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

What Can You Do in Wilderness Areas?

Of all of the types of public land that the United States offers, I’d be hard-pressed to find a set of lands that offer more unique opportunities than Wilderness Areas.

What can you do in Wilderness Areas in the US? Wilderness Areas are a type of public land that is managed by four different U.S. agencies. Each agency will have their specific rules for Wilderness Areas, but no kinds of motorized equipment are allowed.

Keep reading to learn more about what you can and cannot do in the great Wilderness Areas of the United States. We’ll cover where you can find information on the websites of each agency, along with general trends you can expect from a Wilderness Area.

How to Find Information About a Specific Wilderness Area

Before we go over how to find information regarding the rules and regulations for specific Wilderness Areas, it’s helpful to give a general overview of how they are managed.

Wilderness Areas is a specific type of public land in the United States, but they are not not managed by a particular agency. Instead, a Wilderness Area might be managed by any of the following four agencies:

  • U.S. Forest Service
  • Bureau of Land Management
  • Fish and Wildlife Service
  • National Park Service

The last three agencies on that list are under the Department of Interior, while the U.S. Forest Service is run by the U.S. Department of Agriculture. As you can probably imagine, this means that it’s important to do your research before you head out to enjoy a Wilderness Area, as the rules and regulations that apply are not consistent.

A great website for learning more about Wilderness Areas is Wilderness Connect, which is a partnership between a variety of organizations that is dedicated to serving these wilderness areas.

They’ve put together a very helpful tool shows the different Wilderness Areas throughout the US. If you go to their home page and click on the below image, you’ll bring up a map of all the different locations:

It’s a neat tool that is well worth exploring, and they have information available for all of the different wilderness areas.

Wilderness Areas of the U.S. Forest Service

The U.S. Forest Service provides a tool called the Interactive Visitor Map, which allows you to pull up a variety of different maps that display different data layers. They do technically have a Wilderness option on the ‘Layers’ menu on the left-hand side of the screen, but it only pulls up small labels. I didn’t find it to be very helpful, but it’s easy enough to spot Wilderness Areas when you zoom in on the map:

You can see that the Wilderness Areas are colored with a darker shade of green, and if you click anywhere on the shape you should get an info window like this:

If you go to the ‘Forest’ tab in the middle, you’ll see a link that is under the ‘WEBSITE’ section. If you click this link you should be taken to the website of the National Forest that this Wilderness Area belongs to. Once you’re on this website you’ll want to find the ‘Special Places’ menu item in the navigational menu on the left:

Not every National Forest website is organized in the same manner, but you should then be able to find a part of the page that either discusses the various associated wilderness areas, or it links to another page. In this case the Grande Mesa Uncompahgre and Gunnison National Forests (say that five times fast) website has a dedicated page for wilderness areas, as seen below:

Clicking through to that page, you’ll find a list of associated wilderness areas, with separate pages for each. Once you get to the page associated with your specific wilderness area, you’ll find all of the restrictions and rules.

Wilderness Areas of the Bureau of Land Management

The Bureau of Land Management covers such a diverse array of public land that it’s unsurprising that there isn’t a clear method to get information on a specific wilderness area. If you’re interested in finding wilderness areas that are managed by the BLM, you can find them on the map managed by the Wilderness Connect group. The BLM wilderness areas are shaded in yellow like such:

Wilderness Connect will sometimes have site-specific information in their Rules & Regulations section, but you can also look for local rules on the Bureau of Land Management website. Each stat will manage their wilderness areas differently, so your best bet is to select your state in the menu on the far-right of the navigational menu:

If you scroll down you should find a section called ‘Featured Topics’ and in that section you’ll best be served by looking for an icon that most closely aligns with your activity of interest. Exactly what options are available will vary by state, but it could be something like the following:

Wilderness Areas of Fish and Wildlife Service

The website of Fish and Wildlife Service does have a page dedicated to their Wilderness Areas, but I didn’t find any indication of their site containing site-specific rules. They do point people towards the Wilderness Connect group, so I believe that would be the best place to start.

If you go over to the map for Wilderness Connect, you’ll see that the Fish and Wildlife Service wilderness areas are shaded in an orange color, like so:

If you click on the shaded area you will be presented with an information window like the following:

Click on the ‘More >’ link and then you’ll be taken to the individual page for this specific wilderness on Wilderness Connect. There should be a group of icons below the introduction to the page; if you click the ‘Rules and Regulation’ icon you’ll be taken to a page that summarizes the regulations that apply for this site.

It appears that the first section of the rules page covers general rules, while the last section covers site-specific rules and regulations for this wilderness area.

Wilderness Areas for the National Park Service

The website for the National Park Service does have a section dedicated to wilderness areas, but I was unable to get their mapping application working. Much like the Fish and Wildlife Service, your best bet is to start with the Wilderness Connect mapping tool. The wilderness areas for the National Park Service are colored a dark pink in that map, like the following:

Using this map you can pull up the link to the page on Wilderness Connect. Like above, this page will have site-specific rules and regulations.

It’s also worth checking out the website for that specific national park. Using the Zion Wilderness section from the above screenshot as an example, you can head over to this link on the National Park Service website to pull up that specific site. Click the ‘None selected’ drop-down menu and then start typing your park in this search menu:

Once you find your park you can click on it and then you should be taken to the website for that park. There should now be two toolbars on your screen. The top toolbar is for the NPS as a whole, while the bottom toolbar is for your specific park. In the bottom toolbar you’ll be looking for something like ‘Learn About the Park’ –> ‘Management’ –> ‘Laws & Policies’ like so:

The exact name of the last link may vary a bit from park to park, but it should be something similar to that. Once that page loads you’ll be looking for something called the ‘Superintendent’s Compendium.’ This can be in a variety of formats (website, PDF, etc.), but you can expect this to be your best source for park-specific rules and regulations.

Final Thoughts

Hopefully you learned something with this piece and got what you needed in order to start exploring the great Wilderness Areas of the United States. They’re truly an awesome opportunity to experience something that is so hard to find in today’s modern world.

If you liked this post and are interested others like it, feel free to check out any of the following links:

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:

Foraging Nuts

Where to Find Wild Hazelnuts

There’s something fun about eating wild hazelnuts. Sure, the edible part of the nut is a little small, but they’re a blast to snack on, while reminiscing on time spent foraging in nature.

Where can you find wild hazelnuts? Wild hazelnuts, consisting of beaked hazelnuts and American hazelnuts, are widespread throughout the United States. Beaked hazelnuts are mostly across the Northern US and parts of California, while American hazelnuts are fairly widespread throughout the Eastern US. Both thrive in disturbed areas.

In this post, I’m going to walk you through everything you need to know to start identifying areas around you that could feature wild hazelnuts.

The Most Common Types of Wild Hazelnuts

There are two different types of wild hazelnuts that we’ll cover in this post: beaked hazelnuts and American hazelnuts. I won’t get directly into identification of each species, as I think that’s best left up to guidebooks and experienced mentors.

American Hazelnut

American hazelnuts are fairly widespread throughout the Eastern United States. Here’s what you can expect the immature nuts to look like:

The bushes of an American hazelnut are not as tall as the beaked hazelnut, and they take on a more “bushy” appearance.

Beaked Hazelnut

Beaked hazelnuts are also widespread throughout North America, occupying the northern part of the United States and parts of the west coast. Here’s what an immature nut for a beaked hazelnut looks like:

Beaked hazelnuts are noticeably taller than American hazelnuts, and they tend to cluster in tight groups where the main stems are visible for about the bottom two thirds of the shrub.

Common Habitats for Wild Hazelnuts

Both types of wild hazelnuts prefer disturbed areas, meaning that they are frequently found in areas like roadsides, forest edges, openings, and other similar locations.

While they can exist in shadier locations, you’ll want to seek out areas with decent access to sunlight, as that helps a lot with nut production. Trails running through mature forests can have wild hazelnuts, but you’d want to look on the side with the better light access.

Using USDA Reports to Narrow Our Options

This is where this post gets a little technical. Rather than leaving you with a description of potential habitats and then offering a hearty “Good luck!”, we’re going to try to leverage a USDA application to find land with soil that might be suitable for wild hazelnuts.

It’s worth noting that this entire process is based upon a report that doesn’t appear to be universally available across the continental United States. Unfortunately, not everyone will have access to this information.

For those that do have access to data from this report, understand that this can be an incredibly useful tool for a forager, in a million different ways. Sure, maybe this post will be specifically focused on finding areas with a higher likelihood of having wild hazelnuts present, but that’s not where this reports utility has to end. From blackberries to blueberries to cranberries and more, a wide variety of edible plants show up in these reports.

Brief Introduction to This Process

This process is based around a tool called the Web Soil Survey that is provided by the United States Department of Agriculture. The goal of this browser application is two-fold:

  1. Allow users to access a soil map of the United States that indicates which soil types are present where.
  2. Facilitate the easy access of information about the soil types found in a certain area. This includes both scientific properties and information on the suitability of that soil type for different real-world scenarios.

This all may be a little confusing, and that’s OK. In this post we’re going to exclusively focus on the steps you need to take to generate the report we need. If you happen to be interested in learning more about this application and would like to explore it in greater depth, feel free to check out this post that covers everything you could need to get started.

Finding Soil Types That are More Likely to Have Wild Hazelnuts

Alright, let’s get to it: here’s the link to open up a new session of Web Soil Survey. This should be approximately what your screen looks like once you’re loaded:

screenshot of the main page when you load the web soil survey web app

The first thing you’ll want to do is zoom into the local area that you’re interested in learning about. By default the ‘zoom in’ tool is selected when you open the application, so you have to zoom to your area with one of two methods:

  • Pressing single-clicks until the map is zoomed in on your area
  • Clicking and dragging to form a box over the area you want to zoom to, and then the screen should zoom to the highlighted area

You’re probably noticing at this point that the navigation is a bit…dated. Adjusting the zoom and moving the map is a much more manual process than we might be accustomed to in this modern age, but its controlled by a relatively simple toolbar. The first three buttons on the toolbar what you’ll need to navigate, and they are the following:

  • Zoom in
  • Zoom out
  • Pan the screen (moving the map manually)

Again, this isn’t a great interface, but I think you’ll get the hang of it relatively quickly.

One quick note on the area that you’ll zoom into: you’ll want to cover a relatively large area, but there are limits. The tool only allows a selection of 100,000 acres or less, but you’ll be able to keep to that limit pretty easily. For context, a 10-mile square, which is therefore 100 square miles, would only be 64,000 acres.

Once you’ve zoomed in on a location that you’re interested in learning about, you’re next going to have to select an ‘Area of Interest’ with the menu. To select this we’ll need to use the button in the toolbar with the red box, which is located here:

screenshot with an arrow pointing to the area of interest button in the toolbar of the web soil survey web app

After turning this feature on, we’ll then drag a box over the area that we want soil information on, like so:

screenshot of an area of interest being selected in the web soil survey web app

Once you release the mouse, you should be presented with a box that has diagonal lines running through it in the area you selected. There are two possible situations that might give you an error.

First, you may have selected an area that exceeded the size limit of 100,000 acres. If that is the case, all you’ll have to do is draw a new AOI.

Another possibility is that you selected an area that draws from more than one soil survey. You’re still allowed to proceed if this is the case, but it will make the reporting more laborious to work through. If this is the case, I’d recommend that you redraw your AOI if you’re using this application for the first time.

If your screen looks something like this then you’re ready to move onto the next step:

screenshot of a satellite image with the area of interest rectangle visible in the web soil survey web app

What we want to do next is to scroll up to the top of the page, and then look for the third tab which is called the ‘Soil Data Explorer’ and looks like like this:

screenshot with a red arrow pointing to the soil data explorer tab in the web soil survey web app

Clicking this tab will generate a soil map on the screen, and then update the menu on the left with the appropriate features. Once you click through you should notice a second set of tabs; this time we want to click on the ‘Soil Reports’ tab, which is located here:

screenshot with a red arrow pointing to the soil reports tab in the secondary menu of the web soil survey web app

This will bring us to the page that has the report that we’re looking to run. We’ll want to look at the menu on the left, which should look something like this:

menu showing the soil reports available in the web soil survey web app

We’re interested in the ‘Vegetative Productivity’ group of reports, and you can click anywhere on that section to expand the reports. Once expanded, we’re looking for the ‘Rangeland and Forest Vegetation Classification, Productivity, and Plant Composition’ report, which is listed here:

screenshot of the reports available in the vegetative productivity section of the web soil survey web app

Click anywhere on that report title to expand the screen, and then you’ll want to click the ‘View Soil Report’ button, which is found here:

submenu options with a red arrow pointing to the view soil report button in the web soil survey web app

Once you run the report, you may notice that there is a report that shows up underneath the map. Assuming you have data in the report, that area should look something like this:

screenshot of the results of the rangeland and forest vegetation classification report from web soil survey web app

I’d like to draw your attention to the column name that I’ve highlighted in the screenshot above. This is the column that we’ll be most interested in, as it contains the list of plants associated with that specific soil type.

Before we start interpreting the results, I’d like you to quickly do a search on your screen for the ‘hazelnut’ term. This will go through the report results and check whether hazelnuts are mentioned at any point.

It’s possible that you have report results but hazelnuts are mentioned nowhere. This is totally fine, and it turns out that the area I randomly selected for demonstration purposes doesn’t mention hazelnuts at all. This doesn’t necessarily mean that there are no hazelnuts in this area, it’s just that they’re less likely to be found in these types of soils.

After a few tries I eventually found an AOI that included hazelnuts in the results, as this is what I found:

sample report results for the Mudlake soil series in web soil survey web app

One quick navigational note before we start discussing how to use these results. If your selected Area of Interest didn’t include hazelnuts and you’re looking to try a different area, you’ll first have to go back to select another AOI by clicking the ‘Area of Interest (AOI)’ tab at the top of the screen. Then you’ll select a new AOI and then navigate back to the ‘Soil Data Explorer’ tab to run the report again. One final note: you’ll need to click the ‘View Soil Report’ button to generate a report for the new AOI, as the results shown are still for the old AOI.

Making Sense of the Report Results

Alright, so we found a report that mentions some kind of wild hazelnut in it. What exactly does that mean for us? Here’s where it might help to briefly explain the structure of the information in this report.

Take, for instance, the report results that I highlighted above. There are two parts of this report that are most important to understand:

screenshot of report results with the Mudlake soil series name highlighted and a red arrow pointing to the MuB soil map unit in web soil survey web app

The first thing to understand is that the different types of soil on the map are labeled with the map unit symbol, which in this case is the value of ‘MuB’ that the arrow is pointing at. This means that the soil type is MuB. Long story short, soil types are comprised of different soil series, and ‘Mudlake’ is an example of a soil series.

In a (hazel)nut shell, here’s what the report is telling us: beaked hazelnuts are commonly associated with the ‘Mudlake’ soil series, which is a major component of the ‘MuB’ soil type. In English: if we want to find wild hazelnuts, we should be interested any parts of the map that are labeled as the ‘MuB’ soil type.

Hopefully this is all making sense, but I understand if it’s a lot to take in. Taking your time to work slowly through this and experiment with the tool will make it feel much more natural in no time.

Action Steps: How to Actually Use This Approach in the Field

I’m sure this seems cool and all, but the goal here isn’t to have you spend a bunch of time on your computer while you think about nature. We want you to actually get out into nature and on the hunt for delicious hazelnuts. We’re just looking to use technology to make that journey a little easier.

Find a Large Enough Area on Your Soil Map That Features the Desired Soil Series

Going back to the example I was discussing earlier, we now have to take the report results and find the specific soil types that indicate there’s a likely presence of hazelnuts.

In the Area of Interest I was looking at, there are two different soil types that mentioned ‘beaked hazelnuts’ in the results: MuB and PhB. My job is now to scroll back up to the map and search for any instances of these two soil types. Just for reference, here’s what my soil map looks like for my AOI:

screenshot of the soil map in northern wisconsin showing the various soil map units in web soil survey web app

It’s likely difficult to see unless you open the above image in a new tab, but I’m able to pretty quickly spot several different instances of the ‘MuB’ soil type.

After checking a few different areas, the largest section of the ‘MuB’ soil type that I could find is the following:

close up view of a wisconsin soil map with a red box around the MuB soil map unit

There are a few main reasons why I was most interested in this spot. First, it was likely the largest piece I could find that was MuB. Second, I see that this piece happens to have a road running through it. If you remember what we discussed earlier, hazelnuts are usually more prevalent along disturbed areas like roadsides.

Confirm If You Have Public Access to The Land

This is a really important point: just because we find a possible spot on the map, does not mean that we have access to it or the right to forage there (even if it is public land).

You likely already know this, but as foragers it’s our responsibility to ensure that we are always following the rules of the organizations that managed of the land. So our job here is to find a spot that we’re going to check out, and then checking if we have access.

What you’ll need to do is to click the button in the toolbar that is a blue bubble with an ‘i’ in it:

screenshot with a red arrow pointing to the information button in the toolbar of the web soil survey web app

This is the Information tool, and we can use it to pull the GPS coordinates of a specific location on the map. Once you’ve toggled on the tool, you can click anywhere on the map to produce an information window. This window doesn’t show up in the map, but instead shows up as a new report directly below the map, like the following:

screenshot of the identify window in the web soil survey web app

Now that we have the coordinates, we can use them to determine who owns this land. I can confirm that this area is public land, as this bit of land is part of the Nicolet National Forest.

Look for all Trails and Roads Running Through Your Targeted Area

As hazelnuts generally prefer areas that are disturbed, our best bet is to take our target area and look for any trails or roads that cut through it.

It’s worth noting that you’re likely best off picking a location where you can park your car, with the assumption that you’ll park there and go on foot the rest of the way.

Here comes the fun part! Now that we’ve done the technical part, our job is to get out in the woods and see if we can find some hazelnuts.

Things to Keep in Mind While Looking for Wild Hazelnuts

Now that we’ve covered the ‘how’ and ‘where’ of locating wild hazelnuts, let’s take a minute to go over a few key points that any forager should

Learn to Identify the ‘Habit’ of the Bush

This is generally true when foraging for any type of plant, but one of the best things you can do is familiarize yourself with the ‘habit’ of the plant. Generally speaking, this refers to the structure that the plant takes on based on the shape and formation of the branches and leaves.

For example, the beaked hazelnut often has a bush structure that has a sort of open understory on the bottom two-thirds of the plant while the majority of the leaves are in the top third. This means that you can look for an understory that has a lot of main stems that are clustered together and bowing slightly outwards.

On the other hand, the American hazelnut is a shorter bush and is densely leafed throughout the majority of the plant. This may be a little hard to explain in words, but once you get used to seeing each plant you’ll have this ‘habit’ imparted on your brain as a sort of visual structure to be subconsciously scanning the woods for.

As always, your best bet is to heavily rely on your guide books (yes, you’ll use ideally use more than one source) to first positively identify the plant. Once you’ve identified them, your job is to work on building up that instinctual visual memory of how that plant looks.

Easiest to Find the Immature Nuts First

Early to mid-summer will likely be the best time for most people to try to identify wild hazelnuts for the first time. This is because it will force you to look for bushes that have already started producing nuts.

This approach also reduces how much potential time you might have to wait in between positive identification and harvesting. With that being said, any time is better than never, so don’t feel like you need to wait another year just because the timing wasn’t perfect.

Timing is Crucial: Squirrels Won’t Wait for You

It’s hard to stress this enough: when the wild hazelnuts ripen, it will likely be only a matter of days before squirrels and other animals utterly decimate the ripe hazelnuts.

Even being a few days late can be the difference between an awesome harvest and getting skunked, so harvesting wild hazelnuts is often difficult to time unless you live nearby your source.

Just for the record: do try to leave some hazelnuts for the squirrels. They have plenty to eat in the wild, but it’s kind to leave them some, especially given how much they seem to appreciate them.

Final Thoughts

Hopefully you enjoyed this piece and got a few valuable tips that you can apply to your search for the wild hazelnut. I’ve always enjoyed searching around the woods for all sorts of different kinds of wild edibles, and wild hazelnuts are no exception.

If you enjoyed this piece and you’d like to read something similar, be sure to check out the below articles: