Calculate Desertification (MEDALUS)
====================================
The Mediterranean Desertification and Land Use (MEDALUS) is the name of a project supported
by Europe to assess, model and understand the desertification phenomena that increasingly
affect the Mediterranean area. It provides a satisfied result about land degradation vulnerability.
The MEDALUS approach identifies environmentally sensitive areas (ESAs) through the Environmentally
Sensitive Area Index (ESAI). This index can be used to obtain an in-depth understanding of the parameters
causing the desertification threat at a certain point. This approach is simple, robust, widely applicable,
and acceptable to new indicators and parameters and can be adjusted to several level scales. , the method
was used for the analysis of the main indicators identified to be driving forces of land degradation.
.. raw:: html
.. note::
Maintain the same area of interest for all computations within MEDALUS.
.. contents::
1. Soil Quality Index (SQI)
_____________________________
Soil is a crucial factor in evaluating the Environmental Sensitivity of an ecosystem, especially in the arid,
semi-arid and dry sub-humid zones. Soil properties related to desertification and degradation phenomena affect two principal parameters:
(i) water storage and retention capacity;
(ii) erosion resistance.
The formula used to compute the SQI is as shown below:
**SQI = (Parent material x Depth x Texture x Slope x Drainage x Rock Fragments)^1/6**
Default datasets used for sqi are as shown below:
+---------------------------------+--------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| **Indicator** | **Variables** | **Data Source** |
+=================================+================================+======================================================================================================================================================================================+
| Soil Quality Index | Slope | `SRTM Digital Elevation `__ |
+---------------------------------+--------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| | Soil Depth | Custom User Input |
+---------------------------------+--------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| | Rock Fragments | `Harmonized World Soil Database `__ |
+---------------------------------+--------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| | Parent Material | `Digital Sol Map of the world `__ |
+---------------------------------+--------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| | Drainage | `Harmonized World Soil Database `__ |
+---------------------------------+--------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| | Soil Texture | `OpenLandMap Soil texture class (USDA system) `__ |
+---------------------------------+--------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
Soil Quality Index can be calculated in two ways:
a. Using default data (Computed on Google Earth Engine)
b. Using Custom data (Computed locally on device)
a. Using default data (Computed on Google Earth Engine)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Inorder to compute Soil Quality Index using default data use the following steps.
First open the calculate indicators toolbox and select MEDALUS then select the Soil Quality Index
option as shown.
.. image:: /_static/documentation/calculate/medalus_toolbox.png
:align: center
.. image:: /_static/documentation/calculate/medalus_sqi.png
:align: center
Input soil depth (cm) and edit soil texture aggregation method according to case study as shown. This
will reclassify soil depth values according to the definition selected.
.. image:: /_static/documentation/calculate/sqi_toolbox.png
:align: center
.. image:: /_static/documentation/calculate/sqi_texture_agg.png
:align: center
Proceed to select an area of interest and run the computation.
b. Using Custom data (Computed locally on device)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This step requires the data to be available locally.
Raw Data Download
^^^^^^^^^^^^^^^^^^^^^^^^
Inorder to prepare local data, :download:`Harmonized World Soil Database `
Extracting Soil Drainage, Soil Texture and Rock Fragment Layers from HWSD DATA
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
To extract the drainage, texture and soil group variables from the HWSD data follow these
simple steps:
1. Open the HWSD data table on Microsoft Access. The variable to be extracted are D DRAINAGE(code),
D USDA TEX CLASS(code), and T GRAVEL. To initiate the extraction, create a query design in the 'show table'
dialogue that pops up select HWSD DATA, D DRAINAGE and D USDA TEX CLASS tables.
.. image:: /_static/documentation/calculate/sqi1.png
:align: center
In the 'show table' dialogue that pops up select HWSD DATA, D DRAINAGE and D USDA TEX CLASS tables
.. image:: /_static/documentation/calculate/sqi2.png
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2. From the HWSD DATA select the MU GLOBAL and T GRAVEL variables. From the D DRAINAGE and D USDA TEX CLASS tables
select the CODE variable. The resulting query design should have the Fields row populated with the selected variables for
their respective table on the Table row
.. image:: /_static/documentation/calculate/sqi3.png
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3. Click run and the resulting table should have 4 columns populated with data from the
selected variables
.. image:: /_static/documentation/calculate/sqi4.png
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.. image:: /_static/documentation/calculate/sqi5.png
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4. Export the table to MS Excel under the ’External Data’ tab as shown below. On the
dialogue that pops up, give the export table an appropriate name and select ’Excel 97 -
Excel 2003 Workbook’ as the export file format
.. image:: /_static/documentation/calculate/sqi6.png
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.. image:: /_static/documentation/calculate/sqi7.png
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5. In MS Excel, open the just exported sheet and in separate sheets copy the MU Global field
alongside the 3 variables of interest (Drainage, Gravel, Texture class); Paste the variables
separately as shown below to facilitate joining them to the raster dataset in
Q-GIS software.
.. image:: /_static/documentation/calculate/sqi8.png
:align: center
.. image:: /_static/documentation/calculate/sqi9.png
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6. For each of the newly created sheets, copy the tables in MS Excel and paste to MS Access
.. image:: /_static/documentation/calculate/sqi10.png
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.. image:: /_static/documentation/calculate/sqi11.png
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7. Under the Export data tab, select the Export to text file option as shown below.
.. image:: /_static/documentation/calculate/sqi12.png
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8. On the Export text wizard that pops us set the export text as delimited text by checking
the Delimited options then click next.
.. image:: /_static/documentation/calculate/sqi13.png
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9. On the Export text wizard that pops us set the export text as delimited text by checking
the Delimited options fig. 2.15 then click next.
.. image:: /_static/documentation/calculate/sqi14.png
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Click on next to finish the export. Repeat steps 6 to 9 for all the other remaining variables
and save the text files. The text files are used to assign pixel values to the HWSD.bil raster in Q-GIS.
10. Open the HWSD.bil raster file on Qgis together with the vector data for the OSS North Africa States in Q-GIS
.. image:: /_static/documentation/calculate/sqi15.png
:align: center
11. Under the raster tab on the Q-GIS menu bar navigate to 'Extraction' > 'Extract by Mask Layer'
.. image:: /_static/documentation/calculate/sqi16.png
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12. In the Clip Raster by Mask Layer dialogue box, select hwsd raster as the input layer and
the Vector layer as your Mask Layer. Save the output with the desired name to your desired location.
.. image:: /_static/documentation/calculate/sqi17.png
:align: center
The clipped raster should is as shown below:
.. image:: /_static/documentation/calculate/sqi18.png
:align: center
13. On the Q-GIS Menu bar, click on Processing and select the toolbox (or use keyboard shortcut Ctrl+Alt+T) to
open the processing toolbox.
.. image:: /_static/documentation/calculate/sqi19.png
:align: center
14. On the processing toolbox, search for the GRASS-GIS r.recode tool
.. image:: /_static/documentation/calculate/sqi20.png
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15. On the r.recode dialogue, select the clipped HWSD data as the input layer and the .txt
file previously prepared as the file containing the recode rules(for this example we will use the rock fragment).
Save the output to your desired location and click run.
.. image:: /_static/documentation/calculate/sqi21.png
:align: center
Using the HWSD cliped raster as input layer, repeat the step 14 and 15 with the appropriate recoding rules to extract
the remaining variables and save the outputs for the other datasets.
.. image:: /_static/documentation/calculate/sqi22.png
:align: center
Compute Soil Quality Index
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
First Input soil depth (cm) then Select Custom soil
quality datasets instead of default and import sqi datasets as below.
.. image:: /_static/documentation/calculate/sqi_custom.png
:align: center
Select import and select a raster or vector dataset of interest. Select the band number for the
raster dataset. Input the aggregation definition, study year and the reclassified output destination file as shown:
.. image:: /_static/documentation/calculate/sqi_load_raster.png
:align: center
Ensure to reclassifiy values correctly according to case study. Once all 4 datasets are imported proceed
to select an area of interest and run the computation. You will be required to select a destination for your
output file.
.. image:: /_static/documentation/calculate/sqi_aggregation.png
:align: center
2. Vegetation Quality Index (VQI)
_____________________________________________________________
The Vegetation Quality index is derived as the geometric mean of the characteristics of the vegetation. Fire Hazard layers (RI),
Fire Resistance (FR), drought (RS), vegetation erosion protection (PE) and cover plant (CV) according to the
following formula:
**VQI = (RI x PE x RS x CV) ^ ¼**
Default datasets used for vqi are as shown below:
+---------------------------------+--------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| **Indicator** | **Variables** | **Data Source** |
+=================================+================================+======================================================================================================================================================================================+
| Vegetation Quality Index | Fire Risk | `ESA CCI–land cover map v2.0.7–2015 `__ |
+---------------------------------+--------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| | Drought Resistance | `ESA CCI–land cover map v2.0.7–2015 `__ |
+---------------------------------+--------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| | Erosion Protection | `ESA CCI–land cover map v2.0.7–2015 `__ |
+---------------------------------+--------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| | Plant Cover | `PROBA-V C1 Top Of Canopy Daily Synthesis 100m `__ |
+---------------------------------+--------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
To compute vegetation quality index, select Vegetation Quality Index option under the MEDALUS toolbar. For each of the 3 datasets, i.e
- Fire Risk
- Drought Resistance
- Erosion Protection
.. image:: /_static/documentation/calculate/vqi_toolbox.png
:align: center
Select the land cover year and an aggregation definition or use default set aggregation. This will reclassify
land cover classes based on definition provided.
.. image:: /_static/documentation/calculate/vqi_agg.png
:align: center
For Plant cover, select a start and end date. Proceed to select an area of interest and run the computation.
3. Climate Quality Index (CQI)
_____________________________________________________________
Climate quality is assessed on the basis of how it influences water availability to the plants. The climate quality index, according to the MEDALUS approach, is obtained by cross-referencing the three layers of
information namely precipitation and aridity index using the following equation:
**CQI = (precipitation x aridity index) ^ 1/2**
Default datasets used for CQI are:
+---------------------------------+--------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| **Indicator** | **Variables** | **Data Source** |
+=================================+================================+======================================================================================================================================================================================+
| Climate Quality Index | Precipitation | `TerraClimate Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces `__ |
+---------------------------------+--------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| | Potential Evapotranspiration | `TerraClimate Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces `__ |
+---------------------------------+--------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
Climate Quality Index can be calculated in two ways:
a. Using default data (Computed on Google Earth Engine)
b. Using Custom data (Computed locally on device)
a. Using default data (Computed on Google Earth Engine)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Select a year of study between 1979-2020. Proceed to select and area of interest and run the computation.
.. image:: /_static/documentation/calculate/cqi_toolbox.png
:align: center
b. Using Custom data (Computed locally on device)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This step requires the data to be available locally. Load both a potential evapotranspiration and precipitation dataset to the
plugin as shown below.
.. image:: /_static/documentation/calculate/cqi_toolbox_custom.png
:align: center
Proceed to select an area of interest and run the computation. You will be required to select a destination for your
output file.
4. Management Quality Index (MQI)
_____________________________________________________________
The Management quality index, according to the MEDALUS approach, is obtained by cross-referencing the two layers of information
namely Land-Use intensity (LU) and Population Density (PD) using the following equation:
**MQI = (LU X PD)^1/2**
Default datasets used for MQI are:
+---------------------------------+--------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| **Indicator** | **Variables** | **Data Source** |
+=================================+================================+======================================================================================================================================================================================+
| Management Quality Index | Land Use Intensity | `ESA CCI–land cover map v2.0.7–2015 `__ |
+---------------------------------+--------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| | Population Density | `GPWv411: Population Density (Gridded Population of the World Version 4.11) `__ |
+---------------------------------+--------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
To compute vegetation quality index, select Management Quality Index option under the MEDALUS toolbar. Select
the land cover year an and set aggregation definition for Land Use Intensity. This will reclassify land cover classes
based on definition provided.
.. image:: /_static/documentation/calculate/mqi_toolbox.png
:align: center
.. image:: /_static/documentation/calculate/mqi_agg.png
:align: center
Proceed to select an area of interest and run the computation. You will be required to select a destination for your
output file.
Environmentally sensitive area (ESA) Index (Combined Desertification Layer)
_________________________________________________________________________________
The environmentally sensitive area (ESA) index (ESAI) is computed according to the original procedure as a geometric
mean of the four quality values recorded at each location (i.e., in each elementary pixel; Equation 2):
**ESAI = (SQI x VQI x CQI x MQI) ^ 1/4**
To compute the final desertification layer all MEDALUS subindicator must be already computed i.e SQI, VQI, CQI, MQI.
In the MEDALUS toolbox select Calculate **final MEDALUS option** as shown below:
.. image:: /_static/documentation/calculate/esai.png
:align: center
Load all 4 layers to the plugin, select the area of interest and compute the ESAI. You will be required to
select a destination for your output file.
.. note::
All layers will be automatically loaded into the plugin if they are available and loaded within QGIS.
.. image:: /_static/documentation/calculate/esai_toolbox.png
:align: center
.. toctree::
:maxdepth: 3