中国自然资源航空物探遥感中心主办
地质出版社出版

基于时序Sentinel-2数据的江汉平原养殖池提取

陈志洋, 毛德华, 王宗明, 林楠, 贾明明, 任春颖, 王铭. 2025. 基于时序Sentinel-2数据的江汉平原养殖池提取. 自然资源遥感, 37(1): 169-178. doi: 10.6046/zrzyyg.2023278
引用本文: 陈志洋, 毛德华, 王宗明, 林楠, 贾明明, 任春颖, 王铭. 2025. 基于时序Sentinel-2数据的江汉平原养殖池提取. 自然资源遥感, 37(1): 169-178. doi: 10.6046/zrzyyg.2023278
CHEN Zhiyang, MAO Dehua, WANG Zongming, LIN Nan, JIA Mingming, REN Chunying, WANG Ming. 2025. Information extraction of aquaculture ponds in the Jianghan Plain based on Sentinel-2 time-series data. Remote Sensing for Natural Resources, 37(1): 169-178. doi: 10.6046/zrzyyg.2023278
Citation: CHEN Zhiyang, MAO Dehua, WANG Zongming, LIN Nan, JIA Mingming, REN Chunying, WANG Ming. 2025. Information extraction of aquaculture ponds in the Jianghan Plain based on Sentinel-2 time-series data. Remote Sensing for Natural Resources, 37(1): 169-178. doi: 10.6046/zrzyyg.2023278

基于时序Sentinel-2数据的江汉平原养殖池提取

  • 基金项目:

    国家自然科学基金重点项目“全球湿地遥感分类方法研究”(编号:42330109)资助

详细信息
    作者简介: 陈志洋(1997-), 男, 硕士研究生, 研究方向为湿地遥感。Email:1075964123@qq.com
    通讯作者: 王铭(1990-), 男, 博士, 研究方向为湿地遥感。Email:wangming21@iga.cn
  • 中图分类号: P237; |S951.2

Information extraction of aquaculture ponds in the Jianghan Plain based on Sentinel-2 time-series data

More Information
    Corresponding author: WANG Ming
  • 近年来, 水产养殖业的迅速发展引发了一系列的生态环境问题。江汉平原作为我国最重要的淡水养殖基地之一, 研究其养殖池变化对我国的生态保护至关重要。因此, 该文面向江汉平原区域, 基于谷歌地球引擎(Google Earth Engine, GEE)与Sentinel-2密集时间序列影像, 提出了一种结合K均值聚类(K-means)与层次决策树分类算法的养殖池提取与变化监测方法, 实现了2016—2022年逐年的江汉平原养殖池精确提取及时空格局分析。结果表明:结合K-means与融入时间变化特征的层次决策树算法能够实现精准的养殖池分类, 每年总体分类精度均达到91.90%以上, Kappa系数达到0.84以上; 2022年江汉平原的水产养殖池面积为2 126.43 km2, 其中, 43.24%的养殖池集中分布于荆州市, 宜昌市养殖池面积最小仅占0.76%; 江汉平原养殖池在2016—2022年期间的动态变化呈现出明显的空间异质性, 整体呈现增加的趋势, 总面积从1 947.43 km2增加到2 126.43 km2, 增加了9.19%。所提方法为养殖池的精准监测提供了重要参考, 所得数据集对支持江汉平原地区生态保护和对可持续发展目标的评估具有重要的借鉴价值和现实意义。
  • 加载中
  • [1]

    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.

    [2]

    Sun Z, Luo J H, Yang J Z C, et al.Dynamics of coastal aquaculture ponds in Vietnam from 1990 to 2015 using Landsat data[J].IOP Conference Series:Earth and Environmental Science, 2020, 502:012029.

    [3]

    Duan Y, Li X, Zhang L, et al.Mapping national-scale aquaculture ponds based on the Google Earth Engine in the Chinese coastal zone[J].Aquaculture, 2020, 520:734666.

    [4]

    Ottinger M, Bachofer F, Huth J, et al.Mapping aquaculture ponds for the coastal zone of Asia with sentinel-1 and sentinel-2 time series[J].Remote Sensing, 2021, 14(1):153.

    [5]

    Duan Y, Li X, Zhang L, et al.Detecting spatiotemporal changes of large-scale aquaculture ponds regions over 1988-2018 in Jiangsu Province, China using Google Earth Engine[J].Ocean & Coastal Management, 2020, 188:105144.

    [6]

    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.

    [7]

    Li A, Song K, Chen S, et al.Mapping African wetlands for 2020 using multiple spectral, geo-ecological features and Google Earth Engine[J].ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 193:252-268.

    [8]

    Ottinger M, Clauss K, Kuenzer C.Opportunities and challenges for the estimation of aquaculture production based on earth observation data[J].Remote Sensing, 2018, 10(7):1076.

    [9]

    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.

    [10]

    万敏, 刘梦馨, 黄婧, 等.城市八景中的生态智慧考析--以江汉平原为例[J].中国园林, 2022, 38(7):18-25.

    Wan M, Liu M X, Huang J, et al.The research and analysis of ecological wisdom in urban eight scenes:Taking Jianghan Plain as an example[J].Chinese Landscape Architecture, 2022, 38(7):18-25.

    [11]

    Tassi A, Vizzari M.Object-oriented LULC classification in Google Earth Engine combining SNIC, GLCM, and machine learning algorithms[J].Remote Sensing, 2020, 12(22):3776.

    [12]

    Tucker C J.Monitoring the grasslands of the sahel 1984-1985[J].Remote Sensing of Environment, 1979, 8:127-150.

    [13]

    Gao B C.NDWI-a normalized difference water index for remote sensing of vegetation liquid water from space[J].Remote Sensing of Environment, 1996, 58(3):257-266.

    [14]

    Zhu Z, Wang S, Woodcock C E.Improvement and expansion of the Fmask algorithm:Cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and Sentinel 2 images[J].Remote Sensing of Environment, 2015, 159:269-277.

    [15]

    Xu H.Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery[J].International Journal of Remote Sensing, 2006, 27(14):3025-3033.

    [16]

    Zha Y, Gao J, Ni S.Use of normalized difference built-up index in automatically mapping urban areas from TM imagery[J].International Journal of Remote Sensing, 2003, 24(3):583-594.

    [17]

    汪恩良, 徐雷, 韩红卫, 等.基于OTSU算法提取寒区河流流冰密度研究[J].应用基础与工程科学学报, 2021, 29(6):1429-1439.

    Wang E L, Xu L, Han H W, et al.Extracting river ice concentration in cold regions based on the OTSU algorithm[J].Journal of Basic Science and Engineering, 2021, 29(6):1429-1439.

    [18]

    Wang M, Mao D, Xiao X, et al.Interannual changes of coastal aquaculture ponds in China at 10-m spatial resolution during 2016-2021[J].Remote Sensing of Environment, 2023, 284:113347.

    [19]

    Otsu N.A threshold selection method from gray-level histograms[J].IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1):62-66.

    [20]

    Kolli M K, Opp C, Karthe D, et al.Automatic extraction of large-scale aquaculture encroachment areas using Canny Edge Otsu algorithm in Google Earth Engine:The case study of Kolleru Lake, South India[J].Geocarto International, 2022, 37(26):11173-11189.

    [21]

    Vickers N J.Animal communication:When I’m calling you, will you answer too?[J].Current Biology, 2017, 27(14):R713-R715.

    [22]

    Hartigan J A, Wong M A.Algorithm AS 136:A K-means clustering algorithm[J].Applied Statistics, 1979, 28(1):100.

    [23]

    林丹.基于小波变换的K-means算法在遥感图像分类中的应用研究[D].邯郸:河北工程大学, 2015.Lin D.Research on TM classification based on K-means of wavelet transform[D].Handan:Hebei University of Engineering, 2015.

    [24]

    贾凯, 陈水森, 蒋卫国.粤港澳大湾区红树林长时间序列遥感监测[J].遥感学报, 2022, 26(6):1096-1111.

    Jia K, Chen S S, Jiang W G.Long time-series remote sensing monitoring of mangrove forests in the Guangdong-Hong Kong-Macao Greater Bay Area[J].National Remote Sensing Bulletin, 2022, 26(6):1096-1111.

  • 加载中
计量
  • 文章访问数:  110
  • PDF下载数:  14
  • 施引文献:  0
出版历程
收稿日期:  2023-09-14
修回日期:  2024-04-09

目录