D1. Big data science in social-ecological systems towards sustainable landscape management
Martin Schultze (Martin-Luther University), email@example.com
Evelyn Asante-Yeboah (Martin-Luther University)
Praveen Kumar (Jawaharlal Nehru University)
Christine Fürst (Martin-Luther University)
Forecasting sustainable landscape management under future scenarios of climate change aspects, land-use changes and different socio-economic drivers demand integration of new knowledge sources across scientific disciplines. The symposium will bring together scientists, practitioners and students discussing big data approaches, monitoring methods as well as novel modelling concepts relevant to questions such as (i) How does big data facilitate a better understanding of human-nature interactions? and (ii) What frameworks and strategies do ensure a successful implementation to achieve sustainable landscape management practices?
Earth’s ecosystems toward sustainable management practices are often severely impacted by complex social, economic and environmental interactions. Human behaviour that threatens land sustainability is closely connected to multiple spatio-temporal scale processes such as climate change, agricultural (mis-) management or soil depletion. A key challenge to unveil such social-ecological system dynamics requests for novel approaches to reconsider nature provisions to human well-being. The growing awareness of traditional research about land use changes or ecosystem resilience to disturbances can deliver essential information strengthening future nature’s health. Thus, converting the retrospective into forward-looking tools allows to set up sustainable environmental baseline targets to action best land practices. This is often referred to as big data science touching increasingly real-world complexity, including different streams of knowledge. Possibilities occurring from big data are considered pivotal to gain an understanding that can inform stakeholders, practitioners or even decision-makers. The proliferation of social media, earth observing systems and ecological monitoring networks have generated large pools of data sources. These technologies provide the capacity to create, aggregate and cross-reference big data to design alternative land management strategies. Such advances promote significant analytical importance addressing social-ecological system properties along identifying landscape boundaries, essential drivers or ecosystem functions. Complementing the creation of big data in ecosystem management will need the incorporation of simulation systems. Arising innovative modelling techniques from multiple disciplines fosters both (i) a culture of sharing datasets and (ii) supporting an integrated coupling of socio-economic and environmental systems from local to global scales. Recent developments in data-driven approaches such as machine learning allow to process big data, whereas cloud computing sustains a flexible supply of on-demand services. This relation between big data and modern technologies ensures a robust concept according to complex social-ecological questions such as how human interventions impact the stability of environmental processes and biodiversity.
Hence, the symposium aims at bringing together complementary perspectives in simulating social-ecological systems toward future landscape management. Session talks should focus on the following themes:
- Advancement in using multiple data sources such as social media, sensor information as well as networks for modelling social-ecological systems.
- Novel technical solutions to handle big data (e.g. artificial intelligence, open system initiatives), data-sharing practices and cloud computing.
- Supporting decision-makers in designing sustainable land management strategies by involving modern simulation systems
The expected symposium outcome will be a synthesis paper to conceptualize a framework based on the information presented and roundtable discussion. The group dialogue will extent the talks with relevant knowledge or practical experiences from sustainable land management. Additionally, a Special Issue in a peer-reviewed journal can be negotiated.