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Model-based adaptive spatial sampling for occurrence map construction


Peyrard, N and Sabbadin, R and Spring, D and Brook, BW and Mac Nally, R, Model-based adaptive spatial sampling for occurrence map construction, Statistics and Computing, 23, (1) pp. 29-42. ISSN 0960-3174 (2013) [Refereed Article]

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Copyright 2011 Springer Science+Business Media

DOI: doi:10.1007/s11222-011-9287-3


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.

Item Details

Item Type:Refereed Article
Keywords:Hidden Markov random fields, optimal sampling approximation, fire ant sampling for mapping
Research Division:Environmental Sciences
Research Group:Environmental biotechnology
Research Field:Biological control
Objective Division:Environmental Management
Objective Group:Terrestrial systems and management
Objective Field:Control of pests, diseases and exotic species in terrestrial environments
UTAS Author:Brook, BW (Professor Barry Brook)
ID Code:97955
Year Published:2013
Web of Science® Times Cited:16
Deposited By:Biological Sciences
Deposited On:2015-01-22
Last Modified:2017-10-30

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