This document provides an agenda for a workshop on landscape ecology analysis with QGIS. The workshop will cover working with GIS data formats and attributes, projections, georeferencing, and landscape analysis using the LecoS plugin. It includes example data and exercises to analyze land cover composition and heterogeneity for study sites on the island of Gotland. The workshop is intended to help participants learn skills for visualizing spatial data, conducting landscape metrics analysis, and presenting maps in QGIS.

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Landscape ecology analysis with QGIS
Workshop at Lund University (2015) by Martin Jung

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About me
● BSc thesis about plant-pollinator networks in Białowieza
● MSc thesis about broad-scale biodiversity modelling
● Starting a PhD in september on Global biodiversity impacts of land-use
dynamics
● Using QGISQGIS since many years, two plugins (LecoS, QSDM)
Twitter: @Martin_Ecology
Blog: http://conservationecology.wordpress.com/

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Roadplan for today
1 Working with GIS formats and attributes in QGIS
2 Projections
3 Introduction to Georeferencing with QGIS
… Short break somewhere here ...
4 Landscape Analysis with QGIS (LecoS)
5 Questions and problems (...Open end...)

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Now install (if you haven't already)
● Make some room on your computer (at least 1 GB !)
● Install the current Stable Version ( QGIS 2.8 ) on your computer (see
provided folder) for Windows
● MAC-Users: Download QGIS and install python libraries
● (http://www.kyngchaos.com/software/python )
Important!
– Make sure the libraries python-scipy, python-numpy, python-pil and python-
matplotlib are checked

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● Select “Advanced Install”
● “Install from local Folder”
● Mark everything
– Qgis
– Python-numpy
– Python-scipy
– ...
How to install (WIN)
Install from local folder

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GIS data
formats
Source: maprabu.blogspot.com
Format:
*.shp, *.csv, *.gpx, *.kml, ...
Format:
*.tif, *.vrt, *.hdr, *.asc, ...

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Raster vs vector
● Vector
– Advantage: Accuracy, more visually pleasing
– Disadvantage: Space-inefficient. Every vertex needs to be stored.
Algorithms computational intensive.
● Raster
– Advantage: Geogr. Position associated with data, easier for analysis
– Disadvantage: Resolution dependent on cellsize. Often not nice-
looking

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Other data sources
● Google, Bing, Openstreetmaps, ... ( OpenLayers plugin)
● WMS server, for instance the EEA WMS services
– http://discomap.eea.europa.eu/home.html
– Can be loaded into QGIS as WMS layer
Those files can be visualized, but not edited (see WFS) !!!
You can make a screenshot though and georeference this, but check
the publishers license!

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A note on proprietary formats
● Long-term ArcGIS users are often confronted with .lyr files
– Proprietary format by ESRI. Container that stores data and viz
● (Open-source alternative in QGIS ← .qlr )
● TMK: Not convertible without having a ESRI license!
– Arc2Earth extension for ArcMap lets you save .lyr to .sld

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What are Map projections
● Map projections to represent a 3-dimensional structure on a 2d
planar plane
● Many formats and types
– Spherical, azimuthal, equidistant, equal area, …
– Most commonly used:
● WGS84 (lon,lat ← In degree)
● WGS84 Google Pseudo-Mercator (in meter, but inaccurate in tropics!!! )
● WGS84 UTM (in meter, subdivided in lon-lat grids)
http://epsg.io/ ← Lookup projections

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Now hands on in QGIS

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Geo-referencing with QGIS
● GR is the process of
associating data with spatial
coordinates
● In QGIS using the excellent
gdal-referencer
● What we need:
– A non-spatial file
– A reference projection
– Reference points or ROI
Harsjön

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(1) Get the geocode plugin (or use the bar) to jump to
59.55275,18.33490 long-lat
(2) Load Google layers or any other WMS service
(3) Make sure the gdal-georeferencer is available (Raster menu)
(4) Add the Harsjön.tif file to the referencer and add around 5-6
GCP points – Choose linear interpolation
(5) Now you have the rasterized the map and create a new
vector point layer with the study-locations

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Visualizing Gradients
● Add the pH-0915_3.csv to
QGIS via “Add-delimited-
Text-layer”
● Specify RT90 2.5 gon V as
projection (assumed)
● Color the points with the Ph
attribute
● Subset to Skane (Select –
Save Selection).
Computational intensive!!!
● Use the Heatmap plugin (Ph
as weight) or GRASS-
modules in Processing
(v.surf.*)
Vector Raster

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Use the new Heatmap renderer (QGIS 2.8)

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Short Break
Kort paus
Kurze Pause

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LecoS
● Simple Plugin to extract landscape metrics from raster layer
● Inspired by FRAGSTAT
● With Graphical interface, but more options available in
Processing Toolbox

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Example Data set
Use the data within the Gotland zip file

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Research questions
1) What is the most abundant land-cover type in the study
locations?
2) How much combined forest is there in our study sites?
3) How heterogen is the landcover in our study-sites?
4) What is the distance to the Forest-edge for each of our sites?
● (Optional) Neutral Landscape Models with NLMPy

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Landscape analysis preparation
Landscape Layer Studylocations with 500m Buffer

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Research Question 1.)
● Crop your the raster dataset with your buffered Studysites
(overlapping buffers are not correctly cropped. Use DissolveDissolve first)
● Use “LecoS – Land cover statistics” to compute the
“Landscape proportion” of each land cover class
● Use “Groupstats”, “QScatter” or “Statist” to get the aggregated
values
– Or export to csv and open with a spreadsheet program!

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Results Question 1.)
Results Top 3:
30 (Åkermark) Arable land→ 21 %
44 (Barrskog ej på lavmark 7-15 meter) Coniferous
forest →17 %
32 (Betesmark) Pasture → 11 %

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Research Question 2.)
● Isolate all forest cover from both datasets using the raster calculator
(Classes 40 – 50)
●
("Layer_Gotland@1" >= 40 AND "Layer_Gotland@1" <= 50)("Layer_Gotland@1" >= 40 AND "Layer_Gotland@1" <= 50)
● Use “LecoS – Polygon overlay” to compute the Total forest cover for
each buffer
● Ether add to attribute table (Layer needs to be reloaded ) or save as
csv

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Results Question 2.)
● On average:
– 285874 m² (28.58 ha)

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Research Question 3.)
● In order to measure land-cover heterogeneity you have 2
options:
– Ether use the Moving Window in the Processing Toolbox, select
variety and an appropriate window size
– Afterwards extract the mean for each of your buffers using the
polygon-overlay
● Or
– Use the LecoS polygon-overlay tool to compute a “diversity-index”
like Shannon or Simpson index for each of your buffers

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Research
Question 3.)

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Research Question 4.)
The landscape modifier
● Clean Map of small pixels
● Extract the forest edge of the previous
generated forest extract.
● Then use the Proximity tool in the
Raster menu
● Use the Save-As function (rightclick
raster) to correctly set a nodata-value
● Use LecoS to extract the median values
inside the grid per buffer or point

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Results question 4.)
FeatureID 27 + 30
farthest away from forest
edge
~ 942m and 522m

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(Optional) Neutral Landscape Models (NLMpy)
● LecoS has optional NLMpy
support since version 1.9.3
● Neutral Landscape Models
as “Nullmodel” for continuous
and classified landscapes
● Publication:
● onlinelibrary.wiley.com/doi/10
.1111/2041-210X.12308/full

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How to get the library?
● In order to install non-supported libraries you have to use PIP
(python package index).
● NO Guarantee that this will work for your!
● https://pypi.python.org/pypi/nlmpy
– Open the Python console in QGIS (or terminal on Linux)
– Run pip install nlmpy
– Or alternatively download and pip install nlmpy-0.1.1.tar.gz

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Time for Map Presentation?

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Running scripts in the Processing Toolbox
● The Processing toolbox (formerly called Sextante) in QGIS can
be used to write models and script to do repeatable tasks
● Currently Python and R scripts are supported
● In order to create a R-script, make sure that it is enabled in the
Processing options (menu)

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Other useful stuff -
Processing R commands
Command Function
##[datagis]=group Sets the group to “datagis”
##layer = vector or raster Specifies the input layer to use
##distance=number 100 Sets a number field with default 100
##title=string France Get text input. Default is “France”
##field=field layer Select a field from the vector layer “layer”
hist(layer[[field]]) R-command: Histogram for fields from layer
##showplots Has to be set in order to see plot outputs
>t.test(layer[[field]]) Console output with a “>” before command
##output=output vector File output as vector or raster

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Example Script
##[Own Scripts]=group
##showplots
##layer=vector
##y=field layer
##x=field layer
plot(as.numeric( layer[[y]] )~as.numeric( layer[[x]] ),pch=19,bty="l",ylab=paste( y ),xlab=paste( x ) )
fit = lm( layer[[y]]~layer[[x]] )
abline(fit,col="blue",lwd=2)

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Additional examples, help and tutorials
● http://www.gistutor.com/
● http://qgis.spatialthoughts.com/
● Youtube → search QGIS
● http://gis.stackexchange.com
● QGIS-user mailing list → http://lists.osgeo.org/listinfo/qgis-user

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Thanks for your attention!
Open questions....!