Fu, Y., Deng, J., Wang, H. et al. (6 more authors) (2021) A new satellite-derived dataset for marine aquaculture areas in China's coastal region. Earth System Science Data, 13 (5). pp. 1829-1842. ISSN 1866-3508
Abstract
China has witnessed extensive development of the marine aquaculture industry over recent years. However, such rapid and disordered expansion posed risks to coastal environment, economic development, and biodiversity protection. This study aimed to produce an accurate national-scale marine aquaculture map at a spatial resolution of 16 m, using a proposed model based on deep convolution neural networks (CNNs) and applied it to satellite data from China's GF-1 sensor in an end-to-end way. The analyses used homogeneous CNNs to extract high-dimensional features from the input imagery and preserve information at full resolution. Then, a hierarchical cascade architecture was followed to capture multi-scale features and contextual information. This hierarchical cascade homogeneous neural network (HCHNet) was found to achieve better classification performance than current state-of-the-art models (FCN-32s, Deeplab V2, U-Net, and HCNet). The resulting marine aquaculture area map has an overall classification accuracy > 95 % (95.2 %–96.4, 95 % confidence interval). And marine aquaculture was found to cover a total area of ∼ 1100 km2 (1096.8–1110.6 km2, 95 % confidence interval) in China, of which more than 85 % is marine plant culture areas, with 87 % found in the Fujian, Shandong, Liaoning, and Jiangsu provinces. The results confirm the applicability and effectiveness of HCHNet when applied to GF-1 data, identifying notable spatial distributions of different marine aquaculture areas and supporting the sustainable management and ecological assessments of coastal resources at a national scale. The national-scale marine aquaculture map at 16 m spatial resolution is published in the Google Maps kmz file format with georeferencing information at https://doi.org/10.5281/zenodo.3881612 (Fu et al., 2020).
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © Author(s) 2021. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > Centre for Spatial Analysis & Policy (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 Jul 2024 14:10 |
Last Modified: | 19 Jul 2024 14:10 |
Status: | Published |
Publisher: | Copernicus Publications |
Identification Number: | 10.5194/essd-13-1829-2021 |
Related URLs: | |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:214948 |