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

Anders Knudby¹  Chris Roelfsema²  Mitchell Lyons²  Stuart Phinn² 

[1] Department of Geography, University of Waterloo, Waterloo, ON K1Z8G6, Canada;School of Geography, Planning and Environmental Management, University of Queensland, Brisbane, QLD 4072, Australia; E-Mails:

关键词: coral reefs;    IKONOS;    Quickbird;    predictive mapping;    fish;    species richness;    species diversity;    biomass;   

DOI  :  10.3390/rs3030460

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2011 by the authors; licensee MDPI, Basel, Switzerland.