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Kriging-Informed Coverage Sampling: An Integer Programming Approach to Optimizing Information Collection
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DOI：10.1111/gean.70044.png)
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Abstract
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Accurately mapping spatial phenomena with limited observations hinges on selecting sampling sites that minimize predictive uncertainty. We model this task by quantifying unsampled-location uncertainty with ordinary Kriging and framing site selection as an optimization problem. Because the resulting Kriging prediction-variance objective is nonlinear, we derive an integer program approximation called Kriging-informed coverage sampling that bounds the Kriging variance with a set of linear constraints. We prove that Kriging-informed coverage sampling is isomorphic to the classical Maximal Coverage Location Problem, thereby linking geostatistical uncertainty reduction to a well-studied family of location problems and enabling the use of well-established solution techniques. Computational experiments on synthetic landscapes and a remote-sensing case study show that Kriging-informed coverage sampling attains ≥ $$ \ge $$ 90% of the information gain achieved by exact non-linear solution methods while reducing solution times by up to two orders of magnitude.
Keywords:
confident information coverage
coverage
information collection
Kriging
spatial sampling
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