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,
Environ Monit Assess. 2014 Jan;186(1):117-33. doi: 10.1007/s10661-013-3360-7. Epub 2013 Aug 17.
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.
监测和绘制虾场地图，包括其对土地覆盖和土地利用的影响，对于沿海地区的可持续管理和规划至关重要。在这项工作中，提出了一种方法，以建立一种具有成本效益且可重复的程序，该程序利用卫星遥感、基于对象的分类方法和开源软件，以在 2005 年至 2008 年期间实现具有高精度平面和主题精度的水产养殖区域制图。该分析集中在越南中部的塔姆江-考海泻湖（Tam Giang-Cau Hai Lagoon）的两个具有特色的感兴趣区域，这些区域的养殖系统与世界其他沿海水产养殖系统相似：第一个区域主要以当地所称的“退潮”虾塘为特征，这些虾塘是部分淹没的区域；第二个区域则以“涨潮”虾塘为特征，这些虾塘是泻湖沿岸的非淹没区域。该方法基于高分辨率和超高分辨率全色图像 SPOT5 和 Worldview-1 的区域生长分割，以及 SPRING 非商业软件中嵌入的无监督聚类分类器 ISOSEG。结果表明，在有利情况下（涨潮虾塘），通过完全自动的基于对象的分类，分类结果的准确性很高（>95%），该准确性通过基于实地的水产养殖清单进行了测试。在不利情况下（退潮虾塘），由于水与池塘堤岸之间的对比度低，性能会下降。在这些情况下，通过手动勾勒堤岸可以提高自动结果的准确性。Worldview-1 必然具有更好的主题准确性，可以实现高达 1:2000 的精确地图。然而，SPOT5 在正确分类的池塘数量方面提供了可比的结果，但在映射特征的精度方面则提供了较差的结果。该程序还表现出高度的可重复性，因为它应用于在调查期间未经历重大土地覆盖变化的区域的不同空间分辨率的图像。
