Mapping Aquaculture Areas with Multi-Source Spectral and Texture Features: A Case Study in the Pearl River Basin (Guangdong), China
Mapping Aquaculture Areas with Multi-Source Spectral and Texture Features: A Case Study in the Pearl River Basin (Guangdong), China
复制标题
利用多源光谱和纹理特征绘制水产养殖区图：以中国珠江流域（广东）为例
DOI：
10.3390/rs13214320
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发表时间：
2021-10
期刊：
影响因子：
5
通讯作者：
Guofeng Wu
中科院分区：
文献类型：
--
作者：
Yue Xu;Zhongwen Hu;Yinghui Zhang;Jingzhe Wang;Yumeng Yin;Guofeng Wu
近年来，水产养殖在食品工业领域发展迅速；然而，它带来了许多环境问题，例如水污染以及湖泊和沿海湿地地区的围垦。因此，需要对水产养殖业进行评估和管理，其中准确的水产养殖测绘是必不可少的前提。由于内陆和海水养殖区的差异以及大量遥感图像处理的难度，不同水产养殖类型的精确制图仍然具有挑战性。在这项研究中，提出了一种基于多源光谱和纹理特征的新方法来同时绘制内陆和海水养殖区域图。时间序列光学 Sentinel-2 图像首先被用来导出光谱指数以获得纹理特征。然后使用从 Sentinel-1A 的合成孔径雷达 (SAR) 图像得出的后向散射和纹理特征来区分水产养殖区和其他地理实体。最后，应用监督随机森林分类器进行大规模水产养殖区域测绘。为了解决大量遥感图像处理效率低的问题，所提出的方法在Google Earth Engine（GEE）平台上实现。以中国珠江流域（广东省）为例，该方法获得的水产养殖图总体准确率为89.5%，并且该方法在GEE平台上的实施大大提高了大比例尺水产养殖区制图的效率。得出的水产养殖地图可以为水产养殖区的可持续发展和研究区的生态保护提供决策服务，所提出的方法在国家和全球范围内绘制水产养殖图具有巨大的潜力。
Aquaculture has grown rapidly in the field of food industry in recent years; however, it brought many environmental problems, such as water pollution and reclamations of lakes and coastal wetland areas. Thus, the evaluation and management of aquaculture industry are needed, in which accurate aquaculture mapping is an essential prerequisite. Due to the difference between inland and marine aquaculture areas and the difficulty in processing large amounts of remote sensing images, the accurate mapping of different aquaculture types is still challenging. In this study, a novel approach based on multi-source spectral and texture features was proposed to map simultaneously inland and marine aquaculture areas. Time series optical Sentinel-2 images were first employed to derive spectral indices for obtaining texture features. The backscattering and texture features derived from the synthetic aperture radar (SAR) images of Sentinel-1A were then used to distinguish aquaculture areas from other geographical entities. Finally, a supervised Random Forest classifier was applied for large scale aquaculture area mapping. To address the low efficiency in processing large amounts of remote sensing images, the proposed approach was implemented on the Google Earth Engine (GEE) platform. A case study in the Pearl River Basin (Guangdong Province) of China showed that the proposed approach obtained aquaculture map with an overall accuracy of 89.5%, and the implementation of proposed approach on GEE platform greatly improved the efficiency for large scale aquaculture area mapping. The derived aquaculture map may support decision-making services for the sustainable development of aquaculture areas and ecological protection in the study area, and the proposed approach holds great potential for mapping aquacultures on both national and global scales.
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