The Uptake of Earth Observations for Environmental Services Assessments

Photographing the earth from space means more to us now than ever before. Space data feeds the increasing number of models, tools, products, and services that have mainly been created for breaching connectivity gaps and preventing natural disasters. What about supporting our understanding in how people adapt to the natural environment to sustain livelihood? That is, not only human existence and its survival, but human-nature coexistence and its well-being.

Earth Observations (EO) are images collected, stored and transmitted from satellites that continually orbit the globe to systems in charge of processing them as datasets used for specific purposes. EO are collected via remote sensing and in situ data to generate imagery or raw data used to create EO products or services for defense and security purposes, weather forecasting, and measurements and monitoring of earth systems. In the midst of climate change threats and development concerns[1], EO products and services are expected to boost datasets and improve the identification of variables for better measurement and monitoring of ecosystem services. The complex ecological and social relationship between societies and the natural environment is captured in how ecosystem services are understood and used. Based on this premise, efforts to improve ecosystem service assessments are relevant today because ‘the benefits nature provides to people are increasingly recognized and accounted for in assessments of infrastructure, development, agricultural management, conservation prioritization, and sustainable sourcing’ (Ramirez-Reyes et al., 2019).  Though, there is a growing number of publications about ecosystem services, mapping them, as an initial step, is still a challenge (Andrew et al., 2015).

Mapping ecosystem services is just the tip of what current growing initiatives aim to achieve. The real deal comes down to identifying the challenges and opportunities of its provision, trends, demands and trade-offs. EO technologies can be used to support ecosystem services decision-making process on conservation and resource management, but, most importantly, they ‘can be used to create improved and new ecosystem service models that advance both our understanding and assessment processes for the complex-human natural system relationship’ (Ramirez-Reyes et al., 2019). This potential of EO for ecosystem service assessments, however, faces barriers that demand high-skilled expertise, continuous data validation and accessibility.

So far, there are initiatives aiming to include EO in ecosystem services (Ramirez-Reyes et al., 2019). The interdisciplinary bet depicted in these initiatives demonstrates the complexity and variability in EO and its usefulness, yet its funding sources, shareholders, and stakeholders could impact the bias and degree of transparency in data validation and accessibility.

Source: Ramirez-Reyes et al., 2019
An example of how EO data is being applied for assessing ecosystem services in a city is the new 3-year project in Singapore that aims to determine the economic, social and environmental benefits of the country’s forests, parks, waterways, and marine habitats (Hong, 2018, Aug 16). Quantifying all the benefits provided by Singapore’s natural environments looks very ambitious. Nevertheless, this iconic technology-driven megacity aims at assessing the trade-offs between development and conservation. Ultimately, it would be interesting to see how this pilot unfolds and critically analyze the trade-offs proposed out of this assessment. The management and processing of data in the assessment of ecosystem services is crucial since the outcomes are crystalized as trade-offs. As professors Galleguillos & Grêt-Regamey state, ‘details about the production of the dataset and the validation of the data are thus crucial to avoid mistakes in the results and interpretations’ (NSL, retrived on Dec 29, 2019). Also, the access to and quality of that data as well as the ability for the general public to understand it are key to support development-environment trade-offs and the eventual transformation of landscapes intentionally. Big data goes hand in hand with spatial data and the amount of datasets and the way it is used and communicated leave us with more questions than answers: to understand, design, preserve, and exploit landscapes and provisions that sustain the well-being of our future generations, how much and what kind of data should be prioritized?  How does the ‘critical mass’ look like for EO uptake? In policy making and governance, what would decision-making look like knowledge-driven or data-driven?

References

Andrew, M. et al. (2015, May 01). “Spatial data, analysis approaches, and information needs for spatial ecosystem service assessments: a review.” GIScience & Remote Sensing. 52-3. 344-373. Taylor&Francis. DOI: https://doi.org/10.1080/15481603.2015.1033809

Galleguillos, M. and Grêt-Regamey, A. (2018). “Big data for assessing ecosystem services. ETHzürich Network City and Landscape.” Retrived from <https://www.nsl.ethz.ch/big-data-for-assessing-ecosystem-services/>

Hong, J. (2018, Aug 16). “Three-year project will quantify benefits of Singapore’s natural environment.” The Strait Times. Retrived from <https://www.straitstimes.com/singapore/three-year-project-will-quantify-benefits-of-singapores-natural-environment>

Ramirez-Reyes, C. et al. (2019, May 15). “Reimagining the potential of earth observations for ecosystem service assessments.” Science of the Total Environment. 665. 1053-1063. DOI: https://doi.org/10.1016/j.scitotenv.2019.02.150

[1] Under the umbrella of development concerns, development interventions and programs are being undertaken to breach some development gaps. EO, for instance, is also a tool to monitor development programs and interventions. See a ESA’s initiative that supports EO uptake for sustainable development: <http://eo4sd.esa.int/>