Large-Scale Assessment of Coastal Aquaculture Ponds with Sentinel-1 Time Series Data
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 aqu...
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doaj-e03813d9c3da495d9a5d681aae5432172020-11-24T22:40:13ZengMDPI AGRemote Sensing2072-42922017-05-019544010.3390/rs9050440rs9050440Large-Scale Assessment of Coastal Aquaculture Ponds with Sentinel-1 Time Series DataMarco Ottinger0Kersten Clauss1Claudia Kuenzer2Department of Remote Sensing, Institute of Geography and Geology, University of Wuerzburg, D-97074 Wuerzburg, GermanyDepartment of Remote Sensing, Institute of Geography and Geology, University of Wuerzburg, D-97074 Wuerzburg, GermanyGerman Aerospace Center (DLR), German Remote Sensing Data Center (DFD), D-82234 Wessling, GermanyWe 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.http://www.mdpi.com/2072-4292/9/5/440aquacultureSARSentinel-1time seriesimage segmentationremote sensingpondscoastal zoneriver delta |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Marco Ottinger Kersten Clauss Claudia Kuenzer |
spellingShingle |
Marco Ottinger Kersten Clauss Claudia Kuenzer Large-Scale Assessment of Coastal Aquaculture Ponds with Sentinel-1 Time Series Data Remote Sensing aquaculture SAR Sentinel-1 time series image segmentation remote sensing ponds coastal zone river delta |
author_facet |
Marco Ottinger Kersten Clauss Claudia Kuenzer |
author_sort |
Marco Ottinger |
title |
Large-Scale Assessment of Coastal Aquaculture Ponds with Sentinel-1 Time Series Data |
title_short |
Large-Scale Assessment of Coastal Aquaculture Ponds with Sentinel-1 Time Series Data |
title_full |
Large-Scale Assessment of Coastal Aquaculture Ponds with Sentinel-1 Time Series Data |
title_fullStr |
Large-Scale Assessment of Coastal Aquaculture Ponds with Sentinel-1 Time Series Data |
title_full_unstemmed |
Large-Scale Assessment of Coastal Aquaculture Ponds with Sentinel-1 Time Series Data |
title_sort |
large-scale assessment of coastal aquaculture ponds with sentinel-1 time series data |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2017-05-01 |
description |
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. |
topic |
aquaculture SAR Sentinel-1 time series image segmentation remote sensing ponds coastal zone river delta |
url |
http://www.mdpi.com/2072-4292/9/5/440 |
work_keys_str_mv |
AT marcoottinger largescaleassessmentofcoastalaquaculturepondswithsentinel1timeseriesdata AT kerstenclauss largescaleassessmentofcoastalaquaculturepondswithsentinel1timeseriesdata AT claudiakuenzer largescaleassessmentofcoastalaquaculturepondswithsentinel1timeseriesdata |
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1725705351389511680 |