Earth Observation (EO) Data allows us to gather information about the Earth’s land surface, sea, and atmosphere through remote sensing technologies and satellites. It can be particularly useful in collecting data over large areas of land relatively quickly, and can allow us to measure baselines of environmental data relating to biodiversity, land use, and weather forecasting. In some ways, this is crucial for marginalised communities because it can help fight injustice:
EO data can help map and document cultural heritage sites and be used to help protect them against encroachment, or can foster collaboration between indigenous communities, scientists, and policymakers if community members are trained in using and engaging with EO data.
However, this does not come without risks of harming these communities. There are various ethical considerations at play, such as who controls EO information, who benefits from its application, and who the “winners” and “losers” are of the specific projects that EO data aims to benefit. There are also security considerations, as publicly-available EO data can be accessed by diverse stakeholders around the world, and there are concerns that this access might expose vulnerable locations or indigenous communities who may not want to be seen at this level.
If EO Data is so risky, why should it be used?
The good thing about EO Data is that it’s sparked quite a lot of interest over recent years. Its potential has been widely recognised in both understanding what future climate change might physically look like at different scales and understanding how it could be used to advance environmental and social justice even in negative implications of climate change, such as climate migration. Because of this, there’s been a shift in EO research that acknowledges the social and justice impacts of EO information, and how potential disadvantages or risks can be addressed.
In Uganda, climate migration has been a major issue affecting local communities. While empirical research on the numbers of people who have migrated due to environmental or climate change has been lacking, recent studies have shown that hardships relating to socio-economic hardships (such as natural resources scarcity and hunger/food insecurity) are linked to slow-onset climate processes (such as rising temperatures). Additionally, these slow-onset processes can be then compounded on by more sudden environmental shocks, like flooding or drought. Ugandan households are typically dependent on natural resources, yet we have seen increased deforestation, biodiversity loss, and urbanisation increasing the consumption of these natural resources. When natural disasters occur — which they are at an increasing rate due to climate change — this has further negative effects on peoples’ livelihoods and then can induce migration.
Climate migration can occur quite suddenly at times and often disproportionately impacts indigenous communities, and this is where Earth Observation data can come into play.
For policymakers, EO data could allow for “modelling, forecasting, characterising, and understanding the severity of migration flows.” Therefore, EO data has the potential to help us understand how people migrate and which areas might see more outmigration due to climate change or natural disasters. If used properly, EO data can then be used to influence Anticipatory Action policies that are more equitable for multiple stakeholders, ideally those who are most vulnerable, and on multiple scales.
So how can we move forward with EO data relating to climate migration?
A team of researchers, as part of the AfriCultuReS project, has recently been working on providing agriculture sectors in eight African countries with data combining EO data and climate and crop modelling, which can be used to improve food security. Through workshops, this team was able to map some of the reasons — both immediate and underlying — why EO data could fail to produce lasting benefits.
This map splits potential barriers for the successful use of EO data in development projects into two categories: proximate causes (the immediate and more easily observable reasons why EO data does not produce lasting impacts) and ultimate causes (the more systemic issues that can hinder the success of EO data in development). While there are many considerations evident in this map that need to be taken into account on a case-by-case basis, one of the most consistent themes is the need to engage with communities and foster meaningful collaboration in EO data processes, rather than relying on unidirectional communication towards communities. By doing this, we can ensure that EO information is useful both locally, such as in climate adaptation efforts, and internationally, such as in potentially providing aid during natural disasters or other environmental crises.
Conclusion
EO data has many different considerations that must be accounted for if we are to use it to help understand climate migration and use it as a tool to promote social justice in said migration. We know that climate disasters will increase in both frequency and intensity, and the knock-on effects of these will inevitably lead to the emergence of new patterns in migration. With careful consideration of its ethical implications while fostering partnership in Earth Observation design processes and data collection/utilisation, EO Data may provide a valuable tool for predicting migration dynamics and understanding what can be done preventatively and correctively.
