New paper - Contrasting climate niches among co-occurring sub-dominant forbs of the sagebrush steppe

Sarah Barga, Beth Leger, and myself just got a paper accepted in Diversity and Distributions!  It is titled "Contrasting climate niches among co-occurring sub-dominant forbs of the sagebrush steppe". The paper projects species distribution models for ten sub-dominant herbaceous forbs in the Great Basin. We then looked at niche overlap and found very little between the ten species. There was no relationship between phylogentic distance and niche overlap. We also looked at how species responded to temperature and precipitation variability and found that there were differences among different life forms. We hope that our paper findings will help conservationists understand which species may be more or less suitable to climatic variability.

Blended image to classification in ArcMap

A while back I did a vegetation classification in ArcMap using data collected from a drone. The method was fairly simple and I was pretty pleased with the result. I wanted to simultaneously display the image and the classified map. I had seen some pretty nifty blended images on the web that were created in Photoshop, but since I don't have Photoshop on my computer I opted to try to figure out how to do this in ArcMap. In general, I followed the steps to this tutorial - https://blogs.esri.com/esri/arcgis/2008/10/14/fade-to-white-background-effect/

However, I took some liberties and deviated from it a bit. My classification was a raster so in order to accommodate that I sliced the raster up into discrete slices going from north to south. For each raster I set the transparency to increase by 7%. Likewise I did the same with the segment outlines (the black lines).

Below is the resulting image. In case you are interested in the actual vegetation here is what each color represents: blue = sagebrush, green = other shrub, pink = cheatgrass+forbs, tan = bare soil, and gray = dead shrub (rare in this image). The UAV image was take by AboveGeo near Doyle, California. The upper portion of the image is intact sagebrush desert while the lower part was previously burned.

New tool - Patch and Gap Metrics Toolbox for ArcGIS

Forest ecologists, vegetation ecologists, and others are frequently interested in characterizing the structure of patches and gaps on the landscape. Typically, data, such as tree crown size, are collected in quadrats. In order to synthesize these data for each quadrat I developed the Patch and Gap Metrics Toolbox for ArcGIS. This tool takes polygons of quadrats combined with polygons of representing tree crowns, shrub crowns, or some other patch on the landscape, and calculates the number of patches and area of those patches as well as gaps. There is a version of the tool that allows the user to specify a radius to filter the gaps by in order to ensure that only large gaps are included in the output. You can download the tool by clicking HERE.

The Patch and Gap Metrics Toolbox only requires two input layers: 1) a polygon shapefile of patches (need not be dissolved) and 2) a polygon shapefile of quadrats. The quadrats can be any shape or size and can even be overlapping. In addition to this the quadrat polygons need a quadrat ID field upon which dissolving can be based on. Finally, for the version of the tool that accommodates additional gap size criteria there is a parameter that specifies the radius of gaps to be considered. For example, a value of 6 would eliminate any gaps with a diameter less than 12.

Above: There are two inputs required by the Patch and Gap Metrics Toolbox. The picture on the left shows a polygon shapefile representing the patches. Note that adjacent patches do not need to be merged. The tool will do this automatically. The picture on the right shows overlapping quadrats. Quadrats need not be overlapping.

The main output of this tool is a point shapefile representing the centroid of each quadrat that is attributed with the following fields:

COUNT_SHAPE – Count of the number of patches
SUM_ SHAPE – Total area of the patches in the quadrat
MEAN_ SHAPE – Average size of the patches in the quadrat
STD_ SHAPE – Standard deviation of the patches in the quadrat
MIN_SHAPE – Minimum number of patches in the quadrat
MAX_SHAPE - Maximum number of patches in the quadrat
SUM_ SHAPE1 - Total perimeter of patches in the quadrat
COUNT_SHAP_1 – Count of the number of gaps
SUM_ SHAP_1 - Total area of the gaps in the quadrat
MEAN_ SHAP_1  - Average size of the gaps in the quadrat
STD_ SHAP_1 – Standard deviation of the patches in the quadrat
MIN_ SHAP_1 - Minimum number of patches in the quadrat
MAX_ SHAP_1 - Maximum number of patches in the quadrat
SUM_ SHAP_2 – Total perimeter of gaps in the quadrat

In addition to attributing each quadrat centroid with the above values there are also two additional outputs. In the image below the darker green polygons with red outlines show patches as generated by this tool. The tan polygons with purple outlines show the gaps using a 6 meter radius filter. The remaining light green areas are classified as neither patch nor gap.