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Remote Sens. 2017, 9(5), 440; doi:10.3390/rs9050440

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

1
Department of Remote Sensing, Institute of Geography and Geology, University of Wuerzburg, D-97074 Wuerzburg, Germany
2
German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), D-82234 Wessling, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Deepak R. Mishra, Xiaofeng Li and Prasad S. Thenkabail
Received: 10 February 2017 / Revised: 11 April 2017 / Accepted: 27 April 2017 / Published: 4 May 2017
View Full-Text   |   Download PDF [22429 KB, uploaded 4 May 2017]   |  

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. View Full-Text
Keywords: aquaculture; SAR; Sentinel-1; time series; image segmentation; remote sensing; ponds; coastal zone; river delta aquaculture; SAR; Sentinel-1; time series; image segmentation; remote sensing; ponds; coastal zone; river delta
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Ottinger, M.; Clauss, K.; Kuenzer, C. Large-Scale Assessment of Coastal Aquaculture Ponds with Sentinel-1 Time Series Data. Remote Sens. 2017, 9, 440.

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