[GRASS-user] LSMetrics - addon development
bernardo_brandaum at yahoo.com.br
Wed Feb 7 05:55:36 PST 2018
We have been developing a python application called LSMetrics to calculate landscape metrics for environmental management and ecological research. All functions/metrics use GRASS modules in the process of calculation. More information here:https://github.com/LEEClab/LS_METRICS/wiki
Basically we have several python functions, each of which calculates a different metric. All of them are then called by a single function that can coordinate the calculation of several metrics for several maps in a row.Currently the metrics can be calculated for input maps using a grafical interface (we call it from the GRASS shell using "python LS_metrics_v1_0_0.py") or by calling python shell from the GRASS shell and then using python scripting to call the functions (importing functions and then calling them directly). We wish to transform these function into one (or more than one) grass addon.
Then I have two questions:- Do you think it would be better to build a single addon (e.g. called r.ls.metrics) so that each metrics is defined by a parameter (e.g. method = "patch_size", "fragment_size", "structural connectivity" ...), or rather a set of addons, each one corresponding to a single landscape metrics (e.g. r.ls.patch_size, r.ls.fragment_size, ...)?
- Also, I have looked on how to build the addon in GRASS help pages but I am not sure about some details. Can you send me some references to ease that process?
Thanks a lot. Below you can find more information regarding the metrics.BestBernardo Niebuhr
Metrics calculated:Metrics related to structural connectivity:- patch size- fragment size (excludes corridors with a given edge depth)- structural connectivity- proportion of habitat (within a window around each pixel)Metrics related to functional connectivity- functionally connected area (considering the capacity of organisms to cross gaps between habitat patches)- functional connectivityMetrics related to habitat edges- distance from edges- clasisfication edge/core/matrix (considering a given edge depth)- proportion of edge and core (within a window around each pixel)Metrics of landscape diversity(we are currently using the r.diversity add on to calculate that)
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