How to Find Specific Trees Using Satellite Imagery

I’ve always loved spending time in the forest, but ever since graduating from college I’ve lived in large cities. While I still get the chance to explore the woods, this meant that I would spend a lot of time dreaming about the next chance to get away.

To be honest, I’ve spent a lot of time looking at satellite imagery of the forests of my youth. I don’t know exactly what I expected to get out of all that time spent dreaming, but it means I’ve happened to stumble upon a few happy accidents along the way.

All of that brings me to this: there are many, many things that you can do with satellite imagery to make the most out of your time in the woods.

In this post I’m going to lay out everything I know about using satellite images to learn more about your forests. A big part of that is getting to know which kinds of trees live where. Fortunately, with a little luck and some help from some satellite images, we can start to make sense of our time in the woods.

Why You Would Want to Find Trees With Satellite Imagery

I’m sure there will be a few people that think this is all a bit silly. If you’re merely looking to enjoy your local forest by going for a stroll down a path, then I completely agree.

However, if you perhaps have more ambitious plans for your connection to nature, then it can massively pay off if you can get comfortable with a few modern techniques and tools.

Like it or not, we’re living in an entirely unprecedented age when it comes down to access to information. If we’re being honest, there are many downfalls associated with this ever-connected world, and, ironically, those ails are often fixed with a good-old forest bath. In an effort to kill two birds with one stone, let’s find a way to use all of this technology for good and to have it help us spend more time in nature.

So, let’s get to it. Here are some of the things that are possible if we learn how to make the most out of the tremendous resource that is our rich collection of satellite imagery:

  • Identify groves of oak trees in order to find a great spot for gathering acorns
  • Locating swamps with tamarack trees, in order to find plants associated with them
  • Finding a cluster of trees that are unfamiliar to you and then identifying them in the field
  • Observing the fall colors of your nearby woods from space, in order to identify groups of like trees ripe for identification

All of these are a great opportunity to find a way to strengthen your ties and connection to your local forest. Join me as we work to make the forest a little less mysterious with some help from modern technology.

Important Things to Know Before Getting Started

Before we get started and dive into the action steps in the rest of this post, please take a little bit of time to closely read this section. The points covered below will do a lot to make this whole process a lot less confusing, and you really stand to benefit.

The Timing of the Imagery Matters a Lot

There’s no way around it; the timing of when the satellite image was taken makes a huge impact on whether or not we can actually use it. Let me explain what I mean: the satellite image of a mixed forest containing both evergreens and deciduous trees interspersed is difficult to use if it was taken during the summer months.

This makes sense as anything and everything in the woods is green come summer, so all you’re essentially looking at are a huge swath of green trees.

satellite image of forest in summer with deciduous and evergreen trees looking similar
This forest does contain a mix of evergreen and deciduous trees, but satellite images from the growing season make this much more difficult to analyze.

For this reason, I much prefer to find satellite images taken anytime between mid-fall and before the leaves bloom in spring. one of the reasons that fall is okay is because we can leverage the different colors that trees turn before they lose their leaves.

For an example of why this is important, see the below screenshot of satellite imagery from Google Maps that uses growing season imagery on the left but leaves-off imagery on the right.

satellite image from google maps showing a blended section with part leaf off imagery and part leaf on imagery
You’ll occasionally run into spots like this that are blended along a border. This may be temporarily confusing, but understand that it happens with modern mapping applications.

The imagery on the right allows us to quickly understand much more about the landscape. It still is possible to draw some of the same conclusions with the imagery on the left, but the difference are much more subtle and can be hard to spot as a beginner.

We can also use the approximate order in which the different trees lose their leaves but more on that later. On another note, you rarely see satellite imagery from winter used with the snow on the ground.

Very Beneficial to Have a Basic Understanding of Your Local Habitats

The most important point that I can make in the section is that it’s precious to have some basic understanding of your local forests and what happens to your trees throughout the seasons.

I think about it like this: satellite images are merely tools that allow us to apply and search for patterns that we’ve already begun to understand.

This is maybe most clearly explained with a direct example. I spend a lot of time in northern Wisconsin in the Nicolet National Forest, and some of the more common trees up there are maple trees, northern red oaks, tamarack trees, and a variety of evergreens.

I know from experience that the maple trees that dominate the woods lose their leaves early in the season and take on a bright yellow color when they do. On the other hand, northern red oak trees in that area tend to hold onto their leaves much longer, and they have a bright orange color in the fall.

So how can we use this information? I didn’t mean to find this, but one day I was looking at satellite images for that local area, and I noticed that most of the deciduous trees had lost their leaves except for some bright orange clusters of trees.

satellite imagery of a forest in fall with yellow and orange trees scattered throughout
I can confirm that the yellow trees on the left are an evergreen swamp with many tamarack trees present, and that the surrounding trees that are orange are northern red oak trees.

The day earlier, I think it was that I had been walking through the woods and found some northern red oak trees and noticed that they still have their leaves and that they were bright orange. This probably won’t come as a surprise, but I sought out to immediately confirm that all of the bright orange dots I saw on the satellite image were, in fact, northern red oak trees.

Determining Deciduous vs. Evergreen is a Great Starting Point

I understand that the above section may have gotten a little into the weeds, but let’s pull it back for a minute and get back to basics.

One of the easiest things that anybody can do when starting to use satellite imagery to better understand their forests is to always think about the deciduous versus evergreen tree question. Going from treating the forest as just a bunch of green trees to starting to think about which locations have evergreen trees versus which locations are mixed or deciduous is a crucial step to take.

leaf off satellite image showing evergreen trees, deciduous trees and national forest trails
We can glean a great deal of information from a satellite image like this. Note that in addition to seeing evergreen vs. deciduous, it’s also much easier to identify trails and the approximate tree density.

It may sound a little superficial, but it’s valuable to have this framework in mind when you’re out in the woods.

Here’s an example of why this is beneficial: if you’re hiking off-trail through the woods and you know that you’re currently in a patch of woods that is almost entirely deciduous trees but surrounded by evergreens, you’ll be able to leverage that knowledge to keep you from getting lost.

This is because you’ll know that if you ever get to a part of the woods where is the makeup of the forest around you switches from deciduous to evergreen trees, you’ve gone too far and need to turn back.

Using Static Satellite Imagery From Common Applications

We’ll start with the simplest approach before we get into some of the lesser-known methods. As you probably can figure out, the easiest way to start using satellite imagery to identify trees is too visually explore the imagery you find on the mapping applications you already use every day.

For most people it doesn’t have to be any more complicated than opening up Google Maps and toggling on the satellite image option in the bottom left corner of the screen. Now, it’s really beneficial for us to branch out and checks some different applications as well. Here’s a list of some of the mapping applications that are worth checking out for satellite imagery:

The mechanics of all this is straightforward. Just load the mapping application and zoom into the forests that you’re interested in learning about. What you want to see is what type of satellite images they employ.

Of the options that I listed above, I find myself using the Zoom.Earth application most often when I’m looking to quickly check out some satellite images without wanting to load a separate application like Google Earth Pro.

There are a couple of reasons why I prefer their tool. First, the default view when you load the app is recent satellite images. There’s not really a whole lot we can do with this, but I think it’s a pretty neat feature. For example, when I’m loading this on October 5th, 2020 you can see that it indicates that the fall colors in northern Wisconsin are definitely underway:

screenshot of application with near real time satellite imagery of northern wisconsin in october

Unfortunately this is as far zoomed in as this recent satellite imagery will allow us, but I think it’s still kind of cool. On the other hand, once we zoom past this point we are met with the best feature of this application. Here we want to look at the upper left portion of the screen, where we can see something similar to this:

screenshot of application showing buttons to toggle on different satellite imagery layers

You’ll notice that we have two different options available, and we can see that we have the ‘May 2017’ option enabled by default. As you can probably guess, this indicates the time frame in which this layer of satellite imagery was taken. If you look at the layer not toggled on in that screenshot, you’ll notice that you may get a time range that isn’t helpful (5 years, really?). That doesn’t really matter; what matters is that we can easily switch between the different views available. Here’s what it looks like when I switch to the ‘April 2011 – Oct 2016’ layer:

screenshot of application showing leaf off imagery of a local forest

By my guess we’re looking at mostly maple trees (as the leaves are gone) with northern red oak trees scattered throughout. There are a smattering of yellow trees shown, but I would guess only those in the lower right are tamarack trees. The yellow trees in the middle of the screenshot are likely a different type of tree, and I would consider this an opportunity to lace up my boots and try some identification come the fall.

Consider Any and All Online Applications That Offer Satellite Layers

Besides the navigation apps used for driving around, you might also want to consider navigation or exploring apps that you use for various activities like hiking or hunting. These apps will almost always have satellite imagery available as a layer, and they might use a different source. You may notice that a few of the different sources use the same imagery in your location.

This Method is a Game of Luck but Worth a Shot

This method is very much, just a game of luck. The kind of satellite imagery you get from these free resources tends to be very high, and it will vary across a landscape.

For example, here you can see a single screenshot from a Google Maps satellite image, where they’ve clearly blended two different image sources here. The portion on the right is from sometime early in the spring, and the portion on the left appears to be summer when everything is green.

screenshot of google maps satellite imagery with blended area between leaf off and leaf on images

This is why it’s important to check out different satellite imagery sources as merely moving over a couple of miles might greatly change what kind of value you can derive from this approach.

Google Earth is Worth a Shot With the Timelapse Feature

While still a free option, you’ll need to download Google Earth if you want to get the most out of it. As all of Google Earth is focused on satellite imagery, there is a lot of potential to gain valuable insight by exploring their library of images.

You may already know this, but at the top toolbar, they have a time-lapse feature that allows you to explore previous satellite images associated with your area. The images do start to appear in black and white the further you go back, but we can still leverage the ability to view a single habitat throughout a couple of different seasons. Here’s what the time-lapse tool bar looks like:

screenshot of google earth highlighting the timelapse button with its slider bar below

The left hand side of the toolbar has a button that allows you to navigate back towards previous layers, and there are hash-marks on the slider where they have satellite images from. I’ve had mixed results with this tool, but it does occasionally come in handy.

Finding Near-Real-Time Imagery of Local Forests

I have to be honest here: I searched for this functionality for a long time, and I didn’t really ever think that I would find it for free. Needless to say, I was very excited to see that a tool managed by the U.S. Forest Service offered the ability to look up nearly real-time satellite images across the entire United States. To me, this is a game-changer, and I plan on using it a lot going forward.

This functionality is found in a tool called ForWarn II, and I have a separate post that goes into great detail as to how to use this tool most effectively. You don’t necessarily have to read that much about using this tool, as we’ll be employing a straightforward method that doesn’t get into any of the other science.

This tool has a couple of different data layers that allow you to view recent satellite imagery. In my experience, the highest quality images come from a layer referred to as ‘ Imagery’ data layer. Unfortunately, this layer is only available to the following seven states located in the Midwest region: IL, IA, IN, KS, NE, OH, WI, ND, SD.

If you happen to live in one of these seven states, you can use the following link to open up this tool with that layer already loaded. Everyone else will use a layer called ‘High-Resolution Sentinel Imagery’, which does a good job though it may not be as high quality. You can open the tool with that option by opening this link.

One thing to keep in mind when checking this near real-time satellite imagery is that you may occasionally run into issues from clouds. If that’s the case, you may want to toggle on and off a couple of the other different layers as they may offer a better solution that is it blocked from clouds. There’s another layer of recent satellite imagery called the ‘Medium-Resolution Landsat 8 Imagery’ layer. As you probably can deduce, it’s not as clear of imagery as the previous two layers.

Recall earlier where we got a distant view of the fall colors from the Zoom.Earth application? Here’s a much closer example of what we can find with the Imagery layer:

screenshot of satellite imagery data layer in forwarn ii application

This is much closer and is much more useful for us, but do note that the date highlighted above indicates that this image is at least a week old.

Practical Examples of What You Can Do With This

All of this being said, the point of this entire exercise isn’t just to sit here on the computer and look at the outdoors through some fancy tool. Doing that defeats the whole purpose of even being interested in the outdoors in the first place.

Instead, we want to make it easier to discover the outdoors and ultimately make sense of it. To do that, we’re going to cover a few real-life examples of how we can use satellite imagery to make discoveries out in nature.

Finding Groves of Northern Red Oak Trees via Orange Fall Foliage

Like I mentioned earlier in the post, I’ve used basic satellite imagery from Bing Maps to identify locations that had large groves of northern red oak trees.

This works because the satellite image I was looking at was taken and compiled after the leaves on maple trees had fallen and before the leaves for northern red oak trees fell to the ground. This meant that every northern oak tree in the area popped with a bright orange color that made clusters of them easily identifiable in satellite photos.

screenshot of satellite imagery showing trees with red fall colors
I can confirm that much of this area is northern red oak trees.

So what can we do with this? Personally, I’ve got a minor addiction to foraging out in the woods, so I’m mainly using this as a way to identify excellent sources for acorns.

Avoid Hiking Through Dense Brush By Using Leaf-Off Imagery

Maybe you’re like me and you enjoy hiking off-trail in a National Forest. Here’s some advice that I highly hope you consider: do what you can to identify the approximate density of the woods you’ll be navigating.

What do I mean by density? I’m mostly referring to how tightly packed a forest feels at the ground level. For example, a young forest that consists of a dense thicket of quaking aspen trees will be almost un-navigable almost any time of year. The left side of the below satellite image is a good example of what this may look like:

close up screenshot of google maps satellite imagery with a dense forest on the left and a mature forest on the right

As you can see on the left side, it’s very difficult to see the individual trees, so you can expect a dense thicket. However, on the right side you can see not only individual trees, but we can make the following observations:

  • The size of the tree trunks are noticeably larger
  • The space between the trees is much greater

As such, I would guess that it would be significantly easier to navigate through the right side. You don’t have to always use this method, but I think it’s important to keep in mind when you’re doing something like hiking off-trail.

I’ve found that Google Maps has the satellite imagery that is most suitable for this kind of up-close zooming, but your results may vary. As you can probably guess, this type of analysis is much easier to do if you have leaf-off imagery like above, as it’s then easier to get an idea of how many deciduous trees are in a location.

Explore Your Local Forests in Fall as the Leaves Change

Using the near real-time satellite imagery from the ForWarn II tool mentioned earlier, We can look for any interesting patterns happening in the forest. At the same time, the trees have their leaves change color.

screenshot of satellite images with trees changing colors in the fall, as seen from the forwarn ii application
If searching this during the fall, you’ll be able to see clusters of trees that are changing colors. This is particularly useful in the early and late portions of fall, as brightly colored trees stand out more.

One of the easiest things to do is find clusters of trees that all look approximately the same in color and then visit those trees to identify them. All in all, it’s simply a great excuse to get out into the woods and learn something while you’re at it.

Finding Tamarack Swamps From Yellow Fall Foliage

Tamaracks are a fascinating tree, as they are, as far as I know, the only deciduous conifer out there. Yes, that means that they lose their leaves every single year. When they do happen to lose their leaves in the fall, the leaves turn a bright yellow, and they are straightforward to spot if you know where to look.

Tamarack trees prefer wet feet and thrive and swampy locations where they may compete amongst evergreen trees. As such, you can expect satellite imagery of tamarack trees in the fall to show them as a bright yellow tree surrounded by evergreens. Here’s about what you can expect an up-close cluster of tamarack trees to look like in the fall:

screenshot of satellite image in fall with tamarack trees turning yellow and surrounded by evergreen trees with a creek running through

As you can see, it’s effortless to identify them, and if we zoom out will usually be a to spot a few additional clusters, like so in the below screenshot:

screenshot of satellite image with clusters of yellow tamarack trees pointed out with red arrows

Final Thoughts

While this post may have been a bit of a bear to get through, I hope you found value in this approach. I think satellite imagery is a relatively straightforward way to draw some pretty neat conclusions in the right hands.

I hope that by writing this more people start to look differently at the forests around them. Only by digging around and pushing ourselves to learn unfamiliar and new things can we take advantage of such great resources.

If you liked this article, you may find these related articles to be an interesting read:


USDA Web Soil Survey: A Complete How-To Guide

I’m not exactly sure how it ended up this way, but it appears that one of my pastimes is playing around with freely available GIS tools.

While it may be a tad clunky in a few ways, the Web Soil Survey application provided by the United States Department of Agriculture can provide a lot of value to a wide variety of people. So, let’s dive in: this post will go into great detail as to how regular people can get great value out of this humble little tool.

Who Should Be Interested in This Application

I’m not going to lie: this will end up being a pretty long post, with many, many screenshots. The truth is that this kind of post won’t be everyone’s cup of tea, and that’s totally fine. There will be a few slightly technical parts of this post, but we’ll leave the vast majority of the technical stuff for the scientists.

So, let’s cut right to the chase. Here are some real life scenarios that this tool is very well equipped to help with:

  • Identifying the locations where you’re more likely to find a certain native plant growing (foragers, I’m talking to you)
  • Finding a list of recommended trees to plant for your specific type of soil
  • Being able to quickly assess the soil type and other soil information regarding a prospective land purchase
  • Discovering the approximate height a specific tree would reach at maturity on your soil
  • Comparing the yearly rate of wood production for different tree species on different soils

And so on. If any of those ideas are interesting to you, this post should help get you started with the Web Soil Survey application.

Making Sense of the Screen Before We Dive In

Before we start clicking around and navigating all over this thing, I think it makes sense to quickly cover a few aspects of how this is setup. It really isn’t too unique of a setup, but there are a few quirks.

Important: Notice the Tabbed Setup at the Top

The first thing you should really notice is that there is a series of five tabs over the top of the screen. You’ll see these tabs highlighted in the screenshot below:

web soil survey application with area of interest tab highlighted
There’s a menu above this, but that mostly has links that we won’t need for our purposes.

We’ll cover this in more detail later, but understand that this tool is setup for us to navigate the tabs from the left to the right. In other words, we can’t move on to the ‘Soil Map’ or ‘Soil Data Explorer’ tabs until we’ve specified our Area of Interest (AOI).

The AOI is exactly what you think it is: it merely gives the tool a specific area of land to run the reports on. Many of these reports pull in a lot of different information, so they could be quite computationally intensive if we didn’t have to narrow our focus.

Navigating the Map With the Toolbar

It might look like there’s a lot going on with the toolbar, but there’s really only two things that we’re going to focus on: defining you AOI and moving around the map.

First, defining your Area of Interest simply requires that you activate on of the two following buttons:

web soil survey application with the two area of interest buttons highlighted

The square button on the left allows you to select a rectangular AOI, while while the right button allows you to get creative and create your own polygon AOI. Pretty straightforward.

Regarding how you move around the map; I have some bad news here. The unfortunate reality is that we’re going to have to go back to the days of MapQuest. Remember them?

What I’m trying to say is that you won’t be able to move the map or adjust the zoom with merely a dragging click and the scroll of your mouse wheel. Here we’ll need to have the zoom in button activated to zoom in, and we’ll need the same for zooming out.

Moving the map around by dragging is possible, it’s just that you’ll need to first activate the pan button (the white glove) and then you can pan.

You do have the ability to zoom in to a specific area by dragging a box over it while the zoom in button is toggled on. Still, in order to zoom out you’ll need to click individually to back further and further out. Just expect a bit of a retro experience and you’ll be fine.

Information and Reports are on the Left Panel

You’ll notice that the left side of the screen contains a section where you can find a variety of reports or additional layers to include. Here’s what I’m talking about:

web soil survey application with the quick navigation menu items shown on the left side of the screen

Depending on which tab of the tool you’re currently in, this section will display what is available to you. The available reports and layers may vary based on the AOI that you choose, so be aware that things are a bit flexible.

You can expand and then collapse a section by clicking on the downward arrow buttons on the right side of the window. When you expand a window you’ll possibly encounter buttons or drop-down menus relevant to your section. Understand that the map will not change until you click one of the buttons associated with the section.

Here’s an example: if I want to add a layer to the map showing the Headwaters Wilderness are in Wisconsin I’ll need to find it with the drop-down menus and then click one of the ‘View’ buttons like below:

web soil survey application with button to show land owned and operated by the united states forest service

Again, this is easy enough, but it might save you a little time and frustration if you don’t have to discover this one entirely on your own.

Results of a Report Show Up Below the Mapped Area

Until you run a report on your AOI there won’t be anything under the mapped area. However, as soon as you run a report you’ll be able to scroll down and see the table of information that corresponds to that specific report for your AOI. Below is an example of what I mean:

web soil survey application with an area of interest shown that provides soil map unit information

Scroll to the very bottom and you’ll likely get a box of information that provides background on the report that you’re seeing. Here’s an example of what you might expect:

description of the Forestland Planting and Harvesting report from the web soil survey application

This section is actually really helpful and I’d strongly encourage you to get in the habit of scrolling all the way down to the bottom before digging into your results table. Taking a little extra time to learn about what you’re seeing may save you some time on this journey.

Basic Overview of How It Works

Now that we’ve covered the anatomy of the screen, it’s time to start talking about how this tool actually works.

Define Your Area of Interest (AOI)

As discussed briefly above, your job when defining an Area of Interest is to tell the tool which land you want soil information on. Before we designate an AOI, there are two important things for you to keep in mind:

  • AOIs are limited to 100,00 acres in size
  • It’s possible that you’ll define an AOI that requires two different soil data sources

In either case you’ll be notified with a popup window stating as much.

To define your AOI you’ll first need to verify that one of the last two toolbar buttons are activated, like so:

web soil survey application with the area of interest button pressed and highlighted
The shading will indicate which button on the toolbar is toggle on.

Next, you’ll click and drag your mouse to define a region to set as the AOI, like this:

selecting an area of interest in the web soil survey application
Note that the mouse isn’t captured in this screenshot.

After your AOI is selected you should have a map with a box that has diagonal lines running across it, like so:

box with diagonal lines showing a selected area of interest in the web soil survey application

At this point you’ve selected your AOI, but you just haven’t done anything with it.

Switch to the Soil Map Tab to Pull Up Your Information

Now that you’ve found your AOI, we’re going to turn that box with horizontal lines into an actually beneficial map.

To do that we’re simply going to scroll back up to the top of the screen and click on the ‘Soil Map’ tab, which is the second tab. This should change our map into something like this:

close up view of an area of interest with soil map units in web soil survey application
It might be difficult to read, but this is where your AOI is broken into different soil types.

Success! Hopefully you’re now starting to get a better picture of what’s going on here. Just to pull up a map that’s a little less intimidating, here’s a section of the above area where I’ve zoomed in:

very close up view of soil map units in web soil survey application

While they may look like jibberish at this point in time for you, each of the symbols on the map corresponds to a unique type of soil. For example, there’s a puzzle-piece shape of soil on the left side with the ‘VsD’ label. If we scroll down on the window on the left, we’ll find more information about what type of soil this is:

close up of soil map unit with corresponding description on left side of screen in web soil survey application

You can click on the link in order to bring up a window with more information on this specific soil type, but I think most of that data is a little too scientific for our needs. If you’re a gardener you may already have some familiarity with what ‘loamy sand’ could represent, but others don’t need to worry.

Soil Data Explorer: Tabs Within Tabs

We’re really playing with fire now that we’ve made it to the ‘Soil Data Explorer’ tab. This is where all the magic happens. Congrats on making it this far!

There’s a lot of different things going on here, but here’s what you need to know: you’ll now see that we now have a second layer of tabs underneath our original tabs.

closeup of the soil data explorer tab in web soil survey application

I’ll go through each of the tabs in sections below.

Intro to Soils

This tab is mostly useful if you’re looking for more in-depth explanations about what many of these things mean. They have the educational content split apart nicely, it just happens to be a bit of a pain in the butt to navigate.

Either way, you’ll be able to check the boxes of the sections you want to show, and then click the ‘View Selected Topics’ button to update the screen. Feel free to come back to this section if you’ve decided that you would really like to develop a better understanding of soil science.

Suitabilities and Limitations for Use

Many of the reports in this section are geared towards commercial operations like construction companies and large-scale farming operations, so you’ll likely be able to skip over most of this.

There are, however, a few interesting areas that average people might be able to make use of. As there are a million reports found throughout the menu, I’ll just list the menu groupings in this tab that appear to have some value:

  • Land Management
  • Soil Health
  • Vegetative Productivity

Soil Properties and Qualities

There’s no way around it: this section’s for the scientists. Short of you having a degree in soil science, you can feel free to skip over this section and move onto the actually valuable stuff.

Soil Reports: Full of Useful Information

Last and certainly not least, we’ve finally arrived at the tab that can provide the most value for the average person.

There are a ton of really valuable reports that you can find in this part of the tool. I could spend a bunch of time going over all of the different options, but I think the best choice is to cover all of the best reports in the following section.

Download Soils Data and Shopping Cart

I think it’s nice that the tool allows for you to download soil data and prepare a custom report on your Area of Interest, I just don’t know if most people would get any value out of them. Both of these areas are quite self-explanatory, but I think they are more tailored to the academic types.

Practical Examples of How to Use This Tool

We’ve gotten past the boring how-to and we’ve arrived at the part of the post where I actually get into the good stuff. Let’s find some practical applications for all of this!

Finding Common Understory Vegetation for Forests

Without a doubt, this is my favorite application of this tool. I think there’s no reason to hide it, I love bumming around the woods looking for new edible plants to identify and learn about.

What you can do with this is identify which types of plants occupy the understory of a forest. Here’s where we can find this report:

  1. Go to the ‘Soil Reports’ tab in the ‘Soil Data Explorer’
  2. Expand the ‘Vegetative Productivity’ report group
  3. Find the ‘Rangeland and Forest Vegetation Classification, Productivity, and Plant Composition’ report at the bottom
  4. Click the ‘View Soil Report’ button to generate your report

Once the report has loaded you’ll have access to a variety of data fields associated with each soil type. We’re only really focusing on one field here, the ‘Characteristic rangeland or forest understory vegetation’ field in the middle.

What’s the best way for a forager to think about this in real life? Here’s how I approach it: I establish my AOI as a nearby area with a lot of public land that I’m interested in foraging in. Once I set my AOI, I can just skip right to the ‘Soil Data Explorer’ tab and run the report that I need.

Here’s about what my screen will look like once I’ve done all of that:

screenshot showing the soil reports tab in the soil data explorer section of the web soil survey application

Admittedly, there’s a lot going on in this screenshot and it might take a bit to process. Just from the maroon colored tabs and report at the bottom, you can quickly get a good idea of where we are in this tool. The map shows the soil types for our entire AOI, and the section below that is where our report outputted. Towards the right of the report you can see the ‘Characteristic rangeland or…’ section: this is the column that we will be interested in.

This screenshot is a good example of what you can hopefully expect to see included in this report:

breakdown of the understory vegetation report for the web soil survey application

At the top you’ll see an arrow pointing to the ‘MnB’ part: this is the specific soil type that this is information is related to. Below that you’ll see an arrow pointing to ‘Menahga.’ Menahga happens to be a family of soil types, and MnB happens to be a specific variety of that soil. As you may have guessed, the ‘Mn’ portion of the MnB name is shorthand for the Menahga soil family. The ‘B’ portion simply refers to the degree of slopes found in this area, which makes sense once you see the ‘0 to 6 percent slopes’ portion of the soil type description above.

Now it’s time to get to the good stuff: the box on the right is pulling in a list of plants that are commonly associated with this family of soils. Right off the bat I notice the following values:

  • blueberry
  • hazelnut
  • juneberry

Having learned that this family of soils may have these types of wild edibles present, my next job is to go back to the map and look for Menahga soils. Like I mentioned before, the ‘Mn’ portion of the MnB soil type stood for Menahga, so I know that I can be on the lookout for each of the following soil types when I go back to the map:

  • MnA
  • MnB
  • MnC
  • MnD

Just a quick side note before we get back to the report: it seems like the Xx[A-D] soil naming convention is used in a variety of locations. What you may want to internalize is that the A-D portion refers to the severity of the slope, with D being the steepest grades.

Going back up to the map, if I zoom in I can see the following area with ‘MnX’ soil types:

closeup screenshot of the MnC soil map unit highlighted in the web soil survey application
It may be slightly difficult to read, but each of the highlighted labels is ‘MnC’

Now that I’ve located areas that might have some interesting types of wild edibles, my next job is to confirm whether or not this is public land that I have access to. This is a topic for another post, but it still is a very important step.

As you might be able to figure out, this can end up being a very valuable way for a forager to take a more targeted approach when looking for wild edibles.

Now, here comes the caveat: while I’ve had a good amount of success with these reports, I wouldn’t expect to walk into an absolute paradise of wild foods every time you use this. I’ve had many times where I got skunked when looking for something that the report mentioned, so be sure to go in with the right expectations.

I’ve had success using this approach with both mature forests and young forests taking over recently disturbed lands, so be sure to keep an open mind and lace up your hiking boots. You’ve got some hiking to do.

Identify Which Trees Might Be Present

The next report that we’ll be going over is a great way to get a decent idea about which native trees may be present on that type of soil.

This report is found in the same location as the last one, so all you need to look for is the ‘Forestland Productivity’ report in the ‘Vegetative Productivity’ grouping. Having navigated to that report and executed it, this is what my screen looks like:

highlighting the location of the forestland productivity report in the web soil survey application

The information from the report that we’re most interested in is found in the highlighted group at the bottom of the screenshot. We’ll go over each section to give you a decent idea as to what they mean.

Here’s an example of what you might see for results in the report:

report results for forestland productivity for Pence soil series in web soil survey application

Just like the last report, you can tell that the structure is that the data is associated with the name of the group of soil types (Pence in this case), and that the specific soil type is ‘PkC.’

The next column over, which refers to the ‘Common trees‘ heading, is the information that I find most valuable for my purposes. This is a list of the types of trees that you can expect to find associated with this soil type. So if I was interested in finding out where a specific tree may grow, I would use this report to start to narrow down my locations.

The following column is the ‘Site Index‘ and this is where you might see some numbers that are associated with the tree listed to their left. What does this mean? Basically, the site index in this report gives us an approximate height (measured in feet) that a tree is expected to reach after a certain amount of years. I think the length of time may vary, but you can expect something like at least 50 years. Obviously, how tall a tree can grow after X number of years is highly variable depending on a lot of other factors (light, competition, etc.). But this can still be a great way to quickly understand how suitable a certain type of soil is for a specific tree.

The last two columns can provide some interesting information, but I think they’ll mostly just apply to someone trying to manage a forestland for productivity. The column with the numbers in it is the ‘Volume of wood fiber‘ and this is an approximate measure of the volume of wood that an acre those trees would produce in a year. According to the notes below the report, that number is measured in cubic feet per acre per year. The last column is the ‘Trees to manage‘ section, which just mentions what kind of trees are worth promoting if you’re managing a forestland for lumber production.

Planting Suggestions for Bushes and Trees of Varying Heights

This report is also found in the same group as the prior two reports. You’ll want to find the second report in the ‘Vegetative Productivity’ group, which is the ‘Environmental Plantings and Windbreaks’ report. Expand that report open and click the ‘View Soil Report’ button, and your screen should look something like this:

highlighted path to the environmental plantings and windbreaks section of web soil survey application

If you look at the headings highlighted in the bottom portion of that screenshot, you’ll notice that this report is laid out rather simply.

First, you’ll have to find your soil type, but once you do you’ll hopefully see something like this:

report results for the environmental plantings and windbreaks report in web soil survey application

Like the previous two reports, you can see that the information is tied to the Menahga soil family, which is being pulled in from the MnC soil type.

Here’s what’s so valuable about this report: this is an incredibly easy way to find ideas on what kinds of trees and bushes to plant while having an understanding of how they will develop over the next 20 years.

Unlike the site index metric from the last report that talks about how tall a tree may grow after a 50+ year time frame, I feel that a time frame of 20 years is a lot more approachable for a landowner.

This tool also helps you plan for the different layers that you might find in a less mature forest. Overall, I find that this layout is very handy when trying to develop a picture in your mind about how your land might look with enough time.

In-Depth Information on Suitability of Soil For Grape Production

This last one is just for fun, although I know that some people might be able to make use of it.

First, we’ll need to navigate back up to the second layer of tabs, and we’ll need to switch to the ‘Suitabilities and Limitations for Use’ tab. Then we’ll need to expand the ‘Vegetative Productivity’ tab and find one of the reports with ‘American Wine Grape Varieties Site Desirability’ at the beginning of the name.

The area I’m exploring shows four different options, ranging from short to very long. I’ve selected the ‘short’ option and this is what I see in response:

report showing grape growing suitability for different soils in the web soil survey application

There are a few things to notice here. First, you can see that the map has added a color hue used to indicate how suitable that type of land is for growing grapes. Obviously, red areas correspond to land that is generally unsuitable for growing grapes. Looking at the screenshot above and being familiar with this area, I can tell that the red areas here represent swampy land or creeks.

The other thing to notice is that the report below the map contains a column entitled ‘Rating reasons.’ This is the part of this report that contains the most information about growing grapes, but this column is very well organized. If you scroll down to the report you may see something like this:

report results of the wine grape suitability report in web soil survey application

There are a few things to take note of here. First, notice that the soil types are organized a little differently this time around. The report is still organized by the specific soil type (PkC in this case), but that soil type now has information on composition. While the PkC soil type mostly consists of Pence soil, it also has Padus and Sayner soil present. Most people won’t need to worry about this, but it helps make sense out of the highlighted information.

Secondly, you’ll notice that the third column simply has the word ‘High’ in it. This is the overall suitability rating assigned to the PkC soil type, so this states that this soil type is highly suitable for growing grapes.

Last but not least, you can see that information in the highlighted column has numbers present. These numbers fall between a score of 0 and 1, with a higher score indicating that this type of soil is better suited for this purpose. You may notice that only the ‘Site and soil features’ number varies between the different soil classes, as the ‘Growing season length’ and the ‘Slope’ are independent of the soil class.

There are a variety of similar reports in this tool, as much of the functionality is geared towards helping people use their land for the most effective purposes. How effective land may be towards an application like growing grapes depends greatly on your soil. Obviously, it’s much easier for land owners to leverage a tool like this that is provided for free by the USDA, when compared to the alternative of learning everything you can about soil science.

If you are interested in learning more about how reports like this work, your best place to start is at the bottom of the screen below the report results. Here is what that looks like for this report:

report description of the american wine grape varieties site desirability report in web soil survey application
This continues on after the area in the screenshot. Obviously, it appears that grape growing is quite the complicated endeavor.

While the description section might get a little technical at times, I’ve found that it’s almost always written in friendly-enough language.

Final Thoughts

I know that post was a bit of a doozy, but I hope you were able to follow along and you managed to get some value out of the experience. I just can’t help myself, I love taking the time to dig into these undervalued reports and find little nuggets of gold.

If you enjoyed this piece and you’d like to see what else is possible with freely available mapping applications, feel free to check out the below article:

Everything Regular People Should Know About Using ForWarn II


Everything Regular People Should Know About Using ForWarn II

I can’t really hide it: I’m pretty excited to be writing about this program. I understand that that may sound a little odd. I mean, who would be excited about a program named ForWarn II, especially given the slightly ominous sounding name?

Here’s the thing: I’ve been looking for a program like this for a long time. I’ve long loved spending time in nature, but in the last few years I’ve finally made a serious effort to make sense out of it. As I live several hours from the nearest true wilderness, I’ve had to resort to a lot of “remote learning” when trying to make sense of forests and everything in between.

This has been a bit of a struggle, as each tool or piece of software always left a few things lacking. ForWarn II (I’ll just refer to it as ForWarn in the rest of the post) has all of the capabilities I’ve been looking for and much, much more.

What is ForWarn II and Who Uses It?

This section will be brief, but it’s important to give a bit of a background before we dive in and start playing with the bells and whistles.

Publicly Available Software Designed to Detect Changes in the Natural Environment

This software was put together by the U.S. Forest Service (a part of the United States Department of Agriculture), and it is designed to aid in the discovery and tracking of all sorts of things that impact nature: natural or not.

From tornado damage to wildfires to invasive insects, ForWarn II uses satellite imagery to detect changes the vegetative characteristics of our forests in the United States. In addition to monitoring our forests for changes, this software pulls in a bunch of other sources of complementary data.

Appears Mostly Used by Career Scientists

I can’t be totally sure, but I’m pretty convinced that the vast majority of ForWarn users are career scientists. There doesn’t seem to currently be much of an appetite for it in the mainstream, which is a bit of a shame but totally understandable. There’s no way around it: there’s a lot of functionality built into this software, as it appears to be a “jack of all trades” tool designed for a wide variety of scientific purposes.

Why Should I, a Normal Human Being, Use This Program?

I get it. Maybe you’re like me and you just happen to like anything and everything to do with nature. That’s great! So why should you care about using some tool that was designed by and for scientists with a bunch of letters behind their names?

Simply put, because with a little knowledge and practice, ForWarn is a tremendously capable tool, especially for those of us who love nature and want to spend more time outdoors.

Awesome Things are Possible With This Tool

Here are some of the things that are easily possible with this tool:

  • Finding massive colonies of wild ramps in the spring (seen from space, no kidding)
  • Locating groves of northern red oak trees, as seen in the fall when their leaves change to a bright red color
  • Quickly finding National Forest land clear-cut in the last few years, in order to find a potential new raspberry and blackberry spot
  • Locating stands of a specific kind of tree I’m learning to identify as I’m seeking to understand the forest around me
  • Assessing the damage from a recent tornado or windstorm, in order to stay out of the way of the clean-up effort

And so on. If you’re a forager, I’m hoping I now have your attention. Even if you’re not into the whole wild foods scene, the ForWarn tool is an incredible opportunity to start to make sense of the nature that surrounds you. For me, my efforts to make sense of nature has been an amazing journey and I’m extremely grateful for all the ways that the ForWarn tool has aided.

So while the truth is that there’s a bit of a learning curve here, I think there’s a lot of value that the average person can get out of it.

Quick Note: What is NDVI and Why it Matters Here

Before we get into the fine details of how to effectively use ForWarn, there’s one matter we should briefly cover: what on earth is NDVI?

In layman’s terms, the Normalized Difference Vegetation Index (NDVI) is a method of determining how healthy and productive the plants in a location are at a given time. This is done by analyzing satellite photos for certain parts of the light spectrum. Basically, healthier plants more effectively use photosynthesis to convert certain lights into chemical and physical growth.

Healthier plants have a higher NDVI score, and a low score indicates either a dead or dormant plant. For example, a deciduous tree will have the highest NDVI in summer and the lowest NDVI in winter. If you’d like to learn more about the mechanics on how NDVI works, check out our post that goes into greater detail.

How ForWarn II is Setup for Use

There’s no way around it: ForWarn can be a bit overwhelming when you first open it up. This is about what you should expect when you open up ForWarn for the first time:

You can see that there are really four unique parts to this interface:

  • There’s a navigational bar running across the top of the screen featuring a variety of buttons and functions
  • On the left there is a ‘Map Layers’ menu; layers are superimposed onto the base map when their box is checked
  • On the right you have a ‘Map Tools’ menu
  • The main frame has the base map specified in the navigational bar and a layer superimposed over the top of the base map

So, let’s dive in: not all of the options present will be useful for our purposes, so don’t worry if we appear to skip over some features.

The Top Toolbar Has Some Important Functionality

Here are the main things you need to know about the top toolbar.

The Info Button Gives You Data From Map Layers

The info button is the text bubble with the letter ‘i’ that looks like this:

This will likely be the toolbar button you use the most, as this is what allows you to get the exact data from a square you click.

Here’s an example of what I’m talking about: there’s a map layer that I use very frequently that shows the forest type of your region. Here’s an example of what that layer looks like when loaded over a forest:

Each of the colored squares represents a certain type of forest. If you head over to the ‘Map Tools’ menu you would find a key in the ‘Legend’ drop down that specifies the type of forest associated with each color:

As this layer depicts 132 different forest types, it’s extremely difficult if not impossible to understand which shade of red on the main screen represents what forest type.

So in order to tell what each square represents, you’ll need to toggle on the info button. Once the info button has been clicked, clicking on the map will bring up results like the following:

Note the yellow dot that indicates where the Forest Type layer was clicked.

So now we know that the square clicked represents black spruce trees.

Not every layer will have valid data, as sometimes you’ll get null values returned.

Theme Drop-down Menu Controls Your the ‘Map Layers’ Menu Options

The ‘Theme’ drop down menu controls the options that are present in the ‘Map Layers’ menu, as no theme shows all possible layers at one time.

I prefer to leave it on the default theme, which is the following: N. American Vegetation Monitoring Tools. This has all of the layers that I use most frequently, so I just leave it be.

The NDVI Graphing Button: Awesome but Best for Experienced Users

You can find the ‘NDVI Graphing’ button by finding the following icon on the toolbar:

Technically, the purpose of this button is simple: it brings up the NDVI graph for the place on the map you clicked. Here’s an example of one such graph for a forest:

What does this graph mean? In so many words, this represents the previously discussed NDVI values for this piece of land since 2003 (in 8-day increments). If you remember correctly, the NDVI basically indicated how densely vegetated with green plant matter this land is at any time. As you might be able to guess, the great swings in NDVI values each year indicate that this is a deciduous forest.

On the other hand, a dense forest of evergreen trees is still variable in NDVI, but the difference is less stark:

Now here is the bonus part on why this chart is incredibly cool in my book. Notice how there’s a noticeable drop-off in the NDVI peaks between the years 2019 and 2020? A severe storm came through this area in July 2019 and knocked down a bunch of trees in this area. With a certain percentage of trees lost in this area, it produced a temporary drop in the vegetative performance that is clearly measurable.

Here’s an even clearer example: this is a deciduous part of the forest that was utterly devastated in that same storm, and therefore that land was clear-cut in an attempt to salvage the downed timber:

Notice how severe the drop-off is in-between the 2019 summer and the 2020 summer.

This makes sense if you think about it: a mature forest with a dense canopy has a much greater ability to produce green vegetation than newly clear cut land. Yes, the clear-cut land will rebound in no time, but when you compare the productivity to the former forest it has a ways to go.

Everything Else on the Toolbar is Self Explanatory

The rest of the menu is either self-explanatory (zoom in, zoom out, etc.) or mostly unnecessary for our purposes.

The zoom-in, zoom-out, and pan buttons all work as to be expected, but most people will likely just their mouse buttons and scroll wheel. The map behaves just the same as Google Maps, so feel free to scroll and drag to your heart’s content.

The ‘Base Map’ drop down menu is also simple to use. The menu features eight different options to use as a base map, so feel free to use whatever type of base layer you like. I believe the the ‘Imagery’ layer is just the Google Maps satellite image layer, so I typically use that.

The ‘Map Layers’ Menu Shows the Data Sets Available

I’ll just come out and say it straight away: the ‘Map Layers’ menu has an intimidating amount of layers available. There’s just so much here, that it would be easy to get overwhelmed quickly.

In other words, I’m only going to discuss the layers that might be of use to the average person, and we’ll leave the rest behind. Like I mentioned above, the ‘Theme’ drop down menu in the toolbar changes what data layers are available. I’m going to stick to the first theme mentioned in the drop down, as it contains all the information I’m interested in.

So, what exactly am I interested in with this menu? I break it down into the following four ideas:

  • Accessing near real-time satellite imagery
  • Looking for severe changes in NDVI values that might indicate storm damage or logging
  • Historical accounts of U.S. Forest Service logging activity
  • Plots that indicate which types of habitats or trees are where

I’m also interested in the Phenological Regions group of data layers, but I don’t necessarily know how to use the information yet.

Before we get into each of the ideas I mentioned above, here’s how you can adjust the transparency on any layer. For the layer you’re adjusting, click the wheel to the right of the layer name:

Then, adjust the transparency by moving the slider highlighted below, and the main screen will updated behind you:

Nearly Real-Time Satellite Imagery: a Major Win

This was one of the things that I was most excited about when I found out about ForWarn. I’ve been looking for a decent set of near real-time satellite images for awhile now, and here we have a few options.

First things first, you’ll want to use the ‘ Imagery’ section if you’re in one of the following states: IL, IA, IN, KS, NE, OH, WI, ND, and SD. I’m not sure why only parts of the Midwest have access to this layer, but I’m not complaining. Don’t get me wrong, there are other valid options for everyone else, but my impression is that this layer is of the best quality.

Everyone else should check out the ‘High-Resolution Sentinel Imagery’ section, as that provides a decent satellite image for the Continental U.S. You’ll want to select the ‘True Color’ option, as that depicts the colors as seen through human eyes.

Finding Extreme Swings in NDVI Values

This is probably the coolest functionality that ForWarn is capable of, but it’s also rather confusing when you’re first starting out.

Here’s what you need to know: ForWarn is ultimately best known for its ability to detect departures in vegetative performance when compared to past records. What does this mean for us regular people in real life? There are two scenarios that I think are most useful for us:

  • Graphically depicting the impact of real-life events (tornadoes, storms, etc.) on our natural surroundings
  • Allowing us to identify locations undergoing an event like clear-cut logging

Why Be Interested In These Graphical Depictions of Natural Events?

I have two main reasons why I find this feature useful. First, I think it’s just plain cool to be able to graphically depict the impact of a major event like a windstorm. Second, navigating the woods after a major storm is a complete nightmare. Sure, you can get an idea of the level of damage by driving around, but it’s very difficult to understand which parts of the forest were most severely impacted.

With this tool, I’m able to quickly pull up a map that shows me where the storm had the greatest impact. As I’m looking to avoid the areas severely impacted for safety reasons, this allows me to keep enjoying our forests without unnecessary risks.

So how do we make this happen? What we’re looking for is the ‘ForWarn II Near-Real-Time Change Maps’ part of the menu. Expand that you’ll see a wide variety of options.

Here’s more or less what you need to know about these layers: the goal of these layers is to depict the changes in NDVI numbers when compared to previous years. In other words, we’re looking at the relative increase or decrease in vegetative production when compared to the same time of year in previous years.

The different options present here in the layers menu represent the different time frames over which you can compare numbers. For example, the first group is the ‘From Prior Year’ group, and this just compares the current NDVI values to this time last year. Scroll down through the layers and you’ll notice that there are many different options for controlling both the time frame and the type of data to compare (median values, 90th percentile, etc.).

As we’re looking to measure the impact on mature forest from a major event like a tornado, it likely makes sense to use something like the ‘From Prior 10-Year 90th Percentile’ layer. Using this layer allows you to measure the current vegetative performance against the best years in the last ten years.

Doing just that, here’s what a forest can look like one year after a major windstorm that levels hundreds of thousands of acres of mature forest:

As you can probably guess, red means a large drop in vegetative productivity, while blue means an increase.

Therefore, I would be able to use this layer to find the degree in which the different forests around me were impacted by this storm. This saves me loads of time and allows me to focus on the parts of the forest minimally impacted.

Why Would I Want to Find Areas Recently Clear-Cut?

While I understand when people are upset about the clear-cutting of a forest, it’s undeniable that a clear-cut forest undergoes a very interesting transition in the years to follow.

As someone with a passion for foraging, I’ve come to really appreciate forests at all stages of growth. While a mature grove of oak trees can produce a remarkable acorn crop, recently clear-cut land is a gold mine for all sorts of interesting foods.

Therefore, I’m interested in locating parts of the forest that have been recently clear-cut, as I’m interested in how they look in a few years. Here are the steps I use to find these areas:

  1. Look for areas with sharp drops in NDVI levels (we’re looking for dark red on the map)
  2. Toggle off the NDVI layer and ensure that the Base Map is set to Imagery
  3. Pull up the near real-time satellite imagery to see how it compares to the base map

As you can probably guess, there’s a lot that could be said about this, so if you’re interested in learning more, you can head over to this post which covers this topic in detail.

Detailed Logging Activity from the U.S. Forest Service

Unfortunately, this will only be useful for those that are researching National Forests, but it is still well worth mentioning.

The U.S. Forest Service has more than 20 years of historical logging records available in ForWarn. When the data displays correctly, you’ll have access to the following kinds of information about the logging activity in a particular spot:

  • What kind of logging was performed (commercial thinning, clear-cutting, etc.)
  • When the logging was complete
  • Which forest the logging was done on
  • The acreage of the parcel logged

In my experience, not every logging data set from the Forest Service correctly displays this information, but many of them do.

As you may have guessed, if you want to find the details associated with a logging act, you’ll have to turn on the ‘Information’ button on the toolbar and then click on your parcel. That will bring up a screen similar to the following:

For my purposes, I’m mostly looking for land that has been clear-cut. This is due to the fact that clear-cut land allows for the most light access for new plants. As such, I’ve had good luck foraging on land that has been clear-cut in the last 10-15 years.

So where can you find these data layers in the menu? Find the ‘Additional Assessment Maps’ menu item and expand it. Scroll past the first group of layers, where you’ll find the ‘USFS Loggin Activity’ group of layers. This is where you can toggle on individual logging years, or to use the combined option that is listed first.

I prefer to use the individual years, as they show the exact outlines of the locations logged, as opposed to a pixelated map.

Information on Habitat Types and Trees Present

I think one of the most valuable aspects of ForWarn is the ability to pull in information about the expected type of habitats and plant species present in an area. Like the example in the part of this post that explained the ‘Information’ button: there we were able to determine that the land had a value of ‘Black Spruce’ assigned to it on the Forest Type layer.

And here’s the tough part to swallow: it’s likely obvious to you that this type of information is a great over-simplification of what’s actually going on with that piece of land. That’s totally true, as I believe that each pixel of a layer covers about 13 acres of land. But even with this information being an over-simplification of the actual reality, there is still a ton of value we can derive from it. After all, remember the old adage: perfect is the enemy of good enough.

What do I mean by that? Even though I know that that entire parcel won’t be black spruce, I can now reasonably ascertain that I would be able to find black spruce trees if I walked to those coordinates and started searching the land around me. Considering that there are hundreds of millions of acres of forest in the United States, to be able to quickly get in the ballpark of a certain tree without wasting significant time is a very valuable place to be.

Where can you find these layers that show habitat information? They are also in the ‘Additional Assessment Maps’ part of the menu, and the most valuable layers are near the bottom in the ‘Landcover’ group of layers.

In these layers, you’ll most likely be interested in the following options:

  • Forest Type
  • Major Forest Group
  • NLCD 2006
  • 2001 GAP Landfire
  • Land Cover 2010
  • LANDFIRE Vegetation

Feel free to turn the different layers on and off, while checking out what information they contain by using the information tool. Basically, these different layers offer slightly different ways to make sense of the land via classification.

Some layers break down the land into individual tree species (Forest Type, for the most part), while other layers assign predefined habitat types (LANDFIRE Vegetation). I think the point here is to play around with it a little and see what you end up liking.

The ‘Map Tools’ Menu Offers Additional Functionality

Much simpler than the ‘Map Layers’ menu, the ‘Map Tools’ menu has a few important functions.

Here’s the un-expanded view of what this menu looks like:

There are only three things to use here, so this section will be pretty straightforward.

Filtering Out the Noise With the Masks Section

First, we’ll look at the Masks section, which is designed to be used in conjunction with Map Layers from the ‘ForWarn II Near-Real-Time Change Maps’ section. Here are the options in the expanded Masks menu:

This does pretty much what you’d expect it to do. When you turn on a Map Layer that shows the change in NDVI values over a certain time period, you’re allowed to filter to show only certain land types. For example, here’s a map showing the change values for only the grasslands in a heavily wooded regions:

As most of that screenshot consists of woodlands or croplands, there’s very little data to show. However, if you switch it over to deciduous forest then your screen show’s many more values:

Sharing a URL for Your Exact Map, Layers and All

Next up, the ‘Share this Map’ allows you to do just that. There’s a text box where you’ll be able to copy a URL that allows you to share an exact copy of the map you’re looking at with anyone.

You can notice the URL change anytime you make a change to the map.

Legend Conveys Information and Allows for Layer Maintenance

Last but not least, the legend section has two main functions.

First, the main function is to provide a key for the data present on the main screen. Here’s an example of the information available:

Note that there are three layers listed here: state boundaries, Forest Service timber harvests from 2017, and Forest Type

There’s one more thing to be aware of regarding the legend section: clicking on the legend for a layer will actually toggle the layer off on your main screen.

I’ll be honest, this took some getting used to on my part, but it is quite handy. Think of it like this: if you didn’t have this then you would have to dig through the massive ‘Map Layers’ menu to find the few layers you have toggled on.

Conclusions and Next Steps

If you’ve read through the entire post it’s more than possible that you’re a little overwhelmed. That’s more than OK, as this is a lot to take in. With that being said, you don’t have to understand everything right away, and this post was designed to merely give you a taste of what is possible.

Don’t worry, the more you use this the more it will make sense. I’ll be incorporating this program on many posts on this site, the difference being that their I’ll be much more practical.

If you’re interested in seeing a more practical usage of this technology, check out this post: How to Find Forests That Were Recently Logged. In this post I go into great depth on how we can use this application to not only find past logging activity reported by the U.S. Forest Service, but also how to identify logging occurring in real-time.


What is NDVI Imagery and How is it Used?

The use of satellite imagery has greatly advanced what is possible with farming and forestry in the 21st-century.

What is NDVI imagery? NDVI stands for Normalized Difference Vegetation Index, and it is a simple formula designed to measure the approximate amount of photosynthesis plants are undergoing in a location. Satellite images are used to find the levels of red light and near infrared light emitting from a location, and the difference is the NDVI.

This post will quickly go over the NDVI and how it works, and then cover some examples of how it is used in our modern times.

A Simple Breakdown of How NDVI Works

Without going too deep into the weeds on the scientific side of things, keep reading for a quick overview of how the NDVI works, as well as why it works.

NDVI is all About Measuring What Plants Do Best

The light plants receive from the sun is comprised of lights from different parts of spectrum, two of which are the following:

  • Near Infrared Light (NIR)
  • Red Light

The reality is that photosynthesis is limited to a certain part of light spectrum, and most of the light that plants use in creating energy comes from the red and blue parts of the light spectrum.

What Plants Do With Near Infrared (NIR) Light:

The process of photosynthesis has very little use for near infrared light. As such, plants actually re-emit a lot of the near infrared light back into the atmosphere.

What Plants Do With Red Light:

On the other hand, the process of photosynthesis allows for plants to take in red light and convert it into chemical energy. As plants are so efficient at converting red light into energy, very little of the red light is re-emitted into the atmosphere.

So What is the Normalized Difference Vegetative Index Trying to Actually Measure?

As the NDVI is a calculation based around the colors present in an image captured by a satellite, that image is only capturing the light that was re-emitted from the Earth back into the atmosphere.

Like I mentioned above, plants actively undergoing photosynthesis re-emit much light in the near infrared part of the spectrum, and they re-emit very little light in the red part of the spectrum.

As the Normalized Difference Vegetative Index is attempting to measure how much photosynthesis is taking place at a certain location, it merely calculates the difference in the levels of red light and near infrared light.

What Different NDVI Values Mean

Alright, so NDVI values are attempting to calculate the amount of photosynthesis occurring in a satellite image.

What do the numerical results of the NDVI calculation actually mean when it comes down to plants? First things first, the outcome of a NDVI calculation allows for any number in the following range: from -1 to 1.

Positive numbers indicate some level of photosynthesis.

The closer the number is to 1, the more photosynthesis is happening at that location.

A Few Examples of How NDVI is Used in Real Life

Now that we’ve got the theory and calculations out of the way, let’s check out how people are using such a valuable tool.

Real-Time Measurements of Crop Performance

Many farmers with large operations have benefited greatly from NDVI, as it allows for more efficient monitoring of large amounts of crops. For example, a farmer could use NDVI values to search for sections of a field that are under-performing when compared to the expectations at the time for that crop.

Measurements of How Much NDVI Changes Over Time

More than useful for just crops and farming, the NDVI is a major part of detecting natural disasters and other changes that occur in our forests and fields.

screenshot of the ndvi 10 year change layer in the forwarn ii application
This screenshot shows the impact of a windstorm on the Nicolet National Forest. The areas in red are showing a severe drop in vegetative productivity when compared to past production.

Think about it this way: any time there is an event like a tornado that kills a large amount of well-established plants like trees, NDVI is going to be able to measure a drastic change when compared to the performance the past years.

Concluding Thoughts

While a bit technical, I hope you got value out of this article and came away with a new appreciation for the capabilities of satellite imagery. I know I’ve been blown away by the possibilities that are created by this functionality.

If you’d like to learn more about how this technology is applied in the realy world, then check these articles out: