods on very high resolution satellite imagery for
the recognition of linear patterns (fences).
4 | Illegal building e Application of photo-interpretation methods and techniques on a time series of air-
s, air- photos.
rns. e — Application of classification methods on IKONOS multispectral data.
spatial e Application of texture analysis algorithms on a time series of very high resolution
panchromatic satellite imagery ( KVR1000, IKONOS images).
5 | Fragmentation e Application of photo-interpretation methods and techniques for the recognition of
)T XS, linear objects (fences) on air-photos.
s taken e Application of edge detection methods on very high resolution remote sensing images
(e.g. KVR1000 images, IKONOS) for the recognition of linear objects (fences).
| e Application of color composites and/or classification methods on high resolution
T TM, remote sensing images (e.g. SPOT XS, TM images) to recognize surface/spatial
spectral patterns with different spectral signature from the surrounding area.
s taken 6 | Quarries e Application of photo-interpretation methods and techniques on a time series of air-
a photos.
before e Application of color composites on a time series of remote sensing data in order to
high identify quarries and if possible, to determine whether they are active or not.
sensmg Application of the Maximum Likelihood classification on remote sensing data.
R1000, Application of change detection algorithms to monitor changes in quarries.
"photos 7 | Reforestation of|e Application of Color Compositions on satellite imagery in order to identify burnt
burnt forest areas forest areas.
e Application of classification algorithms on remote sensing data in order to identify
burnt forest areas.
e Application of change detection algorithms to indicate temporal changes in forests
tions and forest areas. ; :
could be 8 | Building e Application of photo-interpretation methods and techniques on the air-photos taken
Associations before the year 1974.
e Application of texture analysis algorithms on very high resolution remote sensing
images and/or air-photos taken after the year 1974 to recognize surface objects
(buildings) and their changes during the last years.
ET e Application of change detection algorithms on two data sets (air-photos, KVR1000
taken in images, etc.) to indicate temporal changes in Building Associations.
Table 4. Image Processing requirements
cent air- :
; natural 5 GREEK FOREST LAW: ELCM SPECIFICATIONS
identify 5.1 ELCM specifications
All the above information on legal and remote sensing requirements, as well as image processing methods and
data to techniques, are combined to produce the optimized specifications for the ELCM concerning the Greek Forest Law
(table 5). Seven layers corresponding to the seven thematic categories of Forest Law infringement have been defined
for this ELCM. For each layer, conditioned areas arise from the category that concerns the forests and forest area
characterization (category 1) and potential breach areas arise from the analysis of a thematic category concerning law
infringement (categories 2-7).
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B7. Amsterdam 2000. 1273
