Revision as of 19:13, 12 January 2018

Prerequisites of spatio-temporal image analysis

Correct the pixel intensities as much as possible for uninteresting differences:

1. Sensor calibration
2. Exact spatial co-registration of images (especially pixel-by-pixel comparision)
3. Cloud and cloud shadow masking
4. Haze reduction
5. Atmospheric correction
6. Topographic illumination correction (mountains)
7. Clear definitions and classification scheme

Visualization of multi-temporal images

1. Add subsets of 3 Landsat TM multispectral scenes of 1992 (SUB_LT4_1992-07-30_MUL.tif), 2006 (SUB_LT5_2006-06-11_MUL.tif)and 2010 (SUB_LT5_2010-07-08_MUL.tif) into the QGIS TOC.
2. Create color composites RGB=5-4-3 following [Changing Raster Layer Style].

Globe plugin

MapSwipe plugin

Temporal/Spectral Profile plugin

Change detection techniques

Bitemporal

Post-classification Comparison

Two co-registered satellite images are independently classified to yield thematic maps. Discrete class labels are compared to determine changes using cross-tabulation in which all transitions are presented. Use the Semi Automatic Classification plugin: Postprocessing --> Land cover change

Raster algebra: Difference

1. Add subsets of multispectral 2 Landsat TM images of 1992 (SUB_LT4_1992-07-30_MUL.tif) and 2010 (SUB_LT5_2010-07-08_MUL.tif) into a the QGIS TOC project.

Raster algebra: Ratio

1. Open Toolbox --> OTB --> Feature Extraction --> Radiometric indices.
   • Set tm_920526_mul.tif as Input Image.
   • Set Red Channel to 4 and NIR Channel to 5.
   • Set Available Radiometric Indices to ndvi.
   • Save the Output Image as ndvi1992.
   • Repeat this procedure for the raster file of 2005 and adapt the name of the Output Image to ndvi_2005.
2. Calculate the ratio of both raster images with the Raster --> Raster Calculator.
   • Choose ndvi_2005 from the Raster bands by double clicking on the raster name.
   • Choose the division operator from the Operators by clicking on /.
   • Choose ndvi_1992.tif from the Raster bands by double clicking on the raster name.
   • Save the Output layer as ndvi_ratio and press OK.

Multi-temporal

Multi-temporal color composites

1. Add the raster layers of the years 1992 (tm_920526_mul.tif), 2000 (etm_000515_mul.tif) and 2005 (etm_050623_mul.tif) into a QGIS project. It should be available in the course data.
2. Open Toolbox --> OTB --> Miscellaneous --> Band Math.
3. Calculate the amplitude for each raster layer (1992, 2000, 2005) with the use of the bands 5-4-3.
   • For the year 1992, set tm_920526_mul.tif as Input image list.
   • Type sqrt(im1b4^2 + im1b5^2 + im1b3^2) as Expression.
   • Save theOutput image as amplitude1992.
   • Repeat this procedure for the raster files of 2000 and 2005 and adapt the name of each Output Raster File.
4. Merge the three output raster files with Toolbox --> GDAL/OGR --> [GDAL] Miscellaneous --> Merge
5. Load the three amplitude****.tif files as Input Layers, mark Layer Stack and save Merged output as amplitude_merge.

Principal component analysis

1. Add the raster layers of the years 1992 (tm_920526_mul.tif), 2000 (etm_000515_mul.tif) and 2005 (etm_050623_mul.tif) into a QGIS project. It should be available in the course data.
2. Install PCA plugin under Plugins --> Manage and Install Plugins....
3. Open PCA plugin .
   • Set tm_920526_mul.tif as Input Raster File.
   • Set Number of output Principal Components to 1.
   • Save the Output Raster File as pca1992_1.
   • Repeat this procedure for the raster files of 2000 and 2005 and adapt the name of each Output Raster File.
4. Merge the three output raster files with Toolbox --> GDAL/OGR --> [GDAL] Miscellaneous --> Merge.
5. Load the three pca****_1.tif files as Input Layers, mark Layer Stack and save Merged output as pca_merge.