Mapping Aquaculture Areas with Multi-Source Spectral and Texture Features: A Case Study in the Pearl River Basin (Guangdong), China

Yue Xu,Zhongwen Hu,Yinghui Zhang,Yumeng Yin,Guofeng Wu,Jingzhe Wang

doi:10.3390/rs13214320

Abstract

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.

Highlights

Aquaculture has become one of the fastest-growing food industries [1], and the fishery products of China play an important role in the international seafood market [2], with over 60% of the fish farmed in the world [3]
Based on the above analyses, we found that Normalized difference vegetation index (NDVI), Normalized Difference Water Index (NDWI), and Normalized difference built-up index (NDBI) could better highlight the differences between aquaculture areas and other water bodies and the textures derived from index images; VV and VH images could further enhance the differences
The performances using only spectral indices were poor (Figure 7A and Table 3), and many water pixels were wrongly classified as aquaculture pixels, for some aquaculture facilities are submerged in waters and their spectral signatures are not obliviously different from water

Summary

Introduction

Aquaculture has become one of the fastest-growing food industries [1], and the fishery products of China play an important role in the international seafood market [2], with over 60% of the fish farmed in the world [3]. In China, many lakes and coastal wetlands were reclaimed in the past few years in order to support the fast development of fisheries [5], putting tremendous pressures on environments and hampering regional sustainable developments [6]. Mapping aquaculture areas is an important support to policy development and implementation at regional, national, and global levels, and to measure progress towards sustainable developments [7]. Optical and radar remote sensing images have been increasingly utilized to delineate aquaculture areas [11,12], and many methods have been developed for local [13], regional [14], and national scale [15] aquaculture mapping

Methods

Results

Discussion

Conclusion