Nation-Scale Mapping of Coastal Aquaculture Ponds with Sentinel-1 SAR Data Using Google Earth Engine
文献类型：期刊论文
| 作者 | Sun, Zhe; Luo, Juhua; Yang, Jingzhicheng; Yu, Qiuyan; Zhang, Li; Xue, Kun; Lu, Lirong |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2020 |
| 卷号 | 12期号:18 |
| 英文摘要 | Global rapid expansion of the coastal aquaculture industry has made great contributions to enhance food security, but has also caused a series of ecological and environmental issues. Sustainable management of coastal areas requires the explicit and efficient mapping of the spatial distribution of aquaculture ponds. In this study, a Google Earth Engine (GEE) application was developed for mapping coastal aquaculture ponds at a national scale with a novel classification scheme using Sentinel-1 time series data. Relevant indices used in the classification mainly include the water index, texture, and geometric metrics derived from radar backscatter, which were then used to segment and classify aquaculture ponds. Using this approach, we classified aquaculture ponds for the full extent of the coastal area in Vietnam with an overall accuracy of 90.16% (based on independent sample evaluation). The approach, enabling wall-to-wall mapping and area estimation, is essential to the efficient monitoring and management of aquaculture ponds. The classification results showed that aquaculture ponds are widely distributed in Vietnam's coastal area and are concentrated in the Mekong River Delta and Red River delta (85.14% of the total area), which are facing the increasing collective risk of climate change (e.g., sea level rise and salinity intrusion). Further investigation of the classification results also provides significant insights into the stability and deliverability of the approach. The water index derived from annual median radar backscatter intensity was determined to be efficient at mapping water bodies, likely due to its strong response to water bodies regardless of weather. The geometric metrics considering the spatial variation of radar backscatter patterns were effective at distinguishing aquaculture ponds from other water bodies. The primary use of GEE in this approach makes it replicable and transferable by other users. Our approach lays a solid foundation for intelligent monitoring and management of coastal ecosystems. |
| 源URL | [http://159.226.73.51/handle/332005/20105]  |
| 专题 | 中国科学院南京地理与湖泊研究所 |
| 推荐引用方式 GB/T 7714 | Sun, Zhe,Luo, Juhua,Yang, Jingzhicheng,et al. Nation-Scale Mapping of Coastal Aquaculture Ponds with Sentinel-1 SAR Data Using Google Earth Engine[J]. REMOTE SENSING,2020,12(18). |
| APA | Sun, Zhe.,Luo, Juhua.,Yang, Jingzhicheng.,Yu, Qiuyan.,Zhang, Li.,...&Lu, Lirong.(2020).Nation-Scale Mapping of Coastal Aquaculture Ponds with Sentinel-1 SAR Data Using Google Earth Engine.REMOTE SENSING,12(18). |
| MLA | Sun, Zhe,et al."Nation-Scale Mapping of Coastal Aquaculture Ponds with Sentinel-1 SAR Data Using Google Earth Engine".REMOTE SENSING 12.18(2020). |
入库方式： OAI收割
来源：南京地理与湖泊研究所
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