Tag Archive | landscape metrics

New publication: LecoS now with own reference

As I can see my QGIS plugin LecoS is still widely used and downloaded from the QGIS plugin hub. I have noticed that some people already started referencing ether my blog or the QGIS repository in their outputs, which is fine, but after thinking about it for a while I thought why not make a little descriptive article out of it (being an upstart PhD scholar and scientist an’ all). I am now happy to announce that this article has passed scientific peer-review and is now been published in early view in the Journal of Ecological Informatics.

LecoS — A python plugin for automated landscape ecology analysis

The quantification of landscape structures from remote-sensing products is an important part of many analyses in landscape ecology studies. This paper introduces a new free and open-source tool for conducting landscape ecology analysis. LecoS is able to compute a variety of basic and advanced landscape metrics in an automatized way. The calculation can furthermore be partitioned by iterating through an optional provided polygon layer. The new tool is integrated into the QGIS processing framework and can thus be used as a stand-alone tool or within bigger complex models. For illustration a potential case-study is presented, which tries to quantify pollinator responses on landscape derived metrics at various scales.

The following link provided by Elsevier is still active until the 23 of January 2016. If you need a copy later on and don’t have access to the journal (sorry, I didn’t have the money to pay for open-access fees), then feel free to ether contact me or you can read an earlier prePrint of the manuscript on PeerJ.

So if you are using LecoS in any way for your work, it would be nice if you could reference it using the citation below. That shows me that people are actively using it and gives me incentives to keep on developing it in the future.

Martin Jung, LecoS — A python plugin for automated landscape ecology analysis, Ecological Informatics, Volume 31, January 2016, Pages 18-21, ISSN 1574-9541, http://dx.doi.org/10.1016/j.ecoinf.2015.11.006.

The full sourcecode of LecoS is released on github.

ZonalStatistics, Neighbourhood analysis and more for LecoS

Since QGIS 2.0 stable was released just a while ago, i thought that it would be time to enhance my plugin LecoS a bit more.  Furthermore i also missed some functions, for instance i found no appropriate function to compute ZonalStatistics for a set of rasters of mine.  SAGA has a function to calculate some stats using a categorical and a zone raster layer. However it is lacking a raster output and specific stats. So i added  a new ZonalStatistics function to LecoS and i am sure that it will be of some use to Landscape ecologists and other GIS users out there. See a usecase below!

Furthermore i regularly use a lot of short python scripts to generate and query raster layers using a gdal+numpy backbone. Those custom functions of mine are a lot faster than any other plugin (all hail to numpy), which is why i also implemented some functions that are already available in QGIS through other plugins.

Here is the total changelog from the last LecoS version 1.8.2 to the new 1.9 (note that QGIS 1.8 won’t be supported anymore):

# Version 1.9
### Major Update: ###
- Added new tools to the Processing toolbox for use in complex models
    - Function to count Raster cells -> Output as table
    - Function to query raster values below a point layer
    - Function to intersect two landscape (raster) layers -> Output clipped raster
    - Function to creates a new random raster based on values from a statistical distribution -> Output raster
    - Function to conduct a Neighborhood analysis (analogous to r.neighbors. or Focal Statistics in ArcGis)
    - Function to conduct a connected component labeling of connected patches
    - Function to conduct ZonalStatistics based on two landscapes (ZonalStatistics with raster layers in ArcGIS)
- Improved the overall documentation for the Processing Toolbox and created new simple icons
- Fixed Bug: http://hub.qgis.org/issues/8810

I didn’t create any new graphical interfaces as i believe that sextante aka processing is the future. All new functions were therefore only added to the processing toolbox and not as seperate GUI. This also has the cool advantage that you could use all LecoS tools within more complex multi-algorithms models. The most visible difference to older LecoS versions is that i created a new icon for every function (make them distinguishable) and wrote documentary information.

All currently available processing functions

All currently available processing functions. Notice the new icons

The new function help

The new function help. Was a hell of work writing all this stuff

Click more to see a short tutorial demonstrating the functions using real data.

Read More…

Sextante support

Hey folks,

just a quick update as i am really busy with studying right now. I just added SEXTANTE support to LecoS and although it isn’t as powerful as the original LecoS Gui (only allows you to calculate a single metric at once) you can now address most of the functions (vector analysis excluded) from within the SEXTANTE toolbox.  Simply enable it in the options first to see it in the toolbox.

What is it good for?

Well, you can now batch-process the LecoS functions for multiple rasters and also include the landscape analysis tools in the SEXTANTE modeller. Below is a simple example of a model i just created. It takes a raster (Satellite landcover image) and a polygon (Training Data) as input and then automatically performs a maximum likelihood supervised classification. Afterwards it uses the landscape modifier function “Clean small pixels” to clean up the result and then calculates the total sum of all newly classified landscape pixels with the landscape analysis tool.


Sextante – Supervised classification model

I think this is it for now with new features for LecoS. Unless me or someone else needs a new fancy technique, i will make a feature freeze for now. Please report any bugs and blockers on the bugtracker. Maybe i can mark the whole plugin as stable as soon as QGIS 2.0 will be released.

Interesting paper: The spatial road disturbance index

Following current papers and newly proposed methods is always exciting. Especially when someone proposes a new technique for conservation planning, which

Daily car victims: frogs

Daily car victims: frogs

includes the habitat fragmentation caused by roads. The decrease of road-less areas in Europe certainly has an impact on a wide range of species (Selva et al. 2011), especially if they inhabit large home-ranges and frequently move between for instance forest patches. To give an example: Conservation NGOs in Germany are currently celebrating the return of wolves in east German forests. While this is certainly a good thing it requires a lot of future management actions (possible compensation of farmers, education of the public, …) and furthermore the high fragmentation of east German landscapes might also alter wolf behavior and migration. Many other animals are also affected by fast moving traffic (see the picture of the poor frog) and therefore i am certain that roads will become more and more important in future conservation area prioritizations and landscape planing.

In their recent paper Freudenberger et al (2013) shed light on the current fragmentation of an east German state and introduce a new landscape metric called the spatial road disturbance index (SPROADI). The SPROADI is an aggregated index, which integrates  the (1) traffic intensity on the habitat intersecting road, (2) density and distribution of roads in a landscape and (3) overall fragmentation caused by roads. Mathematically the new index is just a weighted sum of the three equally weighted subindices, which were previously categorized using available quantitative data. While this is certainly not perfect (data maybe biased and thus has to be normalized beforehand, correlations with subindices) i think that due to its simplicity and easy understanding it might become a handy index for landscape planners and biologists conducting impact assessments for roads. Although i am not entirely convinced by the mathematics behind this new index (next rainy sunday i will put some thoughts behind it) i recommend interested people to give their paper a try.

  • Freudenberger et al. “Spatial road disturbance index (SPROADI) for conservation planning: a novel landscape index, demonstrated for the State of Brandenburg, Germany”. Landscape Ecology (2013): 1-17. DOI: http://dx.doi.org/10.1007/s10980-013-9887-8
  • Selva, Nuria, et al. “Roadless and low-traffic areas as conservation targets in Europe.” Environmental management 48.5 (2011): 865-877.

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