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Model-based adaptive spatial sampling for occurrence map construction

journal contribution
posted on 2023-05-18, 06:46 authored by Peyrard, N, Sabbadin, R, Spring, D, Barry BrookBarry Brook, Mac Nally, R
In many environmental management problems, the construction of occurrence maps of species of interest is a prerequisite to their effective management. However, the construction of occurrence maps is a challenging problem because observations are often costly to obtain (thus incomplete) and noisy (thus imperfect). It is therefore critical to develop tools for designing efficient spatial sampling strategies and for addressing data uncertainty. Adaptive sampling strategies are known to be more efficient than non-adaptive strategies. Here, we develop a model-based adaptive spatial sampling method for the construction of occurrence maps. We apply the method to estimate the occurrence of one of the world’s worst invasive species, the red imported fire ant, in and around the city of Brisbane, Australia. Our contribution is threefold: (i) a model of uncertainty about invasion maps using the classical image analysis probabilistic framework of Hidden Markov Random Fields (HMRF), (ii) an original exact method for optimal spatial sampling with HMRF and approximate solution algorithms for this problem, both in the static and adaptive sampling cases, (iii) an empirical evaluation of these methods on simulated problems inspired by the fire ants case study. Our analysis demonstrates that the adaptive strategy can lead to substantial improvement in occurrence mapping.

History

Publication title

Statistics and Computing

Volume

23

Pagination

29-42

ISSN

0960-3174

Department/School

School of Natural Sciences

Publisher

Kluwer Academic Publ

Place of publication

Van Godewijckstraat 30, Dordrecht, Netherlands, 3311 Gz

Rights statement

Copyright 2011 Springer Science+Business Media

Repository Status

  • Restricted

Socio-economic Objectives

Control of pests, diseases and exotic species in terrestrial environments

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