Nation-Scale Mapping of Coastal Aquaculture Ponds with Sentinel-1 SAR Data Using Google Earth Engine

Zhe Sun,Juhua Luo,Kun Xue,Jingzhicheng Yang,Lirong Lu,Li Zhang,Qiuyan Yu

doi:10.3390/rs12183086

Abstract

Global rapid expansion of the coastal aquaculture industry has made great contributions to enhance food security, but has also caused a series of ecological and environmental issues. Sustainable management of coastal areas requires the explicit and efficient mapping of the spatial distribution of aquaculture ponds. In this study, a Google Earth Engine (GEE) application was developed for mapping coastal aquaculture ponds at a national scale with a novel classification scheme using Sentinel-1 time series data. Relevant indices used in the classification mainly include the water index, texture, and geometric metrics derived from radar backscatter, which were then used to segment and classify aquaculture ponds. Using this approach, we classified aquaculture ponds for the full extent of the coastal area in Vietnam with an overall accuracy of 90.16% (based on independent sample evaluation). The approach, enabling wall-to-wall mapping and area estimation, is essential to the efficient monitoring and management of aquaculture ponds. The classification results showed that aquaculture ponds are widely distributed in Vietnam’s coastal area and are concentrated in the Mekong River Delta and Red River delta (85.14% of the total area), which are facing the increasing collective risk of climate change (e.g., sea level rise and salinity intrusion). Further investigation of the classification results also provides significant insights into the stability and deliverability of the approach. The water index derived from annual median radar backscatter intensity was determined to be efficient at mapping water bodies, likely due to its strong response to water bodies regardless of weather. The geometric metrics considering the spatial variation of radar backscatter patterns were effective at distinguishing aquaculture ponds from other water bodies. The primary use of GEE in this approach makes it replicable and transferable by other users. Our approach lays a solid foundation for intelligent monitoring and management of coastal ecosystems.

Highlights

Aquaculture is an important source of food, nutrition, income, and livelihood for hundreds of millions of people around the world [1,2]
The water index SDWI was calculated (Figure 9c) and has the capability to enhance the difference between water and other objects, which corresponded to the two modes in the histogram (Figure 9d)
We presented an approach to efficiently and ecosystem conservation and aquaculture management

Summary

Introduction

Aquaculture is an important source of food, nutrition, income, and livelihood for hundreds of millions of people around the world [1,2]. Driven by the increasing human population, the need to improve social-economic benefits and the escalating protein demands, aquaculture has been one of the fastest-growing food production sectors in the world over the past decades [3,4,5]. While making significant contributions to global food security and social-economic development, expansion of aquaculture inevitably leads to a series of environmental problems such as natural habitat destruction [6], ecosystem degradation [7,8], water eutrophication [9,10,11,12], and landscape fragmentation [5,13]. An accurate, quantitative, and spatially explicit assessment of the distribution of aquaculture ponds is crucial to local, regional and global efforts aimed at improving the sustainability of the aquaculture industry, reducing food insecurity and enhancing ecosystem resilience

Methods

Results

Discussion

Conclusion