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