Training data selection (SCP)

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(Interpretation of selected points)
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==Creating systematic sampling grid==
 
* A processing toolbox model can downloaded from Stud.IP {{typed|text=urban_atlas_systematic_sample.model3}} or by right click on [http://www.gwdg.de/~hfuchs/qgis/urban_atlas_systematic_sample.model3 here] and Save file as ...
 
* Open QGIS and go to the Processing Toolbox; if the Toolbox is not opened click {{mitem|text= Processing --> Toolbox}} to activate it.
 
* In the processing toolbar click {{button|text=Options}} [[File:qgis_processing_options.png]]. In the General tab choose "ignor invalid features with invalid geoemtries".
 
[[File:qgis_processing_invalid_feature.png|500px]]
 
* Go to [[File:qgis-processing_model_iconExample.png]] {{mitem|text= Models --> Open existing models}} and load the previously downloaded model. The model should appear in the Models tab. Double click on it to execute.
 
* Specify the ''European Urban Atlas Vector File'' layer for Göttingen: ''\lucc\DE021L1_GOTTINGEN\Subset-Goe_DE021L1_GOTTINGEN_UA2012_UTM32N.shp'' or download data from other [https://land.copernicus.eu/local/urban-atlas/urban-atlas-2012 European cities]. The original European Urban Atlas data have the spatial reference system EPSG:3035 (ETRS89, LAEA). The Goettingen Urban Atlas file is already transformed to WGS84 /UTM 32. There might occur geometry errors: Run {{mitem|text=Processing Toolbox --> Check validitiy}} and {{mitem|text=Processing Toolbox --> Fix geometries}} repairing invalid vector geometries.
 
* {{mitem|text=Project --> Project Properties --> CRS}}. Check that the Project spatial reference system is set to '''EPSG:32632'''.
 
[[File:qgis_goe_syst_sample.png|400px]]
 
* Click {{button|text=Run}}
 
 
==Stratified random sampling==
 
* {{mitem|text=Vector --> Research Tools --> Random selection within subsets}}
 
* The input layer is ''SCP_systematic_points''
 
* ID Field containing the code for the strata is '''MC_ID'''
 
* Method is equal ''Number of selected features'' in each stratum.
 
* Number of selected features type '''25'''.
 
 
[[File:qgis_stratified_selection.png|400px]]
 
 
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.
 
 
[[File:qgis_save_selection.png|400px]]
 
 
== Interpretation of selected points==
 
 
Prepare main QGIS map viewer:
 
* Install the ''Openlayers'' and the ''Go2nextFeature3'' plugin (if not yet done) {{mitem|text=Plugins --> Manage and Install Plugins --> Install}}.
 
* Load the multiband Sentinel-2 satellite image ''Subset_S2A_MSIL2A_20170619T_MUL.tif''.
 
* Create a false-color composite (RGB = 9-7-3). Set the {{mitem|text=Layer Properies --> Transparency --> Global Transparency}} to 50%. 
 
* Load the comma separated ACII file with stratified sample points: click [[file:QGIS_2.0_Add_delimited.png|100px]] {{mitem|text= Data Source Manger -->  Delimited Text}}. Select the input file ''CE_2020-01-03.csv''. Define X field: ''XCoordinat'' and Y field: ''YCoordinat''. Coordinate Reference System (CRS) is ''EPSG:4326 - WGS84''. {{button|text=OK}}.
 
* Load [http://wiki.awf.forst.uni-goettingen.de/wiki/index.php/Load_a_WMS-Layer#Add_Google_Maps_layers Google maps as background WMS layer]
 
* In {{tool|text=Layers panel}} drag the Sentinel-2 image on top of ''Google Satellite''.
 
* Drag the sample points vector file on top of all other files.
 
* Activate {{mitem|text=Plugins -->Go 2 next feature}}.
 
* Layer is the sample points.
 
* The Attribute is ''id''.
 
* Click the checkbox '''Select''' on.
 
[[File:qgis_go2nextfeature.png|200px]]
 
* In {{tool|text=Layers panel}} right click on the sample point layer and {{button|text=Open Attribute Table}}.
 
* On the lower left of the Attribute Table switch from {{button|text=Show All Features}} to {{button|text=Show Selected Features}}.
 
* Start the edit mode of the point layer by clicking the {{button|text=Toggle editing}} button [[file:QGIS_2.0_Edit.png|25px]].
 
* Arrange the windows on your Desktop as shown below.
 
[[File:qgis_goe_interpret_luc.png|1200px]]
 
* Enter the interpreted land cover class in column '''valid_cl''' of the Attribute Table.
 
* Do not forget to click {{button|text=Save edits}} button [[file:QGIS_2.0_SaveEdits.png|25px]].
 
* To stop the edit mode of the point layer click the {{button|text=Toggle editing}} button [[file:QGIS_2.0_Edit.png|25px]] again.
 
* Export the file in format csv. Mark layer name in Layers Window. Right click {{mitem|text=Export --> Save Features as... }}. Choose file format ''Comma Separated Value''.
 
  
  
 
[[category:QGIS Tutorial]]
 
[[category:QGIS Tutorial]]

Revision as of 13:01, 18 November 2020

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 1 Built-up 230-0-77
2 Agricultural 2 Arable land 255-255-168
2 Agricultural 3 Bare soil 253-191-168
2 Agricultural 4 Pasture 230-230-77
3 Natural and semi-natural areas 5 Broad leaved tree cover 128-255-0
3 Natural and semi-natural areas 6 Coniferous tree cover 0-166-0
5 Water 7 Water 128-242-230
0 Miscellaneous 8 No data (cloud) 255-255-255
0 Miscellaneous 9 No data (shadow) 0-0-0
0 Miscellaneous 10 Unclassified 0-0-0
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