From data to information

An introduction to using remote sensing data and open-source tools for analysis of foreshore characteristics

Edward P. Morris (UCA-CEIMAR) and Bas Oteman (NIOZ)

The capacity of light to carry and convey information is perhaps its most important, and remarkable, characteristic.

Ian Walmsley in 'Light : A very short introduction', Oxford University Press.

Remote sensing 101

Platform, sensor and resolution

Electromagnetic radiation

Has both wave and particle properties at the same time (wave-particle duality).

  • Waves can be described by their amplitude and wavelength
  • Polarisation: waves oscillate in more than one orientation
  • Photons are the elementary particles of eletromagnetic radiation
  • Photons can be interact with matter by scattering and absorption
  • Reflectance is scattering in a defined direction
"Electromagneticwave3D" by Lookang many thanks to Fu-Kwun Hwang and author of Easy Java Simulation = Francisco Esquembre - Own work. Licensed under CC BY-SA 3.0 via Commons.

Electromagnetic wave spectrum

Atmospheric electromagnetic opacity

Passive visible and infrared

platform-sensor Ocean Optics Web Book • All contents Creative Commons logo 2015 Creative Commons Attribution license.

Measures (sun) light

  • day-time only*
  • radiance (light) passes through atmosphere
    • affected by scattering and absorption
    • affected by clouds
    • requires atmospheric correction procedures

Passive visible and infrared

The surfaces of substances have specific scattering and absorption properties

  • allows identification of surfaces (i.e, the blue surface)
  • allows quantification of substances (i.e., the number of blue 'rocks')

Red and green light is absorbed, blue reflected.

Simple reflectance.svg
"Simple reflectance" by User:Phidauex - Own work. Licensed under Public Domain via Commons.

Passive visible and infrared

Passive visible and infrared

All photosynthetic organisms contain chlorophyll giving them a distinctive reflectance spectrum that can be summarised by spectral indices (ratios), e.g., the 'Normalised Difference Vegetation Index' (NDVI).

"Vegetation spectral response" by Licensed under Public Domain via Commons.

NDVI = (NIR - Red)/(NIR + Red)

Passive visible and infrared

The different pigments of photosynthetic organisms result in different 'colours', potentially allowing identification of foreshore vegetation types.


Passive visible and infrared

Passive visible and infrared

Active radio

Synthetic Aperture Radar (SAR)

  • active radio pulses sent and collected
  • effective antena size much larger using movement of satellite and clever processing (Synthetic Aperture).
  • measures polarised radio waves e.g., horizontal and vertical.
  • allows increased spatial resolution.
SAR_diagram.png "A geometric model of a SAR system" by Zhou et al. 2009.

Active Radio

Synthetic Aperture Radar (SAR)

  • most common bands are L, C and X
  • different band frequencies are suited to different applications
    • penetrating canopies: L and P, very high resolution: X, multi-purpose: C
SAR-bands.png "Commonly used frequency bands..." by Moreira et al. 2013.

Active Radio

canopy-radar.png "Surface and volume scattering of a SAR beam for trees " by (Fernandez-Ordonez et al. 2009.).

Surface interactions

  • still water tends to be a specular reflector of radio waves i.e, usually low backscatter.
  • complex interactions with vegetation result in different return signals.
  • buildings often have strong backscatter signal.
  • interactions vary with beam polarization i.e., different polarizations provide more information.

Active Radio

radar-speckle.png "Speckle occurs in SAR images due to the coherent sum of many elemental scatterers within a resolution cell..." by Moreira et al. 2013.


  • caused by the many scatterers in a 'pixel'.
  • coherence of the scattered signal results in strong fluctuations across the image.
  • 'multi-looking' used to reduce speckle at cost of spatial resolution.
    • non-coherent averaging of the intensity image.
  • adaptive filtering can also be used to reduce speckle.

Active Radio

Further resources

How to access remote sensing data

Data availability

Some earth observation satellites with open access data, based on *C=Color, E=Elevation, H=Hyperspectral, M=Multispectral, P=Panchromatic, R=Syntheric Aperature Radar
Name Abbreviation Resolution (m) Availability Return Interval Type* Platform
Landsat Thematic Mapper 5 L5-TM 30,60 1984 to 2013 16 days M Satellite
Landsat Enhanced Thematic Mapper 7 L7-ETM 15,30,60 1995 to 2003 16 days P,M Satellite
Moderate-resolution Imaging Spectroradiometer MODIS 250,500,1000 2000 to present Daily M Satellite
Medium Resolution Imaging Spectrometer ENVISAT-MERIS 300 2002 to present 3 days M Satellite
Landsat Data Continuity Mission (Landsat 8) L8-OLI 15,30,100 2013 to present 16 days P,M Satellite
Copernicus Sentinel-1A S-1A 4,10,25,40 2015 to present 12 (6) days R Satellite
Copernicus Sentinel-2A S-2A 10,20,60 2015 to present 12 (6) days M Satellite

Data type and format

Data type and format

Landsat product types

Generally surface reflectance products are prefered, however see L8-SR user notes and Landsat Higher Level Science Data Products:

  • not completely validated
  • not all scenes can be processed: Landsat 8 Pre-WRS-2 scenes (before April 11, 2013), scenes with a solar zenith angle greater than 76°.
  • Efficancy of L8SR correction will be likely reduced in: Hyper arid or snow covered regions, Low sun angle conditions, Coastal regions where land area is small relative to adjacent water, Areas with extensive cloud contamination.
  • High latitudes (> 65º) may not be valid.

Data format and type

Landsat Standard L1 products

  • Standard Terrain Correction (Level 1T -precision and terrain correction) if possible.
  • Cubic Convolution (CC) resampling method.
  • 30-meter (TM, ETM+, OLI) and 60-meter (MSS) pixel size (reflective bands).
  • Universal Transverse Mercator (UTM) map projection (Polar Stereographic projection for scenes with a center latitude greater than or equal to -63.0 degrees).
  • World Geodetic System (WGS) 84 datum.
  • MAP (North-up) image orientation.
  • GeoTIFF per band, quality assement + metadata (.MTL)

Data format and type

Data type and format

Data type and format

Data type and format

Data type and format

Data type and format

Earth Explorer

  • official bulk dissemination system for the Landsat (and other) products.
  • requires registration (quick and easy); email address used to send download links!
  • no restrictions on data use but requested to acknowledge source:
    • USGS Products: 'Data available from the U.S. Geological Survey.'
    • NASA LP DAAC Products: 'These data are distributed by the Land Processes Distributed Active Archive Center (LP DAAC), located at USGS/EROS, Sioux Falls, SD.'
  • surface reflectance products generated 'on demand'.

Sentinel Scientific Data Hub

Other examples of how to get the data


  • Google Earth Engine (GEE); Landsat archive, Sentinel-1 (available late 2015?), MODIS, MERRIS, ect. Sentinel-2 (planned).
    • Google Storage also mirrors much of these archives. You can install their “gsutil” (free) and then list this directory, which is indexed by sensor (for example, L8 is Landsat 8) and path/row: gsutil ls gs://earthengine-public/landsat/
    • Note Sentinel-1 has undergone further pre-processing in GEE


Other examples of how to get the data


Via commandline using curl:

# download a specific sentinel product
S:\>curl -u User:Password -JO "\

How to extract information

Basic concepts

Georectification: Mapping the data to real world coordinates.

Calibration: Converting the data to real world units

  • at-instrument; instrument-specific digital numbers to units , i.e., radiance
  • top-of-atmosphere; accounting for platform to target geometry.
  • surface; removing atmospheric affects and corrections for topography

Morphological operations: Filtering, changing resolution, ect.

Course classification: Assigning different classes (e.g., clouds, shadows, water) using segmentation or clustering.

Open source image processing (some options)

Sentinel Application Platform (SNAP)

A common architecture for all Sentinel Toolboxes

  • open-source (Java) available at GitHub or precompiled at STEP.
  • successor of the mature BEAM + NEST software.
  • efficient parralization of calculations resulting in good performance on modern PCs.
  • Python bindings available soon?
  • Sensor specific toolboxes (Sentinels, but also many other sensors).
  • Developed by Array Systems and DLR (S-1), C-S (S-2) and Brockmann Consult (S-3) for ESA.

Sentinel Application Platform (SNAP)

Getting help

The power of the Graph Builder and Graph Processing Tool (GPT)

  • graphical building of workflows [graphs]
  • save workflows in XML and share
  • apply to single image or batch process many images
  • run from within the GUI or the command line (GPT)

ESA Research and Service Support (RSS)

'RSS has the mission to support the Earth Observation (EO) comunity in exploiting EO data'

  • supports institutions, scientists and developers.
  • supports data provisioning, access and processing.
  • help to scale up workflows
  • computation on demand and cloud computing service free for 'non-profit' organisations.


Flag_of_Europe.svg" The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 607131. All views presented are those of the author’s, the European Union is not liable for any use that may be made of the information contained therein.

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Now your turn...

Using Sentinel 1 toolbox (S1TBX) to get information about coastal systems

Introduction to the Sentinel 1 toolbox (S1TBX)

This workshop will cover the basics of working with S1TBX to process Sentinel-1 data:

  • open S1A-IW-GRDH product
  • basic pre-processing
  • export data

Using the Graph Builder in S1TBX

  • use the graph builder to save your workflow.
  • take a look at the graph XML format.
  • edit the XML so as apply different types of speckle filter and multilooking, compare results.
  • set up thresholding of the image using band-math and extract 'water'.
  • run a basic classification and compare to the threshold result.

Open S1A-IW-GRDH product

File > Open Product OR Import Raster Data > SAR data > SENTINEL-1


Examine the S1A-IW-GRDH product

Products View expand structure

Open window to display raw data (RIGHT CLICK BAND > Open Image Window)


Subset S1A-IW-GRDH product

Zoom in on area of interest; note image is mirrored

Raster > Subset OR RIGHT CLICK ON IMAGE > Spatial Subset From View


Apply at-sensor and top-of-atmosphere calibration

SAR Processing > Radiometric > Calibrate


Apply a 'speckle' filter

SAR Processing > Speckle Filtering


Apply a geometric correction

SAR Processing > Geometric > Elipsoid Correction


Convert to decibels

Utilities > Dataset Conversion > Linear to/from dB

Raster > Dataset Conversion > Linear to/from dB


Examine the pre-processed product

Look at a profile from water to land:

Analysis > Profile Plot

Add a vector layer:

Vector > Import OR Line Drawing Tool


Look at a histogram for the scene:

Analysis > Histogram


Export the pre-processed product

Choose your prefered export format

File > Export Raster Data > NetCDF4-CF


Using the Graph Builder in S1TBX

Tools > Graph Builder