National Forest Inventory (NFI) in the Czech Republic presented in Graphs and Maps
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00:00
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Transcript: English(auto-generated)
00:08
Thank you. Good morning, everybody. First of all, I would like to introduce myself. My name is Wendula Halova and I am working at Forest Management Institute in the Czech Republic.
00:21
And today I would like to present to you our National Forest Inventory and the results that are presented in the form of maps and graphs. So here is a small introduction to my presentation. First of all, I would like to tell you something about National Forest Inventory in the Czech Republic.
00:42
After all, I'm going to talk about map making tool and graph making tool. And finally, I would like to show you some results of the second cycle of National Forest Inventory. National Forest Inventory in the Czech Republic. It's an independent survey on the state and development of forests and it's independent because it's not
01:06
influenced by the owners of land that is forested and we have three major aims. And the first one is to provide required information about forest for the need of the state administration. And
01:22
the second one is to provide information from the evaluation of forestry activities and the last one is to provide information about health condition of the forest. Our National Forest Inventory is divided into three cycles. The first one was carried out
01:40
from 2001 to 2004. After all, it was a quite long pause and after that we had the second National inventory cycle and now we are at the end of the third National Forest Inventory cycle that is carried out from
02:03
2016 to 2020. So we evaluated the results from the second National inventory cycle now and we use modern technology for data collection, for data storage and data visualization.
02:22
We use photogrammetric interpretation to detect forested areas and at that forested areas, the field survey was carried out. For the evaluation, we used other off-shore data in the form of GIS layers such as digital elevation models or
02:44
auto-photos ATC. So NFI results evaluation is based on the survey sampling methods. We used one-faced estimations and this approach was
03:00
really good automized. So, let's say something about mapmaking tool. We used PostgreSQL to store our data in the form of views and we used QGIS to visualize the data in the form of maps. So here are some important tables for mapmaking and for preparation
03:26
data for visualization. The most important tables are C-cartogram label table. You can see it at the bottom of the presentation and we store a form of labels that are displayed
03:42
directly in the map. And the most important table is the cartogram configuration table. You can see it. You can see it and I would like to say that we store there two important or all the columns are important, but
04:02
the column IDRC1 and IDRC2 are very important because of the source data for our map are stored there. Then we use identification of NFI cycle or NFI grid or
04:23
geographical domain and after that you can see there at the end of the table view name and description. Here is a set of functions that help us to make the concrete view for
04:41
for the desired cartogram. And the most important function is the red one and and this function creates creates a new view from data that we store in the configuration table. And the result of this function are two views, one for World Czech Republic and
05:05
and one for the desired region or regions or geographical domain. Here is the result view map. Sorry, here is the result view and this view is showing growth in the Czech Republic and it's divided into 14
05:24
major regions in the Czech Republic. And you can see the red labeled columns that shows the data from the source views and we have the point column that shows the point estimation of the growth in the area and
05:46
we can see as well a confidence interval for this point and we have there we have there four red depicted columns. It's because one
06:03
two of them are in the whole numbers and two of them are in ratio so that we can display it in a map and in the form of cartogram. Here you can see the result that shows the mean and new growth in the Czech regions between the first and the second national
06:25
inventory cycle. So we automasked quite everything that was possible in QGIS and we have this map. You can see that the growth, the highest growth in the Czech Republic is in the eastern and in the
06:44
southern part of it. So let's move to the graph making tool. We developed it in or we use it in a virtual environment and graphs are made in PostgreSQL and we are using PLR extension.
07:02
Every graph have its own PLR functions and these functions are stored in our database. The result of the process are two images, one in the PNG format and one in SVG format. Here you can see the tables for graph making. The first one is
07:23
C function for plot. It's a dial and you can see there the list of function names and description and the most important table is also a configuration table and you can see at the bottom of
07:40
this slide and there are stored data about source views, about about geographical domain, the restored name of the resulting plot and we store the resulting plots in the database right now in the form of PNG or SVG. It can be both or
08:07
one of them and then we download it, download them from this database. So here is a set of functions that help us to create the images. The most important function is fngetplot function that creates
08:26
one or two images from the information included in the configuration table and as I said we use PLR function for every single graph. Here you can see
08:42
the PLR function for the graph in the Czech Republic. It's for the world Czech Republic and you can see that first of all we select data and we select data in a form of rows.
09:01
We are not using ODBC connections to the database, but we decided to select data in the rows from the view and after that we declare image size, image resolution and margins and the rest of the code is about graph properties. There are defined x and epsilon axis labels, colors and
09:27
so on. And here is the resulting graph and this graph shows growth as you can see on epsilon axis and the x-axis shows age categories and
09:44
the growth is divided into these age categories so that you can see and this graph is for the world Czech Republic. And you can see that the highest amount of growth is in the age category 21 to 30 years and after that it's slightly decreasing.
10:03
So our NFI results are divided into chapters according to the major team and these major teams include for example forest age structure, growth, mortality, dead trees, forest regeneration and
10:23
damages caused by game NTC and we are aimed not only for forest forest factors, but we study the environment and geographical environment and
10:41
we included chapters such as pedology, water streams, erosion in the forested areas. So and I would like to show you results from the chapter forest area and water streams right now. Here this graph shows the amount of forest area in the Czech Republic according to FAO
11:04
declaration so that you can see that forest covers thirty six point eight percent of the Czech of the area of the Czech Republic and not forested areas covers sixty one point three
11:23
percent of the land. Here you can see some maps and this map shows forest area in regions and in and this forested areas is in the elevation below 400 meters about sea level and
11:43
this maps I gave here these maps to to show you the name of the major regions and you can see that the highest amount of forested areas below 400 meters is in the capital city of Prague and in the in the southern Moravian.
12:04
And here is the the same graph, but with labels that we are using and the first row shows the whole amount of of forest area in in these regions below 400 meters and the second row number shows
12:24
the percentage and the percentage is showed in this map. Here is the same example, but you here you can see forested areas in the elevation from 400 to 700 meters in the Czech Republic so that
12:44
the highest amount of forested in in this elevation is situated in the center of the Czech Republic where the Czech Moravian highland is situated. And here is the last
13:00
last map showing this problem and here are showed forested area in the elevation above 700 meters about sea level. So that you can see that the highest amount of forests in this elevation is situated in the South Bohemia where Chumava Mountains are situated.
13:26
So and here is the same information in a graph. As you can see, epsilon epsilon axis shows the names of regions and x-axis shows the representation of elevation
13:42
categories on the forested land. So you can decide which represent visual representation you can use or brings more information for your project and you can use it. And here's something about water streams in the Czech regions in the in Czech Republic.
14:04
Here this map shows water streams that are natural and that are situated in the forested areas. You can see that the highest amount of natural water streams is situated in the eastern part of the republic and
14:22
and it's because there are a lot of forest and in the other regions artificial artificial water streams are is more artificial water streams. So and here is, it's not working.
14:45
So so here you can see a graph and this graph shows the division of water streams to nature and artificial origins and it's also divided into the wideness of a channel. You can see that
15:04
artificial artificially created water streams are not so wide as the natural ones and the wideness that dominate is under one meter only. So and that would be everything for me and
15:26
I would like to thank you for your attentions and if you have any questions, do not hesitate to ask me.
16:10
I think that I'm not able to answer correctly this question because it's not my field but I think that we are not so open to
16:22
provide data and I think that it's a pity but in the Czech Republic it's a problem to provide data. I think only for students it's for free and then it's not so easy to get them. I think I agree with you that
16:51
it's really funny that government spend a lot of money pulling out data and then if you're lucky at least in Italy you get maybe a PDF
17:02
which is then you have to take all the tables out of the PDF and I go crazy because I say if you make the tables from you probably have Excel why don't at least you give us some type of spreadsheet and that's how it's very hard to get through but I think slowly we'll be getting there. But my question was since we have a bit of time is how big are your tables because you do a lot of views
17:23
and maybe joints and did you find that we might slow up the system or are your data not so numerous so you can just because there's materialized views now in Postgres maybe you might want to use
17:43
So big, but we have a lot of them. These tables store only the data and I don't know how to say it, but the storage is not a problem. The problem is the computation of the data.
18:04
It takes a lot of time because a lot of factors go through the computation and and it's quite a problem right now but the storage of the data is not the tables are quite small because of there are only numbers for I think
18:26
I don't know we don't we do not count everything for the smallest regions like catastrophic areas and I think it's not a problem right now maybe it will be a problem in the future
18:46
So any other questions for the speaker? No? Okay well then we just wait for the next one at 9.30 and we thank the speaker again thank you
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