Welcome everyone
I'm Elissa Schuett, I am the Program Manager for the Forest Ecosystem Monitoring Cooperative
and I will be here facilitating this webinar and want to thank
you for joining us and we are excited to share with you our latest online tool,
the Northeastern Forest Inventory Network, or NEFIN.
The Forest Ecosystem Monitoring Cooperative built this tool to aggregate and standardize continuous forest inventory data with our partners
from across the region and this complements other programs and tools by providing a higher
spatial density of data points. Soren Donisvitch, the FEMC Data Analyst will share with you
how to download the data for your own use and demonstrate using the data visualization
tool to help you explore trends across the region. Before I hand it over to Soren, there
are a few housekeeping tips that I want to mention.
First of all, this session is being recorded
and it will be made available then to view by others. So if you know others who
might be interested, we will be able to share this probably sometime next week or so
once we process it.
Also we have been approved for one Category 1 SAF credit so if you are
interested in receiving that credit please let me know and I'll be sure to submit the
attendance reports for this to SAF. I will be monitoring the Q&A box so feel free to
drop any questions in there and there will be a few times throughout the session where
you will be able to ask questions and you'll be able to unmute yourself and so just use
the Q&A box or raise your hand to make that known especially during a few times that Soren
will make that available and then we will also have plenty of time at the end of the
session for further questions and discussion. So I think that I will go ahead and let Soren
get started and again feel free to use the Q&A box to introduce yourself as well.
Alright Soren.
Awesome, thanks a lot. So I'm going to start everything off with the PowerPoint
And we can get things going.
Awesome, so just to reintroduce myself and I'm Soren. I'm the Data Analyst
analyst for the Forest Ecosystem Monitoring Cooperative. In today's webinar we're going
to be going over the Northeastern Forest Inventory Network tool and specifically we're going
to be going over our data downloader. So, like how do you interact with the standardized
data that we've created using our online data download filtering to get the data that you
want that's publicly available. We'll also go over the data visualization tool which is
useful to kind of visualize the database itself to know what exactly you might want to filter to
and download from our data downloader tool.
First off I want to give a huge shout out to
the project team. There've been many different people that have been involved with NEFIN over
the past couple of years. It's a pretty large project. Just want to say thank you to everyone
who's made this project, bringing it over the finish line, and really bring this to fruition.
So I'm going to give you a brief overview of what to expect from this webinar.
Right now we're going to be going over the PowerPoint of this webinar. We're going to go over
the project goals, some background of NEFIN. Really what NEFIN is, what were
the design principles we were using in order to create this
standardized continuous forest inventory network.
What were the design principles but then also why is this useful to the programs
who are involved with NEFIN? What is the utility for them to be continually adding their data
into NEFIN?
And then also why it's useful to you, the users who may be wanting to download
that data. The primary focus of this webinar is how to interact with our data downloader tool.
Then we get to launch into the actual live demo where you can follow along with me.
We're going to be going through that website and going into the data downloader tool using
a bunch of different filters, filtering the data. We can discuss exactly what is the data
that you're getting, what are the limitations, etc. Where to find the things within the web page
And then if we have time, again, we'll go to the data visualization tool and then
to make sure to leave some time, hopefully for questions. I know some people are still,
people are just going to be trickling in, so we'll try to keep things as kind of service
levels as possible and then we can dive later into the fun stuff, which is the live demo
where you can follow along.
Awesome.
So let's go over the goals of the project, really what is NEFIN? So the data accessibility
of continuous forest inventory data across the Northeast has been fairly limited.
There's many different disparate datasets and programs that have been able to be used
for decades from the 1960s.
Many of these programs are disparate and they use different methodology
They store their data differently and so our idea was to be able to create an
easily accessible and usable standardized regiongal network of all of these different programs.
The other aspect of this is we wanted to make the process, the data management, actually
standardized in a semi-automated way so that we don't ask programs to change how they are
measuring the forest, they don't change the plot sizes, they don't change their methodology.
We build a system that each and every year they can upload their new data as they take it,
as they store it, and it would still be able to be automatically processed and standardized
through time, to really lower that barrier for all these programs to continue to want
to incorporate their data into this standardized network.
The other aspect of this is always to kind of better our own networks, both the programs,
which everyone to interconnectivity between data managers across the region,
as well as users of these data as well.
So again, what is NEFIN? NEFIN is primarily comprised of three different aspects, two of which
So NEFIN is primarily comprised of three different aspects, two of which
are really for the program managers, the people who house the data and can take the data.
So that's the data upload system - the data processor system
which is where we standardize everything.
And then what we're going to be really covering today is the data downloader and visualization.
So how do you actually interact with standardized data, get at some of the raw data if it's publicly
available, and then also visualize it as well. So in the data uploading system, it's really
designed to handle continuous forest inventory data. So that's the CFI. It handles both fixed
radius and variable radius plots. So what were the criteria that we were really using for what
is a continuous forest inventory? Really, it needed to be continuous. That means that it's
re-measured through time. We made it mandatory that at least the program had to have been
re-measured twice. The other thing is that it's fixed, so it's spatially fixed. There's either
monumentation, in which case you know that you're returning to the same plot in the
forest through time.
And that's really our basic criteria for what is a continuous forest inventory. We also had
criteria for what data they collected. We made sure that they at least managed to collect tree
data, although many different programs also collected sapling and seedling data. And then
with these metrics, they also all reported what we might consider the standard
forest inventory where you would collect things like DBH, height, status, etc. So again, we also
wanted to make sure that a lot of this is all automated. So the management of metadata changes.
So anybody who's worked with forest inventory data and collected it know that one program
can be completely different from another. They use different species codes. They have different
plot sizes. Some plots are circles and squares. And so we really wanted to make
sure that our system was able to handle all of those different unique metadata and then
keep that going through time, which is really unique to the NEFIN. Also the thing we want to
talk about is allowing for different levels of security. So many programs don't want to share
actual plot coordinates. So similar to how FIA handles things, we fuzz where necessary,
although we do have some programs that do provide true plot coordinates. If you do want to get
true plot coordinates, those are all done through a specific process in which we will help facilitate
the communication between us and the actual programs themselves in order to get access to those data.
So moving on to the data processor. So this is where we standardize everything. And so we wanted
to make sure that everything is standardized. All of the species are in the same species codes.
We use ITIS species codes as well as Latin tags. We also include things like expansion to per acre,
excuse me, per hectare units. So each and every program might have different plot sizes. We've
gone through the trouble of actually in our standardized data creating those expansions to
trees per hectare and basal area per hectare. So that's quite useful for our standardized data.
The other aspect is we do provide QAQC reporting. So we flag things such as missing data,
you know, 1000 foot tall trees, etc, which is useful for programs. And then to get to today
where we're going to be talking about the data downloader and visualization tools. So that's
our extractor. It's how you use our web filtering and downloading to download the standardized data
that's publicly available. The other aspect to date is we're going to go over if we have time,
the data visualization tool. So to give you kind of a broader aspect of what were the design
principles for this network, we wanted to make sure that we didn't require all of the programs
who were involved in this network to change how they're monitoring their forests. We didn't want
them to change everything from metric to imperial and imperial to metric or to change their plot
size. We wanted to make sure that it was easy for all these programs to continually
in an automated fashion be able to update their data within the system. We also tried to work
with them to make as much of the data accessible as possible, although things like plot coordinates
for many of the different programs are not publicly available. And when we go through the data
downloader, I can show you how to see that. And then in the final aspect of this is we want to make
sure any of the standardized data is truly representative of both what the programs wanted to
standardize, but then also everything is truly standardized and really ready for analysis.
And we'll go through exactly how this is actually
happening within NEFIN. So again, we have many different programs that all record things differently.
In the top left hand corner, you can see there's forest inventory as data as delivered.
It's fictitious data, but they record things like species, diameter, tree health.
This is for program A. And then we have program B, right? So program B also records diameter,
but they record things, everything's slightly differently. So you can see that the tree identification
codes are different. The species codes they use are different. One reports in Imperial, one
reports in metric, and there's also ancillary data that's recorded differently, all of which
we have been trying to be able to standardize to a standard set of codes. So in this, we go through
the processing where we process both and we create these standardized data sets where we're able to
standardize to unique codes for trees, for plots, all of which you're able to filter to,
which is useful for analysis, and then as well as standardized species, and then everything
standardized to metric. We also provide that QAQC. A lot of the stuff is recorded only to the program
in order to see if they want to incorporate those data. So if there's missing data or erroneous data,
we provide QAQC, which is useful for programs. We take all these standardized data and processing
codes and QAQC reports, and then we really version everything, which is also extremely useful for
programs themselves. I could go into that, but today we're really working on the data
downloader and how to interact with the database itself, but we will have a later discussion to go
over exactly how useful this is for the programs themselves, a little more nuanced,
but things also are versioned, and then we eventually merge these standardized datasets into the
NEFIN and standardized database, and then we make that database available for web data portal.
I'll give you a basic overview again, higher level. Data managers prep the data,
they upload the data,
they may have changed metadata, but our system is able to automate it, process, and standardize,
and that's what we're going to go through today, which is how us as users can download and interact
with that data.
To give you an overview of what our database actually, the spatial coverage,
this is a map of the Northeastern Forest Inventory Network as it stands today, not all of which are
public. All of these plots are fuzzed, just to let everyone know, but as you can see, currently,
there are 10 that we have standardized within NEFIN. The distribution of plots across the
Northeast is something we should talk about. NEFIN in and of itself is useful data to have,
but you should understand any analysis conducted should understand that a lot of this data is not
evenly distributed across space, and you should know where those plots are and what that might
be representative of with any analysis that you do. This is why I would not say this is in
competition with FIA, FIA, the Forest Inventory and Analysis program is wonderful, really amazing
for population level estimates, but I would say this coupled with FIA are able to enhance the
spatial resolution may fill in temporal gaps within FIA to really get at maybe more a holistic
approach or at least a better amount of data to answer questions we have for many different questions
you might have in the Northeast. So I would say this coupled with FIA or in and of itself is useful
for analysis.
At this point, do we have any questions?
Nothing's coming through the Q&A box, but if anyone wants to speak up, you should be able to
unmute yourselves.
Awesome. Then I think I will continue. We'll go to the fun part which is actually
downloading the data.
So if everyone could please go to the link that's going to be popped into the chat
or go to the URL link that is displayed currently on your screen, that would be great.
I'll give that a quick minute.
The link should be in the chat now.
Awesome. Thank you. So once you navigate to the URL link that's posted in the chat where you can
manually enter it in, again it's www.uvm.edu/femc/nefin, that's
N-E-F-I-N, and you'll navigate to our landing page here.
Awesome. So this is our homepage currently. How you are going to be orientating yourselves within
our web page here. The tool itself is really operated through this ribbon here. Right now we
are in the home page. This is our landing page for the website. And right here it gives you a
couple interesting things like what is the Northeastern Forest Inventory Network
some helpful links, and really what's in the database currently.
We have eight different programs that have semi or public data, the data ranges of the
data, the states of our publicly available data. So that includes data from Massachusetts,
Maine, New York, Vermont, the total number of plots, trees, species, just a basic overview of what is
in NEFIN. But today what we're really going to be going about is the Get Data tool. So if you
scroll back up to the top, the About NEFIN will take you to another web page that will show you
some history about NEFIN. The get data tool is what we're going to be covering today.
The Inventory Programs, this is where you can get some useful information such as contact
information from the inventory programs, it goes over all the inventory programs we have in the
database, and our Data Visualization tool which we might cover later. So let's go back to the
Get Data tool. We're going to click and follow that link. It's going to take us to our data
downloading tool. The first thing that's going to happen is this pop-up is going to occur for you.
So when this pop-up occurs, it's really useful. So this gives you some basic overview of how to
actually use the tool itself. So how do you where the filters, how do you clear the filtering,
what is the search parameters, a bunch of useful information, all of which we're about to cover.
So we don't really spend too much time here. But what you should know is that if you don't
want to ever see this pop-up again, you can click don't show this next time and the cookies will
remember that you don't want to see this pop-up and then you can close. But I like to actually see
this pop-up. And so I'm just going to unclick that box and hit exit. So every time I come back to
this, the extractor, it'll pop up and kind of give me some reminders. It's useful to have.
So this is our data downloader tool. And so you can see again, this is the primary ribbon that
navigates through. And right now we're the Get Data tool. And right here on the top left hand
corner, these are our filters. This is how we're going to be actually interacting with the data
itself. This is how you're going to be filtering to the data that you want to see.
And then the output of whatever filter you search will be output down here. So this in this bottom
left hand corner, you can see right here where that output is going to be. So let's actually
interact with those filters. So again, right here, just to orientate ourselves, we're in the search
results tab and we're currently looking at programs.
And right here, this is the output of our filtering
the data, the title of the program itself, the code, etc. So let's actually interact with this.
So today, let's work with the database, which I know in and out, which is the
FEMC forest ecosystem FHM plots
So we're going to click that. So again, to do that, if you want to select all,
you can select all or if you only want to select a specific program, you can deselect all and then
click on the forest ecosystem FHM plots. So as you can see here, these are filtering by program.
We also provide filters for state if you're interested in all the programs in the state or
some such you can do that. But today, let's only focus on Vermont's plots.
So you can again, do that again, you can deselect all and select one more amount multiple,
but today we're just going to be looking at Vermont. And then we can look at the time range.
So we can select a subset of time that we like to look at either a single year or multiple years.
And for this, let's go from 2000 to the last data updated, which is 2021.
And again, the filters we're going to be using for program is FEMC Vermont and this time range
from 2000 to 2021. And then to execute this query, what you do is you hit search.
And then again, this is going to repopulate and the output of your query is going to be
you output in this window right here. And again, right now we're looking at the forest ecosystem
monitoring FHM plots, the Forest Health Monitoring plots, the code that we use, the state,
organization that has sway of that data currently, and the date range of that program.
Something else that is also useful is this search parameter here. So I'll highlight right here.
So if you go here, this is telling you what is actually being executed, the filter that is being
executed currently and is being displayed. As you can see, we've selected FEMC and it's selected
FEMC's FHM plots here
it's Vermont and the time range. Currently, we're at the programs.
Now let's move to the plots. So we give you the other type of data, we click on it.
And something I would notice
is as you go through these, there's going to be different filtering, but there isn't different
filtering for programs to plots. So as you go over here, it's still retained our information.
There's FEMC Forest Health Monitoring plots, again we're looking at only Vermont,
Only Vermont selected, and then the date range that we had before. And this is what's
going to pop up up for you. And so it gives you the plot ID. So this is NEFIN's plot ID
for this program.
The latitude and longitude. And so for these plots that we've put into
NEFIN are publicly available. This is the true lat and longitude for these Forest Health Monitoring
plots. If you are fuzzing, a way to know whether or not it's fuzzed can be also seen here.
So what we do is we do randomized latitude and longitude fuzzing, and then also rounding. So we do two
digit latitude rounding after fuzzing. So then if you only saw four digits or two decimal places
after, then that's a good way of kind of indicating that it has been fuzzed and is not the true plot
coordinates. And I can cover that again. Something that's also useful here is the results per page.
So if you want to see more results, you can select how many results you'd like to see.
I would give you a word of caution that if you go to, you know, 1,500 things might be a little slow.
So you can expand this maybe to 25 and it'll give you 25 results from your query.
This query right here.
Again, you can see what that query is right here in the search parameters.
Awesome. So let's get to the more fun data, which is looking at trees.
Go to trees. And something I'd like to highlight here is you can see the filters that are available
have changed. There are more filters that you can use to filter through trees.
So as you can see, we still are only looking at FEMC's data. We still are only looking at Vermont
We now have things like species. So if you want to search multiple species or just a single
species, so if I wanted to search, maybe we'll go for sugar maple. So we don't need poison sumac,
but we want sugar maple. You just want to make sure it all is deselected and you can hit sugar
maple. So we're only getting looking at sugar maple. And you can come down here.
You can see there is more information down here,
more filters available. We have tree status. So in the standardized data
set, we take all of the many different codes that are used for identifying status and
we're able to go to a binary primarily, which is whether or not the tree is living or dead.
So we have living or dead and then missing an unknown is the missing is if there was no status
recorded, which occurred rarely for many of the programs, and unknown status, which means it was
likely an erroneous input for status. These very rarely occur, but
it would become NAs. So we'll just remove those. Again, so for tree status, we're only going to be
looking at living and dead. And then diameter, you can look at a subset of diameter ranges
you are interested in, but I'm not going to. We can get every tree that we want. We can look at
subset of tree heights if we want to. And then we also have tree crown class. So this crown class in
the filter, let's you know that there is a crown class identifier. So that program did
look at crown class of some type or has no crown class data. So to know how we did this for our
crown class, we used again a binary. So the we use either overstory or understory or no value.
So overstory or understory, so overstory would be codominate, dominant, and emergent
and understory would be suppressed and intermediate. Not all programs were at that fine
grain with canopy data. So we had to go to what would be considered the common denominator,
which is a binary of overstory and understory. We did this also - somewhat of a more complicated
metric would be the forest health indicator. So here is where you were able to see the presence
or absence of a forest health indicator. So some programs were extremely detailed.
They measured this specific
agent that may be ailing the individual tree. Other programs simply measured that there was
some type of impact on the tree health. And so we had to go to the common denominator,
which is kind of binary, which is either present or absent. Now each of the individual programs
may report many different - so FHM plots, I know that we measure many different forest health
metrics, things like transparency, vigor, etc. And we individually, the program sets the threshold
for what it's considered to be present or absence of what would be considered a forest health flag.
So for us, we measure things like vigor and transparency. And so we set a threshold,
all of which are available within the program data. Like what are the thresholds to
be considered a flagged presence or absence of a forest health indicator. So for this,
we're not actually going to look at that at this data. If we want to talk more about that, we can.
But let's actually execute this query. And again, to go through it, we are looking at the trees.
We're looking at FEMC. We're only looking at sugar maple. We're looking at Vermont the same years.
We're looking at only living and dead. And we didn't modify diameter trees, tree height,
tree crown class or the forest health indicator. We're going to execute that search. Awesome.
And so it ran. And so this was the output. Again, it did filter to the sugar maple.
The states, the year, it was live, the diameter, the height, all the metric, the overstory and
absence. So for those metrics that are standardized, like per hectare, so like trees per hectare
and basal area per hectare, those are going to be in when we download the data, you have to download
the data to be able to visualize that. Something that is also useful here. Again, we'll go back
up to the top again, you see the search parameters. We now are looking at the sugar maple only.
And those tree statuses. But something that's useful here is if the data is fully publicly
available, you are able to click here and see the raw data. So this is actually the data as
delivered stored in a dictionary format or a JSON format if anyone's interested. But it's useful
for us and also everyone else to be able to, QAQC - did the DBH actually in fact line up with our
standardization. So 34.2 centimeters, it did! But it also is how you can get at that ancillary data
so whether or not we measured dieback, whether or not the distance of that species from the plot
center, all interesting information that we didn't really want to throw away. We wanted to make
somewhat available and for download if that program allows for it to be public. I know that this data
is allowed to be public many of the other programs that you are not in which case you would have to
request the raw data.
Awesome. So we'll move on to saplings. And again,
it reset some of the filters. So if you go back down here, we'll only look at sugar maple again.
And then down here it gives us sapling status. If anyone is interested in in-growth, we were
able to capture for many of the different programs if it grew into a tree. So for saplings, we have
Living, Dead, Missing, Unknown status. We'll remove the missing and unknown status and we can look at
the Grew into tree. So this really gets rid of the NAs but if you want to see those NAs, you have
to keep these flagged, or these checked.
Awesome. So I can keep that query and again you're able to see those outputs.
So we'll go - saplings is much like trees but we'll go to seedlings since there is
something I want to talk about here as I'm sure some people are interested in how exactly we were
able to standardize all of the different ways people record seedlings in specific regeneration.
So you go to the seedlings again looking at search results for seedlings.
We are still looking at FEMC's FHM data. We are going to only be looking at sugar maple,
looking at Vermont. But if you come down to here, you can see seedling counts. It gives you all of
the different counts. The counts were able to be standardized as that was something fairly,
it was universal across the programs. But then the seedling size classes. So it's here that we
chose to not modify how programs chose to categorize and tally regeneration. In order to filter this,
for instance, click on this right up here and these icons, these eyes, will be able to
give you pop-ups, they give you understanding of what things are. And so right here the seedling size
classes, it gives our standardized NEFIN code. And then how that program actually
classified that code.
So for regeneration, for MACFI, they had both diameter and also height for classification.
So for instance, if I was interested in in-growth or seedlings about to become
trees, we can look at category 4. And then maybe for the Northeastern Temperate Inventory Network
we can look at something that is only height. So we might look at 150 centimeters in height
and larger, so 13. So this requires you to really understand how each individual program
chose to categorize regeneration in order to make informed decisions about your analysis.
All of these codes, again, are available for download and can be easy to pop-up and see.
So at this point, do we have any questions?
There was one in the chat you might see it about if NEFIN has been used with FIA insect and disease
database or other datasets.
For any level of analysis, currently, no, not that I know of
that has produced any kind of like published material. I do know that the output of the standardized
data is really easy to integrate with FIA's data. So if you were interested in running FES or
look at FIA's raw data is pretty compatible as far as what you're going to actually get. We'll go
through what the data actually looks like when we download it. But I would say no, not that I know
of currently for any published material.
So why don't we go to, we'll go back to trees.
Something that's really important here is the data download. So once you execute that search,
and again, you can see the search parameters for that search, so it did search poison sumac also.
So let's get rid of poison sumac and we'll go back and search again.
And we'll go back up. We're looking at trees. We're looking at search results for trees.
And you can go to data download.
Awesome. So once you go to data download, it gives you option to
download the data that you have queried using our filters. Again, right here, this is
what's being searched and being filtered to. And you have two primary options. You can download
all packages. So this includes if it's available, the original data, the raw data, this and our
standardized data as well as related files such as if there are any files that were uploaded with it
So like methodology information and also understand the download different versions of the data.
And so if you go down to this box right here, you can pick which files you'd like to download,
whether or not you want to download specific versions or the processor data or the Python
functions used for standardization, all of which can be downloaded if it's available publicly.
And then the other option here is to just download the CSV of our standardized data, which is what
we're going to do right now. So again, we've executed this query and then we are going to
download that data and we're going to let it run. And you're going to get a pop up. So if you don't
get a pop up or it doesn't go to your downloads folder, what I would do is make sure that your
browser allows pop-ups for NEFIN and then try rerunning it again. But it does allow for us.
So we're going to download the results and it's downloaded to our downloads folder.
And before we open that up, I do want to let you know that where you can find other useful
information. So this tips, this brings up that pop up that shows you how to navigate the filters
and interact with website, but then our NEFIN standardized field codes. So we've provided all
of the standardization documentation for all of the downloadable information. So for instance,
if you were to download the plot table, which we have currently, it'll give you the description
and definition for all of the fields you'll find in those downloaded CSVs for our trees, tree table,
whether or not it's a tree species number, the taxonomic serial number, diameter, and everything
else. So it'll give you an understanding of what it is you're actually going to be looking
at within the tables, which is useful. But then also, we do that for saplings and seedlings as
well, you're also going to be able to see this for what are NEFIN standardized codes lists for
status, for instance; living, dead, missing, unknown. The codes are one, two, three, and four,
our crown codes for overstory, understory, undetermined
seedling classes. Again, if you're going to be working
with regeneration here, you're really going to want to understand specifically what that program used
for regeneration as far as classes go, and then saplings as well.
This is useful to know when actually working with the data.
So again, let's actually go and you can open up the data we downloaded.
And as you can see, it's a zip file of the plots, programs, and we executed that tree filter,
Let's open it up and see what it looks like.
So this is the execution of the downloaded filter we just did. Again, we made sure that we
were looking at sugar maple, we were looking at FEMC, so you can see - and then the sample
years that we filtered to. Again, diameter and height are all in the metric. We have the crown
class, all those codes that can be seen, whether or not there was a forest health metric identified.
And then something that's useful is because it's public data, we store the raw data in a
JSON formatted dock field. So it's dictionary-like which gives you access to what the raw data
actually looks like. Super useful. And then also, this is where we have those
per hectare expansion factors for all of the different plot types. So it's a true standardized,
so each program's expansion was done, so you don't have to do that yourself and know what those are.
Those information are available, and I can show you where you can find them.
But one way to do that for here is you have the basal area per hectare representation of that
individual tree, and you also have that tree's representation of the count per hectare. One way
to get at a fixed area plot would be to pick one and divide it by that number, and that would give
you the expansion factor. But if you want to find that information yourself, I can show you how to
do that in a little bit. But this is where you would be able to use that information.
This is quite useful as you can kind of build this straight into FES if you want to
run a vegetation simulation.
The states and then also other useful things like the unique tree code for that instance of being
recorded on that plot in that year, and then as well as the individual tree. So right here,
you can see it's FEMC, it's the first plot, first subplot, and this is the first tree. And so
if you wanted to look at the history of a tree or track a tree through time, you can see that
these are all the same trees and those are the diameters through time. And so
it's pretty set up to do a lot of different analyses quite quickly.
So let's exit out of this and I can
show you how to see what those expansion factors really are. You can get that in
some of the information that has to do with this.
So we'll go back to search results and we'll go to programs.
And then we can go here. And again, so a lot of useful information can be seen here.
There're other ways to get to it, but this is one way. You can go to the details. You can see all
the different information about overview of the program, the study range, the contact information,
what data is actually being included, the program definitions. So this gives you an understanding
what type - that it's a fixed plot. It's a circle. What's the projection coordinate system
used for a lot of the GPS information, the diameters, and then also the expansion factors for per
hectare units for trees, seedlings, and sapling plots. It gives you all the information
if you wanted to get that yourself as well. It also gives you access to the downloading the
files. So if you wanted to download all of the version files and standardized files, this is
another way to do it instead of just going through the filtering. But that's also quite useful.
So at this point, do we have any more questions?
And I'm going to take that as a no.
Nothing has come in on the chat. I didn't know if
anyone wanted to raise a hand or speak up right now.
I think you can go ahead and move on.
Yeah, awesome. So it looks like we have, maybe we have 10
minutes and five minutes for questions. So we'll go to a data visualization tool. So again, we've
just kind of executed a query to get at sugar maple for a specific program that allows for
download of those data. And we can go now to the data visualization tool. It may take a while for
you to get these. It is building all of the table structures to be able to visualize. So let's just
give it a moment. And as you can see right here, we have a data visualization app
for the database itself.
So this right here is just a basic overview of the all the fuzz plot locations for individual
program. None of these plots are actually the true plot coordinates. As you can see, if you zoom
in and everything's been rounded to decimals and fuzz randomly in space. So there's really no way
to be able to actually get to the original data from this visualization. But it does give
you useful information. For instance, we're looking at, I believe that's Mount Toby, or Cadwell's
individual plot and the range in which that plot was actually measured. So it's quite useful. And
it's useful also to kind of see where they that inventory program may be representative of the
forest itself. It's good to keep in mind what data you're looking at in space, what that might
represent for forest when looking at these visuals as well. So first off, we can interact with the
tree data. So the primary navigation of this tool is going to be going to the plot map.
We would just add tree data, the sapling data, the seedling data, and then the overall trends.
So what we're going to go through right now, at least in this app, we're going to -
it outputs a visual here. And then the primary kind of how you're going to interact with this
visual is going to be through this control panel on here on the left. We're again,
we're looking at the tree data and we're looking at all states, all programs, all live trees,
and then no filtering of individual species. But for here, for instance, we wanted to look at
balsam fir, you can filter only to balsam fir, although that's not going to tell you much,
it may give you some interesting size class distribution information. But you can filter to
multiple or some species. They can give you date range if you want to look at a subset of years
or all years you can. Again, something that's really useful here is that, or rather, you should
know is that it's filtered to the top 10 species within the query. So there may be multiple other
species here, but that aren't represented visually. We also have a bunch of different
type of plot visualizations if you're interested in box plots, which may give you better information
about the median and the distribution of a species based on basal area per hectare. So again,
the x-axis is basal area per hectare and you can see that we're here at the x-axis.
You can look at the density plots as well, broken up again by the top 10 species.
And then let's go to pie charts. So maybe not the best representation all the time, but it does
give you a breakdown of the pie chart by species and then all other species are being
lumped together here. So it's useful for, again, it's representing only the data that you are
filtering to and that should always be considered. You can also do scatterplots if you all want to
think back to our silvicultural classes. We can think back to a density management diagram or
some type of size frequency relationship. So here it's tree's per hectare and basal area per hectare.
Something that should be noted again is we're looking at individual species. So there's no real
way to do any kind of like management from this based on the plots, but it does give some interesting
kind of oversight into maybe silvics of individual species. So for instance, let's, I don't know,
let's look at a high density species. Maybe like balsamea - maybe balsam fir
versus like let's see, low density, high size. Again, so then here's interesting. So here's the
dog hair thickets that a lot of people in Maine have to wade through again super high dense
stands, high trees per hectare, fairly high size. And then you look at red oak here where
very high size. There's a lot of carbon in there too even at a low density. So quite interesting to
see that kind of represented within these data.
But let's go back again. So to interact with the x-axis would be here.
You have a number of interesting metrics. You can use basal area,
trees per hectare, quadratic mean diameter, quadratic mean diameter being representative of larger
trees within a plot, year, and species. It is possible to break this or that you might get -
So say for this it'll throw you a flag up here saying that you need to select a y-axis or there
might be something that that won't work. But in general it works fairly well to be able to look
at certain sets of data. So again, so let's just go this to none and you can group by species or
year. So from this you're able to get like a rudimentary understanding of the data that you
want to filter to. For instance we can select a program, let's look at Cadwell forest
and you get a distribution for, let's see, the last ranges they had. You can get a lot of
quite useful quick information of what's in that dataset itself or across many different datasets
We can look at maybe all datasets in Vermont. And it's quite useful to, kind of, before even
going in and querying and downloading the data using a data download tool come here and see what
it might actually look like before you download it. But useful. Let's you can do that again for
saplings and seedlings. But seedlings that should be noted are on logarithmic scale. It just makes
it easier to see. So trees, saplings, seedlings. And we can look at trends here. So again you should
preface this with any trends you observe here are representative only of the plots, they wouldn't be
representative of the forest. So again you would really want if you're going to look at like the
regional analysis I would couple this with FIA to get a better representation of the forests
of the Northeast. But it does add a lot of really interesting things. So something here that's also
important to note
based on the dataset itself that we've created for NIFIN is that we do have data sets that span
all the way back to 1960s. But there are these temporal gaps here. And so whatever trends you
see here might be a little less representative and they're only representative of those programs
that actually sampled them on the landscape. But then as you can see through time many programs
started to come back online - come more online. You can see some spikes of like every 10 years
some programs resampling. And then some programs going to annual resampling which is quite nice.
So maybe these trends are a little bit more representative of what the region that we're
looking at. But it's interesting here that it does represent some other findings that we have found
recently and currently is that forest in the Northeast are becoming denser or having a
higher relative density which has implications for the regeneration dynamics, mortality dynamics.
And I just want to just we can just see if this database actually can show that. So right here as you
can see that being sent to be holding fairly steady as far as trees per hectare. So the amount the
frequency of trees on the landscape or the abundance of trees on the landscape seem to be
holding fairly steady at least for what these plots represent which are forested or primarily
forested. And on land bases such as university property or public lands, etc.
But let's - instead we'll go to size. So our hypothesis would be if you know that relative
density is increasing that would assume that like basilar area or average size of trees should be
increasing. So we'll look at quadratic mean diameter which might be representative for larger trees
in the plot. And it does it shows that actually that on average the trend in size for species,
for all species, or all within these plots are indeed actually increasing. So that's interesting
to be able to see and a quick litmus test for these data. I can also do that with basal area,
I'm sure it's probably analogous, and yep, also slightly increasing. So from here you're able to see
some basic overview of what what trends might be even go to individual species. Again any trends
observed here really should be that in the background of your mind should really be that
it's only representative of where those plots are on the landscape. So let's maybe let's go to
eastern hemlock. And so you can see a slight basal area size increase over the last
40, 50, 60 years. So all quite interesting, and in a lot of any kind of analysis that is being done
this can give you a rudimentary visual of what data actually holds so that you can download that data
and answer the questions you're interested in yourself. So that's the data visualization tool.
At this point does anybody have any burning questions? And we can go back to maybe the Get Data page.
Awesome. And why don't we go for questions?
Thanks very much, Soren. And yeah, I welcome people to raise their hand, type in the chat or
Q&A boxes. And happy to open the discussion here.
Hey, I'm sorry this is maybe too basic of a question I cut in a little too late here. But I'm also
just wondering like are there some forest plots that may not necessarily be in this database as
looking around the map? And I noticed that there's some at Sleepers River Research Watershed that I
know of that are not there as well as some here at UVM as well like that are like the Rubenstein
School Forest and others. I was wondering like what types of data are here and and what
is there room to add additional data to the database?
Oh yeah, I can talk on that. So there were many different programs across the Northeast that we
initially looked at and had like contact with and those kind of took priority but any major room
for expansion for any continuous forest inventory data so long as they measure those, it's true,
continuous forest inventory and they measure those basic measurements who would be able to
integrate them into this network. Yes. The one thing that would preface is that because of the
automation process it does take some time to get any of those programs involved so it's easy for
them to continually update their data. So there's some legwork there but yes we would definitely open
to expanding this network.
Okay, fantastic and would I just kind of email and contact you and just
share information about the networks I know of and then see where that goes.
That would be wonderful. Yes, if you could reach out to me or one of my colleagues
I'm sure we can help work through that.
Awesome, well thank you so much.
Thanks Josh, yeah feel free to reach out with more details.
I really appreciate you walking through the data visualization I think that's
a really useful tool to get some ideas to kind of start thinking about where you want to dive in
and really do further analysis. You've talked about some of the other ways that
this might be used like maybe for looking at carbon storage or anything else. Any other
ideas or suggestions for where people might be interested in going with this?
Oh there's so many interesting - when I think about continuous forest inventories I think about how
the Northeast built all of its growth and yields like, you know, like what sustainable yield,
what growth was going on?
What its health trends, mortality trends, regeneration dynamics. I think that a lot of this
data can be used to look at both kind of easy questions like, you know, based on the plots
and the landscape that we're seeing are there increased mortality in beech for instance.
These data are really how you're able to build those analyses and so where I would start
personally is, like, I'm interested in mortality so I would I would look at start looking at
being able to see flags between - is there a flag between forest health metric and in some future
mortality or mortality trends of individual species. There's so many different things you can do
with this data but all of which can kind of be started in the data visualization tool
really do require you to download the data and get into the analysis yourself. It's a good launch.
I don't know maybe updating biometric equations by adding more, you know, eastern hemlock.
There are so many different things you can do.
Yeah I think that, you know, for all of you who are attending we welcome you to think about ways
that this data could be used, and reach out if you have questions or want to dive in deeper
and we're happy to you know think about different ways that you might be looking at using
this data and using the tool.
Definitely and just really thank you everybody for coming and
I hope that you're able to use our new tool.
Yeah, thank you all for attending and like I said at the beginning this was recorded and so
we will be making it available for viewing at a later point so you know keep an eye out on our
website for that link and if you are interested in SAF credits please let me know and I will be
sure to submit. All right thanks everyone have a great afternoon thanks for joining us for lunch.
-Thank you -Thank you so much