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Introduction to Geographic Information Systems with QGIS

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Guillaume Larocque

on 10 November 2016

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Transcript of Introduction to Geographic Information Systems with QGIS

By Guillaume Larocque, research professional,
Quebec Centre for Biodiversity Science

http://qcbs.ca/wiki/introgis/
GDAL - Geospatial Data Abstraction Library. A translator library for raster geospatial data formats.
OGR - Library providing read (and sometimes write) access to a variety of vector file formats.
Python – OSS programming language. “Easy” way to create plugins for QGIS and GRASS.
WMS – Web map service. Provides online access to maps.
DBMS – Database management system. MySQL, PostgreSQL.
Terminology
uDIG
SAGA GIS
ILWIS Open
GvSIG
OpenJUMP GIS
QGIS (QGIS)
GRASS
...?
Desktop Open Source GIS
Projects entering adult life.
Ease of use, versatility, user involvement.
Possibility for integration between different OSS platforms.
Cross-platform (Windows, Mac, Linux)
Alternatives are very expensive. ArcGIS Editor license: ArcView: 1,500$, Spatial Analyst: 2,500$, ArcEditor: $7,000, ArcInfo:+14,000$
Why Open Source GIS?
Free today, free tomorrow.
Transparency.
Community development.
Examples: Python, R, PHP, Linux, LibreOffice, Joomla, Firefox, MySQL, etc.
Some negatives: documentation often lacking, websites not updated, support system not always available.
What is open source software?
Online User Guide
QGIS Training Manual
The Geospatial Desktop
Mailing list QGIS-User
QGIS wiki
QGIS Stackexchange Q&A site.
Getting information on QGIS
Development started in 2002.
User friendly and cross-platform.
Focused on display and query.
Analytical capabilities can be improved with plugins, or performed through GRASS.
Vector analysis with ftools plugin.
Basic raster analysis with GDAL tools plugin.
Active development.
QGIS
Developed in the early 1980s by the US Army as a software management tool for military applications.
Originally command line only.
Focused on raster analysis.
Vector and adequate display capabilities later added.
Now part of OSGeo.
New interface.
GRASS
Desktop GIS
Geospatial libraries
Web mapping
Objectives
Get familiarized with GIS.
QGIS and GRASS
Basic concepts.
Intro to open source GIS tools.
How to find more info.
Should I use these tools?
Reprojection 'on the fly' is good for viewing maps, but not for analysis. Always reproject all layers to common CRS before doing analysis.
Important!
X resolution= (Max X-Min X)/Columns
Y resolution= (Max Y-Min Y)/Rows
Number of columns
Number of
rows
Max X, Max Y
Min X, Min Y
Within QGIS. Basic usage.
With the wxPython interface. Intermediate usage.
With the command line or scripts. Advanced usage.
GRASS
DATABASE: Main GRASS working folder.
LOCATION: Working directory. Unique map projection.
MAPSET: Subdirectories under location.
REGION: Defined by a specific extent and resolution.
GRASS
sp package.
ManageR plugin. Requires R 2.11
Launch R within GRASS. System() function to execute GRASS commands.
Launch GRASS within R with initGRASS() function. execGRASS() function to execute GRASS functions.
Using R with QGIS and GRASS
Introduction to geographic information systems with QGIS and GRASS
http://qcbs.ca
What is GIS?
Maps in the computer.
A set of tools for collecting, storing, retrieving, transforming, analyzing and displaying spatially referenced data.
Used to: make maps, answer questions with spatial aspects.
Divided into thematic layers.
Vectors
Point
Line
Polygon
(X, Y, ID)
Vertices
(ID)
Attribute table
Contains non-spatial information associated with each entity.
Linked to entity through unique ID.
Each column has scrict format.
1
2
Text
Integer
Real (single, double, float)
Shapefile format
.shp
- geographical reference data
.shx
- a positional index of the feature geometry
.dbf
- attribute table
.prj
- Projection/coordinate sytem information

Format developed and used by ESRI. Now adopted by other GIS.
Vector analysis
Buffer
Clip
Dissolve
Multi part to single part
Union
Rasters
Grids, images, matrices.
Rectangular surface divided into cells.
Appropriate for representing quantitative variables.
Photos, digital elevation models, temperature, abundance maps, soil carbon content.
1
0
Attribute values
X resolution= (Max X-Min X)/Columns
Number of columns
Number of
rows
Max X, Max Y
Min X, Min Y
1
0
Projection
Coordinate
Reference
System
Coordinate Reference System
"Geographical systems": defined only by datum.

WGS84. NAD83 = Latitude / Longitude
Systems with projections: projection and datum.
UTM 18N / NAD83. Lambert Conformal Conic / WGS84.
vector = coverage ~ shapefile
Data formats
Operations on data table
Similar to spreadsheet or databases
Mathematical operations (sum to columns, ...)
Logical operations
Geometry calculations (area, length, perimeter)
The first GIS (in 1965)
Projection
Coordinate System
Geodetic datum
Latitude,
Longitude
-W
+E
-S
+N
Degrees, minutes, seconds
45°17'23''N, 73°21'18''W

Decimal degrees
45 + 17/60 + 23/3600 = 45.2897
73 + 21/60 + 18/3600 = -73.3550
WGS84 Ellipsoid/Datum
Raster analysis
Suitable for remote sensing, continuous surfaces, land cover, species abundance/distribution.
Suitable for categorical variables.
Large files.
Easy computation.
Coordinate transformations more difficult and imperfect. Rubber sheeting.
Vector to raster
Rasterization
Loss in detail
Single column per raster

Vector to raster
Interpolation
Inverse distance weighing
(versatile)
Trend surface analysis
. Very smooth surfaces
Spline
(smooth surfaces)
Thiessen polygons
(categorical/patchy)
Triangular irregular networks
(TIN). High density, variable density
Kriging
(versatile, if enough points).
Triangular irregular network (vector)
Satellites / remote sensing
Multi-spectral imagery
Directional or fixed-path.
Compromise resolution vs. coverage
Clouds a big issue.
MODIS
(250m-1km)
Landsat
5, 7 and 8. (30 meters/15 meters panchromatic). Free data in USGS.
SPOT
. 2.5m to 20 meters. $$
Radarsat.
Microwaves. Goes through clouds. Snow/ice cover, land cover.
High resolution
. GEOEYE-1, Ikonos, Quickbird. $$
Treatment of multi-spectral imagery
Color/false-color composites. RGB, various band combinations.
Vegetation index. Normalized difference vegetation index (IR-R)/(R+IR)
Image classification. unsupervised, supervised.
Supervised classification:
training sites -> signatures -> classification
Maximum likelihood classification
Vector or raster?
What are the types of input data?
Raster
: land use change, spatial modeling, multi-criteria evaluation.
Vector
: anything with a complex shape. Land ownership, political boundaries, routes, lakes. Network analysis.
Minimize loss in information.
Other open source GIS tools
Processing in QGIS
PostGIS
R
SAGA
Whitebox
Free tools
Fragstats
http://registration.qcbs.ca/pay
When to use each system?
Small area (<100km): UTM ou MTM.
Large area (>100km): Lambert, Albert.
Other systems for entire planet, continents, or large countries.
To share vector data: lat/long
Full transcript