Remote Sensing Data to Support Integrated Decision Making in Cultural and Natural Heritage Management. Impasses and opportunities for collaboration in agricultural areas

Abstract

Remote and near surface sensing data are widely used in archaeology and heritage management for feature discovery, change detection and monitoring, as an input to predictive modelling, and in the planning process. While global and regional datasets are widely used for some purposes, data are regularly acquired specifically for archaeological projects because of the very high spatial resolution required for feature detection and assessments of archaeological significance and the need for data on subsurface features. The sensing data collected for archaeology cover limited areas and only a few types of sensors, known to produce data efficiently, are regularly employed. Precision agriculture is beginning to produce large quantities of varied sensing data across extensive landscape areas. This situation creates an opportunity to adapt and reuse precision agricultural data for archaeology and heritage work, extending covering and enhancing our understanding of archaeology in contemporary agricultural landscapes. Equally, there is potential for coordinated data collection, collecting data once for multiple applications, and to add value through analyses which bring together perspectives from multiple related domains to model long-term processes in anthropogenic soil systems. This article provides a high-level overview of policy and technological developments which create the potential for sensing data reuse, coordinated data collection, and collaborative analyses across archaeological, agricultural, and agri-environmental applications while underscoring the structural barriers which, at present, constrain this potential. It highlights examples where the development of interoperable data and workflows can promote tighter integration of archaeology and cultural heritage management with sustainable agricultural land management and support integrated decision making.