With mounting pressure on lands, waters, and vital natural resources, information from physical geography about how to steward natural systems has never been more precious. The United Nations (UN) has agreed on a new agenda to address these challenges by proposing 17 Sustainable Development Goals (SDGs) and 169 associated targets for 2030 [1
]. This is a plan of action to protect the planet from degradation, including through sustainable consumption and production, sustainably managing natural resources, and taking urgent action on climate change in order to support the needs of current and future generations.
Data from Earth observation (www.data4sdgs.org
), combined with models from physical geography, demographics, and statistical data, can provide the necessary information to inform about the SDG targets by building indicators at various scales. The aim is to trigger action based on data-driven decision-making processes and inform the public. Indeed, Earth observations operate globally at higher and higher spatial and temporal resolutions, in real-time, becoming a cost-effective solution to monitor progress toward a more sustainable future. Inevitably, geography recognized the importance of location to most of the SDGs and related targets, bringing a new opportunity for geospatial information to play a key role in monitoring and informing about SDGs in the coming decades [2
In order to avoid redundancy and dissonance, the new SDG framework should be linked to existing environmental policies. For instance, SDGs 14 and 15, which are about promoting the sustainable use of water and land ecosystems and halt biodiversity loss, should be in-line with the Aichi targets for 2020 defined by the Convention on Biological Diversity [3
]. The Global Biodiversity Outlook gives an interim assessment of the progress towards these targets [4
]. The Group on Earth Observation Biodiversity Observation Network further outlines the distributed network of databases [5
] needed to measure the Essential Biodiversity Variables [6
] that are useful to assess the Aichi targets. For policy links to the other SDGs presented in Figure 1
, a table is provided in Annex 1.
In all of these cases, the SDG agenda recognizes that data needed for assessing several targets is not broadly available [8
]. This means that data collection must be improved in order to establish national and international baselines. The UN calls for new ways to foster and promote innovation to fill data gaps, mobilize resources to overcome inequalities between developed and developing countries, and between data-poor and data-rich regions, and lead and coordinate the data revolution into its full role for sustainable development. The expectation is that existing international environmental institutions should regularly evaluate the progress toward sustainable development. Scientists have identified five priorities for better contributing to this process: devise metrics; establish monitoring mechanisms; evaluate progress; enhance infrastructures; and standardize and verify data [9
]. These priorities are critical for improving policy and better informing decisions, but must go beyond traditional economic valuation to incorporate societal and environmental values. Furthermore, any measurements or models must be readily accessible, globally applicable, replicable, shareable, and relevant to specific decisions and the stakeholders making them.
However, against this backdrop of ambitious goal setting, the planet has already passed some of its own resource boundaries [10
] through global changes, as demonstrated by physical geography, making the sustainability challenges greater and still more pressing to address (Figure 1
). Among the main drivers of changes, the fifth IPCC synthesis report [11
] demonstrated that greenhouse gas emissions are responsible for global warming in a range between 2 and 6 degrees on average, with more complex changes occurring in the distribution of extreme temperatures and seasonal and annual rainfall. These changes would severely impact numerous aspects of the natural environment, such as biodiversity and ecosystems, soils, freshwater water, and marine resources as well as human activities, such as agriculture, tourism, transport, and energy production [11
]. A second driver of change is land use, which changes and fragmentation are severely impacting the environment, with habitat losses from development that cannot be offset by the creation of protected areas [12
], maintenance of connectivity, ecological corridors [13
], or extensive agriculture. Finally, the UN’s latest projections [14
] suggest that the global population will reach between 9.4 and 10 billion by 2050 (key finding 2, p. 8). This review of worldwide demographic trends is essential for evaluating the progress toward achieving the SDGs and to guide policy and economic decision making in the 21st century.
The sustainability challenges laid out by international organizations are very challenging for society, but fortunately the available tools and approaches are steadily improving to help craft solutions to these challenges (e.g., cost-benefit analyses, ecosystem services, nexus, planetary boundaries; see Annex 2) (Figure 1
). The objective of this paper is to present, through selected case studies and an original framework, the state of the art solutions to lifting the technical barriers to integrating scientific information into sustainable decision making. The aim is to improve the capacity to address complex and related sustainability challenges. The main barriers and solutions presented in Figure 2
to improve information flows are:
User access to data is limited and can be improved by data sharing based on web platforms and interoperability in order to facilitate the use and reuse of high-quality data on the environment.
The increasing size and complexity of data sources can be handled by improved data-processing capacities in order to make better sense of the amount of data confronting decision makers.
Individual software solutions can be improved by the ability to elaborate and iterate on and between them. Data processing, analysis, and reporting workflows can be enhanced by developing application programming interfaces (APIs) for software products in order to enhance third-party development without duplicated efforts.
Limited knowledge transmission and uptake can be enhanced through collaborative engagement, co-creating and applying scientific decision-support tools in order to identify solutions to problems faced by specific stakeholders.
4. General Discussion
Sustainable stewardship faces many challenges at the intersection of physical geography, Earth observation, and policy; and new technologies are needed to better manage information and the knowledge that it can produce. Limited information can come from too little or too much data, fragmented data repositories, and the lack of sharing due to cultural, legal, or technological limits. The use of spatial data infrastructures can facilitate data sharing in a manner that is efficient, scalable, and resilient. The ability to then process data into information and knowledge is critical, and a public platform for connecting developers is essential for innovation. The engagement among and between experts and stakeholders is critical for the development, deployment, and long-term impact of a project. Their success depends on the ability to gain funding from diverse sources, effectively manage projects, and convey the results and lessons learned.
The proposed approach of barriers and solutions for addressing sustainability challenges from physical geography and Earth observation represents a new and original framework based on the combined experience from the coauthors. However, similar challenges exist in many other fields of information systems, where different types of barriers and solutions have been identified, as for instance in the health domain [66
] and also in the energy sector [71
]. While the authors of this paper concentrated here on technical barriers, other domains are also addressing organizational, behavioral/human, and financial barriers. The adopted approach is also closely related to data-information-knowledge-wisdom (DIKW) pyramids [72
], which could clearly be linked to different barriers and the transition from one level to another. Barrier A is about accessing data; Barrier B is essentially about transforming data into information; Barrier C is about generating knowledge; and Barrier D can be seen as a way to reach some kind of common wisdom.
Solutions exist that improve data sharing by exchanging data through web services using interoperability standards, but these solutions and standards are underused. Data sharing could greatly benefit from promoting multidimensional data standards with imbedded metadata, such as NetCDF. Web services should be discoverable by web browsers, as traditional Internet search tools are still mostly used when looking for data instead of dedicated geoportals, such as GEOSS or INSPIRE.
The need for improved data processing solutions will increase with the quantity of new datasets made available, their increased spatial and temporal resolution, and the arrival of data from distributed sensors and the Internet. In particular, choosing the right processing solution (single computer, clusters, grids, clouds) remains an issue when facing large data and/or processing challenges [73
Many developments can be made available to other research groups if they carefully plan and implement APIs. However, a combination of physical and virtual networks is needed in order to improve the participation of various stakeholders in decision-making processes based on the best available scientific information. Finding solutions for measuring the progress towards SDGs, and especially for implementing solutions, depend on large interdisciplinary networks.
The sustainability of the online tools themselves remains a challenge, since there are usually very limited resources to develop and maintain complex information systems for end-users. Funding mechanisms common to academic and government entities are often not compatible with the needs of scoping, developing, and maintaining software and data infrastructure. A much greater priority should be given to environmental information systems that are capable of addressing the sustainability challenges at various geographical scales.
The solutions proposed in this paper represent important contributions toward transforming data into information and knowledge, feeding into the science-policy interface. Capacity-building is essential for this transformation [38
], but the most difficult step remains bringing scientists and decision-makers to the same table to build their project together alongside an adaptive strategy. One way forward is building interdisciplinary projects, where several forms of knowledge (scientific, economic, social) are processed, compared, and integrated in order to provide a holistic context within which decisions may be informed and negotiated.