Sea Surface-Visible Aquaculture Spatial-Temporal Distribution Remote Sensing: A Case Study in Liaoning Province, China from 2000 to 2018

Junmei Kang, Lichun Sui, Xiaomei Yang, Yueming Liu, Zhihua Wang, Jun Wang, Fengshuo Yang, Bin Liu and Yuanzheng Ma
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Junmei Kang: Geological Engineering and Institute of Surveying and Mapping, Chang’an University, Xi’an 710054, China
Lichun Sui: Geological Engineering and Institute of Surveying and Mapping, Chang’an University, Xi’an 710054, China
Xiaomei Yang: State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
Yueming Liu: State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
Zhihua Wang: State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
Jun Wang: Geological Engineering and Institute of Surveying and Mapping, Chang’an University, Xi’an 710054, China
Fengshuo Yang: State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
Bin Liu: State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
Yuanzheng Ma: The Second Topographic Surveying Brigade of Ministry of Natural Resources, Xi’an 710054, China

Sustainability, 2019, vol. 11, issue 24, 1-23

Abstract: Aquaculture plays an important role in providing food and reducing poverty but it affects environmental change and coastal ecosystems. Remote sensing is a technology that is helpful in the spatial-temporal dynamic monitoring of aquaculture, coastal management, and environmental monitoring. Most research focuses on inland and coastal areas, and little attention is paid to the extensive distribution of marine aquaculture. As an example, we use the freely available Landsat data of the developed marine aquaculture Liaoning Province of China and use the object-oriented automatic extraction method to analyze the spatial and temporal distribution information of marine aquaculture from 2000 to 2018. The accuracy evaluation from the randomly distributed sample points in high-resolution remote sensing images shows that the extraction accuracy for all of the five individual years of aquaculture area was higher than 82%. The results showed that (1) in the past 19 years, the area of marine aquaculture in Liaoning Province showed an increasing trend, which increased from 35.41 km 2 in 2000 to 201.83 km 2 in 2018, approximately 5.7 times increase in total area, but the growth rate decreased slightly due to government policy and the environmental quality of the sea area. (2) The centroid of offshore aquaculture in Liaoning Province shows a migration pattern to the northeast, in general, extending from the Dalian Bay sea area to the eastern sea area of the Dalian Chengshantou National Nature Reserve of Coastal Landform in the northeastern direction, and the migration distance reached 48.78 km. Moreover, the migration distance between 2005 and 2010 was the largest of all of the periods, reaching 35.43 km. The new marine aquaculture areas are mainly concentrated in the eastern direction of Xiaoyao Bay, the Changshan Islands, and Guanglu Island in Changhai County. (3) The landscape pattern of marine aquaculture in Liaoning Province is split, large-scale aquaculture and small-scale aquaculture are symbiotic, and landscape ecological activities are active. For local managers, this study can provide valuable supporting data for the assessment of marine aquaculture yield in this region, comprehensive control and management of the marine environment, and stability of the marine ecosystem. For other countries or regions, this work provides a great reference value for monitoring the dynamic spatial distribution of marine aquaculture.

Keywords: remote sensing; marine aquaculture; spatial distribution; dynamic monitoring; Liaoning Province (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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