Identifying new conservation areas: a web multi-criteria approach using Earth Observation and other spatial information
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| Number of Parts | 351 | |
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| License | CC Attribution 3.0 Unported: You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor. | |
| Identifiers | 10.5446/69159 (DOI) | |
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| Production Year | 2022 |
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00:00
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DatabaseGroup actionVariable (mathematics)Function (mathematics)Subject indexingServer (computing)Vector spaceStatisticsFunktionalanalysisSlide ruleAreaStatisticsVariable (mathematics)Demo (music)
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Mathematical singularityVariable (mathematics)AreaPlotterInformationArc (geometry)Limit (category theory)Subject indexingCASE <Informatik>Selectivity (electronic)Term (mathematics)CountingLevel (video gaming)WeightDemo (music)Computer animation
Transcript: English(auto-generated)
00:00
Thank you very much Good afternoon everybody. I hope you had a good break and I hope I won't stop your digesting process with my presentation So briefly, I am Luca Battistella. I'm from the European Commission Joint Research Center. We are based in Ispra
00:22
Lake Maggiore, north of Italy I work as a geospatial developer within a project called Biopama Biopama stands for biodiversity and protected areas management In this context I have developed a tool for addressing
00:44
biodiversity and ecosystem services hotspots globally Using different products and combining them into a multi criteria weighted Analysis toolkit, so
01:02
We normally address protected areas issues so protected areas pressures anthropogenic pressures or Decrease or increase of water availability both seasonal permanent water or Forest loss gain and so on so forth
01:22
With this tool we are going to address Issues that are outside protected areas and to actually find hotspots in terms of biodiversity and ecosystem services So actually many studies Now are highlighting the need to increase
01:44
protected area network and today the main mechanism for Conserve our land is represented by protected areas the global deal for nature
02:00
Has set 30% of the global lands to be formally protected And an additional 20% to be set as a climate stabilization areas, so the famous 30 by 30 Target so today
02:21
just about 50% of the global land are actually formally protected and was estimated that Half of the important sites in terms of biodiversity are actually protected
02:43
Therefore we have another house that can be protected How do we know where? biodiversity ecosystem services natural areas forests are In in the world
03:02
Available for protection, that's why We will be talking about the conservation analyst today. So when we started developing this This product this this application we had a look on the web and we noticed that
03:21
There's a There's plenty of tools addressing biodiversity and ecosystem services layers and information but most of them are there for Visualization purposes and you cannot really interact with information you find
03:42
So we wanted to Give the possibility to use a tool which relies on updated and reliable Information and and data sets to provide decision makers with means to
04:00
identify easily This sort of areas so out there. There are also other tools that deal with this sort of topics, but they are normally Specialized systematic conservation planning tools and they are quite difficult and
04:22
The learning curve is quite steep So we wanted to simplify some things but not to lose The information needed for addressing these sort of issues at least in a preliminary scale when we start the conservation planning process
04:44
So the methodology we adopted is split into three main stages so There's a data collection and data preparation that we will go through in a minute
05:02
Data Dissemination so Providing api's and providing all the sort of services we need To display information and interact with information on the web And then there's the front-end part in which we have developed this sort of application
05:24
That I'll show you later so the data sets Included so far is what we consider a Prediminary list of data set that nothing here is set on stone We can
05:42
increase the number of Data sets that we will call variables from now on Quite easily for now. We have 10 and we are addressing as I say the biodiversity component Through the amphibians birds and mammals
06:01
presence both overall presence and threatened endemic species presence natural areas intact forest Permanent seasonal presence water presence and above and below ground carbon So data preparation
06:23
is quite a long process that we try to automatize using Python and QGIS combined There's a first phase of normalization of the information We of the variables that we show in the platform in the application
06:45
so all the variables are normalized 0 to 1 in order to be able to compare them and to Merging information into one single indicator. So we took the raster we For each cell we subtract the minimum value
07:03
And divided by the maximum minus the minimum value for each pixel. So we get a normalized raster 0 to 1 We reproject using We actually project the information using web Mercator
07:23
Because we are going to show it the information and interact with that in in my box here We resemble the data sets at five kilometers Globally, so five kilometers at the equator is not going to be equal area fortunately
07:43
And then we rescale This all the data sets Using a baseline layer that we created and There you have to use a
08:01
Resempling method when you go through this process and for continuous rasters for instance for above and below ground carbon we used the average resampling method and for discrete rasters such as water presence or Natural areas we use the nearest neighbor method so then we
08:26
We converted the raster to two points why because we want to To perform statistics easily on the web so with raster was a bit more complicated and
08:42
Less fast at least with the technology. I'm used to work with And then we sample all the The point they did the pixels behind the point grid we have created and we store basically
09:02
this information on a postgres database So we import the shapefile with containing the points at five kilometers Resolution of distance between the points and And we import in in in the postgres database also
09:25
the CSV we keep this information separate because we want to Be able to add easily information in the table That is going to be joined then through automatic functions with the with the points
09:45
So aside from this Function, which is easy to understand. So we basically join information and we grab the country code as well to be able to compute country statistics on the fly we have also
10:03
Set up Some some some other functions that create statistics within protected areas in a country or Outside protected areas or the overall scores for for each variable. You will see in the demo
10:22
after after some slides We of course create spatial indexes To Make it faster. We actually store the information in materialized view views in in postgres We noticed that there's a big difference combined with geo server and I'll come to that in a minute
10:45
using materialized views and then as I say we harvest information From the postgres database into geo server and we provide services Through gel web cache as vector ties
11:01
we use vector ties because We think is the fastest way to interact with spatial information and create spatial analysis Directly on the browser at least we found is the fastest
11:20
Solution for us So as you can see That one in the red one is the response of the vector ties and is about ten million points that are loaded in once for some countries The query is very fast for some others like Canada Russia and so on
11:46
the query can take up to Five seconds six seconds. So it's not ideal in the web nowadays So the application development part. I already say you won't be super exhaustive is
12:03
kind of big Bunch of Functions, but I'll go through the four main components one is Visualization purposes one is for
12:21
Weighting and how the weightings works in the in the application And the other one is related to country analysis and to user based analysis So here you can see a sneak peek of
12:41
Madagascar so in the first image we consider Natural state of lands including natural areas from from Copernicus land cover so we basically take areas that are not affected by
13:04
anthropogenic Interventions so no cities. No agriculture and so on so forth and intact forests from WWF the second one is related to The carbon storage both
13:21
Below ground carbon and above ground carbon and the third one is related to Species presence in this case is birds amphibians and mammals So Okay, five minutes I'll go fast
13:40
Is important that you see the demo mainly So this is for computing the The gradient that is applied to the point features We always need to compute the minimum maximum and mean values for all the points that we query
14:01
this is the UI where you can Actually turn on and off or include or exclude variables give way to each variables So basically what in conservation planning we call conservation priorities and remove protected areas if you need
14:21
This is just what I said in a different format Then as I said, you can perform spatial analysis Drawing polygons with infinite arcs and nodes or simply a square if you need
14:41
That's based on path so we use the stack is postgres just server vector tiles Mapbox GL and tough for conducting spatial Analysis and statistics We use points within polygon for extracting the information
15:05
That that are Below the the polygon that the user drops The information Coming from that function will be feeding Rather plot where you can actually compare what's going on in terms of biodiversity and ecosystem services values
15:25
between protected areas in a country in a country overall and the area that actually you have selected Some caveats The tool As I say this design to for decision-makers at low
15:43
high level so not for local authorities This is for addressing Areas for understanding where areas deserve to be protected or not
16:00
The current resolution can be further improved, of course, we are talking about five meters of a solution Probably with Reducing the number of Services by providing one service for each country. We can go down to one kilometer easily or even
16:21
500 meters Next step there are many here. I pointed only three But let's have a look at the demo so I actually proved that the tool exists So you you click on a country you get information about all the variables
16:42
that I was talking about earlier and you get this information inside protected areas or within the country as a whole You have also the index Related to biodiversity at country level or within
17:01
Protected areas within the country in this case. You see protected areas are working or We suppose they are working because the the index is higher inside protected areas then outside protected areas, so you set your Your priorities you give weight you exclude areas that are already protected and you want to see where
17:24
We did set up we need to Throw our attention. So here you see already a not spot Based on the selection we have done So we want to compare now this this area with the rest of the country or with
17:44
protected areas within the country So we select the area as I say you don't have a limit in terms of arcs and nodes that you draw there and the information will be printed in the In the rather plot and in this one that shows just the weighted values
18:04
but this one is in the important one where you can actually Compare information see why that area is important compared to the rest of the country or to the rest Of the areas that are protected within the country. So that's it. Thank you