MARS Webviewer training course

20th - 21th June 2013:

With focus on Africa

Training on Crop monitoring with remote sensing

Joint Research Centre, Ispra , 17th - 21th June 2013

Hugo de Groot, Alterra, Wageningen UR. hugo.degroot@wur.nl

Content

Introduction to the MARSOP3 project and data

Exploring MARSOP3 data by using the webviewer

Extensive demo Hands-on training

JRC: Joint Research CentreJRC: Joint Research Centre

IES: Institute for Environment and SustainabilityIES: Institute for Environment and Sustainability

AGRI4CASTAGRI4CAST

AGRI-ENVAGRI-ENV

FoodSecFoodSec

GeoCAPGeoCAP

MARS unit (Monitoring Agricultural ResourceS)MARS unit (Monitoring Agricultural ResourceS)

MARSOP3: (www.marsop.info)

European CommissionEuropean Commission

MARSOP3 services

MARSOP3 services

Monitoring Agricultural Resources (MARS)Operational services

MARSOP3: list of operational servicesweather monitoring based on interpolated station data

Africarainfall estimates based on MSG and observed rainfall

pan-Europeweather and vegetation indices based on MSG-SEVIRI

pan-Europe and Horn of Africavegetation indices based on MODIS-250m sensor

pan-Europevegetation indices based on METOP-AVHRR sensor

globalvegetation indices based on NOAA-AVHRR sensor

globalvegetation indices based on SPOT-VEGETATION sensor

globalcrop specific drought monitoring

globalweather monitoring based on ECMWF deterministic forecast

pan-Europecrop yield forecast based on ECMWF ensemble models

pan-Europe and Asiacrop yield forecast based on ECMWF deterministic forecast

pan-Europecrop yield forecast based on interpolated station data

pan-Europecrop monitoring based on ECMWF ensemble models

pan-Europe and Asiacrop monitoring based on ECMWF deterministic forecast

pan-Europecrop monitoring based on interpolated station data

pan-Europeweather monitoring based on ECMWF ensemble models

pan-Europe and Asiaweather monitoring based on ECMWF deterministic forecast

pan-Europe

MARS Webviewer MARSOP3 services deliver and store large

amounts of basic and added value data (size is now 7 TB !)

Basic weather data / Remote Sensing based Vegetation Indices

Added value data generated in the various operational levels of the MARSOP3 services through downscaling and aggregation

Online viewer enables user to perform spatial and temporal analysis of global state-of-art data sets in a customized way

Exploring the data by using the Mars webviewerhttp://www.marsop.info

Note:

Possibility to register

(normally access is granted for half a year)

Login to the Mars webviewer

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guest30 ispra

Choices at startup

• Region of interest• Zoom to specific

part

• Result typeMap, Graph, Quicklook

MARSOP3: regions of interest

Demo

Viewer capabilities: Produce MAPS and GRAPHS for spatial and

temporal analysis: On the fly created from the data; based on user choices Large amount of indicators available

View QUICKLOOKS Static, preprocessed results from crop monitoring by remote

sensing. Indicators: • Normalized Difference Vegetation Index (NDVI)• Dry Matter Productivity (DMV)• Fraction of Absorbed Photosyntheticly Active Radiation

(fAPAR)

• Rainfall estimates (for whole Africa only)

Map and graphRainfall anomaly

april / may

Example of weather indicators

Quicklook

MARSOP: list of operational servicesweather monitoring based on interpolated station data

Africarainfall estimates based on MSG and observed rainfall

pan-Europeweather and vegetation indices based on MSG-SEVIRI

pan-Europe and Horn of Africavegetation indices based on MODIS-250m sensor

pan-Europevegetation indices based on METOP-AVHRR sensor

globalvegetation indices based on NOAA-AVHRR sensor

globalvegetation indices based on SPOT-VEGETATION sensor

globalcrop specific drought monitoring

globalweather monitoring based on ECMWF deterministic forecast

pan-Europecrop yield forecast based on ECMWF ensemble models

pan-Europe and Asiacrop yield forecast based on ECMWF deterministic forecast

pan-Europecrop yield forecast based on interpolated station data

pan-Europecrop monitoring based on ECMWF ensemble models

pan-Europe and Asiacrop monitoring based on ECMWF deterministic forecast

pan-Europecrop monitoring based on interpolated station data

pan-Europeweather monitoring based on ECMWF ensemble models

pan-Europe and Asiaweather monitoring based on ECMWF deterministic forecast

pan-Europe

Datasets and resolutions

Quicklook: define contentUser defines which part of the data will be visualized

Hierarchical choices:

•Resolution

•Theme (service) Indicator

•Function

•Time period and other additional parameters

Push the Push the buttonbutton

Quicklook: view resultPossible user actions:

= home, start again

Define content: FunctionUsed everywhere

Default: Year of Interest (YOI),

and default the year of interest is the actual year

Other: Long term average (LTA)

Difference with long term average

Difference with previous year

Difference with any other year (availability depends on situation)Important to see the spatial distribution of temporal effects or anomalies ! Demo

Map: map actionsMap mode buttons:

Activate one, then click inside the map

Map action buttons:

Perform action immediate on click

Additional functionality:

Leave this map window, start other part

Map: define contentUser defines which part of the data will be visualized

Hierarchical choices:

•Resolution

•Theme (service)

•Crop / Landcover

•Indicator

•Function

•Time period and other additional parameters

•Aggregation type

Push the Push the buttonbutton

Map: view result

Map: Layers

= export or print

= home, start again= open additional

map windowMultiple map windows are linked

Demo

Maps and Quicklooks: time out Hands on: Play around with the viewer in Africa Change the resolution and view the result map Change the theme and view the result map Switch between ‘Quicklooks’ and ‘Maps’ Change the indicator and view the result map Play with the function and view the result map Play with the time period and view the result map Open multiple linked maps and play around Go to the ‘Layers’ tab and add additional layers to the

map

Don’t change the legend, and don’t look at graphs

Graphs Always act on at least one ‘spatial entity’, so on a

specific area This ‘spatial entity’ can be of any available

resolution So it can be:

a country (Admin Level 0 = Countries) a district (Admin Level 1 = Districts) a grid cell

Graphs: opening a graph Select a ‘spatial entity’ from the map

Inside the map, activate the ‘Select feature’ tool:

Click inside the map in order to select an area The selected area gets highlighted (maplayer must be visible)

Click on the ‘Add graph window’ button:

Map: opening a graph

= export or print

= home, start again= open additional

map windowMultiple map windows are linked

Shown before

= open graph window for selected spatial entity

Graphs

Select the ‘graph type’:

Demo

Graph types All available years Bar chart Extra options:

One specific year, one indicator, multiple spatial entities Line chart, more spatial entities possible (up to 6) Extra options: and Shift-click !

One specific year, multiple indicators, one spatial entity Line chart, more chart series possible (up to 6) Extra options:

Graph: define contentUser defines which part of the data will be visualized

Push the Push the buttonbutton

Hierarchical choices:

•Theme (service)

•Crop / Landcover

•Indicator

•Function

•Time aggregation

•Overlapping profileNote: Time period is specified in separate tab

Graph: define contentPush the Push the buttonbutton

Time period:

•Default starts at January 1st

•Timescale depends on dataset (Theme)

•For Africa mostly ‘Dekad’ (10 day periods)

• 1 – 10

• 11 – 20

• 21 – end of Month

Define content: Overlapping profileUsed for graphs

Use:

Explore extreme situations

Compare with other years

Note: The Year of Interest (YOI) is excluded in calculating the overlapping profile values !

Maps and GraphsMaps and Graphs are linked

Functionality at the map window for linking Select feature tool, works on the ‘active layer’ The graph gets updated automatically on a change of the selected

area (if the selected area is of the same resolution) The active layer can be changed on the Layers-tab :

Demo

On a resolution changes the active layer changes automatically

Map and graph

Indication of green and healthy

vegetation cover

Example of remote sensing based vegetation index

Maps and Graphs: time out

Hands on: Play around with maps and graphs in Africa

Open a map window and click the ‘Add graph’ button: What happens?

Make sure you can open a graph window from the map window Try the three different graph types, view the graph results With a graph result on the screen: Change the selected spatial entity by selecting

another one from the map Change the theme, the crop / landcover, the indicator and view the result graphs Play with the function and the time period and view the result graphs Play with the overlapping profile and view the result graphs Switch between ‘Africa’, ‘West Africa’ and ‘Horn of Africa’ Open multiple linked maps, open a graph from every map window and play around

Go to ‘Home’ and start a graph: graph only window Question: how to select a ‘spatial entity’

Map legends

Each indicator has a default legend, the system legend.

Possible user actions:

-Edit: Change a legend.-Select: Select a different legend.-Delete: Delete a previous saved legend.

Map legends

Edit legend ‘by hand’:Add / remove legend classesChange legend class range, color or label

Legend type: Auto-calculate other than normal legendsMap legends

Class boundaries get calculated on the fly based on the actual values which correspond with the current user choices for variable and time-period.

Two types:Equal areaEqual width

Map legends

Update map = Preview map with the new legend settings. The legend is not yet saved.Save as = Add this legend to the database storage, for later reuse. It must be stored under a new name.Save = overwrite this legend with the new settings (only available for an earlier saved legend).

Show and / or store the result:

Map legendsSelect legend: Choose from a selection of legends, designed for this indicator

Check ‘Show all’ to choose from all legends:

Map legends

Delete legend: Throw away an earlier saved legend

Demo

Map legends: time out Hands on: Play around with legends Open a map window and view a result map Start the legend editor Add a legend class and view the result map Remove a legend class and view the result map Change the color for some classes and view the result map Play around with the legend types (normal / equal area / equal width) and

view the result maps Save the new legend for later reuse Did you give the new legend a name? If not, what happened? Save some more legends Switch between saved legends and view the result maps Remove a saved legend

Map viewer

Examples: time period: look at growing seasons starting in October or NovemberGo deeper into (and show) aggregation type, within short time period (for Minimum Temperature or so).

Map viewer: Map export facilities Print Save as .pdf Save as .png

Map viewer: export facilities

Print Save as .pdf Save as .png Save as .csv, to

open in Excel

Map viewer: Quicklook export facilities

After download: Print from your browserSave from your browser Demo

Export facilities: time out Hands on: Play around with the export facilities Open a quicklook window and view a quicklook result Download the quicklook Save the quicklook image on your hard disk Open a map window and view the result map Export the result map to your local file system Open a graph window and view the result graph Export the result map to your local file system. Try different formats,

including .csv If you have Excel installed: Open the .csv file in Excel