Time series sentinel-1 SAR data for the mapping of aquaculture ponds in coastal Asia

Ottinger, Marco; Clauss, Kersten; Huth, Juliane; Eisfelder, Christina; Leinenkugel, Patrick; Kuenzer, Claudia

doi:10.1109/igarss.2018.8651419

Cited by 9 publications

(4 citation statements)

References 5 publications

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“…Analysis using Sentinel-1 Image data by utilizing VV polarization. Extraction of water bodies is carried out by determining the boundary value between the backscatter value of water objects and non-water objects [23], [24]. Based on the backscattering curve value, it was found that the water body value was below -13.39dB (Figure 4).…”

Section: Waterbodies Extractionmentioning

confidence: 99%

Flood Modelling Using Integration of Multi-data Analysis and HEC-RAS Model in Mata Allo River, Sulawesi

et al. 2023

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The amount of rainfall in a watershed with steep slopes, small cross-sectional areas, and less water catchment areas. This will cause an increase in water discharge in the river which can cause flooding. These characteristics can be found in Mata Allo River, Enrekang Regency. To identify the most flood-hit areas, the simulating model can be done utilizing the HEC-RAS program. Use of Satellite Imagery Data such as Sentinel-2 for extracting land use data information, and Sentinel-1 for data extraction of actual water bodies/rivers. The analysis is carried out by integrating the interpretation results from multi-sensor images with the results of modeling the flood inundation area using HEC-RAS. Based on the analysis results, the land use classification accuracy is 82.9% for Sentinel-2 data using the random forest algorithm. While for the actual extraction of water bodies using Sentinel-1 imagery was 89.6%. Approaching the threshold value between water and non-water bodies is taken using -13.39. The inundation area in the study area reached 87.66ha at the largest discharge model. The most affected land use after integrating each data is built-up land, most of which are settlements covering an area of 47.26ha.

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Section: Waterbodies Extractionmentioning

confidence: 99%

Flood Modelling Using Integration of Multi-data Analysis and HEC-RAS Model in Mata Allo River, Sulawesi

et al. 2023

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“…However, their coverage is limited, making it difficult to map large areas of aquaculture ponds. Sentinel-1 imagery is increasingly used to map coastal aquaculture ponds due to its all-weather and day/night collection capabilities [28,29]. However, it has more speckle and boundary noise, both of which are challenging to suppress effectively and significantly reduce the signal-to-noise ratio and radiometric resolution [30].…”

Section: Introductionmentioning

confidence: 99%

An Object-Based Approach to Extract Aquaculture Ponds with 10-Meter Resolution Sentinel-2 Images: A Case Study of Wenchang City in Hainan Province

Hu,

Zhang,

Chen

et al. 2024

Remote Sensing

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Coastal aquaculture has made an important contribution to global food security and the economic development of coastal zones in recent decades. However, it has also damaged these coastal zones’ ecosystems. Moreover, coastal aquaculture is poised to play a key role in the achievement of Sustainable Development Goals (SDGs). Consequently, extracting aquaculture has become crucial and valuable. However, due to the limitations of remote sensing image spatial resolution and traditional extraction methods, most research studies focus on aquaculture areas containing dikes rather than individually separable aquaculture ponds (ISAPs). This is not an accurate estimation of these aquaculture areas’ true size. In our study, we propose a rapid and effective object-based method of extracting ISAPs. We chose multi-scale segmentation to generate semantically meaningful image objects for various types of land cover, and then built a decision tree classifier according to the unique features of ISAPs. The results show that our method can remove small rivers and other easily confused features, which has thus far been difficult to accomplish with conventional methods. We obtained an overall precision value of 85.61% with a recall of 84.04%; compared to the support vector machine’s (SVM) overall precision value of 78.85% and recall rate of 61.21%, our method demonstrates greater accuracy and efficiency. We used this method to test the transferability of the algorithm to nearby areas, and the obtained accuracy exceeded 80%. The method proposed in this study could provide a readily available solution for the simple and efficient extracting of ISAPs and shows high spatiotemporal transferability.

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“…Currently, coastal aquaculture research and extraction technologies have reached a high level of maturity, with abundant data sources, diverse results, and significant contributions. Regarding aquaculture extraction technology, perspectives include visual interpretation methods [14], information enhancement methods [15], machine learning-based feature enhancement methods [16][17][18], object-oriented methods [19][20][21], and deep learning methods [22,23]. In terms of data resources, they encompass SAR (synthetic aperture radar) image data, medium-resolution optical image data, and high-resolution optical image data.…”

Section: Introductionmentioning

confidence: 99%

Spatial Distribution and Differentiation Analysis of Coastal Aquaculture in China Based on Remote Sensing Monitoring

Meng,

Yang,

Wang

et al. 2024

Remote Sensing

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Multiple datasets related to pond and marine aquaculture have been published using diverse remote sensing technologies, yet a comprehensive dataset detailing spatial distribution on both land and sea sides is lacking. Firstly, a meticulous comparison of datasets which we selected related to aquaculture ponds and marine, ensuring consistency in trends. Subsequently, the datasets published by our team were edited and integrated to illustrate aquaculture activities on both sides of China’s coastal zone. Finally, a spatial differentiation of coastal aquaculture in major provinces was analyzed. This analysis also utilizes the types of coastline and statistical data, guiding coordinated resource management efforts. The results unveil a distinctive spatial distribution pattern, concentrating aquaculture in the northern regions—Bohai Sea, Jiangsu, Fujian, and Pearl River coasts in Guangdong. The provinces rich in aquaculture resources, such as Shandong, Guangdong, and Liaoning, exhibit extensive coastlines. However, remote sensing monitoring suggests an underestimation of Liaoning’s marine aquaculture compared to statistical yearbook data. Furthermore, southern provinces like Guangdong and Fujian exhibit significantly higher aquaculture output than Liaoning. Zhejiang leads in fishing output. The paper outlines the future development direction of coastal aquaculture, emphasizing a strategic, integrated land–sea approach for sustainable development.

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Time series sentinel-1 SAR data for the mapping of aquaculture ponds in coastal Asia

Cited by 9 publications

References 5 publications

Flood Modelling Using Integration of Multi-data Analysis and HEC-RAS Model in Mata Allo River, Sulawesi

Flood Modelling Using Integration of Multi-data Analysis and HEC-RAS Model in Mata Allo River, Sulawesi

An Object-Based Approach to Extract Aquaculture Ponds with 10-Meter Resolution Sentinel-2 Images: A Case Study of Wenchang City in Hainan Province

Spatial Distribution and Differentiation Analysis of Coastal Aquaculture in China Based on Remote Sensing Monitoring

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