Abstract
Aquaculture makes a crucial contribution to global food security and protein intake and is a basis for many livelihoods. Every second fish consumed today is produced in aquaculture systems, mainly in land-based water ponds situated along the coastal areas. Satellite remote sensing enables high-resolution mapping of pond aquaculture, facilitating inventory analyses to support sustainable development of the planet’s valuable coastal ecosystems. Free, full and open data from the Copernicus earth observation missions opens up new potential for the detection and monitoring of aquaculture from space. High-resolution time series data acquired by active microwave instruments aboard the Sentinel-1 satellites and fully automated, object-based image analysis allow the identification of aquaculture ponds. In view of the diversity and complexity in the production of aquaculture products, yield and production varies greatly among species. Although national statistics on aquaculture production exist, there is a large gap of pond-specific aquaculture production quantities. In this regard, earth observation-based mapping and monitoring of pond aquaculture can be used to estimate production and has great potential for global production projections. For the deltas of the Mekong River, Red River, Pearl River, and Yellow River, as one of the world’s most significant aquaculture production regions, we detected aquaculture ponds from high spatial resolution Sentinel-1 Synthetic Aperture Radar (SAR) data. We collected aquaculture production and yield statistics at national, regional and local levels to link earth observation-based findings to the size, number and distribution of aquaculture ponds with production estimation. With the SAR derived mapping product, it is possible for the first time to assess aquaculture on single pond level at a regional scale and use that information for spatial analyses and production estimation.
Highlights
We present the first production estimation of aquaculture ponds in the coastal zone based on earth observation data
This novel approach includes a large-scale assessment of land-based pond systems, regression analysis, and pond production estimates based on remote sensing techniques and statistical data
Using more than 500 Sentinel-1 scenes, open source tools and software, we developed an object-based image processing chain to detect aquaculture ponds from smoothed time series stacks
Summary
Rising global demand for aquatic protein, such as fish, crustaceans and mollusks, high income and substantial profit potentials made a key contribution to the almost fivefold increase in global aquaculture production from 13 million tons in 1990 to 76 million tons in 2015 (see Figure 1) valued at USD 158.1 billion [1]. The contribution of aquaculture to the global fish production has increased from about 13 percent to 45 percent. Fish and other aquatic food products provide more than 17 percent of all animal protein to the planet’s population [23,24,25]. Maintaining production and supply of aquatic food is crucial to meet rising global demands for protein-rich fish and seafood for the decades [10]
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