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QGIS 2 Cookbook

QGIS 2 Cookbook

By : Mandel, Olaya Ferrero, Anita Graser
5 (1)
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QGIS 2 Cookbook

QGIS 2 Cookbook

5 (1)
By: Mandel, Olaya Ferrero, Anita Graser
Buy this Book

Overview of this book

QGIS is a user-friendly, cross-platform desktop geographic information system used to make maps and analyze spatial data. QGIS allows users to understand, question, interpret, and visualize spatial data in many ways that reveal relationships, patterns, and trends in the form of maps. This book is a collection of simple to advanced techniques that are needed in everyday geospatial work, and shows how to accomplish them with QGIS. You will begin by understanding the different types of data management techniques, as well as how data exploration works. You will then learn how to perform classic vector and raster analysis with QGIS, apart from creating time-based visualizations. Finally, you will learn how to create interactive and visually appealing maps with custom cartography. By the end of this book, you will have all the necessary knowledge to handle spatial data management, exploration, and visualization tasks in QGIS.
Table of Contents (14 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Sections
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Conventions
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Reader feedback
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Customer support
Free Chapter
1
1. Data Input and Output
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1. Data Input and Output
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Introduction
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Finding geospatial data on your computer
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Describing data sources
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Importing data from text files
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Importing KML/KMZ files
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Importing DXF/DWG files
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Opening a NetCDF file
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Saving a vector layer
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Saving a raster layer
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Reprojecting a layer
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Batch format conversion
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Batch reprojection
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Loading vector layers into SpatiaLite
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Loading vector layers into PostGIS
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2. Data Management
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2. Data Management
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Introduction
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Joining layer data
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Cleaning up the attribute table
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Configuring relations
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Joining tables in databases
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Creating views in SpatiaLite
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Creating views in PostGIS
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Creating spatial indexes
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Georeferencing rasters
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Georeferencing vector layers
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Creating raster overviews (pyramids)
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Building virtual rasters (catalogs)
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3. Common Data Preprocessing Steps
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3. Common Data Preprocessing Steps
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Introduction
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Converting points to lines to polygons and back – QGIS
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Converting points to lines to polygons and back – SpatiaLite
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Converting points to lines to polygons and back – PostGIS
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Cropping rasters
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Clipping vectors
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Extracting vectors
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Converting rasters to vectors
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Converting vectors to rasters
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Building DateTime strings
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Geotagging photos
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4. Data Exploration
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4. Data Exploration
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Introduction
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Listing unique values in a column
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Exploring numeric value distribution in a column
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Exploring spatiotemporal vector data using Time Manager
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Creating animations using Time Manager
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Designing time-dependent styles
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Loading BaseMaps with the QuickMapServices plugin
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Loading BaseMaps with the OpenLayers plugin
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Viewing geotagged photos
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5. Classic Vector Analysis
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5. Classic Vector Analysis
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Introduction
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Selecting optimum sites
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Dasymetric mapping
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Calculating regional statistics
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Estimating density heatmaps
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Estimating values based on samples
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6. Network Analysis
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6. Network Analysis
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Introduction
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Creating a simple routing network
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Calculating the shortest paths using the Road graph plugin
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Routing with one-way streets in the Road graph plugin
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Calculating the shortest paths with the QGIS network analysis library
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Routing point sequences
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Automating multiple route computation using batch processing
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Matching points to the nearest line
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Creating a routing network for pgRouting
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Visualizing the pgRouting results in QGIS
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Using the pgRoutingLayer plugin for convenience
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Getting network data from the OSM
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7. Raster Analysis I
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7. Raster Analysis I
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Introduction
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Using the raster calculator
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Preparing elevation data
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Calculating a slope
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Calculating a hillshade layer
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Analyzing hydrology
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Calculating a topographic index
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Automating analysis tasks using the graphical modeler
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8. Raster Analysis II
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8. Raster Analysis II
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Introduction
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Calculating NDVI
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Handling null values
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Setting extents with masks
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Sampling a raster layer
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Visualizing multispectral layers
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Modifying and reclassifying values in raster layers
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Performing supervised classification of raster layers
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9. QGIS and the Web
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9. QGIS and the Web
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Introduction
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Using web services
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Using WFS and WFS-T
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Searching CSW
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Using WMS and WMS Tiles
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Using WCS
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Using GDAL
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Serving web maps with the QGIS server
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Scale-dependent rendering
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Hooking up web clients
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Managing GeoServer from QGIS
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10. Cartography Tips
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10. Cartography Tips
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Introduction
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Using Rule Based Rendering
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Handling transparencies
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Understanding the feature and layer blending modes
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Saving and loading styles
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Configuring data-defined labels
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Creating custom SVG graphics
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Making pretty graticules in any projection
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Making useful graticules in printed maps
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Creating a map series using Atlas
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11. Extending QGIS
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11. Extending QGIS
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Introduction
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Defining custom projections
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Working near the dateline
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Working offline
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Using the QspatiaLite plugin
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Adding plugins with Python dependencies
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Using the Python console
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Writing Processing algorithms
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Writing QGIS plugins
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Using external tools
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12. Up and Coming
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12. Up and Coming
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Introduction
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Preparing LiDAR data
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Opening File Geodatabases with the OpenFileGDB driver
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Using Geopackages
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The PostGIS Topology Editor plugin
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The Topology Checker plugin
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GRASS Topology tools
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Hunting for bugs
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Reporting bugs
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Index
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Index

Visualizing multispectral layers

Multispectral layers can be rendered in different ways depending on how bands are used. This recipe shows you how to do this and discusses the theory behind it.

Getting ready

Open the landsat.qgs project.

How to do it…

  1. The Landsat image, when opened with the default configuration, looks something like the following screenshot:

  2. Double-click on the layer to open its properties and move to the Style section:

    1. Select the band number 4 in the Red band field.

    2. Select the band number 3 in the Green band field.

    3. Select the band number 2 in the Blue band field.

    Your style configuration should be like the following:

  3. Click on OK.

The image should now look like the following:

How it works…

Colors representing a given pixel are defined using the RGB color space, which requires three different components. A normal image (such as the one you will get from a digital camera) has three bands containing the intensity for each one of these three components: red, green, and blue.

Multispectral...

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