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Classification of the Yellow River Delta wetland landscape based on ZY-1 02D hyperspectral imagery

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory Cultivation Base of Urban Environment Process and Simulation, Beijing 100048, China

    Beijing Laboratory of Water Resources Security, Beijing 100048, China

    College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China

  • Email：hy1602311920@163.com
  • Introduction：韩月，研究方向为多源遥感数据融合、生态水文遥感应用。E-mail： hy1602311920@163.com
HAN Yue123,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory Cultivation Base of Urban Environment Process and Simulation, Beijing 100048, China

    Beijing Laboratory of Water Resources Security, Beijing 100048, China

    College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China

  • Email：yke@cnu.edu.cn
  • Introduction：柯樱海，研究方向为多源遥感数据融合、人工智能及数据挖掘方法、生态水文遥感应用。E-mail： yke@cnu.edu.cn
KE Yinghai123*,  
  • Affiliation：

    State Key Laboratory Cultivation Base of Urban Environment Process and Simulation, Beijing 100048, China

    Beijing Laboratory of Water Resources Security, Beijing 100048, China

    College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China

WANG Zhanpeng123,  
  • Affiliation：

    Remote Sensing Satellite General Division, China Academy of Space Technology, Beijing 100086, China

LIANG Deyin4,  
  • Affiliation：

    State Key Laboratory Cultivation Base of Urban Environment Process and Simulation, Beijing 100048, China

    Beijing Laboratory of Water Resources Security, Beijing 100048, China

    College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China

ZHOU Demin123

resumen

The ZY-1 02D satellite was successfully launched in 2019 and officially used in October 2020. It is the first civil hyperspectral satellite independently developed and operated by China and has a wide application prospect. Taking the Yellow River Delta wetland as study area, this work investigated the performance of ZY-1 02D Advanced Hyperspectral Instrument (AHSI) images in wetland landscape classification. First, the ZY-1 02D AHSI spectral curves of typical land cover types in the study area were evaluated. Second, two levels of wetland landscape classification systems, including seven basic classes and nine refined classes considering the difference of vegetation coverage, were developed with the assistance of field surveys and unmanned aerial vehicle images. Then, the random forest algorithm was used for classification, and the tree SHAP method was introduced to sort and select important bands. The most important ZY-1 02D AHSI bands overlapping with Landsat 8 OLI bands were selected for classification, and the classification results were compared. Our results showed the following: (1) ZY-1 02D AHSI data can represent the differences of spectral features of different landscape types. (2) For the two classification systems, the first 40 important bands achieved the highest overall classification accuracy. The classification accuracy of seven basic classes and nine refined classes were 92.18% and 90.76%, respectively. Most of the important bands were visible and near-infrared bands. (3) For the two classification systems, a total of 24 and 29 bands overlapping with Landsat 8 OLI multispectral bands were selected for classification. The classification accuracy reached 90.01% and 89.76%, which were remarkably higher than that of Landsat 8 OLI. (4) Blue and green bands are important for the identification of high- and low-density Spartina alterniflora and Phragmites australis; blue, green, and short-wave infrared bands are important for Phragmites australis identification, and the red band is important for the identification of high- and low-density Suaeda salsa. Results showed that ZY-1 02D AHSI data have advantages in distinguishing different landscape features and differences in vegetation coverage. Our study is expected to provide scientific basis for the application of ZY-1 02D hyperspectral data in wetland ecological resources.

palabra clave

ZY-1 02D;AHSI satellite image;hyperspectral data;vegetation coverage;Random Forest;Tree SHAP

References

  1. 1.
    Chen B Q, Xiao X M, Li X P, Pan L H, Doughty R, Ma J, Dong J W, Qin Y W, Zhao B, Wu Z X, Sun R, Lan G Y, Xie G S, Clinton N and Giri C. 2017. A mangrove forest map of China in 2015: analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform. ISPRS Journal of Photogrammetry and Remote Sensing, 131: 104-120.
  2. 2.
    Cheng X Y, Cai Z Y, Li J, Wen M X, Wang Y M and Zeng D. 2021. A spatial-spectral clustering-based algorithm for endmember extraction and hyperspectral unmixing. International Journal of Remote Sensing, 42(5): 1948-1972.
  3. 3.
    Du P J, Xia J S, Xue Z H, Tan K, Su H J and Bao R. 2016. Review of hyperspectral remote sensing image classification. Journal of Remote Sensing, 20(2): 236-256
  4. 4.
    Gong N, Niu Z G, Qi W and Zhang H Y. 2016. Driving forces of wetland change in China. Journal of Remote Sensing, 20(2): 172-183
  5. 5.
    Hughes G. 1968. On the mean accuracy of statistical pattern recognizers. IEEE Transactions on Information Theory, 14(1): 55-63
  6. 6.
    Jiao L L, Sun W W, Yang G, Ren G B and Liu Y N. 2019. A hierarchical classification framework of satellite multispectral/hyperspectral images for mapping coastal wetlands. Remote Sensing, 11(19): 2238
  7. 7.
    Li C, Chen G, Chu W Q and Hu H H. 2021. Research on main influencing factors of bursting of urban water supply network based on improved SHAP. Bulletin of Science and Technology, 37(1): 79-84
  8. 8.
    Li P, Li D H, Li Z H and Wang H J. 2019. Wetland classification through integration of GF-3 SAR and sentinel-2B multispectral data over the Yellow River Delta. Geomatics and Information Science of Wuhan University, 44(11): 1641-1649
  9. 9.
    Li Y, Li Y R, Wang G H and Shi X. 2020. A hyperspectral image classification algorithm based on the weighted exponential function model. Journal of Geo-Information Science, 22(8): 1642-1653
  10. 10.
    Li Y T, Du Z Y, Wang X, Yang Q S, Chen Z Q, Sun Y Y and Liu D X. 2019. Evaluation of wetland ecosystem services in Yellow River delta nature reserve. Marine Environmental Science, 38(5): 761-768
  11. 11.
    Liu S and Xing G L. 2020. Multi-dimensional convolutional network collaborative unmixing method for hyperspectral image mixed pixels. Acta Geodaetica et Cartographica Sinica, 49(12): 1600-1608
  12. 12.
    Lundberg S M, Erion G G and Lee S I. 2018. Consistent individualized feature attribution for tree ensembles. arXiv: 1802.03888.
  13. 13.
    Lundberg S M and Lee S I. 2017. A unified approach to interpreting model predictions//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach: ACM: 4768-4777
  14. 14.
    Rodríguez-Pérez R and Bajorath J. 2020. Interpretation of compound activity predictions from complex machine learning models using local approximations and Shapley values. Journal of Medicinal Chemistry, 63(16): 8761-8777
  15. 15.
    Slagter B, Tsendbazar N E, Vollrath A and Reiche J. 2020. Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: a case study in the St. Lucia wetlands, South Africa. International Journal of Applied Earth Observation and Geoinformation, 86: 102009
  16. 16.
    Sun Z G, Song H L and Hu X Y. 2017. Response of germination and seeding growth of Suaeda salsa seeds from tidal marshes in the Yellow River estuary to N/P ratio of soil. Wetland Science, 15(1): 10-19
  17. 17.
    Wan L M, Lin Y Y, Zhang H S, Wang F, Liu M F and Lin H. 2020. GF-5 hyperspectral data for species mapping of mangrove in Mai Po, Hong Kong. Remote Sensing, 12(4): 656
  18. 18.
    Wang X P. 2014. Study on the Yellow River Delta Wetland Typical Vegetation Using Hyperspectral Remote Sensing. Dalian: Dalian Maritime University
  19. 19.
    Wang X P, Zhang J, Ma Y and Ren G B. 2014. Comparison of coastal wetland classification accuracy using fused images of different-polarization SAR and TM images. Journal of Marine Sciences, 32(1): 40-46
  20. 20.
    Xu Z T. 2020. Information Extraction and Dynamic Change Analysis of Yellow River Delta Wetland Based on Landsat Data. Qingdao: Qingdao University
  21. 21.
    Zhang L, Gong Z N, Wang Q W, Jin D D and Wang X. 2019. Wetland mapping of Yellow River Delta wetlands based on multi-feature optimization of Sentinel-2 images. Journal of Remote Sensing, 23(2): 313-326
  22. 22.
    Zhang L P and Zhang L F. 2005. Hyperspectral Remote Sensing. Wuhan: Wuhan University Press: 1-8
  23. 23.
    Zhang Y C, Na X D and Zang S Y. 2018. Wetland high precision classification based on the HJ-1A hyperspectral image. Progress in Geography 37(12): 1705-1712

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