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Practical steps on how to make an observation and the different options to compute accounts


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titleTable of Contents

Table of Contents


Compile the accounts in the k.Explorer

To trigger the computation of an output in a specific area, for a specific period, the user should first select the context

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(follow the instructions in 2. Set the context of your analysis),and checks the box of the
account to be computed. There are condition metrics (raw variables, indicators or index) or the ecosystem service to compute their results.

When the knowledge bar turns yellow and the elephant's ball spins, the system is computing the information queried.

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There are several sections to organize the access to the different information made available in the application:
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The first 4 sections were specifically developed for the People EA project and will be the main focus of this guide.
When the 3 dots are displayed horizontally, it means that the options in the menu are hidden Image Modified

By clicking on the 3 dots they get disposed vertically Image Modified and a drop-down menu lists the options available.

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The main output of an observation can be a table
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Before computing any of these results, the guide provides the theoretical background followed to obtain these output in the previous section, so users are invited to carefully read that documentation to be able to select the options of calculations available in the app, as well as to interpret correctly their results.

Users can observe

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Forest Condition Indexes

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Forest Condition Variable (raw values)

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Forest Condition Indicators

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High-resolution Forest Condition Variable

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The selection of one of these options triggers the model(s) underlying the output(s) selected, in case more ecological metrics are selected, the computational flow is continuous. When the whole workflow to compute one metric is completed, the system moves to the next tasks in the queue of computation. The models to be computed follow the order of selection of the user.

Most of the metrics are modelled from different datasets, so the time of computation should be similar, but in case an account shares inputs used in a previous computation, those are not re-calculated, thus one should expect the additional output(s) to be computed faster.

Soil Retention Ecosystem Service

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different accounts, which are organized following the structure provided by the SEEA-EA manual.


As a demonstration of the interoperability between ARIES and OpenEO systems, the Soil erosion model can be computed in two different ways:

    • using the dynamic C-Factor from Open EO → select the OpenEO Cover Management and only after select the Sediment Regulation model:
      select the OpenEO Cover Management
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      Once obtained the Cover Management, call the Sediment Regulation model
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      Once obtained the Cover Management, call the Sediment Regulation model
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      The breakdown of the results focuses on the contribution of the service by type of forest
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    • using the static information about the C-Factor → just select the Sediment Regulation model
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      In this case, the static C-Factor is used in the workflow
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      And the Soil Retention results are comparable but have a coarser resolution
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      The breakdown of the results focuses on the contribution of the service by type of forest
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    • A third option, is to compute the results using the SEEA EA methodology, accessible in the ARIES for SEEA accounting tables section
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      The outputs of the ES are the same
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       but the breakdown of the results focuses on the contribution of each Ecosystem Type
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      Note
      titleObservations are live digital twins
      Any observation done in ARIES happens on the fly and is not a predefined combination (or workflow) of models and data. The system builds the most appropriate strategy to answer such questions using the information available in the system, at the moment in which a query is made. For this reason, as new information is integrated into ARIES, its results improve. 

      ARIES  looks for the "best" available combination of data and models to estimate Soil retention. By selecting the input (C-Factor from OpenEO) and later the model to be computed, we steer the system to build an observation of Soil Retention using the Cover Management dataset previously selected. 



      Tip
      titleWhy do you have to select the OpenEO dataset?
      "How come the OpenEO datasets are not picked automatically by ARIES, besides being more appropriate for this specific query?"
      A legitimate question given the highest temporal and spatial resolution of the data, would mean this dataset should have priority. On the other hand, since the data are not publicly available, they are not chosen to build the model. This is necessary to ensure that only Early Adopters have access to the data, but this may be the case for NSOs and other agencies sharing other sensitive data and information in ARIES. So

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    • this particular dataset is not

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    • available to the whole Integrated Modelling community, but as an Early Adopter, you have the right to observe the model built using the dynamic cover management factor from OpenEO (by observing it before querying the Soil Retention model).


      Tip
      title"Can I substitute or add new information to compute results based on different inputs??
      Yes, the system is built to allow maximum flexibility and local knowledge can be integrated into the system, to adjust or to simply compare results using a different methodology. This means that other datasets can be integrated, parameters can be fine-tuned, and partial or entirely new models can be added as part of the workflow.

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    • For more information on

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The table can be copied or downloaded (click on the symbols at the right bottom of the table to export it)

To explore the geospatial explicit information in the maps used to summarize the results in the table, go back to the View Data section:

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Expand the View Tree to visualize all inputs, intermediate and final output
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Select the map that you want to visualize
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When the knowledge bar turn yellow and the elephant's ball spins, the system is computing the information queried.
In this case, is loading the map

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This can be visualized in the explorer or downloaded as a raster file (.tiff format) for further analyisis in a GIS system.

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This was the main output, but any input and intermediate output of a workflow can be observed by ticking their boxes.

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The results for NDVI are shown below.

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Notice how observations that changes over time in the context selected, have the symbol of a clock next to them, and at the bottom of the menu, you'll see a timeline, in a light blue color.
In this example, as there are just two temporal observations, there is just one separator (small tick in yellow), dividing the 2 temporal observations (2015 and 2016). 

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Selecting a different temporal observation, the map will change and the system displays the result for that year

2015

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2016

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