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There are many instances in which a person might want to obtain the actual underlying data in order to reconstruct a graph.  Maybe you are doing a meta-analysis and you don't have access to the original data.  Perhaps you ran the results years ago and are only now getting around to publishing.  Maybe the software that you used years ago no longer exists so you can't re-run the analysis.

Although there are a number of digitizing software packages out there I'd suggest that for a GIS professional that GIS software might be the way to go.  I recently had a case where I undertook this myself.  As a quick aside it is easier to generate the data while running models.  Always search your software to see if there are options for outputting the data used to build the graphs to some sort of common file type such as a CSV file. For example, Maxent software has a button that needs to checked on to output these files (see below).  It is the write plot data checkbox under the Advanced tab in the older version of Maxent.



Now, back to my case study.  I found myself with these terrible default graphs from Maxent (see below).  No journal editor in their right mind would ever publish something like this.  The font is far too small to read and there is some unnecessary titles in which the information would be in the caption in a journal manuscript.  I needed something cleaner and nicer, but unfortunately I had forgotten to check the above mentioned box years ago when I did the study.



Instead I used ArcMap to take screenshots and digitize the values in order to clean up the data.  Here is my workflow:

First I zoomed in and took more detailed screenshots.  I used standard tools on any windows computer such as the PrtScrn button and the paint software.  This resulted in a graph like the one below.



Next I imported this into ArcMap, which was as simple as dragging the file in from my desktop into an empty ArcMap instance with a data frame with an undefined projection.  In ArcMap I digitized a smooth line designed to replicate the line from the Maxent software like the one below.



I ran a tool called Feature Vertices to Points to convert the lines to points. Feature Vertices to Points uses an Advanced license of ArcMap.  If you don't have that license or want to skip the conversion I'd suggest just digitizing the points directly.


The result of the Feature Vertices to Points to conversion is shown in the image above.  At this point in the process I also digitize in four additional points with known X and Y coordinates on the graph.  Next I run the Add XY tool to obtain the X and Y coordinates (these are in arbitrary units).  I add two additional fields called X and Y which I create as double precision.  In the Field Calculator I use the following formula to calculate the X field:

(((newmax - newmin) / (oldmax - oldmin)) * ( [POINT_X] - oldmin)) + newmin

where newmax is the maximum X value of the digitized reference point as read off of the graph
where newmin is the minimum X value of the digitized reference point as read off of the graph
where oldmax is the maximum X value of the digitized reference point as calculated from the Add XY tool
where oldmin is the minimum X value of the digitized reference point as calculated from the Add XY tool
and [POINT_X] is the value from the POINT_X field in the table

The result of this and a similar formula applied to the Y value is shown below.



Finally I highlight the top rows skipping the four reference points and copy the data into Excel.  As an alternative workflow I could have applied the field calculator formulas in Excel.  Now with a little finessing in Excel (or R or whatever software you prefer) you can get some clean nice graphs like the ones below.

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Matt Forister, myself, and our collaborators at the Xerces Society and the USFWS have a new paper out titled "Host Plants and Climate Structure Habitat Associations of the Western Monarch Butterfly" which is published in the journal Frontiers in Ecology and Evolution. In this paper we model the habitat associations between monarch butterfly, their milkweed host plants, and a number of climatic, topographic, land use, and soil variables. We also perform habitat modeling for 13 of the most common milkweed species in the western U.S. Our study is the first large-scale habitat modeling study focused on the western population of the monarch butterfly which overwinters on the California coast and migrates inland towards the Rocky Mountains. Our paper is open access meaning that anyone can read and download it. You can go to it by clicking HERE.

To facilitate this kind of modeling we used an online citizen-science mapping tool called the Western Monarch and Milkweed Mapper. You can use the WMMM to upload your photos of monarchs and milkweeds in order to help us build better models in the future!  The WMMM also flags recent monarch sightings so that you can see when the monarchs are arriving in your area.  The WMMM also has great resources for learning how to identify the different species of milkweeds.  You can access the Western Monarch and Milkweed Mapper by clicking HERE.

Our habitat modeling work was also made possible by the tremendous amount of legwork by Madeline Steele and Joe Engler (USFWS) who completed a first version of these habitat models in 2016. Most of the variables that we used were originally compiled by Madeline, and the methods that we used followed Madeline's approach.

Finally, we should note that we have made high-resolution maps as well as details about the methodology available in the supplementary material. I would encourage you to check out these resources. For GIS users we have GeoTIFF versions of our maps available. Please contact me and I will send you a link to these files.

Citation: Dilts, T.E., Steele, M.O., Engler, J.D., Pelton, E.M., Jepsen, S.J., McKnight, S.J., Taylor, A.R., Fallon, C.E., Black, S.H., Cruz, E.E., Craver, D.R., & Forister, M.L. (2019) Host plants and climate structure habitat associations of the western monarch butterfly. Frontiers in Ecology and Evolution, 7, 188.
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Everyone should check out the Spring 2019 issue of ArcUser.  It has a nice article by Jay Johnson of Washoe County on how they processed the recent LIDAR flight in the Reno and Sparks area.  Furthermore, the magazine cover is graced by a magnificent image of our area that is the bare earth Digital Elevation Model. You can really see the dramatic topography and almost feel the geomorphic processes taking place in our mountains and valleys!  Check out the article by clicking HERE.
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Adriano Matos and I have put together a new tool for calculating hypsometric integral for ArcMap users. The new tool is called the Hypsometric Integral Toolbox for ArcGIS and is available by clicking HERE. You can also download this tool by clicking on our lab's tools download page HERE.

The hypsometric integral (HI) is one of the most commonly used measures that geomorphologists use to describe the shape of the Earth’s surface.  A hypsometric integral is usually calculated by plotting the cumulative height and the cumulative area under that height for individual watersheds and then taking the area under that curve to get the hypsometric integral.  In a GIS hypsometric integral is calculated by slicing watersheds into elevation bands and plotting the cumulative area for each band. Due to the iterative nature that is required for calculating hypsometric integral it tends to be one of the harder to calculate watershed variables, and thus the need for an automated tool. Although there are instructions online for how to calculate HI in ArcGIS this tool automates the processes and doesn’t require users to do their own plotting or export results to spreadsheets.

This toolbox contains two models.  Hypsometric Integral (for shapefiles only) is the main model that most users will want to run.  Hypsometric Integral (submodel) is a model that is nested within the Hypsometric Integral (for shapefiles only) model and doesn’t need to be run by itself. The tool computes the hypsometric integral for a given watershed. A new shapefile will be created representing the same watershed the user inputs, but includes a new field, "HI," representing hypsometric integral percentages.

In some instances the Hypsometric Integral (for shapefiles) will show up with a red X and won’t be useable.  The workaround for this is to open the Hypsometric Integral (for shapefiles) tool in edit mode (ModelBuilder) delete the Hypsometric Integral (submodel) and drag in your version of the Hypsometric Integral (submodel). Re-connect the following parameters: input DEM, Input Watershed, TempWorkspace, and then connect the output (HI Values for all Watersheds) to the Append tool.  Click save.




In the example map above on the left we have 19 sample watersheds from central Nevada with a Digital Elevation Model. The map above on the right shows the hypsometric integral for each of the watersheds. As you can see smaller more "canyon-like" watersheds have higher values of HI compared to the more open watersheds.
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Recently I've been encountering problems where some geoprocessing tools in ArcGIS have failed to run due to requiring too many values in the attribute table.  I came across this interesting article that I thought that I'd share.  The most interesting aspect of this is that it doesn't only pertain to viewing rasters as the summary might imply.  It is actually a critical step for getting certain tools to run.  The Combine tool in Spatial Analyst is just one tool that can benefit from increasing the number of allowed unique values.  https://support.esri.com/en/technical-article/000010117
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Congratulations Anna Knight on winning Outstanding Student Presentation at the 2018 American Geophysical Union Meeting for her poster “Geomorphic and disturbance controls on vegetation dynamics in Great Basin riparian ecosystems “. Click HERE to view Anna’s poster. You can read more about AGU’s Outstanding Student Presentation by clicking HERE.

AGU is a huge conference with thousands of students attending.  Anna's award is a testament to her hard work and innovative cutting-edge techniques.  Anna is completing her master's thesis under the advisement of Dr. Peter Weisberg.  You can view her webpage by clicking HERE.


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Congratulations Sam Flake and Peter Weisberg on their new paper "Fine‐scale stand structure mediates drought‐induced tree mortality in pinyon–juniper woodlands" published this week in Ecological Applications.  You can view their interesting and excellent paper HERE.
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Here is part four of the blog post on the Miscellaneous Hydrology Tools for ArcGIS. This post covers a tool called the Find Longest Stream Path tool. You can read the original blog post by clicking HERE or download the tool be clicking HERE. It is the fourth and final in this series about the Miscellaneous Hydrology Tools.

Surprisingly, there are no conventional tools in ArcMap that identify the longest stream in a watershed. However, many geomorphic metrics, such as relief ratio and watershed shape, are based on knowing what the longest stream is. In order rectify this situation I built a small model that calculates the longest stream. Unlike the other two tools in the Miscellaneous Hydrology Toolbox it does not provide an absolute answer.  For reasons unknown to me occasionally some watersheds get left out.  Nonetheless I feel that this tool is a useful and helpful addition that some people will enjoy having in their toolbox.

On the left is a sample of watersheds from central Nevada with the longest stream from each watershed shown in bold blue.  All streams are shown in light blue.  Watershed boundaries are in black. Pour points (those places where the stream exits the basin) are shown in green. Channel heads for the longest stream are shown in yellow.

There are four parameters for running this tool.  A input flow stream flow direction tool is required for understanding flow routing.  A standard flow direction raster can be clipped to the stream network to achieve this.  Input watershed polygons are required.  A temporary folder is required for storing outputs.  Finally, a dissolve field (ID for example) is required.  This should coincide with a field with the same name in the watersheds file.

On the right is a picture of the model.  Let me step you through how it works. For each stream flow direction cell the model calculates an upstream and downstream flow length. Using those flow lengths the model generates channel heads and pour points in each watershed. Using cost distance the model identifies the channel head with the greatest cost distance from the pour point. This becomes the end point. The combination of the end point and the pour point is used to create a least-cost path. Finally, grid cells are converted into flow lines.


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