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An object-based image analysis approach for aquaculture ponds precise mapping and monitoring: a case study of Tam Giang-Cau Hai Lagoon, Vietnam

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Abstract

Monitoring and mapping shrimp farms, including their impact on land cover and land use, is critical to the sustainable management and planning of coastal zones. In this work, a methodology was proposed to set up a cost-effective and reproducible procedure that made use of satellite remote sensing, object-based classification approach, and open-source software for mapping aquaculture areas with high planimetric and thematic accuracy between 2005 and 2008. The analysis focused on two characteristic areas of interest of the Tam Giang-Cau Hai Lagoon (in central Vietnam), which have similar farming systems to other coastal aquaculture worldwide: the first was primarily characterised by locally referred “low tide” shrimp ponds, which are partially submerged areas; the second by earthed shrimp ponds, locally referred to as “high tide” ponds, which are non-submerged areas on the lagoon coast. The approach was based on the region-growing segmentation of high- and very high-resolution panchromatic images, SPOT5 and Worldview-1, and the unsupervised clustering classifier ISOSEG embedded on SPRING non-commercial software. The results, the accuracy of which was tested with a field-based aquaculture inventory, showed that in favourable situations (high tide shrimp ponds), the classification results provided high rates of accuracy (>95 %) through a fully automatic object-based classification. In unfavourable situations (low tide shrimp ponds), the performance degraded due to the low contrast between the water and the pond embankments. In these situations, the automatic results were improved by manual delineation of the embankments. Worldview-1 necessarily showed better thematic accuracy, and precise maps have been realised at a scale of up to 1:2,000. However, SPOT5 provided comparable results in terms of number of correctly classified ponds, but less accurate results in terms of the precision of mapped features. The procedure also demonstrated high degrees of reproducibility because it was applied to images with different spatial resolutions in an area that, during the investigated period, did not experience significant land cover changes.

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Aknowledgments

The author would like to thanks Prof. Massimo Sarti, Chief Technical Advisor of the FAO IMOLA Project for providing free access to satellite imagery and reference aquaculture dataset. The author is very grateful to anonymous reviewers for the constructive comments and recommendations, which were very helpful in improving and strengthening this paper.

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  1. Nucleo di Ricerca sulla Desertificazione NRD and Dipartimento di Scienze della Natura e del Territorio DIPNET, Università degli Studi di Sassari, Viale Italia, 39-07100, Sassari, Italia

    Salvatore Gonario Pasquale Virdis

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Virdis, S.G.P. An object-based image analysis approach for aquaculture ponds precise mapping and monitoring: a case study of Tam Giang-Cau Hai Lagoon, Vietnam. Environ Monit Assess 186, 117–133 (2014). https://doi.org/10.1007/s10661-013-3360-7

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