Large-Scale Assessment of Coastal Aquaculture Ponds with Sentinel-1 Time Series Data

Marco Ottinger,Kersten Clauss,Claudia Kuenzer

doi:10.3390/rs9050440

Marco Ottinger, Kersten Clauss + Show 1 more

Open Access

https://doi.org/10.3390/rs9050440

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Journal: Remote sensing	Publication Date: May 4, 2017
Citations: 81	License type: CC BY 4.0

Affiliation: University of Würzburg, German Aerospace Center

Abstract

We present an earth observation based approach to detect aquaculture ponds in coastal areas with dense time series of high spatial resolution Sentinel-1 SAR data. Aquaculture is one of the fastest-growing animal food production sectors worldwide, contributes more than half of the total volume of aquatic foods in human consumption, and offers a great potential for global food security. The key advantages of SAR instruments for aquaculture mapping are their all-weather, day and night imaging capabilities which apply particularly to cloud-prone coastal regions. The different backscatter responses of the pond components (dikes and enclosed water surface) and aquaculture’s distinct rectangular structure allow for separation of aquaculture areas from other natural water bodies. We analyzed the large volume of free and open Sentinel-1 data to derive and map aquaculture pond objects for four study sites covering major river deltas in China and Vietnam. SAR image data were processed to obtain temporally smoothed time series. Terrain information derived from DEM data and accurate coastline data were utilized to identify and mask potential aquaculture areas. An open source segmentation algorithm supported the extraction of aquaculture ponds based on backscatter intensity, size and shape features. We were able to efficiently map aquaculture ponds in coastal areas with an overall accuracy of 0.83 for the four study sites. The approach presented is easily transferable in time and space, and thus holds the potential for continental and global mapping.

Highlights

Aquaculture is one of the fastest growing food production sectors worldwide, an important food supply in many countries, main protein source for hundreds of million people and in the spotlight for its potential to support future food security at global scale [1]
Asia alone generates 90 percent of the total global aquaculture volume which is mainly produced by pond systems in fertile coastal environments
We presented a novel approach to assess coastal aquaculture at large spatial scales using earth observation time series

Summary

Introduction

Aquaculture is one of the fastest growing food production sectors worldwide, an important food supply in many countries, main protein source for hundreds of million people and in the spotlight for its potential to support future food security at global scale [1]. 32.4 million tons in 2000 to 73.8 million tons in 2014 [2] and received a record share of 43.1 percent of the total 168.4 million tons of aquatic organisms such as fish, shrimp and mollusks produced. Asia alone generates 90 percent of the total global aquaculture volume which is mainly produced by pond systems in fertile coastal environments. We developed a framework to process time series of earth observation satellite data to detect and map aquaculture in coastal area at a very large scale. Based on open-source tools, we developed an approach to process large and dense time series of high resolution Synthetic

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