National Forests Technology

How to Add Hiking Trails to Google Earth

Hiking down a trail in the woods is one of the most freeing feelings in the world. Knowing that, let’s see how we can use technology to find great hiking opportunities on our public lands.

How can you add hiking trails to Google Earth? Google Earth comes equipped with trail maps for the National Park System by default. Other organizations like the U.S. Forest Service allow for their trail data to be downloaded as a KML file, which can then be uploaded to Google Earth.

So, how exactly are we going to do that? Keep reading and we’ll go over step-by-step instructions on everything you need to know about adding hiking trails into Google Earth.

Enabling the Default Trails in Google Earth

Before we start exploring open data sets, it would probably help to first check the features that come with Google Earth straight out of the box.

So, what kind of hiking trails can we get with Google Earth? From what I can tell, the only trails available are those that belong to National Parks in the United States. Furthermore, there seems to be very little data associated with the trails that are present. If you happen to be lucky, you can expect to find the name of the trail once you are zoomed in close, but that’s about it.

With that being said, many people will be more than content to roll with this default option, as it appears to do a good job covering the trails in the National Parks.

Finding the Trails Option in the Menu

As you can expect, turning on this layer is dead simple: all you have to do is find it in the Layers menu and then check the box. You’ll looking for the menu in the path of More –> Parks/Recreation Areas –> US National Parks –> Trails, as you can see below:

The ‘More’ group is at the bottom of the Layers menu, so don’t be surprised if you overlook it at first. Enabling the Trails button will end up enabling two features: Trails and Trail Junctions.

The Trails feature is obviously the layer that contains the outline of the trail itself, so that layer is necessary. You’ll often find the name of the trail when you zoom in far enough, as you can see below. Clicking on the trail never appears to do anything based on my experience, but it’s possible that some trails might have additional info.

The Trail Junctions feature is represented on the map with small red circles located along the trail. This information may be useful for some, but I wouldn’t consider it necessary. The small circles are clickable, but I’ve found no junctions that provide a window with additional information. Everything has simply shown a window indicating that I was viewing a ‘Trail Junction,’ as you can see below:

Not the most helpful of stuff, but it is convenient to see the locations of the trail junctions. You’ll see later that while the data direct from the National Park System does have more fields available, it doesn’t often extend beyond the name of the trail. Many of the additional fields are either strictly bureaucratic or they are missing values.

Most people interested in finding trails for National Parks would therefore be best off by sticking to the default trails that come with Google Earth.

Importing Trails Downloaded From the U.S. National Park Service

If you’re looking for more information on the trails in a National Park and you don’t mind searching a little bit, you’re just in luck. The National Park Service maintains an open data hub on the ArcGIS website, which you can find at this link. This data hub covers a wide variety of applications, but they very conveniently provide a Trails category.

The KML files (the type of file we’ll need to upload into Google Earth) are dedicated to a specific National Park, so you’ll have to find the file for your specific park. Feel free to start your search by opening this link, which has a few filters already applied in order to weed out the stuff we don’t need. Once you open that link you should see a page that looks something like this:

We’re mostly interested in the search bar that you can see at the top of the screenshot.

Downloads Organized by National Park in the ArcGiS Data Hub

Your fastest and most reliable option will be to directly search for the trails file associated with the National Park of your interest. So give it a go and search for the trails dataset for your park. Not every park is available and the naming conventions aren’t super consistent, so you may have to try a few different options.

If you can’t find a file dedicated to your park, then you’ll have to use the a filtered dataset of the layer associated with the entire department. Here is the link to that dataset.

Download Your Specific Park Then Upload to Google Earth

Now that you have your KML layer with the trails for your park of choice, you’ll wan to upload it to Google Earth. This is easy enough, just find the Open option in the File menu.

This will add the file to your Temporary Places section, and you’ll want to move it over to the My Places in order to keep it around.

Unfortunately, there’s not much to say about the trail data available from the National Park Service. There are a variety of data fields available with this file, but it turns out that the vast majority of trails don’t have values present.

How to Add U.S. Forest Service Trails to Google Earth

While the U.S. Forest Service does have some great information available, the unfortunate part is that their download file for their trails is nationwide, and therefore is much too large for Google Earth to handle.

No Easy Option for Downloading the U.S. Forest Service Trails

Much like the national parks, information for the U.S. Forest Service can be found at an online hub called the Geospatial Data Discovery. There’s a lot of really cool information here, but the hiking .kml file is simply too large to upload into Google Earth.

This leaves us with a few options:

  • We can download a filtered dataset on a small group of trails (does work, but it is quite slow)
  • We set up an Image Overlay using the WMS Server (slowly updates in Google Earth and looks sub-optimal)

I think most people will be best off by going with the first option. While it is quite slow to process the download, it’s not quite glacial. Also, the download is slow on the ArcGIS end of things, so you’ll be able to let it run in the background without any issue.

How slow are we talking? I downloaded a filtered dataset that contained approximately 300 records in the U.S. Forest Service hiking trail dataset, and it took about 8 minutes to complete. The final file was only 3.8 MB in size, so you can see that this download is slow only because ArcGIS is literally building your filtered dataset before they can send it over to you.

If you’re interested in setting up the Image Overlay, you can check out this post that covers this process is detail. The biggest thing to keep in mind is that your going to be setting up a different WMS server than the one specified in that post. This should be your server for the U.S. Forest Service hiking trails:

Zoom Into Your Region of Interest on the Geospatial Data Discovery Site

You’ll first want to open the link to the data layer for the trails of the U.S. Forest Service. When you open this link you should see something like this:

We’re going to be downloading a filtered dataset, so the first thing we’ll have to do is zoom into our desired location. It may take quite awhile for the trails to appear on the map, as I wasn’t able to see them until the county boundaries appeared.

If you click on the Data tab that you can see in the lower-left corner of the above screenshot, you’ll be able to see the quantity of trails left. Try to keep this number on the lower end (less than a few thousand, ideally), as larger datasets will require additional processing.

Initialize the Download and Let ArcGIS Process it in the Background

Once you are content with the download area you’ve zoomed to, it’s time to initialize the download. As we’re going to use this data in Google Earth, we’re going to need a file format called KML. The button we want is the Download button on the right side of the screen, and in the bottom part of the menu you’ll want to click the KML option, like so:

Clicking that will initiate the processing of your request, and it might take a little while. Your results may vary, but you can probably expect to wait about ten minutes for a file with a thousand or so records.

So, start that download and then take a break. Your download will be hopefully be ready once you get back.

Add Your Filtered Dataset Into Google Earth and Explore

Now that you’ve downloaded your trails into a KML file, it’s time to upload it into Google Earth. This is straightforward enough. Just open Google Earth and then find the Open option in the File menu in the top-left corner of the screen.

You’ll be presented with a file directory that only shows KML and KMZ files by default, so it should be easy enough to find the KML file you just downloaded. After opening this file you should see your screen update to something like this:

The red lines represent the U.S. Forest Service trails that you just downloaded. If you zoom in closer you’ll be able to pull up details for a trail by clicking on it. You’ll see a menu like this:

Admittedly, this table is a bit of an eyesore, but there’s some good information here. First, you’ll notice at the bottom that they list the seasons in which a trail is open to the various modes of use. Second, you’ll find interesting info on the characteristics of the trail in the red box in the middle. There you’ll find info on the trail surface, grade, and approximate width.

The last thing to note is the following: the trails you just uploaded are currently in your Temporary Places part of your Google Earth profile. If you’d like to reference them the next time you come back, it’s easiest just to add them to your My Places panel. This can easily be done by right-clicking on your trails layer and then selecting the ‘Save to My Places’ menu item.

Final Thoughts

Thank you for taking the team to read this post, and I hope you managed to find the answer to your question.

Feel free to check out any of these articles on similar topics:

National Forests Technology

How to Add Forest Service Roads to Google Earth

Forest Service roads are a tremendous asset for those interested in exploring all that our National Forests have to offer.

How can you add U.S. Forest Service roads to Google Earth? Google Earth allows users to create Image Layers based on data from WMS servers. U.S. Forest Service roads are available as a data layer on the Geospatial Data Discovery tool, which can be added as a WMS server.

Let’s get to it: in this post we’ll go over everything you need to know in order to add U.S. Forest Service roads into Google Earth.

How to Add U.S. Forest Service Roads to Google Earth

Before we get into the step-by-step instructions on how to incorporate U.S. Forest Service roads in Google Earth, I need to quickly cover the best options available to us.

A Brief Summary of Our Options

Without digging into the boring details too much, there’s not exactly an easy way to add U.S. Forest Service roads to Google Earth.

The easiest route would be to download the necessary KML file directly from the Geospatial Data Discovery tool that is provided by the Forest Service. They do offer KML files as an output for their data layers, but unfortunately the data layer we need (National Forest System Roads) is much too large to function in Google Earth, as it comes in at over 1 GB in size.

Ideally, we’d be able to filter down to our area and then download the filtered results in a KML file. The unfortunate truth is that the filtered dataset option in ArcGIS is either crazy slow, or straight-up broken (background info for the curious).

So in order to add Forest Service roads into Google Earth we’re left with a workaround that produces a somewhat unsatisfying result. It still works, but it’s a bit laggy and it is rather difficult to discern the road numbers from the created layer.

Add an Image Overlay With the WMS Server Tool

As I mentioned above, we’re unable to import the data on the Forest Service roads directly into Google Earth, but we’ll be able to employ a workaround. To do this we’ll be creating an Image Layer in Google Earth that is based on data from something called a WMS connection. To get started, copy the below URL:

Next, go over to Google Earth and select the ‘Image Overlay’ menu item from the ‘Add’ part of the top menu:

This will open up a window that looks something like this:

Your cursor should already be in the ‘Name’ field at the top. This is what we’re going to see in the Places panel on the left side of the screen, so change the Name to something like ‘Forest Service Roads’.

After you’ve done this, then click on the ‘Refresh’ tab and then click on the ‘WMS Parameters’ button on the right:

This will bring up a new window, and this is where we’ll be needing the WMS URL from before. The top part of this window has a ‘WMS Server’ area with a dropdown menu. You’ll want to click on the ‘Add…’ button highlighted in the below screenshot:

You’ll paste the URL into the ‘Enter WMS Server URL’ window with the red arrow pointing to it. Once you paste it in, click ‘OK’ and then your screen window should update to something like this:

Notice that the Transparent Layers section now has two layers listed if everything worked.

Add the Correct Layer Over to the Selected Layers Section

Now that we’ve established the connection to the WMS server for the U.S. Forest Service roads, we have to add the correct layer over to our Image Layer in Google Earth. There should be two options available:

  • National Forest System Roads closed to motorized uses
  • National Forest System Roads

As you can probably guess, we’re interested in the second layer, as the first layer will only show you roads that are currently closed. We’ll need to move to second layer over to the ‘Selected Layers’ panel on the right. To do that we’ll click on the ‘National Forest System Roads’ layer and then click the ‘Add ->’ button highlighted below:

Once you move the layer over, you can hit the ‘OK’ button for the window. At this point your screen will likely update and show you something that looks like this:

I don’t think I have to tell you that the ‘strange white box at an angle’ is not the intended result from doing all this. We’re going to have to make a few minor tweaks in order to fix this.

Tweak the Values in the Link for Increased Clarity

We’ll make a few small tweaks to the value in the ‘Link’ field at the top of the window. Admittedly, this field might look a little overwhelming to those that don’t spend a lot of time querying APIs, but it’s pretty straightforward. Here is end of the link that Google Earth generated automatically after adding in the WMS layer:

I’ve added red lines to highlight three different parts of the URL, the width, height, and image format.

First, we’re going to adjust the height and width of the image that Google Earth adds to our map. The default value is 512 pixels, as you can see in the above screenshot. We’ll want to bump that up to something like 1920 pixels. I don’t think the exact number you choose matters, you just need to ensure that the height and width are equal. You can update these values by typing the 1920 over the previous value of 512. Ensure that you don’t overwrite the equal sign and ampersand on either sides of your number.

After bumping up the pixel count on your height and width, you’ll want to change the format of the image. As you can see at the end of the URL, the default format is GIF, represented by the ‘image/gif’ part at the end. We’ll need the ‘image/’ part, but you need to change the ‘gif’ part to ‘png’ like so:

After doing all of this, your updated entry in the ‘Link’ field should look something like this:

Adjust the Settings in the Refresh Tab for the Image Overlay

With the entry in the Link field sorted, you next have a make a few tweaks back down in the ‘Refresh’ tab. We’re going to adjust two values:

  • The duration of the View-Based Refresh (default should be 4 seconds)
  • The View Bound Scale (default set to 0.75)

Make the following changes to your settings so that they look like this:

My general understanding is that this will allow the data to refresh much faster, as you won’t have to wait so long for the server to pull over new information.

As a side note, huge thanks to everyone over on StackExchange for their discussion of how to figure out this problem in Google Earth.

Zoom in and Out to Refresh the Image Layer

Now that you’ve gone through all of this work to get this connection established and working just right, it’s time to actually use this info!

You’ll want to zoom in and out to trigger Google Earth into loading the U.S. Forest Service roads onto your screen via the Image Layer we just set up. It does appear that there is a limit to how far zoomed out your screen can be in order to load the roads image.

You don’t have to be super close in zoom, but this was the farthest zoom I could pull off where the road layer was successfully added:

Note the value on the scale on the bottom part of the screenshot for reference.

As you move the screen around and adjust the zoom you’ll notice that it takes a little bit for the roads to appear.

While I’ll be the first to admit that it’s rather convenient that we can hook up this server like this, there is one major caveat for me: the labels for the Forest Service roads are unfortunately barely readable.

I’ve played around with the zoom and the other settings in order to try and improve this, but I haven’t had much luck.

Final Thoughts

Hopefully you were able to get everything working and you are now enjoying the convenience of having Google Earth automatically pull in open U.S. Forest Service roads. I’ll be the first to admit that this solution isn’t ideal, but I’m on the lookout for a better solution. I’ll update this post if I find a better method.

If you’re interested in similar posts, feel free to checkout any of the following:

National Forests Technology

How to Add U.S. Forest Service Boundaries to Google Earth

One of the best parts of the U.S. Forest Service is their online repository of data that is available for the public at no cost.

How can you add U.S. Forest Service Boundaries to Google Earth? The U.S. Forest Service allows for the download of spatial data from the Geospatial Data Discovery tool maintained by ArcGIS. This includes data layers on general boundaries, as well as specific parcel ownership.

Keep reading as we dive into the weeds to go through the different options available when bringing U.S. Forest Service data into Google Earth. We’ll cover the different options available, as well as show step-by-step instructions on how to get it all done.

How to Add U.S. Forest Service Boundaries Into Google Earth

Before we get into the specifics of the different layers, I think it helps to briefly go over some of the terms that I’ll be throwing around in this post.

You might already be familiar, but Google Earth primarily works two proprietary file formats: KML and KMZ. KML stands for Keyhole Markup Language, and you’ll be seeing the .kml and .kmz file extensions a lot when adding files to Google Earth. The .kmz file extension simply refers to a zipped version of .kml, so it may have several .kml files packaged up in a single zipped folder. The files we’ll be working with in the post are .kml files.

The Geospatial Data Discovery Tool is a location where the U.S. Forest Service stores a wide variety of geospatial files free for public use. It’s hosted on the ArcGIS platform that allows you to preview the data and download what you need in a variety of file formats. When you open the Geospatial Data Discovery hub you’ll be taken to a page that looks like this:

You’ll notice a search box at the center of the screenshot. You can directly start searching from there, or you can scroll down and look through the various categories that they have available:

Even if you’re not the kind of person that’s accustomed to digging around open data hubs like this (look, I get that I’m a bit of an odd duck in this regard), I hope you’re intrigued by the wide array of data they have available.

Anyways, back to the task at hand. I’ll be giving you links to the exact data layers that you need in each section, so no need to worry. I just thought it might be helpful to give a little background on this platform first.

Downloading the Approximate Boundaries of the National Forests

I suspect that most of you are going to want to know which individual parcels of land are owned by the U.S. Forest Service, but first we’ll cover a data layer that provides the overall boundaries of the different National Forests and Grasslands. Skip on ahead to the next section of you’re looking for the data layer with the individual parcels.

First, we’re going to open up the Forest Administrative Boundaries layer in GDD. You’ll be greeted with a page that looks something like this:

You’ll notice that I’ve added boxes to highlight two different parts of the screen. First, you’ll see a map in the background that shows all of the different National Forests present in this layer. Second, you’ll notice that there’s a ‘Download’ button with a drop-down menu in the bottom-right portion.

If you explore the map, you might recognize that the shapes on the screen are the general boundaries of the National Forests throughout the United States. These boundaries don’t represent the actual parcels of land owned by the U.S. Forest Service. This might be an obvious point for some, but I think it’s worth pointing out.

To download the Google Earth version of this layer, first click on the ‘Download’ button as seen here:

Then click on the ‘KML’ option found in the ‘Full Dataset’ part of the drop-down menu. This file is a little bit on the large side at 75 MB, but it shouldn’t take too long. Once the download is finished, you can head over to Google Earth and click the ‘Open’ option from the ‘File’ menu:

Then you can navigate to the .kml file you just downloaded and then you can double-click or click the ‘Open’ button to add this layer to your ‘Temporary Places’ folder in Google Earth.

The default styling in Google Earth will likely be fine for your purposes, but if you’re interested in changing the styling, check out below for more details.

You can learn more about each of the units highlighted on the map by clicking anywhere inside the boundaries. This should bring up a info box that looks something like this:

Here you can see the name of the National Forest, as well as the acreage and other attributes of the shape. I believe that the shape area and length are tied to the boundary and not to the National Forest itself, so you can probably disregard that information.

Finding the Actual Parcels Owned by the U.S. Forest Service

Now we get to the good stuff. I’m assuming most are like myself and can make more use out knowing which parcels the U.S. Forest Service actually owns, so here we’ll dive into that.

To get this information we’re going to open the ‘Surface Ownership Parcels, detailed’ layer from GDD. After opening that link you should see something like this:

You’ll notice right away that this dataset is much larger than the last one. First, you can see right below the layer name that there are more than 115,000 records in this dataset, so there’s a lot more going on here. Naturally, this means that this data layer is much larger than the past layer, as it comes in at a whopping 470 MB at the time of writing.

This might be too much for some to download, but you can get an idea of why it’s so useful just by zooming in to an area that you know has a National Forest. You’ll be greeted by something like this:

Here you can see that this layer covers more than a general boundary; it instead shows each individual parcel of land that is owned by the U.S. Forest Service. Once you zoom in to your area, you can check out the ‘Data’ tab by clicking here:

You’ll then see something like this:

This essentially is a data table showing the parcels that are visible on your screen at the time. This data layer has many data fields that are available, but you can see that this is a pretty neat bit of information about the history of the National Forests.

One quick thing to note before moving on: the ‘Download’ button does indicate that it’s possible to download your filtered dataset from the GDD, but I’ve had no luck doing this. I’ve tried two different computers, and each time the download button just spun and never produced a filtered file. So I believe it is only possible to download the full dataset.

You can download this full dataset by using the same option as before, just by clicking the ‘KML’ option in the Download menu just like so:

This will initiate the download for the entire data layer, and it may take awhile depending on the speed of your internet connection.

Adding the U.S. Forest Service Parcels into Google Earth

Now that you have the full dataset downloaded from the Geospatial Data Discovery hub, you can add it into Google Earth with the File –> Open option in the main menu.

Be aware that this is a rather large file for Google Earth to process. I’m running this on a desktop computer that has a lot of RAM and Google Earth definitely struggled a bit. Once you’ve managed to add this data layer to Google Earth (it might take awhile), you should see something like this:

You can see that my version of Google Earth is definitely struggling a bit. Given that, it will probably be best to disable this layer by unchecking the box in the Places menu on the left like so:

Then zoom in to your local area before enabling the layer again. Assuming that your local area has land that is owned by the U.S. Forest Service, you should see something like this:

All of the red lines represent the borders of parcels of land that are owned by the U.S. Forest Service. Admittedly, this is a bit of a mess to look at, but we’re going to show how you can clean it up next.

Altering the Styling in Google Earth for Ease of Use

In its current format it’s a bit difficult to distinguish the exact ownership of the lands, so we’re going to add some styling to make it more obvious. First, go over to your data layer and then right-click to bring up the following menu:

Click the ‘Properties’ menu item and then you should see something similar to this:

Click the ‘Style, Color’ tab that’s pointed out in the above image. In that tab, click the ‘Share Style’ button and then you’ll have to adjust the opacity. Assuming that your Google Earth is behaving like mine, your parcels might have adjusted to include a fill with 100% opacity. That’s not really what we want, so you can then adjust the opacity to something like 15% like so:

It take a little while for Google Earth to process that adjustment, but once it is done processing you should see something like this:

This will make it much easier to distinguish between private parcels and those owned by the U.S. Forest Service. From here you can click around to bring up more information about the different National Forest parcels. When you click you’ll be greeted with an info box that looks something like this:

This is definitely a bit of a mess, but you can see that there’s some cool info in there. For example, this parcel was sold to the Forest Service in April 1934 by a Hines Hardwood & Hemlock Company.

Final Thoughts

Hopefully you enjoyed this post and managed to get everything to work on your end. I think it’s pretty great that the Forest Service provides all of this information for free, and I think it’s really valuable to a lot of people.

If you found this post enjoyable and are interested in similar posts, feel free to check out any of the following:


How to Turn Off 3D Trees in Google Earth

Ah, 3D trees in Google Earth. Like many others, I chuckled a bit the first time I saw one of them rendered on my computer screen.

How do you turn off 3D trees in Google Earth? While the ‘3D Buildings’ section of the Layers menu does contain a ‘Trees’ option, you can disable 3D trees in Google Earth by unchecking the ‘Photorealistic’ menu item. This will disable the entire effect that produces 3D trees.

Keep reading to find step-by-step instructions on exactly how to get rid of the slightly ridiculous 3D trees you see all over your screen.

How to Disable 3D Trees in Google Earth

While I’ll admit that the 3D tree effect is somewhat impressive, it’s just a bit too buggy for my tastes. Just as an example, we have a large sycamore tree sitting at the end of our driveway. Despite the fact that it’s a bit of a pain, the tree is quite beautiful. However, it doesn’t look anything like this:

So, close, yet so far away. Anyways, let’s get on with it and disable these 3D trees.

Easiest to Disable 3D Buildings in its Entirety

The easiest way to get rid of 3D trees in Google Earth is just to disable the entire ‘3D Buildings’ group in the first place. You can find this in the Layers section of the sidebar on the left side of the screen:

If you don’t see a panel like this you’ll have to check that your sidebar is enabled. There are two ways to toggle this sidebar on. First, you can go to the top toolbar and then click on the sidebar button that I’ve highlighted below:

This will bring the sidebar out to the left side of the screen. Alternatively, you can enable the sidebar by going up to the ‘View’ part of the navigation menu and then checking the box for the ‘Sidebar’ option, which should be second in the menu like so:

Lovers of the keyboard will notice the shortcut for this sidebar on the right, which can be used by pressing Ctrl + Alt + B at the same time in Windows (Cmd + Option + B for Mac users).

So to disable 3D trees, the easiest thing to do is to simply uncheck the ‘3D Buildings’ menu item from the Layers menu as shown below:

Assuming that you’re currently viewing a location that has 3D trees present, you’ll be switched back to a view that portrays the trees like a regular satellite image, just perhaps with the tilt adjusted.

The Effect is From the ‘Photorealistic’ Layer

You may have noticed that the ‘3D Buildings’ option in the Layers menu was a group, as there was an icon indicating it was expandable. If you expand this group you’ll see the following options:

As you can see, there are three options in this group: Photorealistic, Gray, and Trees. It truly is counterintuitive in only a way that Google can pull off, but there doesn’t appear to be much of an effect from the Trees option. If you’re in the sub-menu and looking to disable only the trees, the unfortunate reality is that the 3D trees are from the Photorealistic effect, and not from the Trees effect (whatever that is).

For example, this is a random location with the Photorealistic option enabled:

After disabling this option, the screen then adjusts to the following:

The ‘Trees’ Layer Doesn’t Appear to Alter the Screen

I spent a decent amount of time playing around with the different options here, and as far as I can tell, there isn’t really much of an impact from toggling the ‘Trees’ data layer. It’s possible that there might be some minor impact from it, but the effect of making the trees 3D definitely comes from the ‘Photorealistic’ layer.

Watching some old YouTube videos posted by the Google Earth team, it appears that the trees layer was more useful when it was created back in 2010, but it seems that everything is just under the Photorealistic effect at the current moment.

Where 3D Trees Tend to Appear in Google Earth

While I haven’t tested this super thoroughly, it seems that the 3D rendering effect is typically only available to cities and other suburban areas. Small towns don’t appear to consistently have this effect available, as it doesn’t look like Google updates their satellite imagery enough to be able to pull off this effect.

Final Thoughts

Hopefully this article managed to fix your problem and you are now much happier with the appearance of your trees in Google Earth.

If you enjoyed this article and would like to check out other similar articles on this site, feel free to click any of the links below:


What Soil Series am I Standing On?

It’s hard to think of something that people take more for granted than the soil beneath their feet. After all, it’s just dirt, right? Not so fast. There’s a lot to learn about your local soils and modern technology is here to help.

How do you find the soil series of your current location? The SoilWeb application from UC-Davis pulls up the soil map and can show your approximate location. Information on the soil series present is found after clicking the soil map unit.

Keep reading for step-by-step instructions showing everything you need in order to learn exactly what kind of soil series is in your local area.

Finding the Soil Series in Your Immediate Area

In this post we’re going to show a few different methods to find the soil series in your immediate area, but first we’ll briefly cover the different terms you might hear floating around in the world of soil science.

A Brief Overview of Terminology

It probably is easiest to demonstrate the terms with a real-life location. Here is a random location in Northern Wisconsin, as seen in the SoilWeb browser application:

If I click on the shape labeled ‘RoC’ I’ll be presented with the following information in the pane on the left:

There are really two main terms to keep in mind when first learning about soil via the U.S. Department of Agriculture: soil map unit and soil series.

In this case, the soil map unit is represented by the ‘RoC’ symbol. You can think of soil map units as a collection of soil series and other traits (often the slope grade) that recurs through a local region. Each of the symbols on the first screenshot refers to a soil map unit, and the soil map is comprised of the different units. Soil map units are made of different components, which are referred to as a soil series.

For this example, the soil series present in the RoC map unit is actually a mixture. The dominant soil series is Rubicon, as it comprises 90% of the RoC map unit. The other soil series present in this map unit are Croswell, Au Gres, Kalkaska, and Kinross. As I mentioned above, the soil series is the individual component that goes into the U.S. Department of Agriculture maintains the list of soil series and the properties associated with them, and as of this writing there are more than 14,000 soil series in the United States.

Finding Your Soil Series in SoilWeb’s Web Application

While the U.S. Department of Agriculture does have an official app for displaying soil information (Web Soil Survey), it isn’t necessarily the first place I’d look as the interface is pretty dated.

Instead we’ll be using the SoilWeb application, which was put together by the soil science team at the University of California-Davis. It’s available as a web app and as a mobile app, and we’ll start with the web app.

Open the link to the SoilWeb browser application and you should be greeted with a screen that looks something like this:

Once you dismiss the message box, you should then head over to the upper-left corner of the screen to find the following icon:

After you click the icon you’ll likely be prompted with a request for the browser to pass your current location onto the webpage. Approve that and then your screen should update to your current location and also place a blue icon at your approximate location.

Zoom into the map and then click anywhere within the confines of the soil map unit to determine which soil series are present in your current location. The confines of the soil map unit are represented by the yellow lines that you see throughout the map. After clicking within the confines of your map unit you should see something like this on the left side of the screen:

As discussed in the first section, the soil series for this soil map unit are the links highlighted in blue in the ‘Map Unit Composition’ part of the left pane. In this case the soil series are Kennan (twice), Neopit, Rosholt, Hatley, and Keweenaw. The percentages to the left of the blue links indicates the percent of that soil series present in this soil map unit.

Finding Your Soil Series in the SoilWeb Mobile Application

Next we’ll cover finding your soil series with the SoilWeb mobile application. This app is available for both Android and iOS.

If you’ve already found your answer in the section above, there’s no need to download this mobile app unless you would like access to this same information in the field.

It’s dead simple to find the soil series in your local area with the mobile app, simply click on the ‘Get Soil Data’ button at the top of your screen:

This will query the Soil Data Access tables and then your screen should be updated with something that looks similar to this:

It’s worth noting that the data in this view is a formatted a little differently than the browser application. The soil series in the mobile app are found in the light blue boxes at the top of the tall graphics, with this screenshot having values of ‘Plainfield’ and ‘Watseka.’ It’s worth noting that there may be additional soil series found to the right of the two that are currently on the screen. You’ll need to swipe right to check for additional soil series.

You can click the light blue boxes to find more information about that specific soil series.

Finding Your Soil Series in Web Soil Survey

While it’s true that I don’t miss many opportunities to point out that the Web Soil Survey application from the U.S. Department of Agriculture is a bit outdated, there are a lot of really cool things it can do.

With that being said, unless you are really interested in learning more about the soils in your local area, you’ll probably get everything you need from SoilWeb. You can use this link to open Web Soil Survey.

In case you’re interested in seeing what Web Soil Survey is capable of, feel free to check out this post in which I cover this application in great detail.

It’s worth noting that SoilWeb has a very handy link in the upper-right hand portion of the screen that allows you to jump over to Web Soil Survey:

This link takes you over to a session of Web Soil Survey with the boundaries of your previous screen now setup as the Area of Interest. With the AOI already established, you can now head over to the ‘Soil Data Explorer’ tab in order to start exploring the different reports and data available in Web Soil Survey.

Final Thoughts

I hope you enjoyed this piece and were able to find out everything about your local soil that you were looking for. While it’s true that it is easy sometimes to get “information overload” when doing this kind of thing, I think modern tools like SoilWeb are helping a lot.

If you are interested in reading pieces that cover similar content, feel free to check out any of the following articles:


What is a Soil Series?

The soil series: a slightly mysterious thing that takes on the Herculean task of trying to assign a classification to the soil beneath your feet.

What is a soil series? A soil series is a type of classification for soils that is typically named after a local area heavily featuring that kind of soil. This effort was lead by the National Cooperative Soil Survey of the USDA. Soils are grouped together based on their appearance, chemistry, and physical properties.

Now that we have the quick and easy definition out of the way, let’s dive into the rest of this post.

History of the Soil Series: a Brief Overview

In the sections below we’ll briefly cover the history of soil series and how they came to be, and then also provide a little clarity on terminology of the soil science world.

Introduced by the USDA

As you might have gathered in the discussion above, the soil series is a concept specific to the United States. This is because it is the result of a great effort to provide a classification for the soils of the entire continental United States.

As you probably can imagine, this project was a tremendous undertaking, as soil scientists of the USDA were responsible for manually cataloging the properties of local soils. Given the massive size of the United States and the wide variety of soils present, it’s easy to see how this project needed a small-army of soil scientists ready at the helm. In fact, the USDA maintains a list of Million-Acre Mappers, those being scientists that mapped out more than a million acres of land in their careers.

This effort was launched around the beginning of the 20th century, and in 1903 the National Cooperative Soil Survey from the USDA established the idea of the soil series.

Derived From a Variety of Factors

This isn’t always the case, but soil series are almost always given a name that refers to a location where that specific soil series is most prominent. What do I mean by this? If you take the Antigo soil series, you’ll notice that the extent map shows that it is consistently present in the vicinity of Antigo, WI, as seen below:

Going beyond the name of the soil series, a variety of factors are taken into account when deciding how to group soils together in an orderly and logical manner. You might not think about it right away, but all of the following factors and more will play a role in determining a soil series:

  • Soil pH
  • Percentage of different components (i.e. sand, clay)
  • Soil color
  • Depth of different layers
  • Soil structure
  • Drainage capabilities

And so on. Clearly there’s a lot going on here, but the hard-working folks at the USDA have been working on this system for more than a hundred years.

Terms to Keep Straight: Soil Series, Mapping Units, and Soil Description

One of the most confusing aspects of the world of soil series can sometimes be the similar terms being thrown around. This is especially true for non-scientific users that are perhaps looking for quick answers to their end-use focused questions, and might not be so interested in the granular specifics. So in order to keep the confusion to a minimum, let’s define a few terms you might frequently see.

First and foremost, the soil series term refers to a pre-defined type of soil that is found throughout parts of the continental United States. Each soil series has a long list of data fields associated with it.

You may have also have heard the term soil map units. While map units are related to soil series, they are not the same. The relationship here is that soil map units are comprised of individual soil series at specified percentages. An example of this is the ‘PgB’ soil map unit, which is primarily comprised of the following two soil series:

  • Padus (65%)
  • Wabeno (33%)

How to Find the Soil Series for Your Local Area

Now that we have a good understanding of exactly what a soil series is, let’s see how this information can be useful for the average person.

While it is true that the USDA does provide applications that allow for access to this information, they unfortunately aren’t terribly user-friendly as of this time of writing. A better approach is to leverage the work of the soil scientists from the University of California-Davis, which have built modern applications with the USDA data.

If you’re in the browser, a great option is the SoilWeb mapping application, which allows you to zoom into your local area and then explore which soil series are present.

Referencing the example from the previous section, the PgB soil map unit has the following soil series present:

If you then click on the blue links in the Map Unit Composition part of the menu, you’ll then be taken to a screen that looks like this:

This menu provides a jumping-off point for all sorts of information, but it’s important to note that you are now on the soil series section, not the soil map unit.

There’s also a mobile app for both Android and iOS that was developed by the UC-Davis team, and that is best used to pull up information on the soil at your current GPS position.

If you are interested in learning more about finding information on soil series, you can check out this post that goes into great detail on the various applications available.

Final Thoughts

Hopefully you enjoyed reading this piece and came away having learned a little bit about the humble soil series.

If you are interested in learning more about similar topics, feel free to check out any of the following articles:


ForWarn II: Using the Forest Type Data Layer

There are a lot of data layers and features present in ForWarn II, but few are as useful and actionable as the Forest Type data layer.

This layer does exactly what you think it would: it provides a clean category for each piece of forest present on the map. There are a few things to learn in order to get the most out of this data, but there’s plenty we can do with it once we’re comfortable.

Let’s dive in: in this article we’ll go into great detail on everything you need to know about using the Forest Type data layer in the ForWarn II tool.

How to Use This Layer in ForWarn II

Before we get into the finer details of the information present in this data layer, it’s helpful to provide step-by-step instructions on how the mechanics of how you can interact with this layer.

Opening ForWarn II With This Layer On

The first thing that we want to do is open ForWarn II with this data layer present. ForWarn II conveniently allows you to share custom URLs that includes the map positioning and layers present, so you can use this link to open ForWarn II with the Forest Type data layer on.

Once you’ve opened that link you should see something like this:

initial view of forwarn II mapping application from us forest service

Admittedly, there’s a lot going on here for someone unfamiliar with this tool. That’s alright, as we’ll quickly cover the basics of how this is all setup.

First, you’ll notice that there’s a menu on the left that is the ‘Map Layers’ section. None of the layers that you see in the above screenshot are currently visible, so you’ll need to go to that menu and then scroll down to the bottom. What you’ll notice is that there’s a section called ‘Additional Assessment Maps,’ and this is the section that contains the data layer we’re discussing in this post.

map layer menu forwarn ii highlighting additional assessment maps group

Click that section title and then scroll down until you get to the ‘Landcover’ group, which should look like this:

map layers forwarn ii showing Forest Type data layer option checked

You’ll notice that the first layer in this group is the ‘Forest Type’ data layer that we’re interested in. As the box is checked, it means that this layer is visible on the map.

The other thing to notice is that there’s a menu called ‘Map Tools’ on the right side of the screen. There are a couple of things here, but we’re only interested in the Legend section for now, which is found here:

legend layer for forwarn ii mapping application

Click that section title and you’ll be presented with the keys for all of our layers present on the map. This is about what you should see when you expand this layer:

legend of forest type data layer forwarn ii

Hopefully at this point it will be obvious why this data layer is so useful to anyone interested in tree identification. Not just limited to the 19 groups in the above screenshot, you can scroll down to understand just how expansive this layer is:

legend of forest type data layer forwarn ii - bottom section

There’s a ton of information here, and it will be our job to make the most out of it without getting too lost in the weeds. More on that later.

Navigating to Your Area of Interest

Now that we understand a little about what’s actually going on with our screen, let’s start interacting with the map.

The first thing you’ll want to do is to zoom into the location that you’re interested in exploring. We’re fortunate here because this map behaves just like applications like Google Maps and Bing Maps, so you’ll be able to zoom in and out with clicks and scrolling.

Turning on the Information Button

Once you zoom in close enough you’ll be able to tell that the information in the data layer is pixelated, where each pixel corresponds to the forest type for that piece of land. This is a good example of what I’m talking about:

up close zoom of forest type data layer in forwarn ii

You’ll notice that there’s a wide variety of pixel colors in the above screenshot, and we’ll want to know what each one corresponds to. This is where the Information tool comes in handy. You can find it by searching for the text bubble with the ‘i’ in the toolbar at the top of your screen:

information button pointed out with red arrow in forwarn ii

Once you click on this button you’ll be able to easily pull up the Forest Type associated with each of the pixels on your screen.

Making Sense of the Data Layer

Now that we’ve activated the Information tool, we’ll want to check the Forest Type for the different pixels. All you have to do here is to click any of the pixels, and then you should see an information box like the following pop up:

example forest type data from forwarn ii application

There are two things to notice here. First, the arrow in the above screenshot is pointing to a yellow dot that represents exactly where I clicked to pull up this information box. This yellow dot may be covered by the information box, but you can move it down to see it for yourself. I mention this yellow box because sometimes it’s easy to forget where you clicked, so this is worth keeping in mind.

Second and most importantly, you’ll notice that the information box indicates that this pixel represents the following Forest Type for this piece of land:

Chestnut Oak/Black Oak/Scarlet Oak

If we turn to the legend and scroll down until we find item 83, you’ll see that this matches:

showing legend entry for forest type data value in the forwarn ii mapping application

Pretty neat, huh? Hopefully by now you can appreciate why this data layer in ForWarn II might be so useful to us.

How Information is Organized in This Data Layer

There’s a lot of information present in this data layer, so it’s helpful to quickly go over how this information is organized.

A Forest Type can be Individual Species, Groups of Species or a Generic Mix

With a name as generic as the Forest Type layer, you had to know that this wasn’t going to be a clean list of unique species of trees. No, the organization of this data layer much more closely resembles real life: where it’s a bit complicated.

Here’s what you can expect: there are three different ways that Forest Types are organized in ForWarn II. First, you have plenty of types that are just a single species of tree. An example of this is the ‘Black Walnut’ forest type (number 080).

On the other hand, you may have a group of trees in a single forest type. A great example of this is the ‘Black Ash/American Elm/Red Maple’ forest type (number 092).

Taking it even further from that, you may have some forest types that merely represent a general type of habitat or tree. An example of this is the ‘Mixed Upland Hardwoods’ forest type (number 085).

Closely Related Types are Similar Colors on the Scale

This might feel a little frustrating at first, but the scale for this data layer groups similar Forest Types within a similar range of colors on the scale. Here’s a good example of what I’m talking about:

forest type data layer legend in forwarn ii with pine species highlighted in red box

Highlighted in the above screenshot we have ten different types of pine trees all represented by various shade of red or orange. This might seem a little counter intuitive at first, but it makes sense when you think about how this layer can be used to quickly understand the big picture of a forested region.

Important Caveats to Remember While Using This Layer

As much as I love using the information present in this data layer, there are a few caveats to keep in mind while we try to leverage this.

These Values are Often a Drastic Over-Simplification of the Real World

There’s no way around this: the information presented in this data layer is a great over-simplification of the real world.

Everyone knows that our forests aren’t neatly composed of square pixels of a single type of tree. Sure, there are pine plantations that might meet this definition, but the vast majority of forests will be much more complicated in real life.

Many areas may have more than a dozen different species a tree present, but they may be classified by a single species if that species is dominant enough.

Positioning of Pixels Appears to be Off at Times

This is likely due to the sheer amount of land covered by each pixel, but you’ll notice that the pixels don’t exactly cover woodlands neatly when overlayed with satellite imagery.

You can find this best by looking for areas that have a lot of fragmentation and don’t have dense forest cover. Here’s a good example of what I’m talking about:

up close view of pixels of forest type data with satellite imagery of forests in background in forwarn ii

This is definitely a bit nit-picky, but I think it’s the natural result of the sheer scale of each pixel.

Tips and Tricks to Keep in Mind

Once you start using the Forest Type data layer, you’ll want to keep some of the below tips and tricks in mind. This will help you get the most out of this powerful tool.

Keep a Close Eye Out for Slightly Different Colors

You may very well run into the situation where you find a group of pixels and look like the same color at first glance, but upon further inspection it turns out they are different colors. This likely implies that the different forest types are of a similar group of trees or species of trees.

Here’s a good example of what I’m talking about:

close up view of forest type pixels that are blue but slightly different shades of color

The difference is definitely noticeable, but you wouldn’t be faulted for thinking these were at the same type of trees if you quickly glanced at them. In this case the two different colors represent the following Forest Types:

  • Cottonwood
  • Black Ash/American Elm/Red Maple

Just keep this in mind when you’re first looking at an area, as there may be more forest types there than you think.

Think of This Data Layer as a Great Way to Get in the Ballpark of a Tree

One of the best ways to use this data layer is to treat it as a great way to get within the ballpark of a specific tree that you may be looking to find.

This idea alone makes it one of the most useful layers for people learning how to identify trees, as it can help you narrow your focus when spending time in the wild.

Here’s what I mean by that: traditionally, you would just walk out in the woods with a tree identification book when learning to ID trees. By approaching the learning experience differently, we can instead go to a specific part of the woods with a short list of potential species we’re looking to positively identify.

This works extremely well because it narrows our focus down to the trees present in such great volumes that they were classified as that specific Forest Type. Overall, I’ve found this method to greatly cut down on initial confusion when first starting to identify trees.

Smaller Woods of Different Trees Might Not Register

The reality is that each pixel on the screen covers a fairly large piece of land. One of the biggest effects of this is that smaller areas of a certain tree surrounded by a more dominant tree might not even register.

This is probably easiest to demonstrate with an example. In the below screenshot you have an aerial photograph of a patch of evergreen trees surrounded by a deciduous forest that is mostly aspen trees:

screenshot of satellite image of forest that is mixed with deciduous and evergreen trees

This patch of evergreen trees covers a pretty decent sized area, but when you switch to the Forest Type data layer you’ll notice that the entire area is classified as ‘Aspen’:

close up image of forest type data pixels with aspen trees dominating

So while it is useful to understand that the majority of this area is comprised of aspen trees, be sure to keep in mind that you can easily overlook other elements.

Final Thoughts

I hope you benefited from this article and came away having learned a few things. I really get great enjoyment out of working with this data layer, as it’s been tremendously valuable along my journey of learning about the forests around me.

If you’d like to read on other related topics, check out the below articles:


ForWarn II: How to Find Recent Satellite Imagery

Having the ability to pull up recent satellite imagery has been a real game changer for my time spent learning about my local forests. While there are other sites that have recent imagery, only the ForWarn II tool provided by the U.S. Forest Service has been able to provide imagery with enough clarity to be useful.

Sure, part of this fascination with this tool is just how cool I think it is to be able to pull up this recent imagery, but there are many practical reasons to care about this as well. From locating specific types of trees in the fall based on their colors to finding areas that have been recently logged, there’s a lot we can do with this functionality.

In this post, we’ll go into detail on exactly what you need to know about finding recent satellite imagery while using the ForWarn II tool.

Accessing The Different Layers That Have Recent Satellite Imagery

Let’s get to it: there are three different types of layers in ForWarn II that provide recent satellite imagery:

  • Imagery
  • High-Resolution Sentinel Imagery
  • Medium-Resolution Landsat 8 Imagery

Each of these layers comes with their own quirks and functionalities, and below we’ll show you everything you need to know about using them.

A quick note before we get started: ForWarn II allows you to share custom URLs that show the exact map that you’re viewing, which consists of the data layers turned on and the positioning of the map on the screen. Each link that I list below will be custom to include only the data layer that we’re talking about. Imagery Data Layer

Here’s a link to open ForWarn II with the Imagery Data Layer toggled on.

As soon as you open this link you’ll see something like this:

forwarn ii application with the imagery data layer turned on

You might catch in the screenshot above that there are two things worth nothing. First, that the imagery is only available for a few states in the Midwest (the exact list: IL, IA, IN, KS, NE, OH, WI, ND, and SD). Second, you’ll likely see that there are gaps in the coverage of these states.

As far as I know, these gaps correlate to cloud coverage that made it difficult to compile satellite imagery for these areas in this time-frame, but I don’t know for certain.

If you look to the menu on the left side of the screen you’ll notice that there are two different layers in this section:

menu options for imagery data layer in forwarn ii application

You’ll notice that these two layers cover the two most recent time periods of the imagery that is available for this layer. To turn on the older layer all you have to do is toggle off the first box and then check the box below that, like so:

screenshot highlighting previous period satellite imagery from data layer in forwarn ii application

You might catch in the screenshot above that the coverage for this layer is much better when compared to the first layer, so it may be worth checking.

Once you’re at the layer that provides the best coverage, you can zoom in and out by using the tools at the top menu, by scrolling with your mouse wheel, or by double-clicking in.

High-Resolution Sentinel Imagery Data Layer

Here’s a link to ForWarn II with the High-Resolution Sentinel Imagery Data Layer activated.

You’ll immediately notice that we don’t seen any sort of satellite imagery when we load this zoomed out view of the continental United States. This is because this specific layer doesn’t load until you zoom in close enough, which you’ll be able to see in it’s description in the Legend section:

legend entry for sentinel satellite imagery indicating need to zoom in with forwarn ii application

Once we zoom in close enough you’ll see the screen buffer and then it should load satellite imagery like the following:

screenshot showing recent satellite imagery from the sentinel imagery layer in forwarn ii application

You can spot in the left-hand portion of the above screenshot that the menu for this section is organized differently than the previous option. Here you’ll find that this set of layers is organized according to two ideas:

  • A ‘True Color’ vs. ‘Ag False Color’ Distinction
  • Offering the current year and the imagery from one year ago

We’re only going to be interested in the ‘True Color’ layers, as the ‘Ag Color’ layers are for more technical applications that don’t matter here.

We’re also going to default to the true color layer of the current year, but it can be helpful to keep the prior year layer in mind in case we run into coverage issues.

Medium-Resolution Landsat 8 Imagery Data Layer

Here’s the link to open up ForWarn II with the Medium-Resolution Landsat 8 Imagery Data Layer turned on.

When you open this link it will look a little different than the High-Resolution layer, but it behaves the exact same way. Until you zoom in close enough, the map will be all white. Once you’re close enough to bring up satellite imagery, you should see something like this:

screenshot showing recent satellite imagery from landsat 8 imagery data layer in forwarn ii application

You’ll notice that the clarity of this imagery is not as nice as the previously discussed layers, but it still may be suitable for your purposes. The different options in this section are ordered exactly like the High-Resolution section, where they have true/false options as well as the current and prior years.

Just like with the High-Resolution layers, we’ll be interested in the ‘True Color’ layer for the current year primarily, but we can consider the layer for the prior year as a backup option.

How the Different Layers Compare

Now that we’ve covered how you can access each of these different layers of recent satellite imagery, let’s give a brief summary of how they compare.

Accessibility and Coverage

There’s no way around it: each of these layers is bound to run into issues with cloud cover, they just happen to handle it differently. The layer and the Medium-Resolution layer will remove areas impacted by cloud cover, while the High-Resolution Sentinel layers will merely show the clouds in the data layer.

The other thing to keep in mind is that the layer is only available to a select list of states in the Midwest, while the other two layers offer mostly complete coverage throughout the continental United States, depending on cloud cover.

Clarity of the Satellite Imagery

While I find that the layer has the best imagery in the ForWarn II tool, I also think that the High-Resolution Sentinel Imagery data layer isn’t far behind.

With that being said, I do think that the Medium-Resolution imagery layer is useful, it’s just lacking in clarity enough that I prefer to treat it as a last resort option.

How They Function in the Tool

As we discussed earlier, the different layers are organized differently and therefore provide different types of functionality.

The imagery section provides the two most recent 7-day periods of time, and this allows you to get a feel for how things are progressing over time. This may not be useful in the middle of summer when everything is green, but it’s very helpful if you’re using this imagery around seasonal changes in spring and fall.

The High-Resolution layer and Medium-Resolution layer offer imagery for the current year and the prior year, and this may be helpful if you’re running into coverage issues.

For example, this area has a fair amount of coverage issues from cloud cover in the current year imagery:

screenshot showing a recent satellite image with high levels of cloud cover in forwarn ii application

However, if you switch to the prior year you’ll find the coverage is much better:

screenshot showing prior year satellite imagery with low levels of cloud cover in forwarn ii application

This is very helpful and definitely worth keeping in mind if you’re running into issues caused by cloud cover.

How to Best Use These Layers

Assuming that you’re most interested in finding satellite imagery most accurately reflecting the current conditions of a nearby forest, here’s the exact order I would use:

  1. Check the most recent layer if you have coverage
  2. Check the other layer for a slightly delayed set of imagery
  3. Switch to the High-Resolution imagery layer for the current year
  4. Check the prior year imagery of the High-Resolution imagery section
  5. Use the Medium-Resolution Imagery option if everything else failed

This should get you access to the most accurate and clear sets of imagery first, even if you’re checking imagery from prior years before moving to the Medium-Resolution imagery. As trees have relatively consistent behavior from year to year (barring significant differences in weather), the prior year imagery should still be suitable for our purposes.

Final Thoughts

I hope you benefited from this article and came away having learned a few things. There are a lot of really cool things that can be done with recent satellite imagery, and I don’t know of a better way to get it than with ForWarn II.

If you’d like to read on other related topics, feel free to check out the below articles:


How to Find the Soil Type for Your Area

Soil. To many people it seems like a lifeless substance where there isn’t much to learn about it. However, anyone that’s ever tried to grow a plant in any way quickly figured out how much they had to learn about soil.

How can you find the soil type for your area? Most areas have local extensions that analyze soil samples for a reasonable cost. The USDA also maintains a soil map, which details the various soil types present throughout the United States. Different applications allow users to find the scientific properties associated with a soil type.

Keep reading and you’ll find out everything you need to know in order to test your soil and put it in the proper context.

Two Different Approaches

This post will have a lot going on, but it might be most helpful to think of it as two separate methods that are trying to answer the same question:

  • What can I learn about my soil from the testing of samples taken of my soil?
  • What can I learn about the expected properties and potential of my soil based on an existing soil map built by the USDA?

There’s no right answer as to which method is appropriate for your situation, as both can provide unique insights into our soils.

Manually Testing the Actual Soil on Your Land

Without a doubt, this is likely the approach that most people have in mind when first becoming interested in the soils in their area. The idea is that you go out to your land and collect a few samples of your soil. Then either you perform some simple DIY tests, or you send off the samples for analysis at a nearby soil laboratory.

While most of this post will not be focused on this soil testing approach, that doesn’t mean that I don’t find it to be incredibly valuable. I think anyone interested in understanding their local soil should without a doubt perform some sort of soil testing, whether DIY or professional. Whether you’re interested in growing a few flowers in the front yard or you’re starting your own homestead, a soil test will give you valuable feedback. Even if the results of your soil test simply confirms the freely-available information you find in the next approach, that confirmation is still valuable feedback.

Finding Soil Data for Your Location From the USDA

On the other hand, there’s a different approach that we can take in order to quickly gain understanding of our local soil environment with a 10,000 foot view. This doesn’t involve us actually sampling any of our soil, it merely relies on the work that the United States Department of Agriculture already did.

Let me explain what I mean: the USDA underwent a massive effort to produce a soil map for the entire continental United States. This soil map has data on the many physical attributes of soil that we’re interested in, but it also provides helpful ratings and information on how each type of soil is suited for different real-life activities.

Here’s an example of what I’m talking about: there are ratings that indicate how suitable a specific soil series (more on what that means later) is for the production of wine grapes. From construction applications to native vegetation information, there’s quite a lot of valuable information in these reports.

Things to Think About: Context, Desired Outcomes, and Purpose

What ends up being right for you really depends on the context of your situation and the desired outcomes that you’re seeking. I think almost everyone should perform some type of manual testing of soil samples from their land, while not everyone is in a position to gain a lot of value from the USDA approach.

There is one caveat in all of this: if you’re aiming to understand the soils of lands that you don’t technically own (i.e. public land, prospective lands, just for fun research, etc.), then you obviously should only employ the USDA approach.

What I will say is that the USDA approach is generally better if you’re discussing soils from landscapes that haven’t been so greatly disturbed. The data from the USDA indicates what your soil should be based on your location; so if you live in the suburbs and your native soils were trucked away and replaced with less valuable sub-soils, you might be out of luck. I view the USDA information as more valuable for those looking to analyze rural areas, specifically areas that are left a bit more natural such as National Forests and state parks.

Manually Testing Your Actual Soil

The conversation around soil testing can be a little confusing at times, especially if you’re unfamiliar with the different terms floating around.

It’s important to take a little time to understand the basic concepts about soil types before we start digging around in the cupboards for mason jars and dish soap.

Basic Concepts to Keep in Mind Before We Begin

You may have seen heard some of the terms thrown around before:

  • Clay loam
  • Loamy sand
  • Silt loam
  • Silty clay

And so on. These are terms used to describe a specific soil texture, which can be calculated on this page provided by the USDA. At the bottom of the screen you’ll notice a chart that looks like the following:

screenshot of the soil texture chart provided by the usda

What we’re looking at is a graph with three different axes. Overlayed on this chart are the different soil texture groups. For example, you can see that the top section of the graph is almost entirely the ‘clay’ texture. Each of these textures is covering how much of your soil sample is made from these three particle types:

  • Sand
  • Silt
  • Clay

These are in order of the largest to smallest particle sizes, with clay particles being the smallest.

DIY Soil Tests: a Few Different Approaches

Okay, now you’re free to start digging around for mason jars. Despite the fact that these tests are rather simple, there are still some very valuable insights we can obtain from the results.

Using the Jar Test to Get a Rough Estimate of Your Soil Composition

The ‘jar test’ is the iconic version of the DIY soil test. The objective with the jar test is to get a rough estimate of the composition of your soil from the three components discussed earlier.

Here’s an excellent video that slowly goes through the process of the test and then walks you through the science behind it:

Getting an Approximate pH Measurement of Your Soil

You may also be curious about the pH of your local soils. While a pH measurement is usually something that’s best left to the soil scientists with labs and decent equipment, you can get a “rough estimate” of your pH with a few precautions.

This video will walk you through how you can test your soil pH at home with some rudimentary equipment:

The Jar Test for Those With Heavy Clay Soils

Those with heavy clay soils are accustomed to a life where everything’s just a little more difficult than it might need to be. If that’s the case for you, you might want to check out this video that shows a jar test in a region with heavy clay:

Sending Samples in for Lab Testing

Even if you do the DIY soil testing discussed above, it’s almost certainly worth your time to seek out a local lab that is capable of testing your soil professionally.

Most counties or university extensions will have information on locations that provide this kind of testing, so your best bet is to search around to find that page. This page may also have valuable resources on other aspects of soil and gardening, so you might want to bookmark it for future use.

Basic Concepts: Using Existing USDA Soil Maps

This method is all about leveraging the decades of work that soil scientists already did for us. It’s normal to feel a sense of mystery when you step into a natural environment, but the reality is that thousands of people have spent decades studying these natural environments.

A great example of this is the soil map and all of its associated data compiled by scientists that work for the USDA. There was so much work that went into this project that the USDA has a page where they list the ‘Million-Acre Mappers,’ which are past or present USDA employees that did exactly that: map more than a million acres of soil in their careers (side note: they also receive a lapel pin for their work).

My point is this: it’s much easier to take advantage of the product of this tremendous effort than it is to try to learn everything about soil science for yourself.

What Concepts You Should Keep in Mind When Starting Out

Before we dive into the specific details for each application, I think it’s most helpful to cover some basics about soil science and the potential of these applications.

This will help establish a solid baseline of knowledge before we start flooding your screen with unfamiliar maps, symbols, and charts. There’s a lot going on with some of these screens, so it helps to define some key terms to prevent you from feeling overwhelmed.

Keep the Science Side of Things Simple

As I would bet that these applications are mostly used by soil scientists, the reality is that there’s a lot of complex data fields present at any one point of time. Our job is to stick with only the data points we’re interested in, to resist getting lost in the weeds.

Of all of the scientific data fields available in the reports, we’ll stick to the most basic options. The most important fields will be on the composition of the soil itself, which will consist of percent levels of the following materials:

  • Organic matter
  • Sand
  • Clay

Besides that we’ll also maybe dabble in the ‘pH’ field, as I think that’s one of the more approachable fields for just about anyone interested in this kind of stuff. All of the other scientific fields will be left to the scientists.

Quick Notes on How Soil is Classified by the USDA

Before we dive into the different applications, I think it’s important to provide a little background on how soils are classified according to the USDA.

Let’s start with the basics. The USDA has created a ‘soil map’ for the continental United States that is comprised of pre-defined soil units. The map is just like a map of the states in the US, as it contains borders that identify the specific lands that are each soil unit.

Soil units are named with symbols that tell you exactly what soil unit you’re looking at. There are different naming conventions employed throughout the country, but an example of a soil unit is ‘KbC’.

This brings me to the next point: soil units are comprised of pre-defined soil series. This is perhaps best explained in a screenshot:

Here you can see at the top that this is the composition of the KbC soil unit that I mentioned previously. The blue links that you see throughout the table are all the names of soil series that contribute to the KbC soil unit, as soil units can be comprised of multiple soil series.

This brings me to my last point here: the most valuable information that we have available from the USDA is assigned to the soil series, not the soil unit. This may make it a bit confusing when dealing with soil units that aren’t necessarily dominated by a single soil series. Hopefully this makes sense, but I understand that it can be a bit confusing when you first get started.

Focus on Utilizing Application-Based Features

I’m not going to lie: there’s a lot of different data points available with the USDA soil information set, so it’s tempting to try and learn as much as you can (at least for me it is). Your best bet is to forget everything but the most basic soil properties, and then to focus on the most relevant application-based properties?

What do I mean by that? I’m talking about all of the fields that are used to indicate how suitable that soil series is for a specific real-world application. Here’s a good example: there’s a data field that is called the ‘Potential Fire Damage Hazard’ rating, and it provides a rating for a specified soil series. The Seelyeville soil series rates ‘low’ in this field, as this series is typically very poorly drained and often swampy.

The value here for us non-scientists is that we’re leveraging all of the hard work already done by the folks at the USDA, and using their assessments to better help us understand our land. We’re fortunate that there are a wide variety of data fields available to us, from agriculture to forestry to engineering and so on.

Three Different Approaches Using the Same Data

This is an important point that might help add some clarity to this somewhat confusing landscape of smartphone apps and online applications. As far as I can tell, each of these applications uses the exact same data set.

The main difference here is that each application is unique around the way that it formats the data. They are also unique in the manners that they allow users to interact with them.

Exactly how each application works and who they might be best for will be covered in the detailed instructions below. I’ve covered them in order of easiest to hardest, which also happens to be in the order of least to most powerful.

The SoilWeb Application for Mobile Phones: Simple and Straightforward

This application was developed by the California Soil Resource Lab at UC-Davis, and it is built to leverage the work by the USDA soil team while employing modern functionality.

Who is Best Suited for This Option

This is likely going to out me as a huge nerd, but I’m going to do it anyways: this is without a doubt one of my favorite applications for my phone. As there are only 13 reviews in the Google Play store at the time of this writing, I consider this app to be massively underappreciated and I’m more than happy to shed some light on it.

I think anyone that’s interested in soil at all should have this app installed on their phone. If you’re still reading this piece and haven’t abandoned me yet, you’re the perfect candidate for the SoilWeb app.

Here’s where this app is available:

I do have to confess that there is a bit of a limited scope with this app, as it is strictly designed to pull up the soil information at your device’s present location. I think most people will find this more than acceptable, as it’s just enough functionality and probably all that most people need when in the field.

Using the SoilWeb Phone Application for the First Time

When you launch the SoilWeb phone application for the first time you should be presented with something like this:

screenshot of the home page of the soilweb mobile app

All you should have to do to run the app is to click the ‘Get Soil Data’ button at the top of your screen. You’ll then likely be prompted to allow SoilWeb to access the current location of your smartphone.

Once you grant permission your screen should update and you should see something similar to this:

screenshot of the soil web mobile app after pressing get soil data

There are a few things going on here, and we’ll go through each of them in detail in the next section.

Navigating the Results Page for Your Location

Now that you’ve gone through the difficult task of clicking a button or two, let’s actually figure out what the heck’s going on with this screen.

Details About Your Soil Map Unit

As we discussed earlier, the soil map unit is the abbreviation given to the specific type of soil present at your location. Remember, soil map units are derived from soil series. Think of it this way: if a soil map unit was a loaf of bread, then the soil series present would be the flour, water, salt and yeast (or whatever bread needs).

The only part of this screen that is dedicated to this specific soil map unit is the segment highlighted in the below screenshot:

screenshot highlighting the map unit information for a local soil

There are two parts that we need to understand here. First, the part labeled the ‘Map unit’ is the description assigned to this particular soil map unit. Second, we’ll need to click the ‘Details’ button to find the rest of the information directly associated with this map unit. Once we click that button, our screen should show something like this:

screenshot of the map unit data available in the soilweb mobile app

As you can see at the top of the screen, the ‘Map Unit Symbol’ refers to the symbol that the USDA soil map would use to refer to this specific map unit. There is some additional information as you scroll down, but most of that will be irrelevant to the vast majority of people.

Once you are done with this section, click either the back button (sorry iOS users) or the arrow in the upper right section of your screen to return back to the main results page.

Soil Profiles of the Relevant Soil Series are the Majority of the Screen

Now that we’re back to the main results page, we can start to explore the different soil series present in our soil map unit. The majority of the screen here is reserved for the soil profiles of the soil series that this soil map unit consists of:

screenshot from the soilweb mobile app highlighting the graphical depictions of two soil series present

It may look like there are only two soil series present on my screen, but you’ll notice that the percentage only adds up to 97%. Maybe that’s close enough for most, but we’re dealing with scientists here: there’s more to this.

To find the remaining soil series that make up this unit you’ll need to swipe to the left. Once you do that you might be presented with any remaining soil series. Not all soil map units will be comprised of more than two soil series, but many at least three.

Finding Information on each Soil Series

As the Plainfield soil series makes up the vast majority of my soil unit, I’m looking find more information about it. All I have to do to bring up this information is to push anywhere on this light blue area:

arrow pointing to soil series name in soilweb mobile app

This will bring up a screen with three tabs: Description, Details and Links. It should look like this:

screenshot of the description of the plainfield soil series in the soilweb mobile app

Much of the information in the ‘Description’ tab is geared towards scientists, but there area few sections that may be useful for us. First, the main description right under the series name is a great summary and is very friendly to non-scientists. The other relevant sections are down near the bottom of the page, so you might have to scroll a good bit to get there.

Most people would be interested in these two sections:

  • Drainage and Permeability
  • Use and Vegetation

Here’s an example of what the content in these sections may look like:

screenshot showing the use and vegatation section of the plainfield soil series in the soilweb mobile app

Hopefully you can now understand at least part of the reason why we can get so much value out of this application.

The ‘Details’ tab contains many different fields but it is formatted exactly the same in the browser version of SoilWeb. As such, we’ll cover this later in the piece to avoid repetition.

SoilWeb in the Browser: Best of Both Worlds

As this browser application was developed by the same team that built the smartphone app that we just discussed, it’s very easy to leverage what we just learned and apply it here.

Here’s what the team at UC-Davis did with the SoilWeb browser application: they took the USDA’s Web Soil Survey browser app (which will be discussed after this) and applied a much smoother interface while enabling modern the modern functionalities we’ve come to expect from any mapping application.

Who is Best Suited for This Option

As you might expect, this makes the SoilWeb browser application an incredibly functional option that makes the most sense for the vast majority of users. The similarities between the two applications means there’s a low barrier of entry for those already acquainted with the smartphone app

There are additional reporting capabilities that the USDA Web Soil Survey application has over the SoilWeb application, but most people won’t be interested in exploring those capabilities. This makes the SoilWeb application a great choice for anyone that wants to quickly access loads of information about their local soils without having to put up with a slightly frustrating navigational setup.

Introduction to the USDA Soil Map

You may not have familiarity with the soil map put together by the USDA, so this is likely the first time you’ve seen something like this:

screenshot of a soil map in northern wisconsin in the soilweb web application

Don’t worry, it’s not as crazy as it looks. The concept is pretty simple: the USDA took the entire continental United States and then divided the land into different soil map units. These map units are denoted on the screen with the symbols you may have noticed in the above screenshot (e.g. SfD, WaA, W, IsA, etc.).

Much like the world is divided into countries in a way that doesn’t always result in the cleanest or most sensible borders, the soil map has many different map units that interact in many different ways. Let’s zoom in on this map to get a good example of that. Here’s an area that has a couple of interesting things going on:

closeup of the soil map unit labels found in the soilweb web app

You’ll notice that I’ve highlighted two different map units: WaA and SfD. As you may have noticed, the ‘WaA’ unit that I highlighted is entirely surrounded by the ‘SfD’ unit, which covers a large portion of the area in the screenshot.

It’s worth noting that these unit shapes and borders are approximations, as it would be difficult for the USDA to fully covers the complexities and variations that can occur even on a few acres of land. With that being said, this still is very useful for our purposes.

How Information is Organized in the SoilWeb Browser Application

One quick tip before we start looking into specific soil profiles: if you’re in SoilWeb and you’re confused about any of the terminology used, check out their help page. You can find this by clicking the ‘Menu’ button in the upper left corner and then clicking the ‘Help’ button.

The help section is mostly just a collection of frequently used terms, where if you click on the link you’ll be presented with their definition. It’s definitely not necessary to read these, but it’s useful for those who want to learn more.

Soil Map Unit Specific Information

When you click anywhere on the map the screen should update to display all relevant information on the soil map unit clicked. This is about what you can expect to see when you click on a unit:

screenshot of the map unit composition info that comes up once you click on a map unit in the soilweb web app

The first thing you should notice is that the symbol in the parentheses at the top of the screen matches the symbol on the map where the ‘red x’ is located (AoB). The description to the left of that is the name for that particular soil unit.

You can see at the bottom of the screenshot that there’s more information below, but most of this information won’t be useful for our purposes.

Compositional Information on a Soil Series

In the most recent screenshot there is a ‘Map Unit Composition’ section. This shows what soil series are included in this map unit. You can see that 80% of the AoB map unit is comprised of the ‘Antigo’ soil series. This is handy for us, as the rest of the information that we’re interested in is tied to the soil series, not the soil map unit.

To pull up the information on the soil series, click the blue link of the series name. This should bring up a screen that looks like this:

soil profile for the Antigo soil series in the soilweb web app

The stacked bar chart that you see on the right looks interesting, but we’ll ignore it for our purposes. What we’re most interested in is the following menu items on the left side:

  • Org. Matter
  • Clay
  • Sand
  • pH

If you click on the ‘Sand’ button you should be presented with a graph that looks similar to the following:

screenshot of the graph in soilweb web app depicting the levels of sand at various depths in the antigo soil series

I’ll be the first to admit that this is a bit of a funky looking graph, as I’m not accustomed to the data flowing in this direction. With that being said, it’s a pretty simple graph once you understand their intentions.

You’ll notice that the y-axis represents the depth (in centimeters) for this soil series. On the x-axis you have the percent of this soil series that is comprised of sand.

This makes the most sense when you think of the trend line on the graph. For example, at 50 cm of depth we can see that approximately 20% of this soil series is comprised of sand. Switching over to the ‘Clay’ graph, we can see in the below screenshot that this same depth is also approximately 20% clay.

screenshot of the graph in soilweb web app depicting the levels of clay at various depths in the antigo soil series

I hope that this makes sense and that you can see how this would be useful when trying to learn more about our soils.

It’s worth noting that the pH is also displayed with a similar graph, it just happens that the pH is represented on the x-axis.

Suitability Ratings for a Soil Series

Now that we’ve learned about the characteristics of our soil, it’s time to learn about how this soil is suited for different real-life applications.

There are two locations in the menu where we’ll find information relevant to our purposes. First, you’ll find a ‘Forest Productivity’ menu item. This doesn’t always contain information, but if it does you may be presented with something like this:

screenshot of the forest productivity section of a soil series in the soilweb web app

This might be a little confusing to some, but it’s pretty straightforward once you figure out the formatting. We’ll explain what these things mean in detail with an example, so let’s turn our attention to the first entry: sugar maple.

The ‘Site Index Curve Number’ for the sugar maple is 66. This means that sugar maple trees in this type of soil are expected to reach approximately 66 feet in height after a specific number of years.

The other piece of information is the ‘Productivity’ column, which merely refers to the volume of wood (measured in cubic feet) that an acre of this tree would produce in a year. In this case, a stand of mature sugar maples that is one acre in size would be expected to produce 43 cubic feet of wood in a year.

You might also find relevant information in the ‘Soil Suitability Ratings’ section of the menu, which looks like this when you open it:

menu showing different options for the soil suitability ratings menu in soilweb web app

Clicking on the ‘Forestry’ button, we should see something similar to this:

forestry section of the soil suitability ratings menu in the soilweb web app

This clearly demonstrates the main purpose of this section, which is to provide quick ratings for a soil series that cover a wide range of potential uses and applications. You can click around the other buttons in this section to see what other information is available for that soil series.

Additional Information on a Soil Series

There’s one more thing to keep in mind when you’re digging around for information on your soil series, the links in the top bar of the menu:

screenshot of the Antigo soil series in soilweb web app showing the various tabs available

The first thing you need to know is that the ‘Soil Data Explorer’ link (SDE) and the ‘Description’ link bring up the same information, so you can disregard the latter. The reason we’re interested in the SDE is that it neatly compiles a lot of different information sources and functionalities.

This was discussed previously in the phone application part, but there are two main parts that are valuable in the SDE:

  • The initial description under the soil series name
  • The ‘Use and Vegetation’ section

There may be additional sections that could be useful for you, but it depends on your soil series.

The last thing to keep in mind is that the ‘Series Extent Explorer’ link brings up a valuable map that indicates where this soil series is present:

screenshot of the series extent explorer for the antigo soil series in northern wisconsin

I don’t know if there’s necessarily a practical use for this map, but it does seem cool to me. This map can also be found while in the SDE, and all you need to do is click on the ‘Extent’ tab at the very end.

USDA’s Web Soil Survey Application: Most Powerful Option

Last but certainly not least, here we finally get to the soil mapping application provided by the USDA, which is called the Web Soil Survey (the government really does have the most creative names for applications…).

Who is Best Suited for This Option

I don’t anticipate that the majority of people that find this piece will be interested in this application. I think that’s totally fine and I wouldn’t recommend someone start playing around with this application unless they really enjoyed working with SoilWeb’s browser application.

While the SoilWeb application has a ton of information, the simple reality is that the USDA’s Web Soil Survey application has many additional fields that can be really valuable. The application includes everything from reports on the expected native vegetation to the most commonly found trees on that soil series.

This application is a great opportunity to people interested in foraging or people that would like to better understand the natural landscapes around them. Another benefit is that the reports in this application output the results for every soil series present in an area you specify, so you’re not just limited to viewing the data of a single soil series at a time.

The main downside is the fact that the user interface (think more MapQuest and less Google maps) and functionality are a little dated, but that’s more than OK in my book given the value it provides.

Check Out This Post if You’re Interested in Learning More

If any of this sounds interesting to you, you’re more than welcome to check out the following post:

USDA Web Soil Survey: A Complete How-To Guide

In this post I provide step-by-step instructions on how to use this application, as well as demonstrating what reports I found most valuable for my purposes.

Final Thoughts

Whew, we’ve reached the end of the post. I hope this was valuable for you and that you didn’t find it too overwhelming. There are a lot of really amazing things that you can learn about your soil with the right methods and tools, and hopefully you enjoyed this post.

If you’re interested in other posts that go into great detail about how we can make the most out of time in nature, feel free to check out any of the following:

Foraging Technology

Our Favorite Apps for Foraging

I’ll admit that it strikes me as a bit odd to be writing about which smartphone apps we use for foraging. After all, we’re talking about foraging which is essentially the world’s oldest skillset. With that said, the modern day forager can get a lot of wonderful things out of a wide variety of apps.

From navigating the backwoods to quickly saving your secret spots, we’re going to cover all the ways foragers can use apps to make the most out of their time in the field.

Before we get started, here are a few quick things to keep in mind:

Never Trust an App for Plant Identification. Apps that identify plants seem to be all the rage these days, but that doesn’t make it a good idea to use them as the foundation of your identification process. Take the time to learn how to positively identify edible plants the hard way, as it is totally worth the extra effort. Leaving your safety up to machine-learning AI is nothing but a recipe for disaster. Maybe there’s a future where apps can be trusted to greatly assist in plant identification, but we’re not there yet.

Apps are Best Used as Tools, not as Sources of Truth. Whether you’re navigating public lands or using a schedule to time the different harvests, know that you’re fully responsible for yourself while foraging. Even if the app told you that you were on public land, it’s your job to avoid trespassing on others’ land. So remember to treat your apps as merely tools to assist with your foraging.

Think Outside the Box About How Apps Can Help You Forage. There are many more functions than identifying plants that our suite of modern apps can help us with. From timing the seasons perfectly to remembering the location of your secret spot for beaked hazelnuts, the modern forager has a lot on their plate. Use apps to make the little stuff more manageable.

Different Kinds of Apps You Can Use for Foraging

Before we get to the actual apps later on in the post, I thought it would be helpful first to cover what types of functions were looking to cover with our choice of apps. The way that I see it, this breaks down into several different categories.

Navigational and Public Access Mapping Applications

First things first, most foragers will be spending a lot of time on public lands, and they’ll likely spend some time navigating the woods. There are really two parts of that statement that need to be addressed. First, any foragers should be very interested in understanding what public lands are available to them in their local area.

There’s no other way to put it: the more public land you can access, the more potential spots you have at your disposal. The second part of this is regarding navigation. If you’re going to have to travel to spots that are a good distance from your car, you’re going to want to do everything you can to ensure your safe return.

Even if you’re only navigating on trails, it’s just a good idea to have an app that you can use to navigate if you needed it in an emergency. Whether or not you need a navigational app can depend on your setting as navigating a field is very different from navigating a mature forest.

Apps for Organizing Your Spots

You might not think of this right away, but there’s a lot of utility and looking for apps that have the ability to save you time on the little things. What am I talking about here? Think about all the things that you need to successfully execute to have that perfect foraging season. From remembering the locations of all your secret spots to getting in the field, scouting out those spots at the perfect time of year, there are a lot of things that apps can help you with. Just because you don’t end up using your phone to identify the plants you’re gathering doesn’t mean that the phone’s utility ends there. From saving the GPS location and a plant’s identity with a quick snap of a photo to automating reminders about the upcoming seasons, apps can really help us stay on top of our game with little effort.

Local Habitat and Soil Information

As I mentioned earlier, I don’t think it’s a good idea to use apps to identify the plants you’ll be foraging. Plant identification is complicated and best not left up to the chance that your photo is clear enough. With that said, there are a few apps that we can use in the field to help us at least get in the ballpark.

Here’s the bottom line: scientists have spent decades gathering data about a variety of aspects about the natural world. From native plant associations to soil maps to habitat ranges of all sorts of plants, we can our phone to quickly pull up the information relevant to our exact area.

This doesn’t necessarily mean that we know exactly what to expect when we step onto a new piece of land, but it can help us get a better understanding of the big picture. Any forager that is looking to make the most out of their time in nature should always be looking to learn more about how everything relates to each other. With enough practice and the right information at the right time, a forager can learn to anticipate certain plants in new land.

Best Foraging Apps for Land Access and Navigation

Safely navigating the woods is one of the less-appreciated challenges of foraging. Sure, you can stick to foraging only along established trails, but we both know that eventually you’ll be drawn off-trail in search of some delicious wild edible.

Keeping that in mind, let’s allow technology to help us accomplish two main objectives when we’re out in the field. First, let’s have it help us return safely home. Ideally we won’t need to use our phone in order to navigate home, but it can easily serve as a tremendously valuable backup. Second, let’s use technology to ensure that we’re following the laws and only foraging on public lands or lands we have permission to.

OnX Maps: a Boon to the Forager

Before we dive into why I think OnX Maps can be incredible valuable to the forager, I do have to confess that this is a hunting app. It’s branded as a hunting app and the functionality is geared around what hunters need to navigate public lands. I’m a hunter that uses OnX and I understand that this might make some people comfortable, but I would like you to hear me out.

Almost every single feature on OnX Maps is equally valuable to the forager as it is to the hunter. Here’s the biggest reason why every forager that utilizes public land should consider OnX Maps: they have, by far, the cleanest way to quickly assess what public lands around you might be open to foraging. Here are some of the different types of land you can find in a single layer in OnX:

  • National Forests
  • Bureau of Land Management land (BLM)
  • State Parks
  • Land owned by State DNRs (or similar agency)
  • County and City-owned Land

Not only that, but OnX has a layer with the property boundaries of private lands available for quick access. So if you’ve got a buddy with some land and they’re cool with you foraging on it, you can easily stay within the property lines without constantly being a hassle.

From a navigation perspective, OnX covers the basics really well and it offers a clean interface. You can easily record a track, save waypoints, and it even allows you to manage your maps easily from a desktop computer.

There are many, many things that I could say about why OnX Maps is an almost necessary app for any forager that spends a serious amount of time on public lands. Here are some of the top things to keep in mind:

  • You can easily see all of the public lands from all sorts of sources
  • Information such as logging records from the U.S. Forest Service is cleanly integrated into the app
  • Get the following information about a private parcel of land: acreage, owner name, shape, etc.
  • Easily measure the distance of a trail or the size of a shape you draw
  • Quickly pull up local weather information
  • Check integrated trail maps that allow for both mileages and an approximate slope
  • Check the radar or look for active wildfires in your region

The caveat to all of this is obviously that this is a paid application with a yearly subscription. As of current writing in October 2020, the cost is pretty reasonable at $29.99 a year for one state’s data. You’re allowed access to all 50 states with a yearly fee of $99.99, but I don’t think many foragers would cover that much ground to cover the price.

With all that said, here’s an example of what kind of information you can pull up in OnX:

screenshot of the onx maps hunt app showing public land areas in northern wisconsin

The parcels that you see outlined are all publicly owned land that is almost certainly open for recreation. Each of the different color overlays represents a different type of owner (state DNR, National Forest, local city, etc.) and it’s easy to pull up the information associated with each parcel just by tapping on the screen. For example, by clicking on this parcel I can see the following information:

screenshot of the onx hunt app showing ownership information for public lands in northern wisconsin

As you can probably see, this is incredibly useful information to have when scouting for new locations to forage. The reality is that residents of the United States have hundreds of millions of acres of forestland that is open for foraging. A tool like OnX Maps does a fantastic job lowering the barrier to entry, as it effortlessly solves the main question of “where” exactly can we forage.

So while I understand that you might not have anticipated adopting a hunting app, I think it is well worth your serious consideration. It can only improve your time spent foraging, and it has the chance to make a great impact.

BackCountry Navigator TOPO GPS PRO

I like this app for a few simple reasons. First, a disclaimer: I’ve been using this app for almost a decade and I’m also a creature of habit. So I don’t necessarily know if this truly is the best pure navigational app out there, but I do like it.

The main reason I like this app so much is that it offers a rich library of map layers complemented by a powerful navigational interface. This interface isn’t as user-friendly as OnX Maps, but it offers a lot of functionality.

There are several different layers of satellite imagery available, so you can switch them up to find one that suits your needs best. The app also has a variety of topographic maps available, including custom topo maps from the U.S. Forest Service.

Also, there are a ton of options that allow you to customize your main screen, which can be really useful for those looking to make their screen just right. I’ve also found that the tracks are more accurately represented on the screen when compared to OnX.

Here’s where this app is available:

Avenza Maps: Your Best Source for Official Map Layers

While I don’t personally use Avenza Maps much for navigation, it does offer the best collection of freely available maps for use when navigating the wild.

Featuring maps from local counties, the U.S. Forest Service, the National Parks system, ATV/UTV trails, and so much more, every forager should check out what they have to offer in their local area.

Think of it this way: Avenza Maps is the best option for you to bridge the gap between that paper map with all of the detailed information you love and navigation in real life.

It’s hard to summarize what’s all available in Avenza Maps, because it is so specific to your local area. Either way, I suggest that you at least give it a try and check out what free maps they have available. Many of the apps for download are paid maps, but there’s still a lot of great value to be had in the free section.

Here’s where this app is available:

Best Foraging Apps for Keeping Yourself Organized

I’m assuming you are like me: you probably started foraging for something easy like blackberries and then it was only a matter of time before you had a dozen different seasons to keep track of.

This can be a lot of fun, but at some point it becomes easy to miss a season or forget to process your foraged goods in sufficient time. Here’s where we allow technology to the rescue. From scheduling alerts of upcoming seasons to tracking the exact locations of all your secret spots, there’s a lot of ways technology can benefit us.

Google Photos or Photos for iOS

Before I get into why this functionality is so helpful and so valuable, I do have to confess that I resisted this for a very long time on the creepiness factor alone. Here’s the deal: I have no social media presence, I’m quite skeptical of the ways our phones are mined for information, and I’m constantly this close to going back to a flip phone.

With all of that said: man, is it convenient for a forager when you can snap a picture of a newly found plant and have the exact GPS attached directly to your photo. Is it a bit creepy? You bet.

Find a cluster of American hazelnut bushes? Snap a quick picture. Come across serviceberry bushes but it’s a bit too early in the season? Grab a picture. It’s just so easy and so terribly useful.

The real beauty is that the convenience doesn’t have to end in the field. It’s easy enough to add that picture to an album dedicated to that plant species when you get back home. Now, with a photo stored in the correct album and the exact GPS coordinates attached to it, you can easily remember that secret spot any time in the future without any additional work.

Here’s a rundown of how I use Google Photos to keep track of my foraging spots. When I’m in the field I snap a picture of any plant that I want to remember later for foraging. Usually this plant does not have the fruit or nut ripe at this point, as I’m merely saving this location for later in the season. Once I open the app, I scroll down to the pictures that I took when out and about and then tap on the image I’m going to open:

screenshot of pictures of plants in the google photos mobile app

After the image opens up, I go up to the top of the screen and tap the ‘3 dot’ icon to get additional information:

image in google photos app with red arrow pointing to three dot icon to find more options

This then changes the screen to look like the following:

screenshot of google photos app with the button for adding a photo an album highlighted and also gps coordinates highlighted in bottom

I’m interested in two different things when I open up this screen. First, I want to confirm that GPS coordinates were attached to this image. You can see at the bottom of the screenshot that this is the case.

Second, I want to then add this picture into the appropriate album. As you might expect, I organize my foraging pictures by plant species, so I would put this image in the ‘Blackberry’ album. You can do this by clicking the ‘Add to album’ button that is highlighted on the left side of the screenshot. This will pull up a page that allows you to select an existing album or create a new album. I’m going to create a new album, and it’s as simple as clicking this button and then entering a name:

screenshot of creating new album in google photos app

I’m not actually going to create an album for wild blackberries here, as almost every trail in our area is absolutely crawling with them. To find an existing album, go back to the main menu for Google Photos and click the ‘Library’ button at the bottom of the screen:

screenshot of library tab in google photos app showing different plant species organized by album

As you can see in the above screenshot, I have a variety of albums for different species of plants. I’m happy with the simplicity of this approach from a data collection standpoint, but I am looking into better ways to utilize that data once I need it.

I’d love to see an option to pull up a map with pins for all of the different pictures found in an album, but that doesn’t seem to be possible in the Google Photos app. I think it’s possible to extract this data with code, but I think it would require working with an API.

Here’s where this app (or a similar app) is available:

Google Sheets: Free Option for Storing Data

I’ve spent a lot of time working with Google Sheets for a variety of reasons, but I haven’t used it much for foraging. Sheets has a lot of interesting capabilities, and I think it could be very valuable for a forager looking to manage their foraging data if they tend to geek out like me and take a technical approach.

Google Sheets is great for many reasons, but here is a quick summary for those that aren’t familiar with it:

  • Using Google Sheets is free and has a very user-friendly interface
  • The vast majority of people don’t need a separate application on their computer for spreadsheets: an in-browser experience is more than fine
  • A lot of things are possible once you consider integration into the Google suite of apps

Some might be concerned about giving Google too much information, and I understand that concern. However, I don’t fear Google learning about my secret spot for beaked hazelnuts, so I’m comfortable with the trade-off.

Here’s where this app is available:

Notion: an Intriguing Option Worth Exploring

I’ve used productivity and workflow apps in the past (Trello being a good example), but Notion is the first app that I’ve actually managed to use more than a week.

Before I discuss why I think it’s helpful, I think it’s important to state that, yes, Notion can be a little intimidating when you start from scratch. The best and worst part about Notion is that it is basically a blank slate, so there are a million things that you can do with it.

Here’s the deal: if you are interested in using an app to help you better stay on top of the mountain of assorted tasks that come with a great foraging season, Notion might be a great solution for you. It truly is a do-it-all app: everything from note taking to task management to data storage is possible.

I’ve only spent the last month using Notion to manage my work-related tasks and projects, so I have yet to explore how to best use it to manage my foraging headaches. I can promise you that I will be experimenting with this in the future, and I’ll report back with any interesting ideas or thoughts in a future post.

Here’s where this app is available:

Best Foraging Apps for Learning About the Local Environment

While we shouldn’t allow apps to take over our identification of the plants we forage, we can use technology to quickly get the science that we need for our specific location.

SoilWeb: Gain Insights About Your Local Soil

At first glance this app might seem a bit too technical for most people, but hang in there for a minute. The SoilWeb app was developed by the California Soil Resource Lab at UC-Davis, and it’s job is to quickly provide you information about the soil in your current location.

Why might this be useful to the average forager? One of the types of information you can find in this app is referred to as the ‘Use and Vegetation’ sections, which may explain a great amount about your local habitat.

It’s probably easiest to just get to it and show you how to use it. Fortunately, this is perhaps the simplest interface I’ve ever found. When you load the app you should see a screen like the following:

screenshot of the soilweb mobile app with an arrow pointing to the get soil data button at the top

This will come as no surprise, but we’re going to simply click the ‘Get Soil Data’ button the arrow is pointing to in the above screenshot. What this will do is pull your location data (you may need to grant the app permission before it does that) and then it will pull up a screen that may look something like this:

screenshot of possible results from soilweb app query

Let’s quickly walk through what this is saying. First, the soil in my current location consists of two different soil types: Plainfield and Watseka. Second, you can see that the dominant soil type in my location is Plainfield, as it contributes 94%.

The best thing we can do next is to explore the data associated with the soil types that contribute to the majority of our local soil. In my case this means that I’m only interested in the Plainfield soil type, but it’s very possible that you may have two (or possibly three) different soil types to look up depending on your location. Once I click the blue box towards the top of the screen that shows the soil type name and the percent, I’ll be taken to a screen that looks like this:

screenshot of the text description of the plainfield soil series in the soilweb mobile app

As you can see at the top, there are three different tabs that contain information:

  • Description
  • Details
  • Links

We’ll have some use for all three sections, but you’ll probably get the most use out of the Description and Details sections.

The majority of the Description tab will only be useful to soil scientists, but there are a few sections that we can make use of. Most important to us is the ‘Use and Vegetation’ section, which is near the bottom and looks something like this:

screenshot of the use and vegetation section of the plainfield soil series in the soilweb mobile app

If you take a minute to read over the text you’ll quickly realize why this information could be valuable to a forager. Included in the text are the following bits of information:

  • Common ways this type of soil is planted with agricultural crops
  • Approximate areas of distribution of this soil type
  • Specific trees that are commonly present with this soil type

While the screenshot above only contains trees that are commonly present, many other soil types offer a more expansive list of plant species. Needless to say, this app can be a great opportunity to get a quick understanding of what you might expect on a piece of land with only the click of a button.

The other parts of the Description tab that may be useful are the following:

  • Distribution and Extent
  • Geographic Setting
  • Introduction (just the first paragraph at the top)

We can leave everything else to the scientists. Regarding the Details tab, the most valuable information for our purposes is probably in the Forest Productivity and Soil Suitability Ratings sections, as they are the most relevant.

The Links tab contains just that: links to two different soil applications that are designed to show the extent of the currently selected soil type. I’ve found some value in these mapping applications, but the outlines aren’t specific enough for us to really get a whole lot of use out of them. Also, while these browser-based applications can run on a mobile device, you’re probably going to want to switch to a desktop for a better experience.

So while the reality is that the SoilWeb app really only does one trick, it does that trick very well and is often very helpful for our purposes.

Here’s where this app is available:

Any App Featuring Satellite Imagery

I’ve covered this in greater detail in this post, but you can trust me when I say that satellite imagery is a wonderful friend to the forager. From easily finding groves of oak trees to finding a hidden ramps spot that’s just a thick carpet of greens every spring, there’s a lot you can do with the right satellite images.

There isn’t really a clear cut app that can do everything that I’m looking for in regards to satellite imagery, but my point is that you should merely leverage the satellite images on your existing apps.

Here are some of the ways you can use satellite images to benefit your foraging:

  • Finding groves of oak trees based on the fall colors late in the season
  • Locating sections of land recently clear cut
  • Finding tamarack bogs when looking for associated wild edibles

There’s a lot more that could be covered here, but just understand that understanding how use satellite imagery gives you the opportunity to make the most out of your time in the field. There are few other tools with as much potential in this modern age, so take the time to explore how it can help you most in your local environment.

Sun Position: Train Your Brain to Think About the Sun’s Patterns

While I wouldn’t call this a necessary app for a forager, I think it can be a very valuable addition. As you might guess, this app allows you to use augmented reality to show the approximate path of the sun in your current location. It also has the capability to show the sun path for any other day of the year, as the path of the sun varies highly over the different seasons.

Why might you need this? This app can easily serve as a great way to quickly remind yourself about which parts of the landscape will get the most sun throughout the day. This is undoubtedly more useful to the gardener, but a forager is certainly capable of benefiting from this knowledge.

Here’s where this app (or a similar app) is available:

Final Conclusions

I hope that you enjoyed this post and that you found a few interesting ideas that might save you some time or frustration the next time you’re in the field. While apps certainly can get in the way of our enjoyment of nature, I think when they are properly used they can really help open up this world for us.

From helping us successfully navigate our way back to the car to quickly and easily storing the location of that thicket of wild plums, apps are here to make our lives better when used correctly.

If you enjoyed this piece and you’re interested in other pieces that take a deeper look into what’s possible when we use technology to make the most out of nature, check out any of these articles: