QGIS Tutorial 2013/14

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This article has been been tested for QGIS 2.0.1 (32-bit version) on Windows 7. QGIS is also available for operating systems like Windows (64-bit), Mac OSX, several Linux derivates (e.g. Ubuntu) and Android but not yet tested here. Give it a try! If there are problems please report them to one of our QGIS instructors or AWF-Wiki tutors.


Content

Overview of all pages in this category

  1. Foreword
  2. Content

Unit 1

Lecture: Introduction to the remote sensing process

Lab: Course Basics

Installation of QGIS 2.0.1 and R 3.0.2, configuring processing toolbox, data transfer, graphical user interface

  1. QGIS installation
  2. QGIS Plugin installation
  3. R installation
  4. QGIS configuration
  5. Course data
  6. Exercise 01: First steps in QGIS
  7. Exercise 02: GPS applications
  8. Exercise 03a: Multiband layers
  9. Exercise 03b: Georeferencing
  10. Exercise 05: Digital Elevation Model processing
  11. Exercise 06: Digitizing training an test areas
  12. Exercise 07: Unsupervised classification
  13. Exercise 08: Supervised classification
  14. Exercise 09: Segmentation algorithms
  15. Exercise 10: Object-based classification

Unit 2

Lecture: Theory of electromagnetic radiation

Electromagnetic radiation

Lab: First steps in QGIS

First steps in QGIS, color composites and histogram stretching, , subsetting and multilayer stacking, processing toolbox, R scripts and image statistics

  1. Introduction to Quantum GIS
  2. QGIS GUI

Unit 3

Lecture: Overview on Global Navigation Satellite System (GNSS) applications

Lab: GNSS data handling

Overview on GPS Applications, Geocaching, marking waypoints, real time tracking,Up- and downloading waypoints and tracks, OpenStreetMap layers, digital photo links

  1. The GPS tools plugin
  2. Defining an own custom Spatial Reference System (SRS)
  3. Creating GPS waypoints
  4. Outdoors with the GPS receiver
  5. Using the GPS Tracking Plugin
  6. Download from GPS receiver
  7. Digital photo links with the eVIS plugin
  8. Load a WMS-Layer
  9. OpenStreet Map layer

Unit 4

Lecture: Spatial reference systemes, geometric correction

Geometric corrections

Lab: Georeferencing

Georeferencing of topographic maps, aerial photos, satellite images, coordinate and datum transformations

  1. Georeferencing a topographic map
  2. Georeferencing an aerial photograph
  3. Mosaicing
  4. Reprojection of vectors
  5. Reprojection of rasters

Unit 5

Lecture: Overview on satellite sensor technology

Lab: Digital elevation model (DEM) processing

Digital elevation model (DEM) processing, import, interpolation of digital terrain models (DTM), profiles, calculating slope, aspect and contour lines, shaded relief, graphical modeler of processing toolbox

  1. Creating a DEM from vector data
  2. The Profile plugin
  3. Terrain analysis
  4. Evaluation of digital elevation models

Unit 6

Lecture: Image Histogram Modifications

Lab: Digital Terrain Modelling cond.

Unit 7

Lecture: Image Enhancement

Normalized Difference Vegetation Index (NDVI)

Lab: Band math, vegetation indices, spatial filtering, texture indices, PCA

Unit 8

Lecture: Collection of reference data and Accuracy Assessment

Assessment of reference data

Accuracy Assessment

Lab: Digitizing of training and test areas

Assessment of reference data (Training/Testing), systematic and random samples, visual interpretation of Google Maps, on-screen digitizing of training and test areas, land use and cover (LUC) classification key

  1. CORINE LUCCS (Land use / cover classification system)
  2. Creating new vector layers

Digitizing

  1. Configure digitizing options
  2. Digitizing

Sampling tools

  1. Random sampling
  2. Systematic sampling with circular sampling plots
  3. Construction of a regular grid
  4. Using external information sources as reference for training site delineation
  5. Digitizing reference areas

Unit 9

Lecture: Digital classification I

Lab: k-means clustering

Per pixel unsupervised k-means algorithm, self organizing maps (SOM)

Unit 10

Lecture: Digital classification II

Lab: Per pixel supervised classification

Per pixel supervised classification, (Maximum likelihood, Support vector machine algorithm)

Unit 11

Lecture: Segmentation Algorithms

Lab: Mean-Shift Segmentation, feature extraction

Mean shift segmentation, feature extraction, object-based classifcation

Unit 12

Lecture: Object-based classification

Lab: supervised svm classification

Comparison of object-based and pixel-based classification

Unit 13

Lecture: Change detection

Lab: Multitemporal image and map analysis

Change detection techniques

Unit 14

Lecture: Recap

Lab: Map production / Hardcopy

Hardcopy map production

Unit 15

Lecture

Lab

Personal tools
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Variants
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Navigation
Development
Toolbox
Print/export