A Canonical Approach for Quantifying Value of Remote Sensing: Implications for Investments for Monitoring SDGS

There are few frameworks to quantitatively determine value of remote sensing data to inform decisions related to sustainable development. In most cases, value remains vaguely defined and qualitatively discussed. Here, we develop an approach in which decisions, that can be supported by remote sensing data, are systematically structured, and monetized value of possible outcomes are used as a basis for quantifying value of remote sensing. It is shown that a canonical set of decision structures and application of a Bayesian approach leads to a set of analytic expressions that can be used to build insights of the decision problem and to identify driving factors that affect value of remote sensing. The method is demonstrated for water quality monitoring, forest management, and solar power infrastructure inspection. The specific case studied for water quality showed that remote sensing provides value if likelihood of a HAB is 30% or more (even if data quality is not high). In forest management (with specific restoration costs, mangrove loss costs etc.), remote sensing offers value beyond a threshold of cost of field surveys. For solar farm inspection, remote sensing is most valuable for low anomaly rates and high panel replacement costs.