Mapping fish community variables by Integrating field and satellite data, object-based image analysis and modeling in a traditional Fijian fisheries management area

Knudby, Anders, Roelfsema, Chris, Lyons, Mitchell, Phinn, Stuart and Jupiter, Stacy (2011) Mapping fish community variables by Integrating field and satellite data, object-based image analysis and modeling in a traditional Fijian fisheries management area. Remote Sensing, 3 3: 460-483. doi:10.3390/rs3030460

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Author Knudby, Anders
Roelfsema, Chris
Lyons, Mitchell
Phinn, Stuart
Jupiter, Stacy
Title Mapping fish community variables by Integrating field and satellite data, object-based image analysis and modeling in a traditional Fijian fisheries management area
Journal name Remote Sensing   Check publisher's open access policy
ISSN 2072-4292
Publication date 2011-03-01
Year available 2011
Sub-type Article (original research)
DOI 10.3390/rs3030460
Open Access Status DOI
Volume 3
Issue 3
Start page 460
End page 483
Total pages 24
Place of publication Basel, Switzerland
Publisher M D P I AG
Language eng
Abstract The use of marine spatial planning for zoning multi-use areas is growing in both developed and developing countries. Comprehensive maps of marine resources, including those important for local fisheries management and biodiversity conservation, provide a crucial foundation of information for the planning process. Using a combination of field and high spatial resolution satellite data, we use an empirical procedure to create a bathymetric map (RMSE 1.76 m) and object-based image analysis to produce accurate maps of geomorphic and benthic coral reef classes (Kappa values of 0.80 and 0.63; 9 and 33 classes, respectively) covering a large (>260 km(2)) traditional fisheries management area in Fiji. From these maps, we derive per-pixel information on habitat richness, structural complexity, coral cover and the distance from land, and use these variables as input in models to predict fish species richness, diversity and biomass. We show that random forest models outperform five other model types, and that all three fish community variables can be satisfactorily predicted from the high spatial resolution satellite data. We also show geomorphic zone to be the most important predictor on average, with secondary contributions from a range of other variables including benthic class, depth, distance from land, and live coral cover mapped at coarse spatial scales, suggesting that data with lower spatial resolution and lower cost may be sufficient for spatial predictions of the three fish community variables.
Formatted abstract
The use of marine spatial planning for zoning multi-use areas is growing in both developed and developing countries. Comprehensive maps of marine resources, including those important for local fisheries management and biodiversity conservation, provide a crucial foundation of information for the planning process. Using a combination of field and high spatial resolution satellite data, we use an empirical procedure to create a bathymetric map (RMSE 1.76 m) and object-based image analysis to produce accurate maps of geomorphic and benthic coral reef classes (Kappa values of 0.80 and 0.63; 9 and 33 classes, respectively) covering a large (>260 km2) traditional fisheries management area in Fiji. From these maps, we derive per-pixel information on habitat richness, structural complexity, coral cover and the distance from land, and use these variables as input in models to predict fish species richness, diversity and biomass. We show that random forest models outperform five other model types, and that all three fish community variables can be satisfactorily predicted from the high spatial resolution satellite data. We also show geomorphic zone to be the most important predictor on average, with secondary contributions from a range of other variables including benthic class, depth, distance from land, and live coral cover mapped at coarse spatial scales, suggesting that data with lower spatial resolution and lower cost may be sufficient for spatial predictions of the three fish community variables.
Keyword Coral reefs
IKONOS
Quickbird
Predictive mapping
Fish
Species richness
Species diversity
Biomass
Q-Index Code C1
Q-Index Status Confirmed Code
Grant ID 2007-31847
540.01
NA07NOS4630035
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Geography, Planning and Environmental Management Publications
Official 2012 Collection
 
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Citation counts: TR Web of Science Citation Count  Cited 19 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 16 times in Scopus Article | Citations
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Created: Wed, 21 Sep 2011, 20:53:03 EST by Stuart Phinn on behalf of School of Geography, Planning & Env Management