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Quadrennial Series Dataset of Coastal Aquaculture Distribution of China Based on Landsat Images (1990-2022)


YIN Yumeng1ZHANG Yinghui*1HU Zhongwen1XU Yue2WANG Jingzhe3WANG Chen4SHI Tiezhu1WU Guofeng1
1 MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area,Shenzhen University,Shenzhen 518060,China2 College of Urban and Environmental Sciences,Central China Normal University,Wuhan 430079,China3 School of Artificial Intelligence,Shenzhen Polytechnic University,Shenzhen 518055,China4 Satellite Application Center for Ecology and Environment,Ministry of Ecology and Environment of P. R. China,Beijing 100094,China

DOI:10.3974/geodb.2023.09.01.V1

Published:Sep. 2023

Visitors:3856       Data Files Downloaded:208      
Data Downloaded:15740.50 MB      Citations:

Key Words:

China,aquaculture area,Landsat images,long time series

Abstract:

Based on the time series of Landsat images (1990-2022) from Google Earth Engine (GEE) cloud computing platform, the Quadrennial Series Dataset of Coastal Aquaculture Distribution of China Based on Landsat Images (1990-2022) was developed using a multi-featured method. The dataset covers the coastal region of China in 30 m, and is quadrennial from 1990 to 2022. The dataset is archived in .tif format, and consists of 99 data files with data size of 43.4 GB (Compressed to one file with 75.6 MB).

Foundation Item:

Science, Technology and Innovation Commission of Shenzhen Municipality (JCYJ2022082018101617037); National Natural Science Foundation of China (42201347)

Data Citation:

YIN Yumeng, ZHANG Yinghui*, HU Zhongwen, XU Yue, WANG Jingzhe, WANG Chen, SHI Tiezhu, WU Guofeng. Quadrennial Series Dataset of Coastal Aquaculture Distribution of China Based on Landsat Images (1990-2022)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2023. https://doi.org/10.3974/geodb.2023.09.01.V1.

References:


     [1] FAO. The State of World Fisheries and Aquaculture 2020 [M]. 2020.
     [2] Duan, Y., Tian, B., Li, X., et al. Tracking changes in aquaculture ponds on the China coast using 30 years of Landsat images [J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 102: 102383.
     [3] Sun, Z., Luo, J., Yang, J., et al. Nation-scale mapping of coastal aquaculture ponds with Sentinel-1 SAR data using Google Earth Engine [J]. Remote Sensing, 2020, 12(18): 3086.
     [4] Wang, M., Mao, D., Xiao, X. M., et al. Interannual changes of coastal aquaculture ponds in China at 10-m spatial resolution during 2016–2021 [J]. Remote Sensing of Environment, 2023: 15.
     [5] Sridhar, P. N., Surendran, A., Ramana, I. V. Auto‐extraction technique‐based digital classification of saltpans and aquaculture plots using satellite data [J]. International Journal of Remote Sensing, 2008, 29(2): 313-323.
     [6] Ottinger, M., Clauss, K., Kuenzer, C. Aquaculture: Relevance, distribution, impacts and spatial assessments – a review [J]. Ocean & Coastal Management, 2016, 119: 244-266.
     [7] Ren, C., Wang, Z., Zhang, Y., et al. Rapid expansion of coastal aquaculture ponds in China from Landsat observations during 1984–2016 [J]. International Journal of Applied Earth Observation and Geoinformation, 2019, 82: 101902.
     [8] Ottinger, M., Clauss, K., Kuenzer, C. Large-scale assessment of coastal aquaculture ponds with Sentinel-1 time series data [J]. Remote Sensing, 2017, 9(5): 440.
     [9] Otsu, N. Threshold selection method from gray-level histograms [J]. IEEE Transactions on Systems Man and Cybernetics, 1979, 9(1): 62-66.
     [10] Breiman, L. Random forests [J]. Machine Learning, 2001, 45(1): 5-32.
     [11] Pearson, K. Contributions to the mathematical theory of evolution [J]. Philosophical Transactions of the Royal Society of London. A, 1894, 185: 71-110.
     [12] Liu, Y., Wang, Z., Yang, X., et al. Satellite-based monitoring and statistics for raft and cage aquaculture in China’s offshore waters [J]. International Journal of Applied Earth Observation and Geoinformation, 2020, 91: 102118.
     [13] Zhu, Z., Woodcock, C. E. Object-based cloud and cloud shadow detection in Landsat imagery [J]. Remote Sensing of Environment, 2012, 118: 83-94.
     [14] Xie, H., Luo, X., Xu, X., et al. Evaluation of Landsat 8 OLI imagery for unsupervised inland water extraction [J]. International Journal of Remote Sensing, 2016, 37(8): 1826-1844.
     [15] Guo, Q., Pu, R., Li, J., et al. A weighted normalized difference water index for water extraction using Landsat imagery [J]. International Journal of Remote Sensing, 2017, 38(19): 5430-5445.
     [16] Peng, Y., Sengupta, D., Duan, Y., et al. Accurate mapping of Chinese coastal aquaculture ponds using biophysical parameters based on Sentinel-2 time series images [J]. Marine Pollution Bulletin, 2022, 181: 113901.
     [17] Virdis, S. G. P. An object-based image analysis approach for aquaculture ponds precise mapping and monitoring: a case study of Tam Giang-Cau Hai Lagoon, Vietnam [J]. Environmental Monitoring and Assessment, 2014, 186(1): 117-133.
     [18] Diniz, C., Cortinhas, L., Pinheiro, M. L., et al. A large-scale deep-learning approach for multi-temporal aqua and salt-culture mapping [J]. Remote Sensing, 2021, 13(8): 1415.
     [19] Gross, J. W., Heumann, B. W. Can flowers provide better spectral discrimination between herbaceous wetland species than leaves? [J]. Remote Sensing Letters, 2014, 5(10): 892-901.
     

Data Product:

ID Data Name Data Size Operation
1 CAP_MA_China_1990_2022.rar 77491.72KB
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