exploiting copernicus data with ilwis4 webinar...

EXPLOITING COPERNICUS DATA WITH ILWIS4

FACULTY OF GEO-INFORMATION SCIENCE AND EARTH OBSERVATION

Exploiting Copernicus Data with ILWIS4

Webinar material 29 November 2018

Module 1: Introduction to ILWIS4

Presentation (corresponding to webinar time 0”- 4’09’’)

Rob Lemmens

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Credit: ESA – CC BY-SA IGO 3.0

ITC FACULTY OF GEO-INFORMATION SCIENCE AND EARTH OBSERVATION

www.itc.nl


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The Sentinel constellation of satellites

Sentinel‐1 (A/B) – SAR imagingAll weather, day/night applications, interferometry

Sentinel‐2 (A/B) – Multi‐spectral imagingLand applications: urban, forest, agriculture,…Continuity of Landsat, SPOT

Sentinel‐3 (A/B) – Ocean and global land monitoringWide‐swath ocean color, vegetation, sea/land surface temperature, altimetry

Sentinel‐4 (A/B) – Geostationary atmosphericAtmospheric composition monitoring, trans‐boundary pollution

Sentinel‐5 precursor/ Sentinel‐5 (A/B) – Low‐orbit atmosphericAtmospheric composition monitoring

Jason‐CS (A/B) – Low inclination AltimetrySea‐level, wave height and marine wind speed

2014 A /2016 B

2015 A /2017 B

2016 A/ 2018 B

2019 A /2027 B

2017 P / 2021

2020


Copernicus services


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ILWIS: the Integrated Land and Water Information System

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GEONETCastToolbox

ILWIS OBJECTS

OGC services

Third‐partyservices

SNAP, QGIS, etc.

ILWISClient

WEB COMPONENTS

SERVER COMPONENTS

DESKTOP

‐ Rangeland management‐ Hydrological analysis‐ Multi‐criteria evaluation‐ . . .

Application Workflows

Third‐partyclients

Third‐party applications

ILWISdesktop

ILWIS as interoperable software


1 FACULTY OF GEO-INFORMATION SCIENCE AND EARTH OBSERVATION

Module 2: Description of the Interface/Displaying raster and vector

maps

Hands-On (corresponding to webinar time 4’10”- 9’49’’)

Diana Chavarro-Rincon & Rob Lemmens



Interface and data visualization

The first activity consists of displaying and inspecting a satellite image in ILWIS, allowing at the same time the user to get familiar with the ILWIS interface and some of its basic functionalities.

Download the exercise from the link provided for the exercise. Extract the data from the folder “Intro Exercise I” and “Intro Exercise II” into a directory on your hard disk.

Getting started with ILWIS

Start up ILWIS: Double-click the ILWIS icon on your desktop, or double click il-wisclient.exe file from the directory where ILWIS is installed. The ILWIS logo will appear on your screen, followed by the ILWIS Main window.

The ILWIS main window consists of 2 sections (Figure 1): The Workbench, and the Workspaces. Every Workspace contains a single or several data panels that may in-clude catalogs, maps, tables, or any other ILWIS object.

The Workbench is the larger area right to the menu strip. It gives access to most of the actions that can be executed in ILWIS, and contains the following buttons:

• Locator: allows navigating through various folders and subfolders on a local drive or remote database. When this button is activated, a new panel will appear on the left part of the screen (Figure 2). Besides the navigation window, the possibility of adding bookmarks to frequently used folders is also available in this panel Note that the selected option will turn to a darker green color in the workbench.

Figure 1 ILWIS interface


FACULTY OF GEO-INFORMATION SCIENCE AND EARTH OBSERVATION 3

• Operations: ILWIS operations are organized in the form of an alphabetically as-

cending list. The Operations button of the Workbench provides access to that list. • Metadata: provides viewing and editing access to metadata associated with a se-

lected ILWIS object (this button replaces the Properties button of previous ILWIS versions)

• Create: allows the user to build any ILWIS object, including scripts, workflows

and models.

• Messages: supports communications between system and users during working sessions. These communications are presented in the form of a log report that includes events that may occur in software runs, and error messages related to success or failure of an executed action.

• Progress: displays the status of a running operation in terms of the time required

to be completed. • Settings: allows the user to customize the appearance of the Interface and some

ILWIS objects, and to define a location for internal data storage and data sharing. • Info: provides general information regarding the installed ILWIS version as well

as the names of the loaded modules and the contributors. ILWIS main window also allows the simultaneous use of two or more catalogs. To add one, click on the Left or Right buttons either in the Locator window or in the Navigator Shortcut bar.

Figure 2 Locator window - Catalogs

The Workspace includes at least one catalog and may contain one or more data panels. By default, ILWIS starts with an empty catalog the first time it is opened, and it will start with the last that was in use afterwards. The catalog is used to show all the available IL-



WIS objects (on a local drive or a remote database) in the form of icons allowing the us-ers visualization and access to them. Data sources such as maps and tables are visual-ized in ILWIS data panels. In the Workspace you will find the Action bar with tabs that provide access to specific pro-cedures for the kind of data in use. In the case of a catalog, the available tabs are: Filter, Selection, Copy and Refresh. Other options will be available in the Action bar as objects from the catalog e.g. rasters or segments are displayed The Navigation Shortcut bar is located in the upper right corner of the main ILWIS window (Figure 1) and includes quick access to the frequently used and bookmarked folders, as well as direct command for adding new catalogs at the left or right side of the active one.

Catalogs Catalogs in ILWIS are data containers that provide a view of all objects, directories and subdirectories trees within a selected folder on your computer. Every object in a catalog is preceded by an icon; examples of icons representing data objects are presented in Figure 3. To open an object in the catalog, double click on the correspondent icon or drag it into a data panel if another object is already displayed*.

Figure 3 Catalog icons representing ILWIS objects

* ILWIS is capable to read directly the most commonly used geo-data formats, thus no import function is needed.

Exploring raster data

Click the Workbench button Locator. (Note that all the ILWIS buttons are toggles and therefore you can close the window by clicking again on the same button)

In the opened window, navigate to the C:\ILWIS\Intro Exercise I, or the folder where the data for this exercise is located .First, you need to select firs the right directory in the upper tab of the window (Figure 4) and then, the path to the folder that contains the data. Click on the Go button afterwards.

You may add a bookmark to this folder for further use as shown Figure 2.

In the catalog you will find the following objects: the coordinate system, table and segment map named “roads”; the raster multi-band container named NL_S2A2A_20180707_subset.tif, (that corresponds to a Sentinel-2 image of 07/07/2018), and a georeference with the same name.



For technical specification of the Sentinel images please refer to https://senti-nel.esa.int/web/sentinel/missions/sentinel-2

Figure 4 Navigating to the folder containing the data

Double click on the icon . The catalog now shows 8 Sentinel-2 (S2 hereafter) bands with their corresponding georeferences. S2 images originally include 13 bands but for this exercise, only 8 were selected in the visible (VIS) and near infrared (NIR) regions of the electro-magnetic spectrum.

Double click on the second raster map of the list (NL_S2A2A_20180707_Sub-set_2) . A new data panel will open on the right side of the catalog showing the raster map (Figure 5). You can enlarge or reduce the panel size by sliding the left side of the frame and clicking on the Extend button afterwards.

The image corresponds to the S2-Band 2* (0.490 µm, central band) that covers the En-schede area. The original image has a much larger coverage but it has been subset to re-duce the size and computational time during operations.

Click on different pixels of the image. The pixel values (spectral reflectances var-ying from 0 to 1, with a quantification value=10.000) will appear on the screen. In addition, the coordinates of every pixel will be shown in the Position information window available in the upper right side of the data panel.

Use the Zoom In/Out buttons located beside the Extend button to examine the image.

Note that the new data panel shows additional options for the Action bar tabs: Actions for Display, Layers Info and Metadata.

Go to Actions for Display. Make sure that the map NL_S2A2A_20180707_Sub-set_2.tif is selected in the Layers box. Click on Layer Specific in the Data attrib-utes and Layer Info in the Visual properties boxes. Useful information concerning file location, type of data and projection will appear in the Property Editor for the selected layer (Figure 5)

By default, ILWIS applies a 2% stretching to display the image, but this stretching can be modified to improve contrast if needed.

https://sentinel.esa.int/web/sentinel/missions/sentinel-2

https://sentinel.esa.int/web/sentinel/missions/sentinel-2



Figure 5 Displaying satellite bands

Click on Stretch Limits in the Visual properties box. The histogram of the image will be computed and it will be shown on the right side of the window (it may take a few seconds to your system to calculate the histogram). Here you can improve the contrast by moving the sliders below the histogram, or by selecting one of the stretching presets available: 0, 1, 2 or 5% stretching (Figure 6).

Figure 6 Image histogram, stretching options

Click on the Layers Info tab (also in the Action bar), and move the mouse over the image while keeping the left button pressed. You will see the pixel value and the images coordinates (rows and columns) throughout the image.

Go one level up in the catalog by clicking on the icon Note that there is a Metadata button available at the Workbench and another one at the Action bar. The first shows metadata related to the object selected in the catalog, while the second corresponds to the object displayed in the Data panel.

Click on the Metadata button of the Workbench. By doing this, the Object proper-ties panel will open (make sure that the first raster map is selected in the catalog). You will find 3 tabs: General, Data, and Spatial. Select the latter. You will see in-formation on the coordinate system and projection (if applicable) of the selected object, the pixel and raster size and a world map indicating the geographical loca-tion of the data object (Figure 7). Navigate to the main exercise folder. Now select



the segment file ‘roads’. What are the differences in metadata between these two objects?

Click again on the Metadata button to close the window.

Figure 7 Spatial Metadata: raster and segment maps

Now click on the Metadata button of the data panel where the image is displayed, right side in the Action bar (Figure 8). You will find information related to the coor-dinate system, projection (if applicable) and extent of the data panel. In the Over-view window (right side), you can see the selected extend of the view. Zoom in the central part of the image and notice the change in the extend indicator - white rectangle- inside the small image. Click again on the Metadata button.

Figure 8 Data panel, metadata

The objects in the catalog can be seen in different ways depending on which View tab is selected. By default, ILWIS displays contents in a grid. Other options available are: List, Thumb and Spatial.

Click on the tab Thumb of the Catalog/View tabs. All the maps in the catalog will be displayed as large icons with a question mark sign in the center. Click on the refresh button at the top right corner to generate a thumbnail for the selected



map. By doing this, ILWIS creates a directory ‘.ilwis’ in the active catalog and saves the thumbnail for future use; the next time that you click on this option the maps will be shown directly (you may see the thumbnails from the beginning if they have been previously created). Each thumbnail in the catalog includes prod-ucts’ metadata, i.e.: dimension, domain type, name, coordinate system, georefer-ence and data size (Figure 9)

Figure 9 Thumbnails

The content of the selected catalog can also be viewed in spatial format by selecting the Spatial button on the View tabs. If this option is used, you will see a world map showing the geographical location of the objects (marked in red). Clicking at any location on the world map in the catalog will show a preview of the available data in those locations (you will need to zoom in a few times when you have data only in a small area). Figure 10a illustrates the case of a catalog with several products in different locations. The small frame shows the data corresponding to the selected area (you can also use the zooming tools here)

Click on the Spatial tab. In this case you will see only one location highlighted as all the data correspond to the same area in the Netherlands (Figure 10b)

Figure 10 Spatial view examples



All the bands of a multi-band raster can be visualized simultaneously in a synchronized view. This function can be useful in some analysis, e.g. analyzing spectral differences of objects of interest in satellite images.

In the Data panel where the image is displayed, click the Split panel tool located beside the zooming tools. Select the symmetric 4-subpanels view and make sure that the option “Panels are linked” is ticked as shown in Figure 11. You will see the S2 band previously open (NL_S2A2A_20180707_subset_2) in the upper left sub-panel

Drag another 3 bands from the catalog in each of the empty subpanels. You can use e.g. bands _3, _4 and _8 .

Click on any of the images, this will be set as the reference image. You will notice that the frame of the selected image will change from white to light blue. Zoom into the canal in the northern part of the image. Your data panel should look like in Fig-ure 11; in that case, the sub-panel containing the image “_4” is selected for zoom-ing or panning.

To identify which band is in every subpanel, click on Actions for Display /Layers and check the name of the layer marked in blue. This will change as you switch between sub-panels. Can you appreciate the differences among bands? Notice the clear contrast between the 3 bands on the VIS (Bands _2, _3 and _ 4), and the one in the NIR (Band _8) in the lower right sub-panel). This is particularly obvious in the case of the vegetated areas on the western side of the canal.

Figure 11 multi-panel synchronized view

Zoom in/out and pan in different parts of the image until you get familiar with the synchronized view tool. Keep in mind that when the zoom, pan and extend but-tons are active they change their color, and that you need to reactivate them as you change sub-panel. To read the pixel value select the button (located at the right side of the zoom out button).



With the S2 Bands available you can create an RGB image in the form of a true or false color composite (TCC or FCC respectively). In this case, a TCC can be created by using B4, B3, and B2, corresponding to the Red, Green and Blue bands of the S2 image. Refer to Figure 1 of Module 4 to locate the S2 bands in the electromagnetic spectrum.

Close the data panel containing the 4 S2 bands.

Click on the multi-band container NL_S2A2A_20180707_subset and select B4, B3, and B2 (in that order!), while holding the Control key. Go then to Actions for Selec-tion (you will see the bands assigned to the RGB channels), and click on Apply. Your image should look like in Figure 12

In the same way you can visualize other color composites, e,g, B8, B4, B3 for the RGB channels to enhance vegetation using one of the NIR bands of S2.

Figure 12 Color Composite

Adding vector data

You can overlay vector data in a viewer containing the S2 bands as long as they have a defined georeference. In a previous section, the metadata of the objects placed in the cat-alog were examined. Figure 7 showed that the S2 image uses the Reference Coordinate System and projection WGS84/UTM 32N, while the vector map “roads” uses RCS LAT/LON WGS 84. The catalog also includes the point map “places” corresponding to the cities and towns of the area.

Drag the map “roads” in the data panel containing the color composite created in the previous step and zoom in at different locations to check the overlay.

Go to Actions for Display and select the roads map from the Layers window. In Data Attributes, select “type” as attribute and make sure that in Property Editor the Color Scheme is set to “primary colors”. Your screen should look like Figure 13.



Drag the point map “places” into the data panel. You will see the mail urban settle-ments of the area marked with a dark red point.

In the Layers box, you can turn on/off any layer at your convenience for better visualiza-tion.

Figure 13 Segment maps overlay



Module 3: Pre-Processing of Copernicus data in SNAP

DEMO (corresponding to webinar time 9’50”- 15’42’’)

Diana Chavarro-Rincon



Pre-processing of Sentinel-2 data in SNAP

SNAP

The European Space Agency, ASA, has developed free open source toolboxes for the scientific exploitation of Earth Observation missions such us the Copernicus program (https://www.esa.int/Our_Activities/Observing_the_Earth/Copernicus). One of these tools is the Sentinel Application Platform, SNAP, which offers a common architecture for all Sentinel Toolboxes. SNAP works on 32 and 64 bit Windows, Mac OS X and Linux and can be downloaded from http://step.esa.int/main/download/ Downloading Sentinel-2 images Sentinel-2 is one of the satellites of the Copernicus program, for a global, continuous and high quality range of Earth Observations. The Multi Spectral Instrument (MSI) on board the Sentinel-2, provides high resolution measurements in the visible (VIS), near infrared (NIR), and short wave infrared (SWIR) part of the spectrum. The Sentinel-2 mission is dedicated to the acquisition of terrestrial observations for applications such as forest mon-itoring, land cover changes detection, and natural disaster management. To download Sentinel-2 data go to Open Hub https://scihub.copernicus.eu/dhus/#/home.

Click on Login and Sign up to create an account (Figure 1)

Figure 1: Copernicus Data Hub

Once you have an account you will be able to login and start browsing for sentinel-2 im-ages. In this case we are going to work in an agricultural region in south west Spain close to the city of Seville.



Click on the toggle (upper left corner), and fill the fields Sort by, Oder By and Sensing period as in Figure 2. We will start by downloading the image of the 01/06/2017. Here you can also write a time interval if you do not know ex-actly when images have been acquired.

Tick the option Sentinel-2. Leave empty the fields Satellite Platform, and Relative Orbit Number, as in this case we do not have preferences at this regard.

In Product Type, select S2MSI2A (this is a Sentinel-2 image acquired with the Multi Spectral Instrument that carries on board, and processed at the 2A level, i.e. bottom of the atmosphere reflectance in cartographic geometry)

In Cloud Cover% filled with [0 TO 10] indicating a maximum of 10% cloud cover-age

Use the Navigation/Area Selection button located on the right side of the world map, and navigate to southern Spain

Click again on the same button to enable the Area mode. Draw a rectangle over

southern Spain and click on the Search button .

Figure 2: Searching Sentinel-2 images

You will see in the map the footprint of the available images that match your query (Fig-ure 3)

Select the product S2A_MSIL2A_20170601T110651_N0205_R137_T29SQB_20170601T111225 and click on the preview icon.



Figure 3: Examining search output

In addition to the preview of the image you will see the summary of its characteristics (Figure 4); note the Cloud cover percentage = 0.0143 at the bottom of the window.

Figure 4: Image preview

Click on the download icon and navigate to the folder where you want to store the image.

Unzip the downloaded file and explore the content to get familiar with the Senti-nel-2 data structure.



Image pre-processing in SNAP

Open SNAP and click on the icon on the left side of the bar menu. Select the MTD_MSIL2A.mxl file to open the image (You can also select the zipped folder or directly drag it from its location to the Product Explorer window of SNAP (Figure 5)

Figure 5: Opening images in SNAP

Click on the icon beside the product to display the folders containing the differ-ent components of the image.

Click now on the same icon beside de folder Bands. You will see the 13 bands of the image together with the subfolders containing important additional information (Figure 6)

Right click in the root folder and select the option Open RGB image Window. Ac-cept the default combination (True Color Composite, TCC, 432*)

Figure 6: Visualizing Sentinel-2 as a RGB image

Allow the system to display the RGB image. It should look like Figure 7

*For more information on spectral bands and common RGB composites of Sentinel-2 images go to https://www.esa.int/Our_Ac-tivities/Observing_the_Earth/Copernicus/Sentinel-2/Introducing_Sentinel-2 or refer to the User Handbook available at https://sentinels.copernicus.eu/web/sentinel/user-guides/document-library/-/asset_publisher/xlslt4309D5h/content/sentinel-2-user-handbook



Navigate through the image and note the image and geographical coordinates at the low-est right bar. (This RGB composite is intended to present colors that approximately reflect the natural perception of the human eye).

Figure 7: RGB image

The original Sentinel-2 images cover an area of 100kmx100km, but we do not need the whole area for the exercise and therefore we can subset it to our area of interest, AOI. Before doing this, we need to resample the image, i.e. modifying the bands to a spatial resolution different from the original one, in such a way that all the bands have the same pixel size. Figure 8 shows a summary of the spectral and spatial characteristic of the sen-sor bands.

Figure 8: Sentinel-2 Spectral bands

In this case we will resample all the bands to 10m pixels size, i.e. the resolution of B2, B3, B4 and B8.



In the main menu bar click on Raster/Geometric Operations/Resampling (Figure 9). A new window will open.

Figure 9: Resampling operation in SNAP

In the tab I/O Parameters you will see that in the field Target Product/Name the image is selected as shown in Figure 10left panel. (When more than one image appears in the Product Explorer window, you need to make sure that the desired product is selected in this field). Tick on Save as in case you want to save the re-sulting image from this intermediate step (optional), and in that case select an ap-propriate format; you can leave the default BEAM-DIMAP, which is the original Sentinel format. Tick also the option Open in SNAP. In the Resampling Parame-ters tab (Figure 10right panel), select B2 from the context-sensitive menu. Leave the other parameters as the default.

Click on the Run button. The new product will appear in the Product Explorer win-dow preceded by [2].

Figure 10: Resampling menu in SNAP



Close the RGB image previously open. Display a new RGB image as presented in Figure 3.6, but select this time product [2], i.e. the resampled image. You will not notice any obvious differences.

Now that all the bands have the same pixel size, we can subset our image to the AOI.

In the main menu bar click on Raster/Subset (Figure 11 left panel). A new win-dow will open.

In the tab Spatial Subset, select Pixel Coordinates and fill the table as in Figure 11 right panel. The parameters of the tabs Band Subset and Metadata remain as the default. Click OK. The subset image (product [3] will be added to the Product Explorer window).

Figure 11: Subsetting images in SNAP

Display a RGB of the subset as explained above. Select the same band combina-tion (MSI Natural Colors). Explore the newly created image.

The final step is to export the subset image to a format that can be read directly by ILWIS.

In the main menu bar click on File/Export/GeoTiff (Figure 12) and select a folder to store the new image.

Figure 12: Exporting images in SNAP



Module 4: Spectral tool/Map Calculation Radiometric indices – Seville case study

Hands-On (corresponding to webinar time 15’43”- 26’29’’)




Visualizing multi-spectral and multi-temporal data

In this section we will use a multi-temporal Sentinel-2 data set of Southern Spain to be-come familiar with the spectral and temporal data tool (Cross Section) of ILWIS.

Click on the Locator button and navigate to the folder ‘Intro Exercise II’, and add a bookmark there. You will find 9 multi-band containers corresponding to 8 S2* im-ages of:

2017/06/01 – 2017/07/31 - 2017/08/20 – 2017/10/29 – 2017/11/18 – 2018/01/27 – 2018/03/28 and 2018/05/17

*The images correspond to Sentinel 2 – level 2A products, i.e. orthorectified, geo-referenced, atmospheri-cally corrected reflectances.

Note that the image of 2017/06/01 appears two times: the first, includes all the original 12 bands, and the second, together with the other 7 images was subset to 8 bands. The im-ages were renamed as follows:

1. S2A_MSIL2A_20170601 (includes the original 12 bands) 2. SP1_S2AL2A_20170601_sub (same as before but subset to 8 bands) 3. SP2_S2AL2A_20170731_sub 4. SP3_S2AL2A_20170820_sub 5. SP4_S2AL2A_20171029_sub 6. SP5_S2AL2A_20171118_sub 7. SP6_S2AL2A_20180127_sub 8. SP7_S2AL2A_20180328_sub 9. SP8_S2AL2A_20180517_sub

In images 2 to 9, the prefixes “SP1” to “SP8” have been added at the beginning to easily keep track time wise, and the suffix “_sub” at the end to indicate that the amount of bands have been reduced. Figure1 presents an overview of the S2 spectral bands as a function of wavelength.



Figure 1 Sentinel-2 spectral bands

Building spectral plots

Different materials/surfaces in nature reflect electromagnetic (EM) radiation differently in every wave length of the EM spectrum. The radiation reflected as a function of the wave-length is called the spectral signature of the surface. We can obtain some insight into the characteristics of surfaces found in remote sensing imagery by analysing spectral plots from the available bands, and comparing them with theoretical curves (mainly obtained from spectrometers in laboratory conditions).Figure 2 presents the characteristic spectral signatures of the most common surfaces found in nature.

Figure 2 Generic spectral signatures of most common surfaces

Spectral plots can be built from satellite imagery with the available bands. That means that the obtained plot presents a portion of the spectral signature in a discrete way, differ-ent from the continuous measurements obtained in laboratory. They constitute however a useful tool widely used in image interpretation.

Make sure that you are still in the catalog that includes the data for the Intro Exer-cise II (previously bookmarked)

Right click on the multi-band container: -remember that this is the only container whose name does not include the suffix “_sub”-, and open it as a TCC as explained before using bands 4, 3 and 2.

In the Data panel go to Actions for Display, in Data Attributes: Layer Specific and in Visual properties: Cross Section.



Go one level up in the catalog and drag the in Prop-erty Editor/ Data Source(s). You should see the 12 bands* in the small window un-der the Bands box. Here you can select which bands you want to see in the plot. By default, ILWIS includes all the bands available in the container. The data Prop-erty Editor should look like in Figure 3.

* In this part of the exercise we will use the 12 original bands of the S2 image. Bands 1, 5-7, and 9-12 have been resampled to 10m spatial resolution.

Figure 3 Cross section settings

In Property Editor, Pin Manager, tick the option Continuous Mode and click any-where on the displayed image. A new panel with a plot will open on the left side of the image showing the spectral plot of the 12 bands of the container.

Zoom in the agricultural area and move the mouse while keeping the left button pressed. You will see the spectral plot of the different crops and other features in the image.

As mentioned before, S2AL2A (as in this exercise) provide atmospherically corrected (bottom of the atmosphere, BOA) reflectances with a quantification value of 10.000. That means that the pixel value ranges between 0-10.000 (i.e. 0 -1.0, real values), except for a few pixels that might be out of range due to sensor saturation. For a better resolution of the plot, you may want to adjust the Y-axe range to e.g. 0 to 10000:

On the plot panel, go to Dataseries/Continuous pin, in Operations, Set Y-axis range, and set the range as in Figure 4. Tick on Lock Y-axis range and Click on the Apply button afterwards.

Figure 4 Plot Y axis preferences

Click again in Operations and explore the other two options: Set Series Color and Set Series Type.



Running north-south in the image, you will see the Guadalquivir river near the city of Se-ville. This is a natural region of marshy lowlands intensively used for agriculture, espe-cially dedicated to cultivation of rice. Note the difference in landscape between the east-ern and western side of the river, particularly in the southern part of the image.

Click on one of the agricultural parcels southeast the river, e.g. at the image coordi-nates 1998, 2173 (you can type the coordinates in the Property Editor). The data plot should look like in Figure 5. Note the spectral plot and compare it with a typical spectral plot of vegetation presented in Figure 2. Do you notice the sharp rise of pixel value between band 4 and 5 (RED) and 6-7 (NIR)? What pattern do you see in the plot if you click on the river?

Deselect Continuous Mode. The option Add pin and Delete pin will be enabled. Click on the button Add pin. Type 1734 and 2129 in the ‘Column’ and ‘Row’ fields respectively. These image coordinates correspond to a pixel in the river. You will see the pin added in the image.

Figure 5 Spectral plot

Add 4 more pins as in Figure 6. They correspond to different features as indicated in the field ‘Label’. When you finish filling in the table click on ‘Show Chart’. The spectral plot will be updated showing the signature of the 5 pins. You may want to change the Chart type (in the Operations menu) to Line for better visualization.

Can you find differences among the signatures of different crops despite they follow the standard trend for vegetation? (compare pin “crop_rice” with pin “crop_other_1 ” and “crop_other_2 ”). How do they compare with e.g. “bare soil”, “water” or “built” curves?

Enable the option Continuous Mode again and click in different pixels. Can you find pixels corresponding to the features represented by the spectral curves of Fig-ure.6?



Figure 6 Spectral plot of selected features.

Visualizing Time Series

The Cross Section function of ILWIS can be used not only to produce spectral plots from multi-spectral imagery, but also to visualize the pixel variability in e.g thematic maps from multi-temporal series. An example of this application is the temporal analysis of radio-metric indices, which can be easily obtained from the S2 dataset provided for this exer-cise. To achieve that, the MapCalc function is introduced in the next section.

Map calculations MapCalc operation in ILWIS is a mathematical expression of pixel values that supports not only implementation of arithmetic, logical and relational operators, but also execution of combined operators and ILWIS functions, in order to manipulate pixel values of a raster coverage. MapCalc can be used either as a simple raster calculator or as a tool for imple-mentation of complex mathematical expressions to generate analytical or thematic out-puts. The MapCalc syntax, in its general form is: Outputmap = Expression In this part of the exercise, MapCalc will be used to calculate NDVI and NDWI2 radio-metric indices.

NDVI calculation The Normalized Difference Vegetation Index (NDVI) is used to determine vegetation vigor and health by measuring the difference between near-infrared, NIR, radiation, (which veg-etation strongly reflects), and red light (which vegetation absorbs). NDVI is defined by: (NIR- RED) / (NIR+RED), which in terms of S2 images can be ob-tained from the expression: (B8-B4) / (B8+B4). Refer to Figure 1 for S2 spectral bands.



Double click on the first multiband container: “SP1_S2AL2A_20170601_sub.tif” which contains 8 bands of the S2 image corresponding to the 1st of June 2017. Use the tab List instead of the tab Grid to visualize the whole name of the objects if needed. (Make sure that the name of the container includes de suffix “_sub”.

On the Workbench, select Operations and scroll down until you find the MapCalc list of operations. Note, that in the operation list you will find six MapCalc opera-tions numbered from MapCalc 1 to MapCalc 6. The numbers refer to the number of input maps. For example, if the expression contains only one map, you need to select MapCac 1, which allows only one raster input. If the expression includes 4 raster maps, you will need to select MapCalc 4, and so on. Select MapCalc 2 for this calculation. The Operation panel will open.

In this specific case, the first input raster will be Band 4* (red) which will be placed in the upper raster or number field, and the second one, Band 8 (NIR), to be added under the previous raster field. The expression to calculate the NDVI is: (@2-@1)/(@2+@1) and the output map can be named NDVI_SP1. *Refer to the suffix “sub_#” to identify the correct band. To illustrate, in this operation you need to select the bands named “SP1_S2AL2A_20170601_sub_4” and “SP1_S2AL2A_20170601_sub_8”.

Fill in the Operations window as shown in Figure 7. You can drag the input ras-ters from the catalog to the raster or number fields.

Select GeoTiff as output format and click the execute tab. After a few seconds (you may check the Progress bar), the map will be calculated and placed outside the multi-band container.

Go to the previous level in the catalog by clicking on the icon. You will see the newly created map “NDVI_SP1”. Double click on it to display it and click the Op-erations button to close the Operations window. Your NDVI map should look like in Figure 8.

Zoom in the central part of the map and check some of the pixel values. Pixels corresponding to the river should yield negative values while the agricultural fields should result on positive values, in some cases close to 1.

Process the other 7 images using the MapCalc 2 operation. Make sure that you name the maps in a meaningful way e.g. keeping the prefix “NDVI_SP#”



Figure 7:Example MapCal, NDVI calculation

Figure 8 NDVI map from S2 2017/06/01



The NDVI maps for the other 7 images that cover approximately the agricultural year 2017/2018 can be calculated following the procedure explained above. A more effi-cient way, would be processing them using the Workflow ILWIS function, which will be explained later in this document.

Once the 8 maps are calculated open the map “NDVI_SP1”.” and click on the Split panel tool. Select the arrangement.

In the other 3 data subpanels, drag the “NDVI_SP3”, “NDVI_SP5” and “NDVI_SP7”.

Make sure that the option ‘panels are linked’ is selected, and zoom in the central part of the image as in Figure 9. Note the differences in NDVI values and try to correlate with the date of the original image. Note the huge contrast in the rice fields (west to river) between the NDVI_SP1 and NDVI_SP2 maps. You can identify every map by clicking on the subpanel and checking the Ac-tions for Display box info.

Close the panel with the synchronized views. With the series of NDVI maps, you can get some insight into the vegetative cycle of the different crops and other type of vegetation present in the marshes, by building a plot of the temporal variability. In order to create such a plot, the 8 NDVI maps need to be added into a Multi-band container.

Click on the Create button of the Workbench and select Raster Coverage. Give a meaningful name to the stack of maps, e.g. NDVI_2017_2018.

Drag the georeference of any of the maps (they must be identical) in the cor-responding field. Leave the other parameters with the default values. Make sure that the option Drag & Drop is selected

Figure 9 NDVI maps, synchronized view



Drag the 8 NDVI maps in the Drag & Drop box as in Figure 10 and click on Apply

The new Multi-band container will be created. Allow some time for the soft-ware to write all the bands.

Figure10 Multi-band container settings

Open the multi-band container recently created. The content maps will be named just with a number ranging from 1 to 8. Open map 1

In the Data panel go to Actions for Display, in Data Attributes: Layer Specific and in Visual properties: Cross Section. In the Property Editor click on Data Source(s) and drag the created NDVI_2017_2018 container (you may need to go one level up in the catalog). Make sure that the 8 maps are included in the Bands box. Zoom into the southeast part of the displayed map, i.e. the agricultural area.

In Property Editor/ Pin Manager, tick the option Continuous Mode and click any-where on the displayed map. A new panel with a chart will open on the left side of the map showing a plot with the temporal variability of the NDVI series for any se-lected pixel (Figure 11)

Figure 11 NDVI, times series plot



Right click on the multi-band container “SP1_S2A_L2A_20170601_sub” and open it as a Color Composite.

In the Data panel go to Actions for Display, in Data Attributes: Layer Specific and in Visual properties: Cross Section. In the Property Editor click on Data Source(s) and drag the NDVI container NDVI_2017_2018

Add pins in the same locations used before (as in Figure 5) to compare the sea-sonal changes of the NDVI for different features in the image (remember to dese-lect first the option Continuous Mode). When the table is complete click on Show Chart. The temporal plot should look like in Figure 12.

Can you comment about the agricultural cycle of the crops, e.g. what is the time of the year of full maturity for rice (purple line)? What can you say about the annual variability of the 2 crops selected (light and dark green lines)? What can cause the pick in the map#6 (01/27/2018) for the pin located in the river (blue line)?

Figure 12 NDVI temporal variation selected features



Module 5: Time series visualization

Change detection: Lake Urmia case study

DEMO (corresponding to webinar time 26’30”- 30’32’’)




Visualizing multi-temporal data for change detection

This section shows an example of change detection from a multi-temporal satellite data by the combination of Copernicus and Landsat images of the Lake Urmia. Lake Urmia, located in a mountainous region in North-West Iran, is one of the country’s most important ecosystems and used to be a paradise for birds and tourists. Until the late 90’s the lake was well preserved. At its greatest extent, Lake Urmia was the largest lake in the Middle East. Unfortunately, due to climate change and human intervention, the lake has shrunk to 10% of its original size in the last 20 years. To monitor the changes, a dataset of Landsat (5 and 8) & Sentinel-2 images covering the period 2000-2018 was selected as presented in Table 1. The images were downloaded from the Earth Explorer data repository of the USGS (https://earthexplorer.usgs.gov/), and the Copernicus Open Access Hub (https://scihub.copernicus.eu/)

Table 1: Multi-temporal dataset

The pre-processing of the images was made in SNAP, as explained in Module 3 of this series. The pre-processing (for the 7 images) consisted in: resampling all the bands to 20m resolution, subsetting spatially to the area of interest and spectrally to the VIS/NIR bands, and finally exporting to GeoTiff format for the processing in ILWIS. The images were renamed keeping all useful information on the name, i.e., the abbreviation of the mission and sensor: LT05, LC08, and S2A_MSI for Landsat 5, Landsat 8 and Sentinel-2 respectively followed by the level of processing and year of acquisition. To illustrate, S2A_MSIL1C_2018_sub.tif corresponds to a Sentinel 2A MSI image, level 1C acquired on 2018 and subset to the AOI.

Open ILWIS, move to the folder containing the images in GeoTiff format and book-mark the folder from the Locator

Click into the multi-raster container of the 2000 image (LT05_L1TP_2000_sub) and open a TCC. For that select bands 3,2,1 (in that order) while holding the Ctrl key, click then in Actions for Selection/(RGB)Apply. The TCC will be shown in the data panel.

Open all the other images in the same way for an initial inspection. You can appreci-ate the evolution of the lake as summarized in Figure 1.



Note the slight recovery of the lake between 2014 and 2016, which corresponds to some efforts of the Iranian Government to rescue the lake. Unfortunately, its current situation is precarious as observed in the 2018 image.

Figure 1: Evolution of the Lake Urmia from 2000 to 2018 from satellite imagery

In order to map these changes we will use the Normalize Differential Water Index (McFeeters 1996, named NDWI2 by other authors). This index enhances the contrast be-tween the reflectance of water in the GREEN band (normally the highest in EM spectrum) with the low, almost null reflectance in the NIR band. NDWI is defined by the expression: (GREEN - NIR) / (GREEN + NIR). The procedure to calculate NDWI in ILWIS is similar to the one presented in Module 4 for NDVI calculation.

Before starting with the operations, create a folder NDWI outside the folder that contains the images.

Select MapCalc2 from the Operations menu In this specific case, the first input raster will be Band 2 (green) which will be placed in the upper raster or number field, and the second one, Band 4 (NIR), to be added under the previous raster field. The expression to calculate the NDWI is: (@1-@2)/(@1+@2) and the output map can be named NDWI_2000.

Fill in the Operations window as shown in Figure 2 using Band 2 and Band 4. For

that, navigate to the 2000 container using the icon and drag the bands to the MapCalc panel. Give NDWI_2000 as an output name (new raster coverage).

Select GeoTiff as output format, navigate back to the newly created NDWI folder and click on execute. After a few seconds (you may check the Progress bar) the map will appear in the catalog.



Figure 2: NDWI calculation for the 2000 image

Double click on the new NDWI map. It should look like in Figure 3 (upper left). Check some pixel values by clicking on the map. You should get positive values for wet pixels and negative values for all the other pixels.

Repeat the same procedure for the other 6 images. The maps should look like in Figure 3.

Figure 3: Time series of NDWI for the selected dataset



Use the linked view to compare some of the maps You can build a multi-temporal plot of NDWI to check the variability of water levels throughout a hydrological year in the same way that the NDVI temporal plot was built in Module 4.

Create a multi-raster container with all the NDWI maps using the option Cre-ate/Raster Coverage and drag all the NDWI maps on the calculator and name it NDWI_2000-2018 (for more details refer to Module 4 page 10)

Open the NDWI map of 2000 from the newly created container (it will appear there just as map ‘1”)

In the Data panel go to Actions for Display, in Data Attributes: Layer Specific and in Visual properties: Cross Section. In the Property Editor click on Data Source(s) and drag the created NDWI container (you may need to go one level up in the catalog). Make sure that the 7 maps are included in the Bands box.

In Property Editor/ Pin Manager, tick the option Continuous Mode and click any-where on the map. A new panel with a chart will open on the left side of the map. It should look like in Figure 4.

Check the variability of the index for pixels located at the borders of what it ap-pears to be the lake in 2000. The temporal recovery of the lake is evident by the NDWI increase in those pixels between 2010 and 2014 as well as the posterior decline on the surface area.

Figure 4: temporal variability of NDWI for the lake Urmia



Based on these maps we can create a water mask to separate pixels containing water. We can do that by applying a threshold to the water index. In general terms pixels with NDWI>0 correspond to water. This criterion however, can be adjusted in certain cases e.g. in swampy areas to distinguish between wet soil and actual water bodies, by slightly increasing the threshold. We can use in this case a threshold of 0.25 as an initial ap-proach.

From the menu Operations Select MapCalc 1. The Operation panel will open.

Fill in the Expression field with: Iff (@1 > 0.25, 1, 0), where @1 is one of NDWI maps. Give a meaningful name to new map. e.g. water_mask_2000 if you used the NDWI_2000 map. The calculator should look like in Figure 5.

Select GeoTiff as output format and click the execute tab. After a few seconds, the map will be calculated.

Figure 5: Water mask for the 2000 image

Calculate the water masks for the other 6 images in the same way. The 7 masks should look like in Figure 6.

Figure 6: Evolution of water masks for the period 2000-2018



The water masks contain only pixels with 0 or 1 value. The surface area of the lake for every map can be easily estimated from the amount of pixels with value=1 multiplied by the pixel area. Another interesting way to visualize the changes of the lake is by means of a multi-tem-poral RGB image. For that we can select the most recent image (2018), and the oldest (2000) and for the R and B channels respectively, and an image of an intermediate year, e.g. 2010 for the G channel.

From the catalog containing all the water masks select the masks of 2018, 2010 and 2000 (in that order) and visualize as a RGB image.

The resulting image shows the smallest area, i.e. form the 2018 map, in white (as the pix-els contain the value=1 in the three RGB channels). Pixels contained only in the largest mask (i.e from the 2000 map) will appear in blue, and pixels of the intermediate mask (from the 2010 map in this case) will be represented in cyan as they have pixel value=1 in the R and G channels and value=0 in the R, as in Figure 7.

Figure 7 multi-temporal RGB image of the water mask in the lake Urmia. R=2018, G=2010 and B=2000

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