Abstract
Understanding socio-ecological systems and the discovery, recovery and adaptation of land knowledge are key challenges for sustainable land use. The analysis of sustainable agricultural systems and practices, for instance, requires interdisciplinary and transdisciplinary research and coordinated data acquisition, data integration and analysis. However, datasets, which are acquired using remote sensing, geospatial analysis and simulation techniques, are often limited by narrow disciplinary boundaries and therefore fall short in enabling a holistic approach across multiple domains and scales. In this work, we demonstrate a new workflow for interdisciplinary data acquisition and integration, focusing on terraced vineyards in Tuscany, Italy. We used multi-modal data acquisition and performed data integration via a voxelised point cloud that we term a composite voxel model. The latter facilitates a multi-domain and multi-scale data-integrated approach for advancing the discovery and recovery of land knowledge. This approach enables integration, correlation and analysis of data pertaining to different domains and scales in a single data structure.
Highlights
Advancing the understanding of socio-ecological systems is a key challenge for sustainable development
We present a new workflow that combines (1) multi-source remote sensing data, (2) data from open source geographic information systems (GIS), (3) data obtained from simulations and (4) the integration of the obtained datasets into a voxelised point cloud, which we term a composite voxel model (CVM), which enables targeted inquiry for land knowledge recovery
We present a novel approach for the spatial and temporal integration of remote sensing data and Geographic Information Systems (GIS) methods related to solar performance into a CVM, which involves a convergence of point clouds and 2.5D geoscientific datasets
Summary
Advancing the understanding of socio-ecological systems is a key challenge for sustainable development. This necessitates interdisciplinary [1,2] and transdisciplinary [3]. Addressing the recovery and adaptation of land knowledge requires data integration across a range of disciplines. In the context of our research, this includes agronomy, biology, soil science, hydrology, microclimatology, and environmental science, as well as expert knowledge in data acquisition, data science and information modelling. To progress beyond the limits of discipline-specific approaches, it is necessary to develop a consolidated interdisciplinary approach for targeted data acquisition, correlation and integration. We present a new workflow that combines (1) multi-source remote sensing data, (2) data from open source geographic information systems (GIS),
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