grass gl30

Assessment of Land Coverage maps

with GRASSMaria Antonia Brovelli, Monia Elisa Molinari

GEOlab

GEOlab, Politecnico di Milano – Como Campus, via Valleggio 11, 22100, Como, Italy

4th High Level Forum on Global Geospatial Information Management (UN-GGIM)Side Event: Globeland30 18-19 April 2016Caucus 11, UNECA Conference Center Addis Ababa, Ethiopia

Index

Comparison methodology

Datasets

Data Processing

Case study: Lombardy Region (Italy)

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A B C D

A fAA fAB fAC fAD

B fBA fBB fBC fBD

C fCA fCB fCC fCD

D fDA fDB fDC fDD

CONFUSION MATRIX

CLA

SSIF

ICA

TIO

N

The confusion matrix (Congalton and Green, 1999) is derived through a pixel by pixel comparison between a classified map and a reference one.

GROUND TRUTH (REFERENCE)

Comparison methodology3


A B C D

A fAA fAB fAC fAD fA+

B fBA fBB fBC fBD fB+

C fCA fCB fCC fCD fC+

D fDA fDB fDC fDD fD+

f+A f+B f+C f+D n

CLA

SSIF

ICA

TIO

N

f+i = marginal sum of column i (i = A, B, C, D)

fi+ = marginal sum of row i (i = A, B, C, D)

n = total number of cells

CONFUSION MATRIX


4



A B C D





f+A f+B f+C f+D n

CLA

SSIF

ICA

TIO

N

CONFUSION MATRIX DERIVED STATISTICS

(i = A, B, C, D)

Overall accuracy: evaluates the percentage of correctly classified pixels

𝑶𝑨 = 𝑓𝑖𝑖𝑛


5



A B C D





f+A f+B f+C f+D n


CLA

SSIF

ICA

TIO

N


(i = A, B, C, D)

Allocation disagreement: the amount of difference due to the less then optimal match in the spatial allocation of the categories (Pontius and Millones, 2011)

𝑨𝑫 = (2 ∗ 𝑚𝑖𝑛

𝑓+𝑖𝑛−𝑓𝑖𝑖𝑛,𝑓𝑖+𝑛−𝑓𝑖𝑖𝑛)

2

6



A B C D





f+A f+B f+C f+D n


CLA

SSIF

ICA

TIO

N


Quantity disagreement: the amount of difference due to the less than perfect match in the proportion of categories (Pontius and Millones, 2011)

𝑸𝑫= 𝑓+𝑖𝑛−𝑓𝑖+𝑛

2

(i = A, B, C, D)

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8


A B C D





f+A f+B f+C f+D n


CLA

SSIF

ICA

TIO

N


User’s accuracy: percentage of classified pixels which correctly match the ground truth

𝑼𝑨𝒊 =𝑓𝑖𝑖𝑓𝑖+

(i = A, B, C, D)


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Producer’s accuracy: percentage of pixels of ground truth correctly detected in the classified map

𝑷𝑨𝒊 =𝑓𝑖𝑖𝑓+𝑖

A B C D





f+A f+B f+C f+D n


CLA

SSIF

ICA

TIO

N


(i = A, B, C, D)


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Datasets: GlobeLand30 (GL30)

UTM32 UTM33

• FORMAT: RASTER

• YEARS: 2000, 2010

• REFERENCE SYSTEM: WGS84/UTM32 - WGS84/UTM33

Chen et al., 2014

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Datasets: Italian Land Cover Data

Friuli Venezia GiuliaLand Cover 2000 - 1:25’000

VenetoLand Cover 2007/2009 - 1:10’000

Emilia RomagnaLand Cover 2003 - 1:10’000Land Cover 2008 - 1:10’000

SardegnaLand Cover 1997/2000 - 1:25’000Land Cover 2003/2006 - 1:25’000

LiguriaLand Cover 2000 - 1:10’000Land Cover 2012 - 1:10’000

LombardiaLand Cover 1999/2000 - 1:10’000Land Cover 2012 - 1:10’000

Autonomous Province of BolzanoLand Cover 2000 - 1:10’000

Autonomous Province of TrentoLand Cover 2000 - 1:10’000

AbruzzoLand Cover 1997- 1:25’000

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Data Processing

REGIONGL30GL30

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Data Processing

RE-PROJECTION RE-PROJECTION

REGIONGL30GL30

v.proj r.proj

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Data Processing

REGIONGL30GL30


MERGING

v.proj r.proj

r.patch

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Data Processing

REGIONGL30GL30


v.proj r.proj

MERGING

r.patch

RASTERIZATION

v.to.rast

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Data Processing

REGIONGL30GL30


v.proj r.proj

MERGING

r.patch

RASTERIZATION

v.to.rast

RECLASSIFICATION RECLASSIFICATION

r.reclass r.reclass

CORINE LEGEND GLOBELAND30 LEGEND

Artificial surfaces Artificial cover

Agricultural areas Croplands

Forests and semi natural areasMixed forest, Broadleaf forest, Coniferous forest,

Grass, Shrub, Bare land, Permanent ice or snow

Wetlands Wetlands

Water bodies Water

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Data Processing

REGIONGL30GL30


v.proj r.proj

MERGING

r.patch

RASTERIZATION

v.to.rast

RECLASSIFICATION RECLASSIFICATION

r.reclass r.reclass

COMPARISON 1

r.kappa

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Lombardy Region case study

The procedure has been applied taking into account the influence on the comparison results of:

• Different rasterization resolutions and methods

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• Different classification schemes

I METHOD

II METHOD

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• Different classification schemes

• The co-location tolerance (Gallego, 2001) of GL30, which is equal to 70 m

All cells belonging to a buffer of 70 m around GL30 classes border were eliminated and the confusion matrix and statistics were calculated on the other pixels.

(Brovelli et al., 2015)

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DUSAF 1.1 DUSAF 2.0 DUSAF 2.1 DUSAF 3.0 DUSAF 4.0

YEAR 1999 - 2000 2005 - 2007 2007 2009 2012

SCALE 1:10’000 1:10’000 1:10’000 1:10’000 1:10’000

REF SYS WGS84/UTM32N WGS84/UTM32N WGS84/UTM32N WGS84/UTM32N WGS84/UTM32N

LEGEND CORINE CORINE CORINE CORINE CORINE

SOURCE Aerial photosAerial photos and regional databases

Aerial photos and regional databases



EXTENT whole region whole region whole region BS, MI, MB, SO, CR whole region

DUSAF Land Cover Database

DUSAF (“Use Categories of Agricultural and Forest Soils”) isa land cover database created in 2000-2001 within aproject promoted and funded by Lombardy Region.

The database provides a polygonal layer representing theland use and cover. Currently five releases are available.

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GL30 2000 – DUSAF1.1

OVERALL STATISTICS I METHOD

RESOLUTION OA [%] AD [%] QD [%]

NO BUFFER

30 m 86.39 11.61 2.00

30 m (prevalence) 86.50 11.52 1.98

5 m 86.34 11.66 2.00

BUFFER

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GL30 2000 – DUSAF1.1



NO BUFFER

30 m 86.39 11.61 2.00

30 m (prevalence) 86.50 11.52 1.98

5 m 86.34 11.66 2.00

BUFFER

30 m 90.53 7.40 2.07

30 m (prevalence) 90.64 7.33 2.03

5 m 90.83 7.10 2.07

• No significant changes in the results with different input dataset resolutions• The removal of the cells influenced by the co-location tolerance (buffer case)

leads to an increase of the OA

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GL30 2000 – DUSAF1.1



NO BUFFER

30 m 86.39 11.61 2.00

30 m (prevalence) 86.50 11.52 1.98

5 m 86.34 11.66 2.00

BUFFER

30 m 90.53 7.40 2.07

30 m (prevalence) 90.64 7.33 2.03

5 m 90.83 7.10 2.07

C1: artificial surfaces C2: Agricultural areas C3: Forests and semi natural areas C4: wetlands C5: water bodies

PER-CLASS AGREEMENT MEASURES (30 m)

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GL30 2000 – DUSAF1.1

OVERALL STATISTICS II METHOD


NO BUFFER

30 m 77.18 17.97 4.85

30 m (prevalence) 77.30 17.84 4.85

5 m 77.13 18.02 4.85

BUFFER

30 m 82.03 12.82 5.15

30 m (prevalence) 82.15 12.74 5.10

5 m 82.43 12.36 5.21

9.26.3 2.85

Differences with respect the I method

• The introduction of a greater level of classification detail entails an increase of both the allocation and quantity disagreement values.

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GL30 2000 – DUSAF1.1



NO BUFFER

30 m 77.18 17.97 4.85

30 m (prevalence) 77.30 17.84 4.85

5 m 77.13 18.02 4.85

BUFFER

30 m 82.03 12.82 5.15

30 m (prevalence) 82.15 12.74 5.10

5 m 82.43 12.36 5.21


9.26.3 2.85

Differences with respect the I method

C31: forests C32: scrub and herbaceous vegetation C330: open space C335: glaciers /permanent snow

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GL30 2010 – DUSAF4.0



NO BUFFER

30 m 79.88 16.12 4.00

30 m (prevalence) 80.01 16.09 3.89

5 m 79.83 16.17 4.00

BUFFER

30 m 85.06 10.74 4.19

30 m (prevalence) 85.20 10.68 4.20

5 m 85.57 10.19 4.24


2.7

1.85 0.85Differences with respect the year 2000

2000 value

C31: forests C32: scrub and herbaceous vegetation C330: open space C335: glaciers /permanent snow

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GL30 - Italian regions

*OVERALL STATISTICS I METHOD - 2000

* No buffer 30 m resolution case

*OVERALL STATISTICS I METHOD - 2010

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GL30 - Italian regions

*OVERALL STATISTICS II METHOD - 2010

*OVERALL STATISTICS II METHOD - 2000

* No buffer 30 m resolution case

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Conclusions

• The thematic accuracy assessment performed between GlobeLand30 and Italian regional land cover maps shows overall accuracy varying between:

81% - 92% (I method)62% - 81% (II method)

• The disagreement can be due to the fact that the images were taken on different dates, to the different thematic classification and resolution.

• Removing the part of the disagreement due to the co-location tolerance the overall accuracy increases:

84% - 96 % (I method)65% - 86% (II method)

• In addition, in most of literature the reference data has been used as an accurate representation of the reality but they may contain errors.

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References

• Brovelli, M.A., Molinari, M.E., Hussein, E., Chen, J., Li, R. The First Comprehensive Accuracy Assessment of GlobeLand30 at a National Level: Methodology and Results. Remote Sens. 2015, 7(4), 4191-4212; doi:10.3390/rs70404191

• Chen, J.; Chen, J.; Liao, A.; Cao, X.; Chen, L.; Chen, X.; He, C.; Han, G.; Peng, S.; Lu, M.; et al. Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS J. Photogram. Remote Sens. 2014

• Congalton, R.G.; Green, K. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices; Lewis Publishers: Boca Raton, FL, USA, 1999; p. 137

• Gallego, J. Comparing CORINE Land Cover with a more Detailed Database in Arezzo (Italy). Towards Agri-Environmental Indicators; Topic report 6/2001 European Environment Agency 2001; European Environment Agency: Copenhagen, Danmark, 2001; pp. 118–125

• GRASS Development Team, 2015. Geographic Resources Analysis Support System (GRASS) Software, Version 6.4.5. Open Source Geospatial Foundation. http://grass.osgeo.org

• Pontius, R.G.; Millones, M. Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment. Int. J. Remote Sens. 2011, 32, 4407–4429

http://grass.osgeo.org/

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What is GRASS GIS

GEOlab, Politecnico di Milano – Como Campus

http://www.openstreetmap.org/

Geographic Resources Analysis Support System

• 1982: first developments carried out at USA/CERL (Construction Engineering Research Laboratory)

• 1985: version 1.0 was released

• 1998: development transferred to an international GRASS Development Team led by Markus Neteler

• 1999: GRASS 5.0 release under General Public License (GPL)

• 2016: stable long term release GRASS 7.0.3

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Download GRASS GIS


http://www.openstreetmap.org/https://grass.osgeo.org/download/

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Starting with GRASS GIS

1. GIS Database directory: directory containing all the GRASS GIS data

2. Location: it is a sort of data library for the region of interest. It contains datasets with the same coordinate system

3. Mapset: it is a location subdirectory where GIS maps are organized thematically or geographically

DATABASE

LOCATION 1 LOCATION 2 LOCATION N

PERMANENT MAPSET 1 MAPSET N

MULTI-USER

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Location

To create a Location click on New button in the GRASS GIS startup window.

Then, in the GRASS GIS Location window define:

The path to the GIS database directory

The name of the location

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Location




The name for the project location

The reference system

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Location






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Location






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Mapset

Once the location is created, the system requires information about:

region extents

mapset name

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GRASS GIS interface

Layer Manager Toolbar to manage displayed map

layers Menu bar with all GRASS GIS

functions Python shell

Map Display 2D and 3D view of the maps Tools for map navigation Tools for map analysis Map elements Tools for export and printing

Bash Shell It runs GRASS GIS modules without

GUI

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GRASS GIS modules

PREFIX CLASS FUNCTION

d.* display Visualization

db.* database Database management

g.* general General file operations

i.* image Image processing

ps.* postscript Map creation in Postscript

r.* raster Raster analysis

r3.* voxel Voxel analysis

v.* vector Vector analysis

t.* timeseries Temporal data processing

m.* miscellaneous Miscellaneous functions

GRASS is a very powerful GIS suite with over than 400 standard modules in the core version.

https://grass.osgeo.org/grass70/manuals/index.html

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Data import: v.in.ogr

v.in.ogr module converts OGR vector layers to GRASS vector map.

https://grass.osgeo.org/grass70/manuals/v.in.ogr

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Data import: r.in.gdal

r.in.gdal module imports GDAL supported raster file into a binary raster map layer.

https://grass.osgeo.org/grass70/manuals/r.in.gdal

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GRASS region

g.region module manages the boundary definitions for the geographic region.

The region is a geographic area with defined boundaries (N,W,S,E) and specific E-W and N-S resolutions of its smallest units (cells). Most of the raster and displays modules are affected by the current region settings.

https://grass.osgeo.org/grass70/manuals/g.region

Define the current region specifying coordinates and resolutions

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GRASS region

g.region module manages the boundary definitions for the geographic region.

The region is a geographic area with defined boundaries (N,W,S,E) and specific E-W and N-S resolutions of its smallest units (cells). Most of the raster and displays modules are affected by the current region settings.

Define the current region settings to match the extension of a raster/vector map

https://grass.osgeo.org/grass70/manuals/g.region

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Re-projection: v.proj / r.proj

v.proj / r.proj modules re-projects a vector / raster map from one location to the current location.

1. Create a location in the layer reference system

2. Import the layer into the location

3. Create the location in the reference system you want to re-project the layer into

4. Run v.proj (for vector map) or r.proj (for raster map) modules

https://grass.osgeo.org/grass70/manuals/v.proj.html

https://grass.osgeo.org/grass70/manuals/r.proj.html

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Merging: r.patch

r.patch module creates a composite raster map layer by using known category values from one (or more) map layer(s) to fill in areas of "no data" in another map layer.

https://grass.osgeo.org/grass70/manuals/r.patch.html

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Rasterization: v.to.rast

v.to.rast converts a vector map into a raster map. The resolution of the output raster map is the same of the current region.

https://grass.osgeo.org/grass70/manuals/v.to.rast.html

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Reclassification: r.reclass

r.reclass reclassifies a raster map based on category values.

https://grass.osgeo.org/grass70/manuals/r.reclass.html

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Matrix confusion calculation: r.kappa

r.kappa calculates error matrix for accuracy assessment of classification result.

https://grass.osgeo.org/grass70/manuals/r.kappa.html

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Matrix confusion calculation: r.kappa

r.kappa calculates error matrix for accuracy assessment of classification result.

https://grass.osgeo.org/grass70/manuals/r.kappa.html

Stats.txt

Confusion matrix

Commission and omission errors

Overall accuracy