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.

Honorable Mention for the Outstanding Paper Award in Landscape Ecology

I was really excited to learn that a paper that I was an author on had been chosen for the Honorable Mention for the Outstanding Paper Award in Landscape Ecology by the US Chapter of the International Association for Landscape Ecology. Last month I attended the meeting in Baltimore, MD to accept this award on behalf of our team and got to meet some excellent folks and catch up with some friends.  The paper is entitled "Dilts TE, Weisberg PJ, Leitner P, Matocq MD, Inman RD, Nussear KE, Esque TC (2016) Multiscale connectivity and graph theory highlight critical areas for conservation under climate change. Ecological Applications 26:1222-1237." In their e-mail the awards committee wrote "Your paper provides a valuable contribution to the science of landscape ecology by providing a unifying, multi-scale framework and some valuable guidance for when and where to apply differing approaches for assessing species-specific connectivity. We were also excited by the application of graph theory to both climate change and land use change scenarios."   The photo above is me with Ross Meentemeyer taken by Jeff Hepinstall-Cymerman. Thank you US-IALE for this honor.

Paper is now a pre-print

Our paper "Multi-scale connectivity and graph theory highlight critical areas for conservation under climate change" in Ecological Applications is now a pre-print - http://www.esajournals.org/toc/ecap/0/0 .

New tool - Multiple Raster Zonal Statistics

I just released a new tool called Multiple Raster Zonal Statistics for ArcGIS 10.1. This handy little tool can pull attributes such as cell counts from a whole directory of classified rasters potentially saving a lot of time.  I've also posted a link to it from our lab's download page.

Here is an example of how I've been using the tool.

I'm in the process of classifiying NAIP imagery which will be used to generate training and validation for a Landsat model of tree cover.  I've got 1348 random polygons scattered across 19 different NAIP tiles. In the left figure the purple polygons represent NAIP tiles and the tiny red dots are random points. Finding the cell count would involve 4 classes x 19 tiles = 76 runs of the Zonal Statistics tool in ArcGIS.

The Multiple Raster Zonal Statistics tool automates the process using two loops.  First it loops through the NAIP tiles and then it loops through each class in the categorical raster. The resulting output has same number of polygons as the original input, but is attributed with cell counts from each categorical class. You can see on the map in the figure on the right that the polygons are colored according to percent tree cover.

Some caveats: You may need to edit the code slightly if you wish to process more than 4 categories. There are instructions on how to do so in the python script itself. Before running the tool you will also need an index shapefile attributed with the name of each raster image. I did this using the create bounding box for geodatasets tool in the Marine Geospatial Ecology Tools.