Please wait a minute...
 
Remote Sensing for Natural Resources    2022, Vol. 34 Issue (4) : 33-41     DOI: 10.6046/zrzyyg.2021438
|
Identification of mariculture areas in Guangdong Province and remote sensing monitoring of their spatial and temporal changes based on the U-Net convolutional neural network
SU Wei1(), LIN Yangyang1, YUE Wen1(), CHEN Yingbiao2
1. Land Investigation and Planning Institute of Guangdong Province, Guangzhou 511453, China
2. School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
Download: PDF(4614 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

The mariculture industry occupies an important position in the marine economy of Guangdong Province. Timely and accurate knowledge of the spatial distribution and area changing trends of mariculture areas can greatly promote the sustainable development of the mariculture industry. Conventional interpretation methods for remote sensing images have problems of poor repeatability, low applicability, and high subjective arbitrariness. By contrast, the U-Net convolutional neural network, which belongs to the deep learning network model, can better extract the features of the object with higher extraction precision. Therefore, based on the multi-temporal Landsat TM/OLI remote sensing images, this study identified the mariculture areas (enclosed-sea and open-cage aquaculture areas) in Guangdong from 1998 to 2021 using the U-Net model as the interpretation model. The area trend analysis of mariculture areas was made. The changing characteristics of mariculture areas in terms of spatial distribution patterns were studied. The results are as follows. Compared with network models such as K-Means cluster analysis and DBN, the U-Net model with higher interpretation precision is more suitable for the interpretation of mariculture areas in Guangdong. The mariculture areas in Guangdong are mainly distributed in the western portion of Guangdong, such as Zhanjiang, Jiangmen, and Yangjiang. The mariculture areas in Guangdong can be classified into three levels in terms of area. They have small changes and keep a relatively stable state. The mariculture areas in Guangdong showed a spatial trend of outward expansion from 1998 to 2014 and inward contraction from 2014 to 2021. This study will provide data and technical support for the scientific management of the mariculture areas in Guangdong.
Keywords mariculture area      remote sensing      deep learning      U-Net model      Guangdong Province     
ZTFLH:  TP79  
Issue Date: 27 December 2022
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Wei SU
Yangyang LIN
Wen YUE
Yingbiao CHEN
Cite this article:   
Wei SU,Yangyang LIN,Wen YUE, et al. Identification of mariculture areas in Guangdong Province and remote sensing monitoring of their spatial and temporal changes based on the U-Net convolutional neural network[J]. Remote Sensing for Natural Resources, 2022, 34(4): 33-41.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021438     OR     https://www.gtzyyg.com/EN/Y2022/V34/I4/33
Fig.1  Location map of the study area
Fig.2  Network structure of U-Net
Fig.3  Comparison of three types of models to identify mariculture areas
Tab.1  Distribution of mariculture areas in Guangdong from 1998 to 2021
城市 1998年 2002年 2006年 2010年 2014年 2018年 2021年
潮州 130 87 20 49 43 46 16
汕头 646 495 479 688 1743 547 317
揭阳 64 93 93 160 103 140 92
汕尾 827 797 1 014 1 054 1 076 1 009 836
惠州 1 152 1 569 1 531 1 462 1 399 1 228 1 337
深圳 1 814 2 186 2 557 1 210 887 699 143
东莞 110 150 42 70 144 58 23
广州 19 225 137 99 215 557 597
中山 783 847 956 1 660 1 066 745 242
珠海 3 103 3 282 3 101 4 272 3 535 3 949 3 758
江门 6 050 7 339 8 506 7 588 7 938 7 684 7 567
阳江 5 880 5 567 7 163 6 579 6 658 6 982 6 632
茂名 1 032 1 305 1 378 1 680 1 427 1 114 1 317
湛江 26 344 24 798 28 232 25 317 26 211 26 187 26 626
Tab.2  Area of mariculture areas in coastal cities of Guangdong form 1998 to 2021(hm2)
城市 1998—
2002年		 2002—
2006年		 2006—
2010年		 2010—
2014年		 2014—
2018年		 2018—
2021年
潮州 -8.3 -19.3 36.3 -3.1 1.7 -21.7
汕头 -5.8 -0.8 10.9 38.3 -17.2 -14.0
揭阳 11.3 0.0 18.0 -8.9 9.0 -11.4
汕尾 -0.9 6.8 1.0 0.5 -1.6 -5.7
惠州 9.0 -0.6 -1.1 -1.1 -3.1 3.0
深圳 5.1 4.2 -13.2 -6.7 -5.3 -26.5
东莞 9.1 -18.0 16.7 26.4 -14.9 -20.1
广州 271.1 -9.8 -6.9 29.3 39.8 2.4
中山 2.0 3.2 18.4 -8.9 -7.5 -22.5
珠海 1.4 -1.4 9.4 -4.3 2.9 -1.6
江门 5.3 4.0 -2.7 1.2 -0.8 -0.5
阳江 -1.3 7.2 -2.0 0.3 1.2 -1.7
茂名 6.6 1.4 5.5 -3.8 -5.5 6.1
湛江 -1.5 3.5 -2.6 0.9 0.0 0.6
Tab.3  Dynamic of mariculture areas in coastal cities of Guangdong form 1998 to 2021(%)
Fig.4  Spatial pattern of mariculture areas in Guangdong from 1998 to 2021
[1] 杨正勇, 刘东, 彭乐威. 中国海水养殖业绿色发展:水平测度、区域对比及发展对策研究[J]. 生态经济, 2021, 37(11):128-135.
[1] Yang Z Y, Liu D, Peng L W. Green development of mariculture in China:Estimation,regional comparison and research on development strategy[J]. Ecological Economy, 2021, 37(11):128-135.
[2] 黄文积, 袁蓓. 高质量发展要求下我国海水养殖业竞争力评价[J]. 中国渔业经济, 2021, 39(4):60-67.
[2] Huang W J, Yuan B. Competitiveness evaluation of China’s mariculture industry under the requirements of high-quality development[J]. Chinese Fisheries Economics, 2021, 39(4):60-67.
[3] 翟路, 孙兆群, 王波, 等. 基于灰色预测模型的我国海洋渔业发展趋势研究[J]. 江苏农业科学, 2019, 47(13):342-346.
[3] Zhai L, Sun Z Q, Wang B, et al. Study on development of China’s marine fisheries based on GM (1,1) model[J]. Jiangsu Agricultural Sciences, 2019, 47(13):342-346.
[4] 郑智腾, 范海生, 王洁, 等. 改进型双支网络模型的遥感海水网箱养殖区智能提取方法[J]. 国土资源遥感, 2020, 32(4):120-129.doi:10.6046/gtzyyg.2020.04.17.
doi: 10.6046/gtzyyg.2020.04.17
[4] Zheng Z T, Fan H S, Wang J, et al. An improved double-branch network method for intelligently extracting marinecage culture area[J]. Remote Sensing for Land and Resources, 2020, 32(4):120-129.doi:10.6046/gtzyyg.2020.04.17.
doi: 10.6046/gtzyyg.2020.04.17
[5] Oyinlola M A, Reygondeau G, Wabnitz C C C, et al. Global estimation of areas with suitable environmental conditions for mariculture species[J]. PLoS One, 2018, 13(1):e191086.
[6] Feng Z H, Zhang T, Wang J X, et al. Spatio-temporal features of microplastics pollution in macroalgae growing in an important mariculture area,China[J]. Science of the Total Environment, 2020, 719:137490.
doi: 10.1016/j.scitotenv.2020.137490 url: https://linkinghub.elsevier.com/retrieve/pii/S0048969720310019
[7] 武易天, 陈甫, 马勇, 等. 基于Landsat8数据的近海养殖区自动提取方法研究[J]. 国土资源遥感, 2018, 30(3):96-105.doi:10.6046/gtzyyg.2018.03.14.
doi: 10.6046/gtzyyg.2018.03.14
[7] Wu Y T, Chen F, Ma Y, et al. Research on automatic extraction method for coastal aquaculture area using Landsat8 data[J]. Remote Sensing for Land and Resources, 2018, 30(3):96-105.doi:10.6046/gtzyyg.2018.03.14.
doi: 10.6046/gtzyyg.2018.03.14
[8] 杨智威, 陈颖彪, 吴志峰, 等. 基于自然区块的城市热环境空间分异性研究[J]. 地理科学进展, 2019, 38(12):1944-1956.
doi: 10.18306/dlkxjz.2019.12.010
[8] Yang Z W, Chen Y B, Wu Z F, et al. Spatial variability of urban thermal environment based on natural blocks[J]. Progress in Geography, 2019, 38(12):1944-1956.
doi: 10.18306/dlkxjz.2019.12.010
[9] 李阳, 袁琳, 赵志远, 等. 基于无人机低空遥感和现场调查的潮滩地形反演研究[J]. 自然资源遥感, 2021, 33(3):80-88.doi:10.6046/zrzyyg.2020336.
doi: 10.6046/zrzyyg.2020336
[9] Li Y, Yuan L, Zhao Z Y, et al. Inversion of tidal flat topography based on unmanned aerial vehicle low-altitude remote sensing and field surveys[J]. Remote Sensing for Natural Resources, 2021, 33(3):80-88.doi:10.6046/zrzyyg.2020336.
doi: 10.6046/zrzyyg.2020336
[10] 徐京萍, 赵建华, 张丰收, 等. 面向对象的池塘养殖用海信息提取[J]. 国土资源遥感, 2013, 25(1):82-85.doi:10.6046/gtzyyg.2013.01.15.
doi: 10.6046/gtzyyg.2013.01.15
[10] Xu J P, Zhao J H, Zhang F S, et al. Object-oriented information extraction of pond aquaculture[J]. Remote Sensing for Land and Resources, 2013, 25(1):82-85.doi:10.6046/gtzyyg.2013.01.15.
doi: 10.6046/gtzyyg.2013.01.15
[11] Wang Z H, Yang X M, Liu Y M, et al. Extraction of coastal raft cultivation area with heterogeneous water background by thresholding object-based visually salient NDVI from high spatial resolution imagery[J]. Remote Sensing Letters, 2018, 9(9):839-846.
doi: 10.1080/2150704X.2018.1468103 url: https://www.tandfonline.com/doi/full/10.1080/2150704X.2018.1468103
[12] 陈思明. 三沙湾海水养殖区时空动态变化及其影响因素[J]. 生态学杂志, 2021, 40(4):1137-1145.
[12] Chen S M. Spatiotemporal dynamics of mariculture area in Sansha Bay and its driving factors[J]. Chinese Journal of Ecology, 2021, 40(4):1137-1145.
[13] 王芳, 夏丽华, 陈智斌, 等. 基于关联规则面向对象的海岸带海水养殖模式遥感识别[J]. 农业工程学报, 2018, 34(12):210-217.
[13] Wang F, Xia L H, Chen Z B, et al. Remote sensing identification of coastal zone mariculture modes based on association-rules object-oriented method[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(12):210-217.
[14] Lu Y M, Shao W, Sun J. Extraction of offshore aquaculture areas from medium-resolution remote sensing images based on deep learning[J]. Remote Sensing, 2021, 13(19):3854.
doi: 10.3390/rs13193854 url: https://www.mdpi.com/2072-4292/13/19/3854
[15] Alom M Z, Yakopcic C, Hasan M, et al. Recurrent residual U-Net for medical image segmentation[J]. Journal of Medical Imaging, 2019, 6(1):014006.
[16] Wei S S, Zhang H, Wang C, et al. Multi-temporal SAR data large-scale crop mapping based on U-Net model[J]. Remote Sensing, 2019, 11(1):68.
doi: 10.3390/rs11010068 url: https://www.mdpi.com/2072-4292/11/1/68
[17] Cui B G, Fei D, Shao G H, et al. Extracting raft aquaculture areas from remote sensing images via an improved U-Net with a PSE structure[J]. Remote Sensing, 2019, 11(17):2053.
doi: 10.3390/rs11172053 url: https://www.mdpi.com/2072-4292/11/17/2053
[18] Falk T, Mai D, Bensch R, et al. U-Net:Deep learning for cell counting,detection,and morphometry[J]. Nature Methods, 2019, 16(1):67-70.
doi: 10.1038/s41592-018-0261-2 url: https://doi.org/10.1038/s41592-018-0261-2
[19] 杨瑞, 祁元, 苏阳. 深度学习U-Net方法及其在高分辨卫星影像分类中的应用[J]. 遥感技术与应用, 2020, 35(4):767-774.
[19] Yang R, Qi Y, Su Y. U-Net neural networks and its application in high resolution satellite image classification[J]. Remote Sensing Technology and Application, 2020, 35(4):767-774.
[20] 杨智威, 陈颖彪, 千庆兰, 等. 人口空间化下公共医疗服务水平匹配度评价——以广州市为例[J]. 地理与地理信息科学, 2019, 35(2):74-82.
[20] Yang Z W, Chen Y B, Qian Q L, et al. Evaluation of the matching degree of public medical service level based on population spatialization:A case study of Guangzhou[J]. Geography and Geo-Information Science, 2019, 35(2):74-82.
[21] Nogueira K, Penatti O A B, Dos S J A. Towards better exploiting convolutional neural networks for remote sensing scene classification[J]. Pattern Recognition, 2017, 61:539-556.
doi: 10.1016/j.patcog.2016.07.001 url: https://linkinghub.elsevier.com/retrieve/pii/S0031320316301509
[22] Shao Z F, Zhou W X, Deng X Q, et al. Multilabel remote sensing image retrieval based on fully convolutional network[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13:318-328.
doi: 10.1109/JSTARS.2019.2961634 url: https://ieeexplore.ieee.org/document/8954885/
[23] Freudenberg M, Nölke N, Agostini A, et al. Large-scale palm tree detection in high resolution satellite images using U-Net[J]. Remote Sensing, 2019, 11(3):312.
doi: 10.3390/rs11030312 url: http://www.mdpi.com/2072-4292/11/3/312
[24] Qin P, Cai Y L, Wang X L. Small waterbody extraction with improved U-Net using Zhuhai-1 hyperspectral remote sensing images[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 19:1-5.
[25] Liu Z Q, Cao Y W, Wang Y Z, et al. Computer vision-based concrete crack detection using U-Net fully convolutional networks[J]. Automation in Construction, 2019, 104:129-139.
doi: 10.1016/j.autcon.2019.04.005 url: https://linkinghub.elsevier.com/retrieve/pii/S0926580519301244
[26] Zhang Z, Wu C D, Coleman S, et al. DENSE-Inception U-Net for medical image segmentation[J]. Computer Methods and Programs in Biomedicine, 2020, 192:105395.
doi: 10.1016/j.cmpb.2020.105395 url: https://linkinghub.elsevier.com/retrieve/pii/S0169260719307904
[27] Ibtehaz N, Rahman M S. MultiResUNet:Rethinking the U-Net architecture for multimodal biomedical image segmentation[J]. Neural Networks, 2020, 121:74-87.
doi: S0893-6080(19)30250-3 pmid: 31536901
[28] 杨智威, 陈颖彪, 郑子豪, 等. 广东省A级旅游点空间分布特征与可达性测度[J]. 地理空间信息, 2019, 17(6):51-55.
[28] Yang Z W, Chen Y B, Zheng Z H, et al. Spatial accessibility and spatial distribution characteristics of A-grade tourist spots in Guangdong Province[J]. Geospatial Information, 2019, 17(6):51-55.
[1] NIU Xianghua, HUANG Wei, HUANG Rui, JIANG Sili. A high-fidelity method for thin cloud removal from remote sensing images based on attentional feature fusion[J]. Remote Sensing for Natural Resources, 2023, 35(3): 116-123.
[2] DONG Ting, FU Weiqi, SHAO Pan, GAO Lipeng, WU Changdong. Detection of changes in SAR images based on an improved fully-connected conditional random field[J]. Remote Sensing for Natural Resources, 2023, 35(3): 134-144.
[3] LIN Jiahui, LIU Guang, FAN Jinghui, ZHAO Hongli, BAI Shibiao, PAN Hongyu. Extracting information about mining subsidence by combining an improved U-Net model and D-InSAR[J]. Remote Sensing for Natural Resources, 2023, 35(3): 145-152.
[4] WANG Jianqiang, ZOU Zhaohui, LIU Rongbo, LIU Zhisong. A method for extracting information on coastal aquacultural ponds from remote sensing images based on a U2-Net deep learning model[J]. Remote Sensing for Natural Resources, 2023, 35(3): 17-24.
[5] TANG Hui, ZOU Juan, YIN Xianghong, YU Shuchen, HE Qiuhua, ZHAO Dong, ZOU Cong, LUO Jianqiang. River and lake sand mining in the Dongting Lake area: Supervision based on high-resolution remote sensing images and typical case analysis[J]. Remote Sensing for Natural Resources, 2023, 35(3): 302-309.
[6] YU Hang, AN Na, WANG Jie, XING Yu, XU Wenjia, BU Fan, WANG Xiaohong, YANG Jinzhong. High-resolution remote sensing-based dynamic monitoring of coal mine collapse areas in southwestern Guizhou: A case study of coal mine collapse areas in Liupanshui City[J]. Remote Sensing for Natural Resources, 2023, 35(3): 310-318.
[7] WANG Jing, WANG Jia, XU Jiangqi, HUANG Shaodong, LIU Dongyun. Exploring ecological environment quality of typical coastal cities based on an improved remote sensing ecological index: A case study of Zhanjiang City[J]. Remote Sensing for Natural Resources, 2023, 35(3): 43-52.
[8] XU Xinyu, LI Xiaojun, ZHAO Heting, GAI Junfei. Pansharpening algorithm of remote sensing images based on NSCT and PCNN[J]. Remote Sensing for Natural Resources, 2023, 35(3): 64-70.
[9] LIU Li, DONG Xianmin, LIU Juan. A performance evaluation method for semantic segmentation models of remote sensing images considering surface features[J]. Remote Sensing for Natural Resources, 2023, 35(3): 80-87.
[10] FANG He, ZHANG Yuhui, HE Yue, LI Zhengquan, FAN Gaofeng, XU Dong, ZHANG Chunyang, HE Zhonghua. Spatio-temporal variations of vegetation ecological quality in Zhejiang Province and their driving factors[J]. Remote Sensing for Natural Resources, 2023, 35(2): 245-254.
[11] ZHANG Xian, LI Wei, CHEN Li, YANG Zhaoying, DOU Baocheng, LI Yu, CHEN Haomin. Research progress and prospect of remote sensing-based feature extraction of opencast mining areas[J]. Remote Sensing for Natural Resources, 2023, 35(2): 25-33.
[12] MA Shibin, PI Yingnan, WANG Jia, ZHANG Kun, LI Shenghui, PENG Xi. High-efficiency supervision method for green geological exploration based on remote sensing[J]. Remote Sensing for Natural Resources, 2023, 35(2): 255-263.
[13] WANG Ping. Application of thermal infrared remote sensing in monitoring the steel overcapacity cutting[J]. Remote Sensing for Natural Resources, 2023, 35(2): 271-276.
[14] LI Tianchi, WANG Daoru, ZHAO Liang, FAN Renfu. Classification and change analysis of the substrate of the Yongle Atoll in the Xisha Islands based on Landsat8 remote sensing data[J]. Remote Sensing for Natural Resources, 2023, 35(2): 70-79.
[15] DIAO Mingguang, LIU Yong, GUO Ningbo, LI Wenji, JIANG Jikang, WANG Yunxiao. Mask R-CNN-based intelligent identification of sparse woods from remote sensing images[J]. Remote Sensing for Natural Resources, 2023, 35(2): 97-104.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech