Toolkit summary. Toolkit contents

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1 Using satellite imagery to understand the benefits that marine nature reserves provide for society: a toolkit for the use and visualisation of satellite products Toolkit summary This toolkit gives an introduction, advice and access to open-source resources for satellite remote sensing and mapping of intertidal marine features. Specifically, this toolkit provides an example for the mapping and sub-feature classification of an area of saltmarsh on The Wash, Lincolnshire, using Sentinel 2 data. Three saltmarsh parish regions are considered and correspond to the stakeholder derived saltmarsh classification schemes undertaken through this Valuing Nature Placement (VNP) Scheme project2017/2018. Click here for access to all project information. This toolkit gives an entry level tutorial for the use of the Sentinel Application Platform (SNAP) for image analysis, QGIS for image visualisation and Scribus for image publishing. This toolkit was funded through the NERC Valuing Nature Placement scheme, led by The Wash and North Norfolk Marine Partnership in collaboration with the Institute of Estuarine and Coastal Studies, University of Hull, Marine Conservation Society and Lincolnshire Wildlife Trust. This toolkit was created by the WNNMP Project Manager Sam Lew with an Introduction from IECS Director and Satellite Oceanography Rodney Forster. Thanks goes to Daryl Burdon and Shona Thomson for reviewing the toolkit. Please get in touch with the WNNMP Project Manager with any suggestions for amendments and improvements to this toolkit sam.lew@wnnmp.co.uk Toolkit contents 1. Introduction to satellite remote sensing of saltmarsh habitats 2. Data/image acquisition 3. Data/image correction 4. Data/image analysis in SNAP - Sentinel Application Platform 5. Data/image visualisation in QGIS 6. Image publishing in Scribus 7. Other useful data products and maps 8. Bibliography 9. Appendix 1

2 1. Introduction to satellite remote sensing of saltmarsh habitats Remote sensing refers to any process which can capture data without direct contact with the object of interest. This includes; aerial photography and images/video from unmanned aerial vehicles (drones) satellite images from orbit radar images sonar and acoustic mapping from ships sensors carried on birds or mammals A source of electromagnetic radiation is used to illuminate the target. Typically, this is the light of the sun, which is referred to as passive remote sensing. Active remote sensing techniques use a high-energy beam such as laser or radar to visualise a target object. Remote sensing of saltmarsh habitats most often measures reflected sunlight in the visible and near-infrared part of the electromagnetic spectrum to determine composition of the marsh surface. The earliest remote sensing images in use today were generated by military surveillance aircraft during and after the second world war. Black and white photographs from this period are very useful for detecting changes in the position of the shoreline, caused by storm events, sediment erosion/deposition or land reclamation (van der Wal & Pye 2004). Regular airborne surveys using a variety of cameras and spectral scanners have provided a reliable record of saltmarsh and intertidal change over the past fifty years (Thomson et al. 2003). Satellite-based remote sensing of the high shore and intertidal can potentially cover a greater area than airborne techniques, and at a lower cost. The Landsat and SPOT series of satellites were the first instruments to provide spatial and spectral data at an adequate resolution for studies of UK terrestrial habitats. Landsat 8, carrying the Operational Land Imager (OLI), produces spectrally-resolved images at 30m resolution. Overpass frequency of Landsat 8 is every 16 days. More recently, the European Space Agency, EUMETSAT and EU funded Copernicus programme has launched a series of Sentinel satellites. Imaging the earth and its oceans with optical sensors at 10 m resolution, the twin pair of Sentinel 2 A and B satellites can provide an image every 5 days. Combining the availability of Landsat-8 and the Sentinel-2 pair gives a revisit time for UK latitudes of 3-4 days (Li & Roy 2017). 2

3 Figure: From Li and Roy (2017) Cloud coverage of the target is an obstacle to remote sensing in the UK. However, the high temporal frequency of overpasses now available allows approximately one clear scene per month of sites such as The Wash and North Norfolk coast. For the low shore intertidal, timing of low tide with respective to satellite overpass is important. This restriction is lower for the upper shore and saltmarsh zones. 2. Data/image acquisition Section 2 gives a quick introduction to a number but not exhaustive list of platforms that allows users to view and download satellite data products. These platforms are referred to as DDD (Discover, Display and Download) Services. 1. EO (Earth Observation) Browser This browser provides access to an archive of sun synchronous products (MODIS, Landsat and Sentinel) to view but without access to the downloadable data files Data searches specify time and date of overpass, tile name, cloud cover (%) and whether the data is atmospheric corrected (L2) or not (L1) EO browser also allows visualisation of custom, true (4, 3, 2) and false (8, 4, 3) colour spectral band combinations Downloadable geo-corrected.jpegs are available for download 3

4 2. Copernicus European Union programme for earth observation using Sentinel 1, 2 and 3 satellite data The Copernicus browser provides access to an archive of all Sentinel data to view and with access to downloadable data files Data searches specify time and date of overpass, Sentinel platform (1, 2, and 3), product type (L1 and L2) and cloud cover (%) o If the Sentinel platform is specified in search criteria only, all data products will be shown for a given time window Geographically referenced tiles for products are specified 3. Earth Explorer: United States Geographic Services 3. Data/image correction When the signal is returned from earth and received by the satellite there will be a degree of atmospheric noise that will affect the received data. Some data products provided by the Copernicus Programme have already been corrected (L2) and are referred to as Analysis Ready Data. Some products have not had any atmospheric filters or corrections applied (L1). Tools are available to apply atmospheric correction that can be tailored to the user s requirements. 1. Acolite This software is for atmospheric correction and processing of Landsat, Sentinel and other data products Red, green and blue images can be created in various files types including; o Geotiff can be directly loaded into GIS o NetCDF can be directly loaded into SNAP as with files downloaded from Copernicus An Acolite manual is available that provides detailed information on how to upload and process L1 files and descriptions of various output parameters, i.e. different types of corrected data products, e.g. cloud correction o _ pdf 4

5 2. ICOR from VITO This software is for atmospheric correction and processing of Landsat and Sentinel data products and links directly with SNAP 4. Data/image analysis Having acquired Analysis Ready Data (L2) or having corrected data (L1), you can now view, manipulate and analyse the satellite data product using SNAP, which can be downloaded using the following link or directly The tile used in this toolkit is tile T31UCU (The Wash and North Norfolk coast) at 1050 on 9 th April 2017 L2 data. Open SNAP and import the downloaded Sentinel L2 data file: File Import Optical Sensors Sentinel 2 S2-MSI L2A (options available to import L1 data) Select downloaded Sentinel L2 file - open sub folder and select MTD MSIL2A (Metadata Microsoft Level 2A) file. 5

6 The file will appear in the Product Explorer window (left hand column). You can now open an image of the download Sentinel 2 tile in SNAP: Right click file Open RGB (red, green, blue) Image Window Choose band combinations There are a series of default band combinations to choose from, for example; Natural colours (bands 2, 3 and 4 are within the visible spectrum) False colour Infra-red (bands 3, 4 and 8 band 8 is near Infrared) To start with, choose Natural Colours to view the satellite tile in the visible spectrum. Each band s optical signal can be manipulated to vary the relative intensity of each band using the Colour Manipulation function (bottom left on the screengrab below). Have a play around to get your preferred view and make a note of the values ranges. You will need them later for GIS processing. As the Sentinel 2 satellite samples multiple bands that have different widths (10m, 20m and 60m) the image pixels must be resampled to the same size to allow for comparative analysis of pixel properties. Raster Geometric operations Resampling IMPORTANT save as BEAM-DIMAP file. Now specify the resampling parameters, i.e. define the size of the sample product by pixel resolution, choose 10 m. 6

7 Run (this can take a while depending on the processing capacity of your machine). IMPORTANT when the processing complete window appears do not tick the box that says, Don t show this message anymore. This appears to destabilise SNAP. Many open-source software s have quirks. This appears to be SNAP s initiate meltdown button including keyboard disconnection. Just click OK. A second file will open in the Product Explorer window: file name _resampled. Open RGB image as above. Define and create a subset of the tile that you want to analyse this helps to speed up processing time In the left-hand Navigation Panel zoom in on the desired subset and make a subset of the image Raster Subset Specify product subset select bands that you wish to represent in the subset (B1 to B12 include all Sentinel 2 bands) and click OK. (if you have saved the BEAM-DIMAP file when resampling the tile this should be quick). 7

8 A third file will open in the Product Explorer window: file name _subset. Open RGB image as above. Now you can create a classification scheme to identify pixels with shared optical properties within a specified region or polygon. You can manually draw a polygon in SNAP using the polygon drawing tool found in the SNAP toolbar. You can save the polygon dimensional properties by right clicking the polygon and select WKT from geometry. WKT stands for Well-Known-Text. Save these coordinates in a text file and if you want to use the polygon again right click an image in SNAP and select Geometry from WKT. The georeferenced polygon will appear. 8

9 The Well-Known-Text file for the polygon used in this toolkit can be found in Appendix 1. Alternatively, you can import a shapefile into SNAP. This function may also be useful for defining boundaries if you are drawing a polygon by hand using the polygon drawing tool. To import a shape file: Vector Import ESRI Shapefile Once you have created the region you would like to classify with a polygon, you are ready to run an unsupervised classification scheme using the K-Means Cluster Analysis Method. For more information on K- Means clustering see the SNAP help function. Raster Classification Unsupervised K-Means Cluster Analysis This time do not save BEAM-DIMAP file. Another SNAP quirk You can also select the directory and the name of the classification scheme. Under processing parameters, specify: the number of classification clusters (default is 14). Select 6 for now the source bands you would like to use in the classification scheme (select all for now) selected your ROI (Region of Interest) mask, i.e. your polygon. If you have drawn a polygon using the polygon drawing tool it will be called geometry. Run. 9

10 A fourth file will open in the Product Explorer window: file name _K_Means. Open the sub-folders by clicking [+]. The last file in the list is called Bands. Within this folder is a file called class_indices. Right click this file and Open Image Window. You now have a classification scheme for the area within your polygon. Files can now be exported for use and visualisation in GIS. Export both subsets: subset{ }_resampled and _kmeans Select file in Product Explore window File Export GeoTIFF 5. Data/image visualisation in QGIS QGIS can now be used to isolated and visualise the classification scheme in distinct layer and further mapping layers can be added to create informative products. Download the latest version of QGIS Import your image layers into the QGIS work window Layers to import include; Sentinel 2 subset image, K-Means classification subset 10

11 Layer Add layer Add Raster choose Sentinel 2 subset You will have noticed that the colour scheme in QGIS is different to SNAP. This is because QGIS imports different default band combination to the ones you previously specified in SNAP, i.e. Natural Colours (bands 2, 3 and 4 in the blue, green and red spectra, respectively). To change the bands, double click the file in the layers panel (bottom left). Under Style you have options for Band rendering Colour rendering and Resampling. Under Band rendering, allocate bands 4, 3 and 2 to colour spectra red, green and blue, respectively, i.e. Natural Colours. You will also need to specify the minimum and maximum values that you made of note of from the SNAP Colour Manipulation window. You will notice that the relative scaling of the spectra values is one order of magnitude different between QGIS and SNAP. Just in case you didn t make a note, use these values: Red: , Green: , Blue: Click Apply. 11

12 Now import the K_Means classification. Layer Add layer Add Raster choose Sentinel 2 subset kmeans 12

13 The Raster layers now need to be converted to separate polygons to separate each of the K-Means classes Raster Conversion Polygonise Choose K-Means file from drop down menu and give the output file a suitable name Now the polygons that you just created must be classified as separate categories rather than one aggregated group Layer properties Style Categorise Classify Now the K-Means classes are grouped into each class. You can now use the Layers panel to deselect the original imported kmeans file. Zoom in to your classification scheme. Each classified layer is now made up of many separate polygons. The polygons in each class must be grouped into a single layer. I.e. 6 classes will give 6 layers. In the Layer Panel select one class only and highlight the whole class using Pointer tool for in the QGIS toolbar. 13

14 Layer toggle editing (allows editing and merging of polygons) Edit merge selected features layer (un)toggle editing Save layer 14

15 By saving you have merged all the polygons in a single class into one layer. Now repeat the process for all classes. Play around with the Layer Properties window. Try choosing a different colour scheme. 15

16 When you have merge all classes creating 6 class layers and one Sentinel image layer the final version of the GIS map is made in the QGIS Composer. Select the QGIS composer and give the file a suitable name. Now you need to paste your QGIS map into the composer. Layout Map Drag cursor across the window to the desired size (map will appear) You can also add a legend, North arrow and other finishing touches in the composer window. The map you have made in the QGIS composer window must now be saved as a scalable vector graphics or SVG file to retain the layers to create a layered portable document file or layered.pdf Composer export as SVG name file In the export options make sure that you export map layers as SVG groups. You are now readt dreate your layered PDF. 16

17 6. Image publication in Scribus Although PDF file can be printed in QGIS, Scribus allows the user to create layered PDFs that allows the user to activate and deactivate layers in a PDF file as in the QGIS work window. Download the latest version of Scribus The Scribus toolkit process is based on Open the SVG file that you previously saved in QGIS. Now create some blank layers in the layers window so you can allocate layers from your SVG file. Windows Layers add 7 layers The file will have no distinct layers as all layers are grouped by default in the SVG file. Right click on the image and select ungroup. Make sure you have selected the background layer in the layers window. Ungroup the Sentinel 2 image from the classification layers. You may have to delete a blank surface layer. Then right click again and select ungroup. Again, make sure you have selected the background layer in the layers window before ungrouping layers. 17

18 You will now have the Sentinel image separated from the classification layers. Right click again and ungroup the classification layers. Right click on the layer that is highlighted and choose Send to Layers and choose one of the empty layers that you previously created. You can give this layer a name. Turn it off to see what other layers are left. Continue this process of ungrouping and sending to layers until all layers are assigned. Name and order all layers appropriately before creating a layered PDF. 18

19 To create the layered PDF: File Export save as PDF (ignore errors) Under File Options - Compatibility choose PDF 1.5 (Acrobat 6) and select Include layers Now you have saved a layered PDF. Open in a PDF viewer that allows viewing of layers (Acrobat) and click on the layers icon (left) to view layers and select/deselect them to view. 7. Other useful data products and maps Magic maps saltmarsh extent maps for the Environment Agency marine charts and environment maps Ordnance Survey shapefiles JNCC protected sites website shapefiles available for MPA boundaries 19

20 8. Bibliography Li J, Roy DP (2017) A global analysis of Sentinel-2a, Sentinel-2b and Landsat-8 data revisit intervals and implications for terrestrial monitoring. Remote Sens 9 Thomson AG, Fuller RM, Yates MG, Brown SL, Cox R, Wadsworth RA (2003) The use of airborne remote sensing for extensive mapping of intertidal sediments and saltmarshes in eastern England. Int J Remote Sens 24: Wal D van der, Pye K (2004) Patterns, rates and possible causes of saltmarsh erosion in the Greater Thames area (UK). Geomorphology 61:

21 Appendix 1 Well-Known-Text coordinates for the polygon used in Section 5. POLYGON (( , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , )) 21