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Welcome everyone

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I'm Elissa Schuett, I am the Program Manager for the Forest Ecosystem Monitoring Cooperative

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and I will be here facilitating this webinar and want to thank

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you for joining us and we are excited to share with you our latest online tool,

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the Northeastern Forest Inventory Network, or NEFIN.

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The Forest Ecosystem Monitoring Cooperative built this tool to aggregate 
and standardize continuous forest inventory data with our partners

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from across the region and this complements other programs and tools by providing a higher

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spatial density of data points. Soren Donisvitch, the FEMC Data Analyst will share with you

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how to download the data for your own use and demonstrate using the data visualization

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tool to help you explore trends across the region. Before I hand it over to Soren, there

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are a few housekeeping tips that I want to mention. 

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First of all, this session is being recorded

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and it will be made available then to view by others. So if you know others who

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might be interested, we will be able to share this probably sometime next week or so

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once we process it. 

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Also we have been approved for one Category 1 SAF credit so if you are

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interested in receiving that credit please let me know and I'll be sure to submit the

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attendance reports for this to SAF. I will be monitoring the Q&A box so feel free to

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drop any questions in there and there will be a few times throughout the session where

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you will be able to ask questions and you'll be able to unmute yourself and so just use

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the Q&A box or raise your hand to make that known especially during a few times that Soren

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will make that available and then we will also have plenty of time at the end of the

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session for further questions and discussion. So I think that I will go ahead and let Soren

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get started and again feel free to use the Q&A box to introduce yourself as well.

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Alright Soren. 

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Awesome, thanks a lot. So I'm going to start everything off with the PowerPoint

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And we can get things going. 

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Awesome, so just to reintroduce myself and I'm Soren. I'm the Data Analyst

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analyst for the Forest Ecosystem Monitoring Cooperative. In today's webinar we're going

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to be going over the Northeastern Forest Inventory Network tool and specifically we're going

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to be going over our data downloader. So, like how do you interact with the standardized

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data that we've created using our online data download filtering to get the data that you

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want that's publicly available. We'll also go over the data visualization tool which is

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useful to kind of visualize the database itself to know what exactly you might want to filter to

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and download from our data downloader tool.

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First off I want to give a huge shout out to

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the project team. There've been many different people that have been involved with NEFIN over

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the past couple of years. It's a pretty large project. Just want to say thank you to everyone

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who's made this project, bringing it over the finish line, and really bring this to fruition.

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So I'm going to give you a brief overview of what to expect from this webinar.

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Right now we're going to be going over the PowerPoint of this webinar. We're going to go over

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the project goals, some background of NEFIN. Really what NEFIN is, what were

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the design principles we were using in order to create this

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standardized continuous forest inventory network. 

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What were the design principles but then also why is this useful to the programs

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who are involved with NEFIN? What is the utility for them to be continually adding their data

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into NEFIN? 

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And then also why it's useful to you, the users who may be wanting to download

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that data. The primary focus of this webinar is how to interact with our data downloader tool.

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Then we get to launch into the actual live demo where you can follow along with me.

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We're going to be going through that website and going into the data downloader tool using

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a bunch of different filters, filtering the data. We can discuss exactly what is the data

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that you're getting, what are the limitations, etc. Where to find the things within the web page

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And then if we have time, again, we'll go to the data visualization tool and then

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to make sure to leave some time, hopefully for questions. I know some people are still,

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people are just going to be trickling in, so we'll try to keep things as kind of service

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levels as possible and then we can dive later into the fun stuff, which is the live demo

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where you can follow along.

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Awesome.

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So let's go over the goals of the project, really what is NEFIN? So the data accessibility

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of continuous forest inventory data across the Northeast has been fairly limited.

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There's many different disparate datasets and programs that have been able to be used

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for decades from the 1960s. 

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Many of these programs are disparate and they use different methodology

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They store their data differently and so our idea was to be able to create an

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easily accessible and usable standardized regiongal network of all of these different programs.

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The other aspect of this is we wanted to make the process, the data management, actually

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standardized in a semi-automated way so that we don't ask programs to change how they are

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measuring the forest, they don't change the plot sizes, they don't change their methodology.

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We build a system that each and every year they can upload their new data as they take it,

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as they store it, and it would still be able to be automatically processed and standardized

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through time, to really lower that barrier for all these programs to continue to want

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to incorporate their data into this standardized network.

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The other aspect of this is always to kind of better our own networks, both the programs,

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which everyone to interconnectivity between data managers across the region,

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as well as users of these data as well.

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So again, what is NEFIN? NEFIN is primarily comprised of three different aspects, two of which

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So NEFIN is primarily comprised of three different aspects, two of which

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are really for the program managers, the people who house the data and can take the data.

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So that's the data upload system - the data processor system

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which is where we standardize everything.

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And then what we're going to be really covering today is the data downloader and visualization.

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So how do you actually interact with standardized data, get at some of the raw data if it's publicly

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available, and then also visualize it as well. So in the data uploading system, it's really

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designed to handle continuous forest inventory data. So that's the CFI. It handles both fixed

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radius and variable radius plots. So what were the criteria that we were really using for what

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is a continuous forest inventory? Really, it needed to be continuous. That means that it's

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re-measured through time. We made it mandatory that at least the program had to have been

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re-measured twice. The other thing is that it's fixed, so it's spatially fixed. There's either

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monumentation, in which case you know that you're returning to the same plot in the 

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forest through time.

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And that's really our basic criteria for what is a continuous forest inventory. We also had

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criteria for what data they collected. We made sure that they at least managed to collect tree

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data, although many different programs also collected sapling and seedling data. And then

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with these metrics, they also all reported what we might consider the standard

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forest inventory where you would collect things like DBH, height, status, etc. So again, we also

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wanted to make sure that a lot of this is all automated. So the management of metadata changes.

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So anybody who's worked with forest inventory data and collected it know that one program

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can be completely different from another. They use different species codes. They have different

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plot sizes. Some plots are circles and squares. And so we really wanted to make

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sure that our system was able to handle all of those different unique metadata and then

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keep that going through time, which is really unique to the NEFIN. Also the thing we want to

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talk about is allowing for different levels of security. So many programs don't want to share

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actual plot coordinates. So similar to how FIA handles things, we fuzz where necessary,

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although we do have some programs that do provide true plot coordinates. If you do want to get

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true plot coordinates, those are all done through a specific process in which we will help facilitate

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the communication between us and the actual programs themselves in order to get access to those data.

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So moving on to the data processor. So this is where we standardize everything. And so we wanted

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to make sure that everything is standardized. All of the species are in the same species codes.

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We use ITIS species codes as well as Latin tags. We also include things like expansion to per acre,

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excuse me, per hectare units. So each and every program might have different plot sizes. We've

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gone through the trouble of actually in our standardized data creating those expansions to

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trees per hectare and basal area per hectare. So that's quite useful for our standardized data.

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The other aspect is we do provide QAQC reporting. So we flag things such as missing data,

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you know, 1000 foot tall trees, etc, which is useful for programs. And then to get to today

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where we're going to be talking about the data downloader and visualization tools. So that's

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our extractor. It's how you use our web filtering and downloading to download the standardized data

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that's publicly available. The other aspect to date is we're going to go over if we have time,

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the data visualization tool. So to give you kind of a broader aspect of what were the design

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principles for this network, we wanted to make sure that we didn't require all of the programs

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who were involved in this network to change how they're monitoring their forests. We didn't want

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them to change everything from metric to imperial and imperial to metric or to change their plot

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size. We wanted to make sure that it was easy for all these programs to continually

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in an automated fashion be able to update their data within the system. We also tried to work

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with them to make as much of the data accessible as possible, although things like plot coordinates

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for many of the different programs are not publicly available. And when we go through the data

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downloader, I can show you how to see that. And then in the final aspect of this is we want to make

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sure any of the standardized data is truly representative of both what the programs wanted to

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standardize, but then also everything is truly standardized and really ready for analysis.

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And we'll go through exactly how this is actually

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happening within NEFIN. So again, we have many different programs that all record things differently.

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In the top left hand corner, you can see there's forest inventory as data as delivered.

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It's fictitious data, but they record things like species, diameter, tree health.

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This is for program A. And then we have program B, right? So program B also records diameter,

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but they record things, everything's slightly differently. So you can see that the tree identification

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codes are different. The species codes they use are different. One reports in Imperial, one

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reports in metric, and there's also ancillary data that's recorded differently, all of which

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we have been trying to be able to standardize to a standard set of codes. So in this, we go through

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the processing where we process both and we create these standardized data sets where we're able to

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standardize to unique codes for trees, for plots, all of which you're able to filter to,

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which is useful for analysis, and then as well as standardized species, and then everything

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standardized to metric. We also provide that QAQC. A lot of the stuff is recorded only to the program

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in order to see if they want to incorporate those data. So if there's missing data or erroneous data,

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we provide QAQC, which is useful for programs. We take all these standardized data and processing

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codes and QAQC reports, and then we really version everything, which is also extremely useful for

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programs themselves. I could go into that, but today we're really working on the data

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downloader and how to interact with the database itself, but we will have a later discussion to go

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over exactly how useful this is for the programs themselves, a little more nuanced,

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but things also are versioned, and then we eventually merge these standardized datasets into the

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NEFIN and standardized database, and then we make that database available for web data portal.

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I'll give you a basic overview again, higher level. Data managers prep the data, 

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they upload the data,

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they may have changed metadata, but our system is able to automate it, process, and standardize,

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and that's what we're going to go through today, which is how us as users can download and interact

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with that data.  

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To give you an overview of what our database actually, the spatial coverage,

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this is a map of the Northeastern Forest Inventory Network as it stands today, not all of which are

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public. All of these plots are fuzzed, just to let everyone know, but as you can see, currently,

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there are 10 that we have standardized within NEFIN. The distribution of plots across the

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Northeast is something we should talk about. NEFIN in and of itself is useful data to have,

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but you should understand any analysis conducted should understand that a lot of this data is not

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evenly distributed across space, and you should know where those plots are and what that might

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be representative of with any analysis that you do. This is why I would not say this is in

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competition with FIA, FIA, the Forest Inventory and Analysis program is wonderful, really amazing

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for population level estimates, but I would say this coupled with FIA are able to enhance the

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spatial resolution may fill in temporal gaps within FIA to really get at maybe more a holistic

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approach or at least a better amount of data to answer questions we have for many different questions

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you might have in the Northeast. So I would say this coupled with FIA or in and of itself is useful

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for analysis.

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At this point, do we have any questions?

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Nothing's coming through the Q&A box, but if anyone wants to speak up, you should be able to

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unmute yourselves.

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Awesome. Then I think I will continue. We'll go to the fun part which is actually 

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downloading the data.

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So if everyone could please go to the link that's going to be popped into the chat

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or go to the URL link that is displayed currently on your screen, that would be great.

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I'll give that a quick minute.

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The link should be in the chat now.

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Awesome. Thank you. So once you navigate to the URL link that's posted in the chat where you can

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manually enter it in, again it's www.uvm.edu/femc/nefin, that's

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N-E-F-I-N, and you'll navigate to our landing page here.

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Awesome. So this is our homepage currently. How you are going to be orientating yourselves within

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our web page here. The tool itself is really operated through this ribbon here. Right now we

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are in the home page. This is our landing page for the website. And right here it gives you a

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couple interesting things like what is the Northeastern Forest Inventory Network

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some helpful links, and really what's in the database currently.

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We have eight different programs that have semi or public data, the data ranges of the

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data, the states of our publicly available data. So that includes data from Massachusetts,

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Maine, New York, Vermont, the total number of plots, trees, species, just a basic overview of what is

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in NEFIN. But today what we're really going to be going about is the Get Data tool. So if you

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scroll back up to the top, the About NEFIN will take you to another web page that will show you

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some history about NEFIN. The get data tool is what we're going to be covering today.

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The Inventory Programs, this is where you can get some useful information such as contact

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information from the inventory programs, it goes over all the inventory programs we have in the

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database, and our Data Visualization tool which we might cover later. So let's go back to the

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Get Data tool. We're going to click and follow that link. It's going to take us to our data

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downloading tool. The first thing that's going to happen is this pop-up is going to occur for you.

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So when this pop-up occurs, it's really useful. So this gives you some basic overview of how to

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actually use the tool itself. So how do you where the filters, how do you clear the filtering,

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what is the search parameters, a bunch of useful information, all of which we're about to cover.

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So we don't really spend too much time here. But what you should know is that if you don't

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want to ever see this pop-up again, you can click don't show this next time and the cookies will

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remember that you don't want to see this pop-up and then you can close. But I like to actually see

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this pop-up. And so I'm just going to unclick that box and hit exit. So every time I come back to

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this, the extractor, it'll pop up and kind of give me some reminders. It's useful to have.

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So this is our data downloader tool. And so you can see again, this is the primary ribbon that

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navigates through. And right now we're the Get Data tool. And right here on the top left hand

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corner, these are our filters. This is how we're going to be actually interacting with the data

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itself. This is how you're going to be filtering to the data that you want to see.

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And then the output of whatever filter you search will be output down here. So this in this bottom

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left hand corner, you can see right here where that output is going to be. So let's actually

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interact with those filters. So again, right here, just to orientate ourselves, we're in the search

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results tab and we're currently looking at programs. 

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And right here, this is the output of our filtering

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the data, the title of the program itself, the code, etc. So let's actually interact with this.

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So today, let's work with the database, which I know in and out, which is the 

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FEMC forest ecosystem FHM plots

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So we're going to click that. So again, to do that, if you want to select all,

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you can select all or if you only want to select a specific program, you can deselect all and then

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click on the forest ecosystem FHM plots. So as you can see here, these are filtering by program.

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We also provide filters for state if you're interested in all the programs in the state or

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some such you can do that. But today, let's only focus on Vermont's plots.

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So you can again, do that again, you can deselect all and select one more amount multiple,

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but today we're just going to be looking at Vermont. And then we can look at the time range.

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So we can select a subset of time that we like to look at either a single year or multiple years.

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And for this, let's go from 2000 to the last data updated, which is 2021.

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And again, the filters we're going to be using for program is FEMC Vermont and this time range

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from 2000 to 2021. And then to execute this query, what you do is you hit search.

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And then again, this is going to repopulate and the output of your query is going to be

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you output in this window right here. And again, right now we're looking at the forest ecosystem

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monitoring FHM plots, the Forest Health Monitoring plots, the code that we use, the state, 

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organization that has sway of that data currently, and the date range of that program.

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Something else that is also useful is this search parameter here. So I'll highlight right here.

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So if you go here, this is telling you what is actually being executed, the filter that is being 

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executed currently and is being displayed. As you can see, we've selected FEMC and it's selected

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FEMC's FHM plots here

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it's Vermont and the time range. Currently, we're at the programs.

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Now let's move to the plots. So we give you the other type of data, we click on it. 

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And something I would notice

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is as you go through these, there's going to be different filtering, but there isn't different

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filtering for programs to plots. So as you go over here, it's still retained our information.

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There's FEMC Forest Health Monitoring plots, again we're looking at only Vermont,

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Only Vermont selected, and then the date range that we had before. And this is what's

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going to pop up up for you. And so it gives you the plot ID. So this is NEFIN's plot ID 

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for this program.

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The latitude and longitude. And so for these plots that we've put into

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NEFIN are publicly available. This is the true lat and longitude for these Forest Health Monitoring

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plots. If you are fuzzing, a way to know whether or not it's fuzzed can be also seen here.

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So what we do is we do randomized latitude and longitude fuzzing, and then also rounding. So we do two

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digit latitude rounding after fuzzing. So then if you only saw four digits or two decimal places

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after, then that's a good way of kind of indicating that it has been fuzzed and is not the true plot

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coordinates. And I can cover that again. Something that's also useful here is the results per page.

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So if you want to see more results, you can select how many results you'd like to see. 

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I would give you a word of caution that if you go to, you know, 1,500 things might be a little slow.

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So you can expand this maybe to 25 and it'll give you 25 results from your query. 

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This query right here.

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Again, you can see what that query is right here in the search parameters.

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Awesome. So let's get to the more fun data, which is looking at trees.

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Go to trees. And something I'd like to highlight here is you can see the filters that are available

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have changed. There are more filters that you can use to filter through trees.

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So as you can see, we still are only looking at FEMC's data. We still are only looking at Vermont

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We now have things like species. So if you want to search multiple species or just a single

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species, so if I wanted to search, maybe we'll go for sugar maple. So we don't need poison sumac,

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but we want sugar maple. You just want to make sure it all is deselected and you can hit sugar

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maple. So we're only getting looking at sugar maple. And you can come down here. 

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You can see there is more information down here,

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more filters available. We have tree status. So in the standardized data

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set, we take all of the many different codes that are used for identifying status and

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we're able to go to a binary primarily, which is whether or not the tree is living or dead.

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So we have living or dead and then missing an unknown is the missing is if there was no status

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recorded, which occurred rarely for many of the programs, and unknown status, which means it was

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likely an erroneous input for status. These very rarely occur, but

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it would become NAs. So we'll just remove those. Again, so for tree status, we're only going to be

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looking at living and dead. And then diameter, you can look at a subset of diameter ranges

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you are interested in, but I'm not going to. We can get every tree that we want. We can look at

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subset of tree heights if we want to. And then we also have tree crown class. So this crown class in

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the filter, let's you know that there is a crown class identifier. So that program did

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look at crown class of some type or has no crown class data. So to know how we did this for our

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crown class, we used again a binary. So the we use either overstory or understory or no value.

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So overstory or understory, so overstory would be codominate, dominant, and emergent

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and understory would be suppressed and intermediate. Not all programs were at that fine

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grain with canopy data. So we had to go to what would be considered the common denominator,

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which is a binary of overstory and understory. We did this also - somewhat of a more complicated

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metric would be the forest health indicator. So here is where you were able to see the presence

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or absence of a forest health indicator. So some programs were extremely detailed. 

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They measured this specific

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agent that may be ailing the individual tree. Other programs simply measured that there was

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some type of impact on the tree health. And so we had to go to the common denominator,

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which is kind of binary, which is either present or absent. Now each of the individual programs

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may report many different - so FHM plots, I know that we measure many different forest health

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metrics, things like transparency, vigor, etc. And we individually, the program sets the threshold

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for what it's considered to be present or absence of what would be considered a forest health flag.

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So for us, we measure things like vigor and transparency. And so we set a threshold,

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all of which are available within the program data. Like what are the thresholds to

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be considered a flagged presence or absence of a forest health indicator. So for this,

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we're not actually going to look at that at this data. If we want to talk more about that, we can.

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But let's actually execute this query. And again, to go through it, we are looking at the trees.

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We're looking at FEMC. We're only looking at sugar maple. We're looking at Vermont the same years.

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We're looking at only living and dead. And we didn't modify diameter trees, tree height,

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tree crown class or the forest health indicator. We're going to execute that search. Awesome.

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And so it ran. And so this was the output. Again, it did filter to the sugar maple.

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The states, the year, it was live, the diameter, the height, all the metric, the overstory and

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absence. So for those metrics that are standardized, like per hectare, so like trees per hectare

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and basal area per hectare, those are going to be in when we download the data, you have to download

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the data to be able to visualize that. Something that is also useful here. Again, we'll go back

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up to the top again, you see the search parameters. We now are looking at the sugar maple only.

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And those tree statuses. But something that's useful here is if the data is fully publicly

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available, you are able to click here and see the raw data. So this is actually the data as

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delivered stored in a dictionary format or a JSON format if anyone's interested. But it's useful

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for us and also everyone else to be able to, QAQC - did the DBH actually in fact line up with our

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standardization. So 34.2 centimeters, it did! But it also is how you can get at that ancillary data

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so whether or not we measured dieback, whether or not the distance of that species from the plot

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center, all interesting information that we didn't really want to throw away. We wanted to make

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somewhat available and for download if that program allows for it to be public. I know that this data

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is allowed to be public many of the other programs that you are not in which case you would have to

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request the raw data. 

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Awesome. So we'll move on to saplings. And again,

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it reset some of the filters. So if you go back down here, we'll only look at sugar maple again.

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And then down here it gives us sapling status. If anyone is interested in in-growth, we were

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able to capture for many of the different programs if it grew into a tree. So for saplings, we have

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Living, Dead, Missing, Unknown status. We'll remove the missing and unknown status and we can look at

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the Grew into tree. So this really gets rid of the NAs but if you want to see those NAs, you have

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to keep these flagged, or these checked. 

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Awesome. So I can keep that query and again you're able to see those outputs.

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So we'll go - saplings is much like trees but we'll go to seedlings since there is

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something I want to talk about here as I'm sure some people are interested in how exactly we were

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able to standardize all of the different ways people record seedlings in specific regeneration.

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So you go to the seedlings again looking at search results for seedlings.

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We are still looking at FEMC's FHM data. We are going to only be looking at sugar maple,

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looking at Vermont. But if you come down to here, you can see seedling counts. It gives you all of

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the different counts. The counts were able to be standardized as that was something fairly,

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it was universal across the programs. But then the seedling size classes. So it's here that we

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chose to not modify how programs chose to categorize and tally regeneration. In order to filter this,

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for instance, click on this right up here and these icons, these eyes, will be able to

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give you pop-ups, they give you understanding of what things are. And so right here the seedling size

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classes, it gives our standardized NEFIN code. And then how that program actually

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classified that code.

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So for regeneration, for MACFI, they had both diameter and also height for classification.

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So for instance, if I was interested in in-growth or seedlings about to become

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trees, we can look at category 4. And then maybe for the Northeastern Temperate Inventory Network

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we can look at something that is only height. So we might look at 150 centimeters in height

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and larger, so 13. So this requires you to really understand how each individual program

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chose to categorize regeneration in order to make informed decisions about your analysis.

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All of these codes, again, are available for download and can be easy to pop-up and see.

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So at this point, do we have any questions?

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There was one in the chat you might see it about if NEFIN has been used with FIA insect and disease

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database or other datasets. 

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For any level of analysis, currently, no, not that I know of

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that has produced any kind of like published material. I do know that the output of the standardized

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data is really easy to integrate with FIA's data. So if you were interested in running FES or

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look at FIA's raw data is pretty compatible as far as what you're going to actually get. We'll go

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through what the data actually looks like when we download it. But I would say no, not that I know

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of currently for any published material. 

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So why don't we go to, we'll go back to trees.

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Something that's really important here is the data download. So once you execute that search,

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and again, you can see the search parameters for that search, so it did search poison sumac also.

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So let's get rid of poison sumac and we'll go back and search again.

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And we'll go back up. We're looking at trees. We're looking at search results for trees.

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And you can go to data download. 

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Awesome. So once you go to data download, it gives you option to

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download the data that you have queried using our filters. Again, right here, this is

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what's being searched and being filtered to. And you have two primary options. You can download

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all packages. So this includes if it's available, the original data, the raw data, this and our

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standardized data as well as related files such as if there are any files that were uploaded with it

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So like methodology information and also understand the download different versions of the data.

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And so if you go down to this box right here, you can pick which files you'd like to download,

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whether or not you want to download specific versions or the processor data or the Python

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functions used for standardization, all of which can be downloaded if it's available publicly.

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And then the other option here is to just download the CSV of our standardized data, which is what

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we're going to do right now. So again, we've executed this query and then we are going to

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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

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get a pop up or it doesn't go to your downloads folder, what I would do is make sure that your

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browser allows pop-ups for NEFIN and then try rerunning it again. But it does allow for us.

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So we're going to download the results and it's downloaded to our downloads folder.

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And before we open that up, I do want to let you know that where you can find other useful

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information. So this tips, this brings up that pop up that shows you how to navigate the filters

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and interact with website, but then our NEFIN standardized field codes. So we've provided all

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of the standardization documentation for all of the downloadable information. So for instance,

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if you were to download the plot table, which we have currently, it'll give you the description

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and definition for all of the fields you'll find in those downloaded CSVs for our trees, tree table,

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whether or not it's a tree species number, the taxonomic serial number, diameter, and everything

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else. So it'll give you an understanding of what it is you're actually going to be looking

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at within the tables, which is useful. But then also, we do that for saplings and seedlings as

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well, you're also going to be able to see this for what are NEFIN standardized codes lists for

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status, for instance; living, dead, missing, unknown. The codes are one, two, three, and four,

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our crown codes for overstory, understory, undetermined

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seedling classes. Again, if you're going to be working

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with regeneration here, you're really going to want to understand specifically what that program used

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for regeneration as far as classes go, and then saplings as well. 

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This is useful to know when actually working with the data. 

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So again, let's actually go and you can open up the data we downloaded.

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And as you can see, it's a zip file of the plots, programs, and we executed that tree filter,

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Let's open it up and see what it looks like.

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So this is the execution of the downloaded filter we just did. Again, we made sure that we

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were looking at sugar maple, we were looking at FEMC, so you can see - and then the sample

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years that we filtered to. Again, diameter and height are all in the metric. We have the crown

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class, all those codes that can be seen, whether or not there was a forest health metric identified.

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And then something that's useful is because it's public data, we store the raw data in a

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JSON formatted dock field. So it's dictionary-like which gives you access to what the raw data

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actually looks like. Super useful. And then also, this is where we have those

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per hectare expansion factors for all of the different plot types. So it's a true standardized,

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so each program's expansion was done, so you don't have to do that yourself and know what those are.

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Those information are available, and I can show you where you can find them.

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But one way to do that for here is you have the basal area per hectare representation of that

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00:37:55,000 --> 00:38:01,000
individual tree, and you also have that tree's representation of the count per hectare. One way

373
00:38:01,000 --> 00:38:06,000
to get at a fixed area plot would be to pick one and divide it by that number, and that would give

374
00:38:06,000 --> 00:38:11,000
you the expansion factor. But if you want to find that information yourself, I can show you how to

375
00:38:11,000 --> 00:38:15,000
do that in a little bit. But this is where you would be able to use that information.

376
00:38:15,000 --> 00:38:20,000
This is quite useful as you can kind of build this straight into FES if you want to 

376
00:38:20,000 --> 00:38:22,000
run a vegetation simulation.

377
00:38:24,000 --> 00:38:30,000
The states and then also other useful things like the unique tree code for that instance of being

378
00:38:30,000 --> 00:38:36,000
recorded on that plot in that year, and then as well as the individual tree. So right here,

379
00:38:36,000 --> 00:38:42,000
you can see it's FEMC, it's the first plot, first subplot, and this is the first tree. And so

380
00:38:42,000 --> 00:38:47,000
if you wanted to look at the history of a tree or track a tree through time, you can see that

381
00:38:47,000 --> 00:38:51,000
these are all the same trees and those are the diameters through time. And so

382
00:38:51,000 --> 00:38:55,000
it's pretty set up to do a lot of different analyses quite quickly. 

382
00:38:55,000 --> 00:38:57,000
So let's exit out of this and I can

383
00:38:57,000 --> 00:39:06,000
show you how to see what those expansion factors really are. You can get that in

384
00:39:06,000 --> 00:39:09,000
some of the information that has to do with this.

384
00:39:09,000 --> 00:39:14,000
So we'll go back to search results and we'll go to programs.

385
00:39:14,000 --> 00:39:20,000
And then we can go here. And again, so a lot of useful information can be seen here.

386
00:39:20,000 --> 00:39:26,000
There're other ways to get to it, but this is one way. You can go to the details. You can see all

387
00:39:26,000 --> 00:39:31,000
the different information about overview of the program, the study range, the contact information,

388
00:39:31,000 --> 00:39:38,000
what data is actually being included, the program definitions. So this gives you an understanding

389
00:39:38,000 --> 00:39:43,000
what type - that it's a fixed plot. It's a circle. What's the projection coordinate system

390
00:39:43,000 --> 00:39:53,000
used for a lot of the GPS information, the diameters, and then also the expansion factors for per

391
00:39:53,000 --> 00:39:59,000
hectare units for trees, seedlings, and sapling plots. It gives you all the information

392
00:39:59,000 --> 00:40:06,000
if you wanted to get that yourself as well. It also gives you access to the downloading the

393
00:40:06,000 --> 00:40:10,000
files. So if you wanted to download all of the version files and standardized files, this is

394
00:40:10,000 --> 00:40:16,000
another way to do it instead of just going through the filtering. But that's also quite useful.

395
00:40:16,000 --> 00:40:31,000
So at this point, do we have any more questions?

396
00:40:31,000 --> 00:40:32,000
And I'm going to take that as a no.

396
00:40:32,000 --> 00:40:37,000
Nothing has come in on the chat. I didn't know if

397
00:40:37,000 --> 00:40:49,000
anyone wanted to raise a hand or speak up right now.

398
00:40:51,000 --> 00:40:55,000
I think you can go ahead and move on. 

398
00:40:55,000 --> 00:40:57,000
Yeah, awesome. So it looks like we have, maybe we have 10

399
00:40:57,000 --> 00:41:02,000
minutes and five minutes for questions. So we'll go to a data visualization tool. So again, we've

400
00:41:02,000 --> 00:41:09,000
just kind of executed a query to get at sugar maple for a specific program that allows for

401
00:41:09,000 --> 00:41:17,000
download of those data. And we can go now to the data visualization tool. It may take a while for

402
00:41:17,000 --> 00:41:21,000
you to get these. It is building all of the table structures to be able to visualize. So let's just

403
00:41:21,000 --> 00:41:27,000
give it a moment. And as you can see right here, we have a data visualization app 

403
00:41:21,000 --> 00:41:28,000
for the database itself. 

404
00:41:28,000 --> 00:41:33,000
So this right here is just a basic overview of the all the fuzz plot locations for individual

405
00:41:33,000 --> 00:41:39,000
program. None of these plots are actually the true plot coordinates. As you can see, if you zoom

406
00:41:39,000 --> 00:41:45,000
in and everything's been rounded to decimals and fuzz randomly in space. So there's really no way

407
00:41:45,000 --> 00:41:50,000
to be able to actually get to the original data from this visualization. But it does give

408
00:41:50,000 --> 00:41:58,000
you useful information. For instance, we're looking at, I believe that's Mount Toby, or Cadwell's

409
00:41:58,000 --> 00:42:03,000
individual plot and the range in which that plot was actually measured. So it's quite useful. And

410
00:42:03,000 --> 00:42:08,000
it's useful also to kind of see where they that inventory program may be representative of the

411
00:42:08,000 --> 00:42:16,000
forest itself. It's good to keep in mind what data you're looking at in space, what that might

412
00:42:16,000 --> 00:42:22,000
represent for forest when looking at these visuals as well. So first off, we can interact with the

413
00:42:22,000 --> 00:42:27,000
tree data. So the primary navigation of this tool is going to be going to the plot map.

414
00:42:27,000 --> 00:42:32,000
We would just add tree data, the sapling data, the seedling data, and then the overall trends.

415
00:42:33,000 --> 00:42:37,000
So what we're going to go through right now, at least in this app, we're going to -

416
00:42:39,000 --> 00:42:45,000
it outputs a visual here. And then the primary kind of how you're going to interact with this

417
00:42:45,000 --> 00:42:48,000
visual is going to be through this control panel on here on the left. We're again,

418
00:42:48,000 --> 00:42:54,000
we're looking at the tree data and we're looking at all states, all programs, all live trees,

419
00:42:54,000 --> 00:42:59,000
and then no filtering of individual species. But for here, for instance, we wanted to look at

420
00:43:00,000 --> 00:43:05,000
balsam fir, you can filter only to balsam fir, although that's not going to tell you much,

421
00:43:05,000 --> 00:43:12,000
it may give you some interesting size class distribution information. But you can filter to

422
00:43:12,000 --> 00:43:19,000
multiple or some species. They can give you date range if you want to look at a subset of years

423
00:43:20,000 --> 00:43:27,000
or all years you can. Again, something that's really useful here is that, or rather, you should

424
00:43:27,000 --> 00:43:32,000
know is that it's filtered to the top 10 species within the query. So there may be multiple other

425
00:43:32,000 --> 00:43:38,000
species here, but that aren't represented visually. We also have a bunch of different

426
00:43:38,000 --> 00:43:43,000
type of plot visualizations if you're interested in box plots, which may give you better information

427
00:43:43,000 --> 00:43:48,000
about the median and the distribution of a species based on basal area per hectare. So again,

428
00:43:48,000 --> 00:43:52,000
the x-axis is basal area per hectare and you can see that we're here at the x-axis.

429
00:43:53,000 --> 00:43:59,000
You can look at the density plots as well, broken up again by the top 10 species.

430
00:44:00,000 --> 00:44:07,000
And then let's go to pie charts. So maybe not the best representation all the time, but it does

431
00:44:07,000 --> 00:44:14,000
give you a breakdown of the pie chart by species and then all other species are being

432
00:44:14,000 --> 00:44:20,000
lumped together here. So it's useful for, again, it's representing only the data that you are

433
00:44:20,000 --> 00:44:26,000
filtering to and that should always be considered. You can also do scatterplots if you all want to

434
00:44:26,000 --> 00:44:31,000
think back to our silvicultural classes. We can think back to a density management diagram or

435
00:44:31,000 --> 00:44:37,000
some type of size frequency relationship. So here it's tree's per hectare and basal area per hectare.

436
00:44:37,000 --> 00:44:42,000
Something that should be noted again is we're looking at individual species. So there's no real

437
00:44:42,000 --> 00:44:47,000
way to do any kind of like management from this based on the plots, but it does give some interesting

438
00:44:47,000 --> 00:44:52,000
kind of oversight into maybe silvics of individual species. So for instance, let's, I don't know,

439
00:44:52,000 --> 00:44:57,000
let's look at a high density species. Maybe like balsamea - maybe balsam fir

440
00:44:57,000 --> 00:45:06,000
versus like let's see, low density, high size. Again, so then here's interesting. So here's the

441
00:45:06,000 --> 00:45:11,000
dog hair thickets that a lot of people in Maine have to wade through again super high dense

442
00:45:12,000 --> 00:45:19,000
stands, high trees per hectare, fairly high size. And then you look at red oak here where

443
00:45:19,000 --> 00:45:25,000
very high size. There's a lot of carbon in there too even at a low density. So quite interesting to

444
00:45:25,000 --> 00:45:28,000
see that kind of represented within these data. 

444
00:45:31,000 --> 00:45:35,000
But let's go back again. So to interact with the x-axis would be here.

445
00:45:35,000 --> 00:45:37,000
You have a number of interesting metrics. You can use basal area,

446
00:45:37,000 --> 00:45:43,000
trees per hectare, quadratic mean diameter, quadratic mean diameter being representative of larger

447
00:45:43,000 --> 00:45:51,000
trees within a plot, year, and species. It is possible to break this or that you might get -

448
00:45:51,000 --> 00:45:56,000
So say for this it'll throw you a flag up here saying that you need to select a y-axis or there

449
00:45:56,000 --> 00:46:02,000
might be something that that won't work. But in general it works fairly well to be able to look

450
00:46:02,000 --> 00:46:08,000
at certain sets of data. So again, so let's just go this to none and you can group by species or

451
00:46:08,000 --> 00:46:13,000
year. So from this you're able to get like a rudimentary understanding of the data that you

452
00:46:13,000 --> 00:46:23,000
want to filter to. For instance we can select a program, let's look at Cadwell forest

453
00:46:23,000 --> 00:46:29,000
and you get a distribution for, let's see, the last ranges they had. You can get a lot of

454
00:46:29,000 --> 00:46:34,000
quite useful quick information of what's in that dataset itself or across many different datasets

455
00:46:34,000 --> 00:46:41,000
We can look at maybe all datasets in Vermont. And it's quite useful to, kind of, before even

456
00:46:41,000 --> 00:46:45,000
going in and querying and downloading the data using a data download tool come here and see what

457
00:46:45,000 --> 00:46:50,000
it might actually look like before you download it. But useful. Let's you can do that again for

458
00:46:50,000 --> 00:46:58,000
saplings and seedlings. But seedlings that should be noted are on logarithmic scale. It just makes

459
00:46:58,000 --> 00:47:05,000
it easier to see. So trees, saplings, seedlings. And we can look at trends here. So again you should

460
00:47:05,000 --> 00:47:10,000
preface this with any trends you observe here are representative only of the plots, they wouldn't be

461
00:47:10,000 --> 00:47:14,000
representative of the forest. So again you would really want if you're going to look at like the

462
00:47:14,000 --> 00:47:19,000
regional analysis I would couple this with FIA to get a better representation of the forests

463
00:47:20,000 --> 00:47:24,000
of the Northeast. But it does add a lot of really interesting things. So something here that's also 

463
00:47:24,000 --> 00:47:26,000
important to note

464
00:47:26,000 --> 00:47:31,000
based on the dataset itself that we've created for NIFIN is that we do have data sets that span

465
00:47:31,000 --> 00:47:37,000
all the way back to 1960s. But there are these temporal gaps here. And so whatever trends you

466
00:47:37,000 --> 00:47:41,000
see here might be a little less representative and they're only representative of those programs

467
00:47:41,000 --> 00:47:46,000
that actually sampled them on the landscape. But then as you can see through time many programs

468
00:47:46,000 --> 00:47:50,000
started to come back online - come more online. You can see some spikes of like every 10 years

469
00:47:50,000 --> 00:47:55,000
some programs resampling. And then some programs going to annual resampling which is quite nice.

470
00:47:55,000 --> 00:48:00,000
So maybe these trends are a little bit more representative of what the region that we're

471
00:48:00,000 --> 00:48:06,000
looking at. But it's interesting here that it does represent some other findings that we have found

472
00:48:06,000 --> 00:48:12,000
recently and currently is that forest in the Northeast are becoming denser or having a

473
00:48:12,000 --> 00:48:18,000
higher relative density which has implications for the regeneration dynamics, mortality dynamics.

474
00:48:18,000 --> 00:48:23,000
And I just want to just we can just see if this database actually can show that. So right here as you

475
00:48:23,000 --> 00:48:29,000
can see that being sent to be holding fairly steady as far as trees per hectare. So the amount the

476
00:48:29,000 --> 00:48:33,000
frequency of trees on the landscape or the abundance of trees on the landscape seem to be

477
00:48:33,000 --> 00:48:38,000
holding fairly steady at least for what these plots represent which are forested or primarily

478
00:48:38,000 --> 00:48:45,000
forested. And on land bases such as university property or public lands, etc.

479
00:48:47,000 --> 00:48:52,000
But let's - instead we'll go to size. So our hypothesis would be if you know that relative

480
00:48:52,000 --> 00:48:57,000
density is increasing that would assume that like basilar area or average size of trees should be

481
00:48:57,000 --> 00:49:02,000
increasing. So we'll look at quadratic mean diameter which might be representative for larger trees

482
00:49:02,000 --> 00:49:09,000
in the plot. And it does it shows that actually that on average the trend in size for species,

483
00:49:09,000 --> 00:49:16,000
for all species, or all within these plots are indeed actually increasing. So that's interesting

484
00:49:16,000 --> 00:49:24,000
to be able to see and a quick litmus test for these data. I can also do that with basal area,

485
00:49:24,000 --> 00:49:29,000
I'm sure it's probably analogous, and yep, also slightly increasing. So from here you're able to see 

486
00:49:29,000 --> 00:49:36,000
some basic overview of what what trends might be even go to individual species. Again any trends

487
00:49:36,000 --> 00:49:42,000
observed here really should be that in the background of your mind should really be that

488
00:49:42,000 --> 00:49:48,000
it's only representative of where those plots are on the landscape. So let's maybe let's go to

489
00:49:48,000 --> 00:49:57,000
eastern hemlock. And so you can see a slight basal area size increase over the last

490
00:49:57,000 --> 00:50:04,000
40, 50, 60 years. So all quite interesting, and in a lot of any kind of analysis that is being done

491
00:50:04,000 --> 00:50:09,000
this can give you a rudimentary visual of what data actually holds so that you can download that data

492
00:50:09,000 --> 00:50:16,000
and answer the questions you're interested in yourself. So that's the data visualization tool.

493
00:50:16,000 --> 00:50:34,000
At this point does anybody have any burning questions? And we can go back to maybe the Get Data page.

495
00:50:35,000 --> 00:50:37,000
Awesome. And why don't we go for questions?

496
00:50:37,000 --> 00:50:47,000
Thanks very much, Soren. And yeah, I welcome people to raise their hand, type in the chat or

497
00:50:47,000 --> 00:50:53,000
Q&A boxes. And happy to open the discussion here.

498
00:51:03,000 --> 00:51:08,000
Hey, I'm sorry this is maybe too basic of a question I cut in a little too late here. But I'm also

499
00:51:08,000 --> 00:51:14,000
just wondering like are there some forest plots that may not necessarily be in this database as

500
00:51:14,000 --> 00:51:20,000
looking around the map? And I noticed that there's some at Sleepers River Research Watershed that I

501
00:51:20,000 --> 00:51:28,000
know of that are not there as well as some here at UVM as well like that are like the Rubenstein

502
00:51:28,000 --> 00:51:33,000
School Forest and others. I was wondering like what types of data are here and and what

503
00:51:35,000 --> 00:51:38,000
is there room to add additional data to the database?

504
00:51:39,000 --> 00:51:45,000
Oh yeah, I can talk on that. So there were many different programs across the Northeast that we

505
00:51:45,000 --> 00:51:51,000
initially looked at and had like contact with and those kind of took priority but any major room

506
00:51:51,000 --> 00:51:56,000
for expansion for any continuous forest inventory data so long as they measure those, it's true,

507
00:51:56,000 --> 00:52:00,000
continuous forest inventory and they measure those basic measurements who would be able to

508
00:52:00,000 --> 00:52:07,000
integrate them into this network. Yes. The one thing that would preface is that because of the

509
00:52:07,000 --> 00:52:14,000
automation process it does take some time to get any of those programs involved so it's easy for

510
00:52:14,000 --> 00:52:19,000
them to continually update their data. So there's some legwork there but yes we would definitely open

511
00:52:19,000 --> 00:52:25,000
to expanding this network. 

511
00:52:25,000 --> 00:52:27,000
Okay, fantastic and would I just kind of email and contact you and just

512
00:52:29,000 --> 00:52:32,000
share information about the networks I know of and then see where that goes.

513
00:52:32,000 --> 00:52:36,000
That would be wonderful. Yes, if you could reach out to me or one of my colleagues 

513
00:52:36,000 --> 00:52:39,000
I'm sure we can help work through that.

514
00:52:39,000 --> 00:52:41,000
Awesome, well thank you so much. 

00:52:41,000 --> 00:52:44,500
Thanks Josh, yeah feel free to reach out with more details.

515
00:53:02,500 --> 00:53:06,000
I really appreciate you walking through the data visualization I think that's

516
00:53:06,000 --> 00:53:17,000
a really useful tool to get some ideas to kind of start thinking about where you want to dive in

517
00:53:17,000 --> 00:53:27,000
and really do further analysis. You've talked about some of the other ways that

518
00:53:27,000 --> 00:53:36,000
this might be used like maybe for looking at carbon storage or anything else. Any other

519
00:53:38,000 --> 00:53:44,000
ideas or suggestions for where people might be interested in going with this?

520
00:53:44,000 --> 00:53:46,000
Oh there's so many interesting - when I think about continuous forest inventories I think about how 

521
00:53:46,000 --> 00:53:52,000
the Northeast built all of its growth and yields like, you know, like what sustainable yield,

521
00:53:46,000 --> 00:53:52,000
what growth was going on? 

522
00:53:52,000 --> 00:53:58,000
What its health trends, mortality trends, regeneration dynamics. I think that a lot of this

523
00:53:58,000 --> 00:54:05,000
data can be used to look at both kind of easy questions like, you know, based on the plots

524
00:54:05,000 --> 00:54:09,000
and the landscape that we're seeing are there increased mortality in beech for instance.

525
00:54:11,000 --> 00:54:18,000
These data are really how you're able to build those analyses and so where I would start

526
00:54:18,000 --> 00:54:22,000
personally is, like, I'm interested in mortality so I would I would look at start looking at

527
00:54:22,000 --> 00:54:30,000
being able to see flags between - is there a flag between forest health metric and in some future

528
00:54:30,000 --> 00:54:33,000
mortality or mortality trends of individual species. There's so many different things you can do

529
00:54:34,000 --> 00:54:39,000
with this data but all of which can kind of be started in the data visualization tool

530
00:54:39,000 --> 00:54:50,000
really do require you to download the data and get into the analysis yourself. It's a good launch.

531
00:54:50,000 --> 00:54:54,000
I don't know maybe updating biometric equations by adding more, you know, eastern hemlock.

532
00:54:54,000 --> 00:54:55,000
There are so many different things you can do.

533
00:55:03,000 --> 00:55:10,000
Yeah I think that, you know, for all of you who are attending we welcome you to think about ways

534
00:55:10,000 --> 00:55:19,000
that this data could be used, and reach out if you have questions or want to dive in deeper

535
00:55:19,000 --> 00:55:28,000
and we're happy to you know think about different ways that you might be looking at using

536
00:55:28,000 --> 00:55:34,000
this data and using the tool. 

536
00:55:34,000 --> 00:55:37,000
Definitely and just really thank you everybody for coming and

537
00:55:38,000 --> 00:55:41,000
I hope that you're able to use our new tool.

538
00:55:45,000 --> 00:55:50,000
Yeah, thank you all for attending and like I said at the beginning this was recorded and so

539
00:55:50,000 --> 00:55:56,000
we will be making it available for viewing at a later point so you know keep an eye out on our

540
00:55:56,000 --> 00:56:03,000
website for that link and if you are interested in SAF credits please let me know and I will be

541
00:56:03,000 --> 00:56:11,000
sure to submit. All right thanks everyone have a great afternoon thanks for joining us for lunch.

542
00:56:11,000 --> 00:56:41,000
-Thank you
-Thank you so much