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onlinegis [2021/11/11 12:48]
elijah formatting file extensions and menu navigation to stand out. 		onlinegis [2022/04/04 18:03]
elijah
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-======= Introduction to Open Source Geographic ​information systems ​with QGIS =======+======= Introduction to Open Source Geographic ​Information Systems ​with QGIS =======
  
  
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 ====== Extra exercise 1 - Manipulation of rasters and intro to satellite imagery ====== ====== Extra exercise 1 - Manipulation of rasters and intro to satellite imagery ======
  
-**Objective:​ create an RGB false color composite image and an NDVI (Normalized Difference Vegetation Index) map, associate it with a color palette and extract the mean NDVI for parcs and fields of the region. Find which park is least vegetated** ​+**Objective:​ create an RGB false color composite image and an NDVI (Normalized Difference Vegetation Index) map, associate it with a color palette and extract the mean NDVI for parks and fields of the region. Find which park is least vegetated.** 
  
-**Step 1**: We will first create a false-color composite image to better visualize the contrasts. To do so, find the merge function under Raster>​Miscellaneous. Choose Landsat images 4,3 and 2 (you might need to click on them in that order, depending on your operating system) as the input files, specify Landsat_432.tif as the Output, and select "Put each input file into a separate band" option. In the Symbologie properties ​of the new layer, the Landsat band 4 should become band 1 (Red), Landsat band 3 should become band 2 (Green) and Landsat band 2 should become band 3 (Blue). Yes, this is confusing. ​+**Step 1**: We will first create a false-color composite image to better visualize the contrasts. To do so, find the merge function under ''>​Raster >​Miscellaneous''​. Choose Landsat images 4,3 and 2 (you might need to click on them in that order, depending on your operating system) as the input files, specify Landsat_432.tif as the output, and select "Put each input file into a separate band" option. In the Symbology Properties ​of the new layer, the Landsat band 4 should become band 1 (Red), Landsat band 3 should become band 2 (Green) and Landsat band 2 should become band 3 (Blue). ​(Yes, this is confusing.)
  
 **Step 2**: In the Symbology section, you'll notice that "​Multiband color" is used as the Render type. You can play with the brightness, contrast and saturation to improve the visual appearance of the image. Notice how this image can easily allow you to distinguish the vegetation, suburbs, cities and rivers. You can also create a 5-4-3 composite and compare the two images. **Step 2**: In the Symbology section, you'll notice that "​Multiband color" is used as the Render type. You can play with the brightness, contrast and saturation to improve the visual appearance of the image. Notice how this image can easily allow you to distinguish the vegetation, suburbs, cities and rivers. You can also create a 5-4-3 composite and compare the two images.
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 **Step 4**: In the properties menu of that layer, select Single band pseudo color as the Render type, choose the RdYlGn color map and click on Classify. On this map, dark green areas represent areas that are densely vegetated while reddish areas are not vegetated. ​ **Step 4**: In the properties menu of that layer, select Single band pseudo color as the Render type, choose the RdYlGn color map and click on Classify. On this map, dark green areas represent areas that are densely vegetated while reddish areas are not vegetated. ​
  
-**Step 5**: Add the layer containing the parcs and fields (parcs_terrains_sports.shp) to the canvas and save it as a new file with the CRS NAD83 / UTM 18N. Add the new file to the canvas and remove the old one. +**Step 5**: Add the layer containing the parks and fields (parcs_terrains_sports.shp) to the canvas and save it as a new file with the CRS NAD83 / UTM 18N. Add the new file to the canvas and remove the old one. 
  
-**Step 6**: Find the Zonal statistics menu in the Procesing Toobox. Choose the parcs et terrains as the vector layer and the NDVI image as the raster layer. ​+**Step 6**: Find the Zonal Statistics entry in the Processing Toolbox. Choose the 'parcs et terrains' ​as the vector layer and the NDVI image as the raster layer. ​
  
-**Step 7**: By looking at the attribute table of the parcs et terrains layer, you will see that columns were added with the statistics of the NDVI cells for each field and park. Which park has the the lowest Mean NDVI (click on the column name to sort)? Where is it located (Click on the zoom to selection icon)?+**Step 7**: By looking at the attribute table of the 'parcs et terrains' ​layer, you will see that columns were added with the statistics of the NDVI cells for each field and park. Which park has the the lowest Mean NDVI (click on the column name to sort)? Where is it located (Click on the zoom to selection icon)?
  
 ++++ Answer | ++++ Answer |
  
-The park is located North of autoroute 40 in Ville Saint-Laurent (number 305, mean ndvi: -0.31). If you add a Google satellite layer to the canvas, you will note that there are no parks at that location, explaining the very low NDVI value.  ​+The park is located North of autoroute 40 in Ville Saint-Laurent (number 305, mean NDVI: -0.31). If you add a Google satellite layer to the canvas, you will note that there are no parks at that location, explaining the very low NDVI value.  ​
  
 ++++ ++++
  
 **CHALLENGE**:​ **CHALLENGE**:​
-Go to the USGS EarthExplorer website [[http://​earthexplorer.usgs.gov/​]] and create an account by clicking on register. Then, choose a region anywhere in the world about the size of the Montreal region that you think could have seen a lot of land use change in the last 30 years. Search for a  Landsat 4-5TM (Collection 1 Level 1) image dating anywhere between 1980-1990 for that region and taken in the summer months that contains less than 20% clouds. Download that file as a Level 1 product. Note the path/row information of this image and search for an equivalent image for 2010-2015 (same months, row and path). Bring them to QGIS, and calculate an NDVI value for each time period. ​Substract ​the later NDVI from the earlier NDVI and view the file with {{:​ndvidiff.qml|this palette}} (right-click the link...save as) to see the difference in vegetation cover between the two time periods. ​+Go to the USGS EarthExplorer website [[http://​earthexplorer.usgs.gov/​]] and create an account by clicking on register. Then, choose a region anywhere in the world about the size of the Montreal region that you think could have seen a lot of land use change in the last 30 years. Search for a  Landsat 4-5TM (Collection 1 Level 1) image dating anywhere between 1980-1990 for that region and taken in the summer months that contains less than 20% clouds. Download that file as a Level 1 product. Note the path/row information of this image and search for an equivalent image for 2010-2015 (same months, row and path). Bring them to QGIS, and calculate an NDVI value for each time period. ​Subtract ​the later NDVI from the earlier NDVI and view the file with {{:​ndvidiff.qml|this palette}} (right-click the link... ​''​Save ​as''​) to see the difference in vegetation cover between the two time periods. ​
  
 **CHALLENGE 2** **CHALLENGE 2**
-Use the Cluster Analysis for Grids function in Processing Toolbox>​SAGA to perform an unsupervised classification of your images using bands 3,4,5 and 7. +Use the Cluster Analysis for Grids function in ''>​Processing Toolbox >SAGA'' ​to perform an unsupervised classification of your images using bands 3,4,5 and 7. 
  
 ====== Extra exercise 2 - Using GRASS with QGIS Processing toolbox ====== ====== Extra exercise 2 - Using GRASS with QGIS Processing toolbox ======