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

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

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

DIGITIZATION IN GIS MANUAL AUTOMATIC SEMI-AUTOMATIC Digitization of Hannover town plan scale 1:20.000 (Illert, 1990).

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

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).

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

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

STUDY AREA

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

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

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

THERMAL BANDS: Landsat 4-5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) Wavelength Useful for mapping Band 1 - blue 0.45 - 0.52 Bathymetric mapping, distinguishing soil from vegetation and deciduous from coniferous vegetation Band 2 - green 0.52 - 0.60 Emphasizes peak vegetation, which is useful for assessing plant vigor Band 3 - red 0.63 - 0.69 Discriminates vegetation slopes Band 4 - Near Infrared 0.77 - 0.90 Emphasizes biomass content and shorelines Band 5 - Short-wave Infrared 1.55 - 1.75 Discriminates moisture content of soil and vegetation; penetrates thin clouds Band 6 - Thermal Infrared 10.40 - 12.50 Thermal mapping and estimated soil moisture Band 7 - Short-wave Infrared 2.09 - 2.35 Hydrothermally altered rocks associated with mineral deposits Band 8 - Panchromatic (Landsat 7 only) 0.52 - 0.90 15 meter resolution, sharper image definition

AUTOMATIC DIGITIZING OF GLACIERS  

AUTOMATIC DIGITIZING OF GLACIERS  

AUTOMATIC DIGITIZING OF GLACIERS  

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

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

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)

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

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

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)

GLACIALIZED AREA (SQ M) RESULTS YEAR DATE GLACIALIZED AREA (SQ M) GLACIALIZED AREA (km2) BUFFER AREA ERR % 1984 25/07/1984 149143816 149,14 24285009 16,28% 1988 13/08/1988 99248566 99,25 12042120 12,13% 2003 30/07/2003 80374456 80,37 9979180 12,42% 2004 01/08/2004 86402956 86,40 11670125 13,51% 2007 25/07/2007 71741758 71,74 6269265 8,74% 2009 31/08/2009 77501096 77,50 12480506 16,10% 2010 25/08/2010 80858183 80,86 13356108 16,52% 2011 21/08/2011 69996039 70,00 7664414 10,95% (manual) 1990-91 39090000 39,09 382136 0,98% (manual) 2003 32142165 32,14 31013 0,10% (manual) 2007 29762036 29,76 30120 (manual) 2012 27820898 27,82 61391 0,22%

RESULTS

RESULTS: AUTO vs. MANUAL YEAR DATE GLACIALIZED AREA (SQ M) GLACIALIZED AREA (km2) BUFFER AREA ERR % 1984 25/07/1984 149143816 149,14 24285009 16,28% 1988 13/08/1988 99248566 99,25 12042120 12,13% 2003 30/07/2003 80374456 80,37 9979180 12,42% 2004 01/08/2004 86402956 86,40 11670125 13,51% 2007 25/07/2007 71741758 71,74 6269265 8,74% 2009 31/08/2009 77501096 77,50 12480506 16,10% 2010 25/08/2010 80858183 80,86 13356108 16,52% 2011 21/08/2011 69996039 70,00 7664414 10,95% (manual) 1990-91 39090000 39,09 382136 0,98% (manual) 2003 32142165 32,14 31013 0,10% (manual) 2007 29762036 29,76 30120 (manual) 2012 27820898 27,82 61391 0,22%

RESULTS: AUTO vs. MANUAL YEAR DATE GLACIALIZED AREA (SQ M) GLACIALIZED AREA (km2) BUFFER AREA ERR % 1984 25/07/1984 149143816 149,14 24285009 16,28% 1988 13/08/1988 99248566 99,25 12042120 12,13% 2003 30/07/2003 80374456 80,37 9979180 12,42% 2004 01/08/2004 86402956 86,40 11670125 13,51% 2007 25/07/2007 71741758 71,74 6269265 8,74% 2009 31/08/2009 77501096 77,50 12480506 16,10% 2010 25/08/2010 80858183 80,86 13356108 16,52% 2011 21/08/2011 69996039 70,00 7664414 10,95% (manual) 1990-91 39090000 39,09 382136 0,98% (manual) 2003 32142165 32,14 31013 0,10% (manual) 2007 29762036 29,76 30120 (manual) 2012 27820898 27,82 61391 0,22%

RESULTS: AUTO vs. MANUAL     Starting Glaciated area (m2) Loss of % Error Loss % Glaciated area Loss % per year Automatic (1988-2011) 99248566 29252526 11,64% 29,47% 1,28% Manual (1991-2012) 39090000 11269102 0,66% 28,83% 1,34%

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)

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, 919-- 933. 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), 510--518. 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), 104-125. 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). 

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!