Model-based adaptive spatial sampling for occurrence map construction

Nathalie Peyrard, Regis Sabbadin, Daniel A. Spring, Barry Brook, Ralph MAC NALLY

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)29-42
Number of pages14
JournalStatistics and Computing
Volume23
DOIs
Publication statusPublished - 2013
Externally publishedYes

Fingerprint

Model-based
Sampling
Adaptive Sampling
Sampling Strategy
Adaptive Strategies
Random Field
Fires
Environmental Management
Uncertainty
Environmental management
Exact Method
Invasion
Sampling Methods
Threefolds
Image Analysis
Imperfect
Image analysis
Approximate Solution
Evaluation
Estimate

Cite this

Peyrard, Nathalie ; Sabbadin, Regis ; Spring, Daniel A. ; Brook, Barry ; MAC NALLY, Ralph. / Model-based adaptive spatial sampling for occurrence map construction. In: Statistics and Computing. 2013 ; Vol. 23. pp. 29-42.
@article{ef6001faf56a455f9917900b00415f8f,
title = "Model-based adaptive spatial sampling for occurrence map construction",
abstract = "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.",
keywords = "Hidden Markov random fields, Optimal sampling approximation, Fire ant sampling for mapping.",
author = "Nathalie Peyrard and Regis Sabbadin and Spring, {Daniel A.} and Barry Brook and {MAC NALLY}, Ralph",
year = "2013",
doi = "10.1007/s11222-011-9287-3",
language = "English",
volume = "23",
pages = "29--42",
journal = "Statistics and Computing",
issn = "0960-3174",
publisher = "Springer",

}

Model-based adaptive spatial sampling for occurrence map construction. / Peyrard, Nathalie; Sabbadin, Regis; Spring, Daniel A.; Brook, Barry; MAC NALLY, Ralph.

In: Statistics and Computing, Vol. 23, 2013, p. 29-42.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Model-based adaptive spatial sampling for occurrence map construction

AU - Peyrard, Nathalie

AU - Sabbadin, Regis

AU - Spring, Daniel A.

AU - Brook, Barry

AU - MAC NALLY, Ralph

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - Hidden Markov random fields

KW - Optimal sampling approximation

KW - Fire ant sampling for mapping.

U2 - 10.1007/s11222-011-9287-3

DO - 10.1007/s11222-011-9287-3

M3 - Article

VL - 23

SP - 29

EP - 42

JO - Statistics and Computing

JF - Statistics and Computing

SN - 0960-3174

ER -