Solutions and Data
The CommonSensing web-based solutions that will be delivered to the governments in Fiji, Vanuatu and the Solomon Islands are currently under development. We are expecting to deliver these solutions to the nations over the course of 2020.
The below information describes how IPP CommonSensing will deliver satellite data and geospatial solutions to the nations by explaining the challenges and procedures required before satellite data can be analysed and accessed by geospatial experts, governments, and policy advisors.
Advancements in technology have led to a dramatic increase in the availability of satellite imagery. Global initiatives such as the EU’s Copernicus Programme, have secured access to freely available satellite imagery covering the whole world approximately every 12 days (6 days in higher latitudes), until at least 2030. The availability of open source data has opened up new opportunities to monitor the Earth across wide scales and through time, allowing us to take the Pulse of our Planet, and supports the development of space based monitoring applications which can enable reporting against global initiatives such as three global priority engagement areas: The United Nations 2030 Agenda for Sustainable Development; the Sendai Framework for Disaster Risk Reduction; and the Paris Climate Agreement – to help drive progress towards inclusive, sustainable development.
This influx of EO data comes with a requirement to find faster, cost-effective ways to analyse EO imagery at scale, and drives the requirement to get data in the hands of policy makers to enable evidence-based decision making. Therefore, there is a challenge to make the analysis and interpretation of satellite imagery easier to achieve across wider global audiences worldwide.
The Common Sensing project will bring satellite data and geospatial solutions to policy makers in the Small Island Developing States of the South Pacific. Common Sensing is providing timing, efficient access to Analysis Ready Data for Sentinel-1, Sentinel-2, Landsat-5, Landsat-7, Landsat-8 and SPOT 1-5 over a period of 30 years (1990 – Present) via the Open Data Cube and a geospatial decision support system.
Analysis Ready Data
Before Earth Observation data can be accurately used, the user needs to undertake a series of pre-processing steps to correct the imagery. To facilitate ease of access and use of Earth Observation data by non-expert users, Common Sensing solution leverages Analysis Ready Data (ARD). The Committee on Earth Observation Satellites (CEOS) defines Analysis Ready Data for Land (CARD4L) as “satellite data that have been processed to a minimum set of requirements and organized into a form that allows immediate analysis without additional user effort” (Killough, 2016). As per the CEOS specification, optical ARD is defined as Surface Reflectance. Data collected with multispectral sensors operating in the Visible (VIS), near-infrared (NIR) and shortwave infrared (SWIR) wavelengths is corrected for effects of atmosphere and cloud cover, illumination and terrain distortion. Radar ARD, is defined as normalised radar backscatter which has been orbit corrected and orthorectified to correct for effects of terrain. This ARD is prepared and hosted in the Open Data Cube (ODC).
Open Data Cube
The Open Data Cube (ODC) is an Open Source Geospatial Data Management and Analysis Software project that helps harness the power of EO data. At its core, the ODC is a set of Python libraries and PostgreSQL database that helps you work with geospatial raster data. The ODC seeks to increase the value and impact of global Earth observation satellite data by providing an open and freely accessible exploitation architecture. The ODC project seeks to foster a community to develop, sustain, and grow the technology and the breadth and depth of its applications for societal benefit. Data Cubes are a new way to store, organise, manage and analyse EO data at scale and provide access to large spatio-temporal archives that are processed and organized to allow immediate analysis across time and across data sets with minimum user effort. This removes barriers to full exploitation of EO data and supports use by non-expert users.