Training data selection (SCP)

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| Agricultural
 
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| Arable land
 
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| Bare soil
 
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| Pasture
 
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| Coniferous tree cover
 
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==Creating systematic sampling grid==
 
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* Use a processing toolbox model downloaded from here [http://www.gwdg.de/~hfuchs/qgis/urban_atlas_systematic_sample.model urban_atlas_systematic_sample.model] (Right click, Save as ..). Note, save the file with the extension '''*.model'''.
 
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* Open the {{mitem|text=Processing --> Toolbox}} and {{mitem|text= Models --> Tools --> Add model from file}}. Load the previously downloaded model.
 
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* The model should appear in the Models tab.
 
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* Double click to open the model.
 
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* Specify an original ''European Urban Vectors'' layer for Göttingen: ''geodata_lab01\vector\DE021L1_GOTTINGEN\Subset-Goe_DE021L1_GOTTINGEN_UA2012_UTM32N.shp'' or download data from  other [https://land.copernicus.eu/local/urban-atlas/change-2006-2009/view European cities].
 
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* {{mitem|text=Project --> Project Properties --> CRS}}. Check that the Project spatial reference system is set to '''EPSG:32632'''.
 
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[[File:qgis_goe_syst_sample.png|400px]]
 
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* Click {{button|text=OK}}
 
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==Stratified random sampling==
 
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* {{mitem|text=Vector --> Research Tools --> Random selection within subsets}}
 
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* The input layer is ''SCP_systematic_points''
 
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* ID Field containing the code for the strata is '''MC_ID'''
 
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* Method is equal ''Number of selected features'' in each stratum.
 
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* Number of selected features type '''25'''.
 
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[[File:qgis_stratified_selection.png|400px]]
 
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The module selects a subset of the layer ''SCP_systematic_points''. The selected points need to be saved as an ESRI shapefile. Right click on the layer ''SCP_systematic_points'', {{button|text=Save as...}}. Make sure to check the box: '''Save selected only..''as shown below.
 
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[[File:qgis_save_selection.png|400px]]
 
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== Interpretation of selected points==
 
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Prepare main QGIS map viewer:
 
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* Install the ''Openlayers'' plugin (if not yet done) {{mitem|text=Plugins --> Manage and Install Plugins --> Install}}.
 
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* Install the ''Go2nextFeature'' plugin.
 
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* Load the multiband Sentinel-2 satellite image as surface reflectance (BOA)(see [Preparing raster data (Converting DN to reflectance)]).
 
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* 
 
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* Load the vector file with stratified sample points ''stratified_sample_25.shp''.
 
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* Load Google maps as background WMS layer. {{mitem|text=Web --> OpenLayers Plugin --> Google Maps --> Google Satellite}}.
 
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* In {{tool|text=Layers panel}} drag the GCP vector file on top of ''Google Satellite''.
 
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[[category:QGIS Tutorial]]
 

Latest revision as of 09:50, 24 November 2020

[edit] Defining land use/cover classes

Before starting to map land cover classes using Sentinel-2 satellite images we need clear definitions and a classification scheme. An example for a hierarchical land use and land cover (LUC) classification scheme is the European Urban Atlas. The scheme defines 5 meta classes where the class 1. Artificial surfaces has many sub classes as shown in figure A.

Figure A:LUC classification scheme of the European Urban Atlas
Figure B: LUC classes for Göttingen

The specification of other meta classes is not as detailed. If we are more interested in forest and open area classes we need to adapt and modify this scheme. On the lowest level not all classes defined in the European Urban Atlas do also appear in the surroundings of Göttingen (figure B). In addition we need to consider the phenolgical development of vegetation at specific acquisiton dates and to specify more classes which can possibly be identified in multispectral feature space of the satellite image. An example of an adapted simplified scheme is shown in the table below.

MC ID MC info C ID C info RGB code
1 Artificial surfaces 11 Built-up 230-0-77
2 Agricultural 22 Arable land 255-255-168
2 Agricultural 23 Bare soil 253-191-168
2 Agricultural 24 Pasture 230-230-77
3 Natural and semi-natural areas 31 Broad leaved tree cover 128-255-0
3 Natural and semi-natural areas 32 Coniferous tree cover 0-166-0
5 Water 51 Water 128-242-230
6 Miscellaneous 61 Cloud 255-255-255
6 Miscellaneous 62 Cloud shadow 100-50-0
6 Miscellaneous 63 Unclassified 0-0-0
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