B.2VegetationAttributeSymbols153
AREA(m2) PERIMETER(m) FTEMA VEG1 VEG2 TILLEGG1 TILLEGG2 KARTSIGN SAU STORFE
26214,33 1189,1 4351 2e 2e 2 2
7250,12 379,9 4351 2c x 2cx 1 1
380450,44 5773,72 4351 2c 2e x} v 2cx}/ev 1 1
16212,53 874,09 4351 3b s 3bs 3 3
84689,94 1785,13 4351 2c 2b v} } 2cv}/b} 1 1
42837,05 1448,21 4351 2c 1b x 2cx/1b 1 1
11332,58 521,25 4351 1b 1b 2 2
25330,33 897,23 4351 9c 3b s 9c/3bs 1 2
43724,45 960,27 4351 2c 2e x x 2cx/ex 1 1
288425,72 3756,93 4351 2c v} 2cv} 1 1
492,06 109,09 4351 9a 9a 1 1
2648,62 241,21 4351 9c 9c 1 2
35310,98 1261,04 4351 3b 9a s 3bs/9a 3 3
87122,13 2135,26 4351 2c 2e x x 2cx/ex 1 1
65379,5 1325,93 4351 2e x 2ex 1 1
34012,02 1195,55 4351 2e 3b s 2e/3bs 2 2
33150,78 1273,7 4351 9a 2e 9a/2e 1 1
7710,34 354,02 4351 1b 1b 2 2
70996,03 1603,04 4351 2c 2e x x 2cx/ex 1 1
38106,5 792,24 4351 2e 3b s 2e/3bs 2 2
48877,43 1987,68 4351 9c 9a 9c/a 1 2
51928,6 1631,44 4351 2c x 2cx 1 1
39747,43 1167,32 4351 2e v 2ev 2 2
Table B.1: This is an excerpt from the attribute table of the Venabygd vegetation vector file produced by NIJOS. Each line corresponds to a polygon which has a number of attributes associated with it (identified in the columns). FTEMA is the SOSI code given for the area type.
Tillegg is the additional attribute sign. These signs are described in the dataset chapter. Kartsign is the sign symbol written on the map for that particular polygon. Sau is the code given for the grazing quality of the polygon for sheep, and Strofe is the grazing quality given to the polygon for cattle.
Figure B.1: This table describes all the additional attributes that a vegetation type can have.
A vegetation type can have up to 2 additional attributes. Copied from Bryn and Rekdal (2002).
Appendix C
Comparison Tables: Airphotos, unsupervised & supervised
classification, satellite image
omparisonTables:Airphotos,unsupervised&supervisedclassification,satelliteimage
Aerial Photo Satellite Image Unsupervised Supervised
2
Lavhei (2cx)>50%lavdekning Very pale area with touches of light red
Majority: 47, 51, 56, 60, 52, 46, 45, 44, 41
Minority: 40, 57
Almost classified uniformly as Less Good
4
Rishei (2ex)>50%lavdekning The lav shows up as white in this band combination
Majority: 52, 58, 46, 44, 42, 40 Minority: 57, 38
Mottled coloured polygon giv-ing a divided classification.
ComparisonTables:Airphotos,unsupervised&supervisedclassification,satelliteimage157
Table C.1 Less Good Grazing – continued from previous page
Aerial Photo Satellite Image Unsupervised Supervised
5
Rishei (2ex)>50%lavdekning Fair amount of light blue in areal photo, and quite a pink satellite image. This is the same vegetation type as in example 4, but here the polygons look quite different crown coverage. Area has25− 50%lavdekning
Combination of various shades of green and pink
Majority: 40, 41, 39, 42, 44 Minority: 43, 46, 36, 56, 57
Classified as mostly Good qual-ity, but this polygon is of Less Good grazing quality.
omparisonTables:Airphotos,unsupervised&supervisedclassification,satelliteimage
7
Bl˚abærbjørkeskog (4b}) Almost homogenous colour in the satellite image although you can see the dark speckle from the trees in the aerial photo
Majority: 45, 43 Minority: 41, 46
Very homogenous unsupervised classification
Very homogenous for a super-vised classification, this could provide good training data for Less Good.
8
Lav og lynrik furuskog (6a*). At least 25% Gran tree in this area
Majority: 25, 27, 26, 29, 31, 32, 34, 35, 36, 37
Minority: 28, 22
A very dark green polygon to be of Less Good quality. Classi-fied as 25% Spruce (*) in with the Pine. Inhomogeneous clas-sifications.
ComparisonTables:Airphotos,unsupervised&supervisedclassification,satelliteimage159
Table C.1 Less Good Grazing – continued from previous page
Aerial Photo Satellite Image Unsupervised Supervised
9
lav og lynrik furuskog (6a&) Very mottled satellite image Majority: 32, 34, 35, 36, 43, 28, 29, 30, 37
Minority: 40, 41, 42, 38, 39, 57, 46, 26, 44
Unclear supervised classifica-tion. Certainly not a good indi-cation of Less Good for classifi-cation purposes.
10
Majourity lav og lynrik granskog and 25-50% lavdekn-ing, Minority Bl˚abærgranskog (7av/7b)
Very dark purples in this poly-gon
Majority: 28, 22, 25, 26, 30 Minority: 29, 32
Good supervised classification, most pixels being shown as Less Good, with a few pixels classi-fied as Very Good.
omparisonTables:Airphotos,unsupervised&supervisedclassification,satelliteimage
11
Fattig Sumpskog (8c&) Very homogenous looking satel-lite polygon
Majority: 55, 49, 38 Minority: 33, 57
Satellite image looks very ho-mogeous but these classification results show otherwise. No pix-els were classified as Less Good Quality. Was classified as Good, and Very Good
12
Rismyr (9a) Can distinctly see the road that was masked out on the satellite image on the aerial photo
Majority: 59, 49, 58, 52, 46 Minority: 40, 41, 42, 55, 57, 33
Very complex polygon. This is verified by the areal photo, satellite image and unsuper-vised classification results.
ComparisonTables:Airphotos,unsupervised&supervisedclassification,satelliteimage161
Table C.2: Selected Polygons - Good Grazing Quality
Aerial Photo Satellite Image Unsupervised Supervised
1
Right side: Rishei / Lavhei with > 50% lavdekning (2e/2cx)
Both polygons look fairly similar in the aerial photo and satellite image
Majority: 6, 41, 43, 46 Minority: 42, 38, 44, 45, 44
Classified as a mixture of Less Good and Good
Left side: Rishei (2e) Majority: 56, 51, 41, 45,
43
Minority: 44, 60, 46, 39, 30
Both polygons show a similar classification pat-tern
omparisonTables:Airphotos,unsupervised&supervisedclassification,satelliteimage
Aerial Photo Satellite Image Unsupervised Supervised
2
Rishei (2e) Same type as example 1, but here the satellite image shows varying reflectances and more green (i.e. near infrared (NIR))
Majority: 41, 58, 44, 57, 42, 46, 56
Minority: 43, 39, 45, 52
Poor classification, mix-ture of all three classes.
ComparisonTables:Airphotos,unsupervised&supervisedclassification,satelliteimage163
Table C.2 Good Grazing – continued from previous page
Aerial Photo Satellite Image Unsupervised Supervised
3
Bl˚abærbjørkeskog (4b) Majority: 38, 36, 40, 44,
57, 55, 42, 34, 37
Minority: 35, 41, 39, 43, 46, 33, 32, 58
This showed fairly good results, with most pixels receiving the correct Good quality class name.
5
Majority Rik sumpskog and Minority Grasmyr which is kalkkrevende myrvegetasjon (8d&/9ck)
Nice homogenous polygon Majority: 31, 34, 33, 55, 38, 49, 57
Minority: 32, 39
Quite homogeous, and ob-viously a good example of NIJOS style Good grazing quality area.
omparisonTables:Airphotos,unsupervised&supervisedclassification,satelliteimage
Aerial Photo Satellite Image Unsupervised Supervised
6
Bl˚abærbjøkeskog (4b) Same vegetation type as example 3, although this polygon is darker in both aerial photo and satellite image
Majority: 29, 26, 27, 22, 24, 31, 34, 28
Minority: 32, 33, 25, 54, 48, 38, 21
Mixed classification
7
Bl˚abærgranskog (7b&) Can see a large man made cleared area that has been included in the polygon.
Majority: 31, 42, 28, 29, 26, 32, 34, 58, 30, 37, 38, 57, 27, 24
Minority: 33, 35, 36, 55, 49, 22, 21, 20, 19, 18, 17
Mixed classification
ComparisonTables:Airphotos,unsupervised&supervisedclassification,satelliteimage165
Table C.2 Good Grazing – continued from previous page
Aerial Photo Satellite Image Unsupervised Supervised
8
Grasmyr which has >
50% grass coverage and
> 50% coverage of Vier (9cgs)
Majority: 40, 42, 58, 57, 55
Minority: 49, 63, 38, 39, 33
Complex polygon. Super-vised classification results don’t show a a good repre-sentation of Good grazing.
omparisonTables:Airphotos,unsupervised&supervisedclassification,satelliteimage
Aerial Photo Satellite Image Unsupervised Supervised
1
Top: Hgstaudeeng with
> 50% vier coverage and grasmyr (Veg3bs/9c!) Middle: Hgstaudeeng with > 50% vier cov-erage (3bs) Bottom:
Hgstaudeeng with > 50%
vier coverage and Rishei (3bs/2e)
The area that had a stream running through has been masked out for classifica-tion. Can be seen on the aerial photo.
Majority: 23, 24, 27, 31, 32, 53, 55;
Minority: 20, 22, 55, 43
Classification was fairly uniformly Good grazing, however this polygon is defined as Very Good grazing, so that is incor-rect.
ComparisonTables:Airphotos,unsupervised&supervisedclassification,satelliteimage167
Table C.3 Very Good Grazing – continued from previous page
Aerial Photo Satellite Image Unsupervised Supervised
3
Rishei with > 50% grass coverage (2eg)
Polygon contains stream, roads and houses so most was actually masked out for classification. The sur-rounding areas of these are often affected however and can affect classification
Majority: 23, 24, 27, 31, 32, 33, 53, 57;
Minority: 20, 43, 47
Mixed classification, mostly which is Good.
5
Bl˚abærbjørkeskog with >
50%grass coverage (4bg)
Majority: 19, 20, 21, 23, 33, 31
Minority: 21, 53
Classified almost uni-formly as Good, but this polygon is of Very Good grazing quality.
omparisonTables:Airphotos,unsupervised&supervisedclassification,satelliteimage
Aerial Photo Satellite Image Unsupervised Supervised
6
Oreskog (4e) A large polygon with
many different colours.
Majority: 14, 15, 16, 17, 19, 29, 33, 45, 46, 53 Minority: 33, 14, 29
A Large polygon to be classified as one vege-tation type, and shows a large range of values.
Classification showed pix-els from all grazing quality classes.
7
Engfuruskog (6c) Majority: 53, 31, 29
Minority: 20, 23, 33
Classified as mostly Good with only a few spots of Very Good.
Appendix D
Acronyms & Abbreviations
asr ’at satellite radiance’
DEM digital elevation model DN digital number
EC European Commission EM electromagnetic
ESA European Space Agency ETM Enhanced Thematic Mapper G Good
GIS geographic information system
GMES Global Monitoring for Environment and Security JM Jeffries-Matusita
LAI leaf area index LG Less Good
masl Meters above sea level MIR Mid Infrared
MLC Maximum Likelihood Classifier
MODIS MODerate Resolution Imaging Spectroradiometer MSS Multispectral Scanner
NDVI normalised difference vegetation index NIJOS Norwegian Institute for Land Inventory NIR near infrared
NPS National Parks Service
NR the Norwegian computing centre ONP Nature Protection Observatory RGB red, green, blue
ROI region of interest SWIR short wave infrared TIN triangular irregular network TM Thematic Mapper
USGS United States Geological Survey VG Very Good
VMP Vegetation Mapping Program