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I believe you can use some map algebra (raster > raster calculator) before you can preform your volume measurements in grass. Assuming that your bathymetric data use positive values to represent the sea depth, and using your example for the range as 50 the min_depth and 200 the max_depth. For each of the raster cells you need to "remove" anything below the ...


2

There is r.skyview addon: https://grass.osgeo.org/grass72/manuals/addons/r.skyview.html. It's a visualization technique, but it computes terrain openness. See the referenced paper at the bottom of the manual page for details. I don't know how to use it with your specific requirements, but it's a start.


1

Using the finest resolution possible is not neccessarily the right choice when doing habitat modeling. I can't think of any papers to cite right now but consider this: When creating a habitat model (HM) the resolution highly depends on the species you are looking at. A mouse has a very different moving and cognition range from an elephant. Thus, using a 1x1 ...


1

The issue you have is that your data covers a very small area, such that you couldn't possibly make any inference about the species response to different climate variables. Were there any other attributes collected with the species data relating to physical habitat? This could be slope, elevation, soil type, vegetation type etc. You might be able to find ...


1

Getting temperature estimates at finer resolution than 1 km is not likely to happen without you placing your own temperature loggers around your study site. You're going to have to look into using different explanatory variables. If you hope to be able to determine any drivers of distribution in an area that small, especially for wildlife (vs. plants because ...


1

I worked on the problem through the day and came up with the following solution. I hope it will come handy for everyone. # Creating the objects x <- stack() threshold.m <- vector("numeric", 2) auc.m1 <- vector("numeric", 2) l.T1 <- list() l.model <- list() run = 2 for(i in 1:run){ # Creating the k-fold data group.k1 ...


1

You could do it directly in GEE: var shape = out.reduceToVectors({ geometry: geometry, crs: ee.Projection('EPSG:4326'), scale: 10, maxPixels: 1e13 }) Export.table.toDrive({ collection: shape, description: "yourDescription", folder: "yourFolder", fileNamePrefix:"yourName", fileFormat:"KML" }) But I do not recommend to do it directly ...


1

If you are fine with using ArcMap for this analysis alone, there is a tool called Minimum Bounding Geometry (Select Polygon) and Kernel Density. I found it to be way simpler than using Sextante/R in QGIS.


1

You should be able to load the SLD styles in QGIS for layers. I'm not very familiar with Ordinance Survey data, but it looks like you should be able to just import the SLD styles by going to the layer proprties -> Style -> Load Style -> select .sld option. Below are a couple of screen-shots that should help. (This is just showing how to import the SLD ...


1

Take a look at the object class definition for "MCHu". The help indicates "The class "MCHu" is basically a list of objects of class SpatialPolygonsDataFrame, with one data frame per animal". As such, you can just extract a SpatialPolygonsDataFrame, for each animal, from the list object. library(adehabitatHR) data(puechabonsp) locs <- puechabonsp$relocs ...


1

I would use SAGA or python. SAGA: -import raster to grid -reclassify all points greater than your min-depth to NODATA -reclassify all points less than max-depth to NODATA at this point you can visualize your band of habitat. then you can do several different things but I would: -create constant grid with value min-depth (not, max-depth is the deeper ...


1

There's another GRASS module that you might find applicable to your case: v.rast.stats. This module creates a table of univariate statistics for each polygon in a vector layer, from the values in a raster. You will get: sum=the total of all cell values with each polygon, which is your habitat volume. And for free you also will have max, min, mean, std, etc. ...


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