1. Attempt in automatic digitization of glaciated areas in Lombardy
Elia Lipreri
Alpine Glaciology and Climatology - UNIMI

2. Workflow Bibliographic research (recent publications) Work planning
Data acquisition
Creation of a digital catalog
Critical analysis of the raster
Auto-digitization of glacial perimeters and error calculation
Final data processing
Results and conclusions

3. DIGITIZATION IN GIS
The process of converting the geographic features on an analog map into digital format. (ESRI GIS Dictionary).

4. DIGITIZATION IN GIS
MANUAL
AUTOMATIC
SEMI-AUTOMATIC
Digitization of Hannover town plan scale 1: (Illert, 1990).

5. DIGITIZATION IN GIS: MANUAL
High resolution orthophotos: from 0,5 m to 0,125 m!!! (aerial photos or drones)

6. CLASSIFICATION METHOD
GLACIERS AND GIS
CLASSIFICATION METHOD
SUITABLE TERRAIN TYPE
manual digitization
any
spectral band ratio and threshold
clean glacier ice and snow
normalized difference snow index
geomorphometric-based methods
debris-covered glaciers
thermal band methods
clean or lightly debris covered glaciers
Summary of glacier outline extraction methods (Raup et al., 2007).

7. MANUAL DIGITIZING OF A GLACIER
Pizzo Scalino (SO). Ortofoto 2012.

8. MANUAL DIGITIZING OF A GLACIER
Pizzo Scalino (SO). Ortofoto e shapefile 2012.

9. STUDY AREA

10. DATA SOURCE: http://earthexplorer.usgs.gov/
Filters: cloud cover under 10%, data taken from July (the end of) to August.

11. DATA SOURCE: http://earthexplorer.usgs.gov/

12. DATA SOURCE: http://earthexplorer.usgs.gov/

13. THERMAL BANDS: Landsat 4-5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+)
Wavelength
Useful for mapping
Band 1 - blue
Bathymetric mapping, distinguishing soil from vegetation and deciduous from coniferous vegetation
Band 2 - green
Emphasizes peak vegetation, which is useful for assessing plant vigor
Band 3 - red
Discriminates vegetation slopes
Band 4 - Near Infrared
Emphasizes biomass content and shorelines
Band 5 - Short-wave Infrared
Discriminates moisture content of soil and vegetation; penetrates thin clouds
Band 6 - Thermal Infrared
Thermal mapping and estimated soil moisture
Band 7 - Short-wave Infrared
Hydrothermally altered rocks associated with mineral deposits
Band 8 - Panchromatic (Landsat 7 only)
15 meter resolution, sharper image definition

14. AUTOMATIC DIGITIZING OF GLACIERS

15. AUTOMATIC DIGITIZING OF GLACIERS

16. AUTOMATIC DIGITIZING OF GLACIERS

17. AUTOMATIC DIGITIZING OF GLACIERS
For Landsat 7 ETM+ spectral ranges: Debris-covered ice delineation for valley glaciers: The debris is transported by the general down-slope movement of a glacier towards the terminus and is deposited in the glacier forefield1. If local surface slope is too high, debris usually slides farther down until a more gentle slope allows accumulation. (Paul et al., 2004).
ICE
Fig. Schematic cross-section of typical debris-covered glaciers.
1 The region between the current leading edge of the glacier and the moraines of latest maximum

18. SLOPE THRESHOLD
(i.e. in Switzerland = 24°)

19. Debris cover All study area in 2011:
Disgrazia
Ventina
Predarossa
All study area in 2011:
Debris cover (in grey) = 13,2% (4500 pixels)
«Clean» ice (in blue) = 86,8% (29574 pixels)
According to
Alifu (2013)

20. AUTOMATIC DIGITIZING OF GLACIERS
In ArcMap, Image Analysis: Arithmetic function

21. Results LIGHT GRAY = fine debris covered glaciers
DARK GRAY/BLACK = ice

22. POST-PROCESSING
I rejected all the polygons under 2000 square meters in order to remove all the features that represented just snow not melted during the hot season (so I suppose that there’s was no plurennial ice under there).
I calculated the error using the ArcGIS tool “BUFFER” for any features. The resolution of the satellite images is 30 m, so:
Linear unit = 15 meters (left) and 15 meters (right)
End type = FLAT (because it derives from a automatic work)
Dissolve type = ALL (it simplifies the work)

23. GLACIALIZED AREA (SQ M)
RESULTS
YEAR
DATE
GLACIALIZED AREA (SQ M)
GLACIALIZED AREA (km2)
BUFFER AREA
ERR %
1984
25/07/1984
149,14
16,28%
1988
13/08/1988
99,25
12,13%
2003
30/07/2003
80,37
12,42%
2004
01/08/2004
86,40
13,51%
2007
25/07/2007
71,74
8,74%
2009
31/08/2009
77,50
16,10%
2010
25/08/2010
80,86
16,52%
2011
21/08/2011
70,00
10,95%
(manual)
39,09
382136
0,98%
(manual) 2003
32,14
31013
0,10%
(manual) 2007
29,76
30120
(manual) 2012
27,82
61391
0,22%

24. RESULTS

25. RESULTS: AUTO vs. MANUAL
YEAR
DATE
GLACIALIZED AREA (SQ M)
GLACIALIZED AREA (km2)
BUFFER AREA
ERR %
1984
25/07/1984
149,14
16,28%
1988
13/08/1988
99,25
12,13%
2003
30/07/2003
80,37
12,42%
2004
01/08/2004
86,40
13,51%
2007
25/07/2007
71,74
8,74%
2009
31/08/2009
77,50
16,10%
2010
25/08/2010
80,86
16,52%
2011
21/08/2011
70,00
10,95%
(manual)
39,09
382136
0,98%
(manual) 2003
32,14
31013
0,10%
(manual) 2007
29,76
30120
(manual) 2012
27,82
61391
0,22%

26. RESULTS: AUTO vs. MANUAL
YEAR
DATE
GLACIALIZED AREA (SQ M)
GLACIALIZED AREA (km2)
BUFFER AREA
ERR %
1984
25/07/1984
149,14
16,28%
1988
13/08/1988
99,25
12,13%
2003
30/07/2003
80,37
12,42%
2004
01/08/2004
86,40
13,51%
2007
25/07/2007
71,74
8,74%
2009
31/08/2009
77,50
16,10%
2010
25/08/2010
80,86
16,52%
2011
21/08/2011
70,00
10,95%
(manual)
39,09
382136
0,98%
(manual) 2003
32,14
31013
0,10%
(manual) 2007
29,76
30120
(manual) 2012
27,82
61391
0,22%

27. RESULTS: AUTO vs. MANUAL
Starting
Glaciated area (m2)
Loss of
% Error
Loss %
Glaciated area
Loss % per year
Automatic ( )
11,64%
29,47%
1,28%
Manual ( )
0,66%
28,83%
1,34%

28. PROS & CONS Faster analysis
More frequency of data (and much more of that in the future)
Data starts from 1982 (instead of high resolution orthophotos that starts later, in this area)
Many applications (in flow velocities, climate changes, land cover studies, etc.)
Lower resolution (and higher error too)
Needing of manual corrections (in all methods I read)
Very bad results in studying small glaciers (because of the high buffer of error)
No applications in glaciers’ inventory (in Italy)

29. BIBLIOGRAPHY
Alifu, H. & Tateishi, R. (2013), Mapping of debris-covered glacier using combination of Landsat band ratio imagery and digital elevation model, in 'The conference of The Remote Sensing Society of Japan'. 
Illert, A. (1990), 'Automatic digitization of large scale maps', ACSM ASPR5,
Paul, F.; Huggel, C. & Kääb, A. (2004), 'Combining satellite multispectral image data and a digital elevation model for mapping debris-covered glaciers', Remote sensing of Environment 89(4),
Raup, B.; Kääb, A.; Kargel, J. S.; Bishop, M. P.; Hamilton, G.; Lee, E.; Paul, F.; Rau, F.; Soltesz, D.; Khalsa, S. J. S. & others (2007), 'Remote sensing and GIS technology in the Global Land Ice Measurements from Space (GLIMS) project', Computers & Geosciences 33(1),
Shukla, A. & Ali, I. (2016), 'A hierarchical knowledge-based classification for glacier terrain mapping: a case study from Kolahoi Glacier, Kashmir Himalaya', Annals of Glaciology 57(71), 1.
Smith, T.; Bookhagen, B. & Cannon, F. (2015), 'Improving semi-automated glacier mapping with a multi-method approach: applications in central Asia.', Cryosphere 9(5). 

30. Thank you for the attention!
“Negative results are just what I want. they’re just as valuable to me as positive results. I can never find the thing that does the job best until I find the ones that don’t.”
Thomas A. Edison
Thank you for the attention!