This section shows the option to compute accounts that record the condition of ecosystems in terms of selected characteristics, at specific points in time. Over time, they record the changes to their condition and provide valuable information on the health of ecosystems.
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Forest Condition Accounts
At this stage of the project, condition accounts are focused on Forest Ecosystems, due to the ecosystems' relevance in terms of ecological conditions and the availability of information from remote sensing data.
Forest Condition Indexes
the first option is to compile the Forest Condition Index to estimate the overall condition based on a set of experts-defined weighted and selected indicators
The symbols next to the Indexes define the output produced as a result of that computation, while the first Condition Index produces maps and a table, the other two indexes produce maps, and can be compared to show how conditions estimates differ when using diverse methodologies.
This option triggers the computation of the variables and indicators' maps involved in the calculation of the index, and generate the index's map and table.
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The options in this section only triggers the computation of the table of the Forest Condition Index. To visualize Variables and Indicators results in a table, you must select that particular output from the drop-down menu in the section below. |
Forest Condition Variable (raw values)
within the section of the Condition metrics, the value of the Raw variables to compile the Variable Forest Ecosystem Condition Account(s),
This option triggers the computation of the variable's map and table.
Forest Condition Indicators
Switch to the Indicators option, to compile the Forest Condition Indicators Account(s),
This option triggers the computation of the variable's map (not the variable's table)and the indicator's map and table.
High-resolution Forest Condition Variable
These variables are placed in another section to differentiate them from the other raw variables.
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High-resolution observation should only be observed over small contexts, taking as reference the location designed as testing areas in the project (i.e. level 3 of the nomenclature of territorial units for statistics - NUTS3). |
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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. |
The workflow to compute Forest Conditions Accounts
Each variable used in the computational workflow of an indicator is initially observed extracting its original vale (raw value) from the dataset over that context
There are reference values delimiting good and bad conditions by class of forest for each variable
Max reference (good condition)
Min reference (bad condition)
The raw value is re-scaled against the reference values for each forest class observed in the context
and assume values between 0 and 1
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Whenever there is no observation for a variable, the original value in the dataset does not cover the spatio-temporal context observed. |
As a direct consequence of this missing observation, also the indicator and the index cannot be computed for that period.
Indicator
Index
But when the same maps are observed in a different period, in this case over 2018, the results are estimated
Indicator
Index
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Considered the lack of data coverage over large portions of Europe of the dataset used for estimating the variable Soil Organic Carbon (SOC), the model is adjusted for redistributing the indicator SOC weight to the others indicators. Thanks to this adjustment, the Condition Index can still be computed over those areas where the data are missing, as explained in the previous section. |