Practical Material and Trainings
 

Topic 1: Electromagnatic Radiation

Assignment explanation on  EMR: Spectral reflectance Curves

- Assignment:

  EMR01-P01_ Reflectance Curve Assignment.pdf

EMR01-P01_ Reflectance Curve Assignment_Answers.pdf

EMR01-P01_ Reflectance Curve Assignment_ TMT Palestine.html

- Learning aim

• Calculate reflectance curves from laboratory measurements and compare with other sources and materials.

- Expected Completion time

• 3 hours 

- Resources needed for the exercise

• PC with MS EXCEL spreadsheet

• Data required to download:  EMR01-P01_EM_Radiation_Exercise_reflectance.xlsx

 

Topic 2: Sensors and image characteristics

- Downloading Sentinel and Landsat imagery

In the beginning of the Earth observation era imagery was poor in temporal and spatial resolution and also very expensive. Costs for full images of 80 m resolution were prohibitive so variants of getting quarter scenes and single bands was the affordable option.

Today this is all history. Medium to high resolution spatio-temporal imagery is free to be downloaded by any user. Temporal, spatial, radiometric and spectral resolutions have increased exponentially. 

In this Demo-lecture, we demonstrate how to search, filter, select, and download Remote Sensing images for the 2 most recognized data sources:

• Data hub from ESA: mainly for the Copernicus program and the Sentinel series: https://scihub.copernicus.eu/ (Links to an external site.) (then click on Data HUB)
• Earth Explorer from Nasa: a variety of satellite EO sensors of NASA (it links to S2 as well): https://earthexplorer.usgs.gov/

- Video explanation of the practical

RSS02-D01_ Demo. Downloading Sentinel and Landsat imagery_ TMT Palestine.html

 

Topic 3: Sensor Calibration

Exercise Information

The main objective of the practical is to understand the transfer from Digital Number back to physical units of radiance in all bands. Then, we need to understand the conversion of radiance (or depending of the sensor, of DN) to reflectance for the shortwave bands and from radiance to brightness temperature for the longwave bands.

These concepts were seen in the chapter of EMR but now are applied to the calibration of images.

Simultaneously, the use of the image raster calculator in QGIS is introduced together with some standard operations commonly done during Remote Sensing pre-processing

-Video explanation of the practical:

CAL02-P01_ Sensor calibration and image calculation_ TMT Palestine.html

Learning objectives

After this exercise you are able to:

• Understand the process of satellite image calibration. (application to Landsat 8)
• Calibrate manually an image.
• Calibrate an image using suitable plugins.
• Use the raster calculator in QGIS.

- Resources

Background Information

Prior to the execution of this exercise, the student is requested to attend the lecture or audio lecture on Sensor calibration / radiometric correction (See previous section)

Support material

• They are included in the directories of the exercise.

Equipment/software

• PC with QGIS 3.10 LTR

Data for the exercise

DOWNLOADS:

• Handout: to guide the exercise and questions: CAL02-P01_Guide_radiometric_calibration&indexes_participants.pdf
• Data files (zipped with 7z): https://surfdrive.surf.nl/files/index.php/s/peSfaZ5rHBTh1U2

- Answer:

CAL02-P01_Answers_radiometric_calibration&indexes.pdf

 

Topic 4: Georeferencing and geocoding

- Learning aim

The objective of the practical is to understand and execute the procedure of assigning a spatial reference to image data and to understand the difference between georeferencing and geocoding in the making.

After this exercise you are able to:

• Select the adequate projection fitting your project.

• Georeference a scanned topographic map using the its coordinate system grid.

• Derive ground control points from scanned topographic maps matching the image.

• Georeference a satellite image with support of an already georeferenced topographic map.

• Display a vector (shape) file on an raster image and evaluate the geometrical correction result

- Resources

Background Information

Prior to the execution of this exercise, the student is requested to attend the lecture on Coordinate systems and Georeferencing.

Model Answers

• You can get the answers of this assignment here:  GG03-P01_Answers_georeferencing&geocoding.pdf

Support material

• Demo (not compulsory): How_to_get_coordinates_from_a_topomap_DEMO.exe

• VIDEO: How to do georeferencing using the georeferencer en QGIS. Georeferencing from itc elearning on Vimeo.:  GG03-P01_ Georeferencing and Geocoding PRACTICAL_ TMT Palestine.html

Video of the practical

GG03-P01_ Georeferencing and Geocoding PRACTICAL_ TMT Palestine.html

-Data for the exercise

DOWNLOADS:

• Handout: to guide the exercise and questions: GG03-P01_Georeferencing&geocoding_Guide_STUD.pdf

• Data files (zipped with 7z):https://surfdrive.surf.nl/files/index.php/s/rUWimAJyavZUu4S

-After unzipping the data files you get:

• Part of a SPOT image of 27 July 2011 sp_no_ref.img (no georeference), covering East-Twente in ERDAS imagine file format

• Scanned topographical map 28H in TIF format (unknown scanned resolution)

• Scanned geocoded or georeferenced topographical maps 29C and 34F in ERDAS .IMG format

• Road center lines (TOP10NL) from central database (wegdeel_lijn) in shape file format

- Products

Ground control points, geometric models and geocoded images.

 

Topic 5: Digital Image Classification Practice:

-Practical: Exercise Information

The main objective of the practical is go through the Digital Image Classification procedure. That consists on image uploading and pre-processing, training and clustering, image classification and assessment.

The technique of classification in QGIS is based on the material taught in the lectures but differs in the sampling technique. It is based on the application of a clustering distance from a center in the feature spaces and an "on-the-fly" classification allowing the user to interact quickly with the system to get a better result.

Moreover it works with the concept of "macroclasses" and sub-classes of these macro's. I.e. A macroclass would be "Vegetation" and "Subclasses" could be grass, wheat, maize, etc... This allows better and easier grouping of classes when the subclasses can't be identified by spectrum.

-Learning objectives

After this exercise you are able to:

• Use the spectral signatures of pure objects to recognize surface elements

• Develop a training set for classification.

• Make a simple supervised classification of a Sentinel-2 image using the Semi-Automatic classification plugin SCP of QGIS

Resources

Background Information

Prior to the execution of this exercise, the student is requested to attend the 4 short lectures in Digital Image Classification and the corresponding quizzes.

Video of the practical

DIC04-P01_ Digital Image Classification Practice_ TMT Palestine.html

Support material

• See the printed practical guide in Digital image classification.

Equipment

• PC with QGIS 3.4 and excel software (optional) 

-Data for the exercise

DOWNLOADS:

• Handout: to guide the exercise: Practical_DIC_Sentinel2_Delft.pdf

• Landsat 8 Zip Data files:https://surfdrive.surf.nl/files/index.php/s/CXp8gSJo6pFZBWW

-Products

Digital Classified Image in QGIS.