Précédent|Prochain

Refined wetland classification of international wetland cities based on the random forest algorithm and knowledge-driven rules： A case study of Changde city， China

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

  • Email：dengyawen@mail.bnu.edu.cn
  • Introduction：邓雅文，研究方向为生态水文遥感和湿地遥感。E-mail： dengyawen@mail.bnu.edu.cn
DENG Yawen,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

  • Email：jiangweiguo@bnu.edu.cn
  • Introduction：蒋卫国，研究方向为水文遥感大数据分析、洪水灾害监测评价及湿地生态空间模拟研究。E-mail：jiangweiguo@bnu.edu.cn
JIANG Weiguo*,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

WANG Xiaoya,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

PENG Kaifeng

résumé

Obtaining refined wetland resource information is important for the restoration, protection, management, and utilization of wetlands in international wetland cities and for regional sustainable development. At present, refined wetland classification research for international wetland cities is lacking, especially for the detailed classification of water bodies in wetlands. Refined wetland classification results could provide vital information support for the nomination and assessment of potential or existing international wetland cities.This study takes Changde City, a typical international wetland city, as the case study area. On the basis of the Google Earth Engine (GEE) cloud computing platform and Sentinel 1/2 time series remote sensing data and terrain data in 2020, the minimum redundancy-maximum correlation algorithm and the recursive gradient boosting tree algorithm are first used to optimize the wetland classification feature set. Then, an intelligent model for refined urban wetland classification integrating pixel-based random forest and object-oriented knowledge rule decision model is constructed to realize the refined classification of wetland resources in Changde City.Using multisource remote sensing data, the GEE cloud computing platform, a machine learning algorithm, and knowledge-driven rule-based model, this study can accurately and efficiently extract refine wetland information of international wetland cities. The methodology developed in this study could be operationally transferred to other urban wetland mapping and has great application potential in the nomination and management of international wetland cities as well as the restoration, protection, and sustainable development and utilization of wetland resources.The results are as follows(1) The number of features before and after the optimization of wetland classification features is reduced from 63 to 16, and the overall accuracy is reduced by 0.9%. The characteristics of water index, vegetation frequency, antd built-up area index in the dry period are of great importance, and feature optimization can reduce the redundancy of feature data and improve the classification efficiency. (2) The results of the fine classification of wetlands in Changde City include eight wetland types: rivers, lakes, reservoirs, aquaculture ponds/pits, canals, mudflats, sedge and reed with an overall accuracy of 91.53% and a kappa coefficient of 0.89. These results meet the requirements for the fine wetland classification of international wetland cities, indicating that the precision of the urban wetland refine classification method framework is high. (3) Wetlands in Changde City are mainly distributed in the eastern and western parts of the Dongting Lake Plain, showing a spatial pattern of more in the east and less in the west.

mots-clés

wetland classification;Random Forest;knowledge-based rules;Sentinel-1/2;international wetland cities;Changde city

References

  1. 1.
    Adeli S, Salehi B, Mahdianpari M, Quackenbush L J, Brisco B, Tamiminia H and Shaw S. 2020. Wetland monitoring using SAR data: a meta-analysis and comprehensive review. Remote Sensing, 12(14): 2190
  2. 2.
    Amani M, Brisco B, Afshar M, Mirmazloumi S M, Mahdavi S, Mirzadeh S M J, Huang W M and Granger J. 2019. A generalized supervised classification scheme to produce provincial wetland inventory maps: an application of Google Earth Engine for big geo data processing. Big Earth Data, 3(4): 378-394
  3. 3.
    Amani M, Ghorbanian A, Ahmadi S A, Kakooei M, Moghimi A, Mirmazloumi S M, Moghaddam S H A, Mahdavi S, Ghahremanloo M, Parsian S, Wu Q S and Brisco B. 2020. Google Earth Engine cloud computing platform for remote sensing big data applications: a comprehensive review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 5326-5350
  4. 4.
    Amani M, Salehi B, Mahdavi S, Granger J E, Brisco B and Hanson A. 2017. Wetland classification using multi-source and multi-temporal optical remote sensing data in Newfoundland and Labrador, Canada. Canadian Journal of Remote Sensing, 43(4): 360-373
  5. 5.
    Berhane T M, Lane C R, Wu Q S, Autrey B C, Anenkhonov O A, Chepinoga V V and Liu H X. 2018. Decision-tree, rule-based, and random forest classification of high-resolution multispectral imagery for wetland mapping and inventory. Remote Sensing, 10(4): 580
  6. 6.
    Breiman L. 2001. Random forests. Machine Learning, 45(1): 5-32
  7. 7.
    Chatziantoniou A, Psomiadis E and Petropoulos G P. 2017. Co-Orbital Sentinel 1 and 2 for LULC mapping with emphasis on wetlands in a mediterranean setting based on machine learning. Remote Sensing, 9(12): 1259
  8. 8.
    Chen J and Su J. 2020. Fully promote the great wetland protection and create a “new business card” for the green development of Changde. Forestry and Ecology, (2): 6-7
  9. 9.
    Chen Y, An L J, Chen H and Feng C. 2022. Declaration of “International Wetland City” and wetland environmentally friendly behavior: an empirical study based on Huai’an. Journal of Nanjing Normal University (Natural Science Edition), 45(1): 96-103
  10. 10.
    Ding C and Peng H C. 2005. Minimum redundancy feature selection from microarray gene expression data. Journal of Bioinformatics and Computational Biology, 3(2): 185-205
  11. 11.
    Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D and Moore R. 2017. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote sensing of Environment, 202: 18-27
  12. 12.
    Guo M, Li J, Sheng C L, Xu J W and Wu L. 2017. A review of wetland remote sensing. Sensors, 17(4): 777
  13. 13.
    Han Y, Ke Y H, Wang Z P, Liang D Y and Zhou D M. 2021. Classification of the Yellow River Delta wetland landscape based on ZY1-02hyperspectral imageryD. National Remote Sensing Bulletin
  14. 14.
    He R, Zhao Y L, Xu Z G, Duam C C, Tao Y S and Peng J. 2016. Changes of land use and waterfowl habitats in western Dongting Lake from 1989 to 2013. Journal of Southwest Forestry University, 36(4): 115-120
  15. 15.
    Hird J N, DeLancey E R, McDermid G J and Kariyeva J. 2017. Google Earth Engine, open-access satellite data, and machine learning in support of large-area probabilistic wetland mapping. Remote Sensing, 9(12): 1315
  16. 16.
    Huang Q, Jiang J H, Lai X J and Sun Z D. 2013. Changes of landscape structure in Dongting Lake wetlands and the evaluation on impacts from operation of the Three Gorges Project. Resources and Environment in the Yangtze Basin, 22(7): 922-927
  17. 17.
    Kaplan G and Avdan U. 2019. Evaluating the utilization of the red edge and radar bands from sentinel sensors for wetland classification. Catena, 178: 109-119
  18. 18.
    Lei J R, Xu H B, Cui L J, Zhang M Y, Li W and Li J. 2018. A new identity of cities: the wetland city accreditation of the Ramsar Convention. Wetland Science and Management, 14(3): 23-26
  19. 19.
    Li C H, Zheng X K, Niu S F, Cai Y P, Shen N and Pang A P. 2009. Research progress in protection and restoration of urban wetlands. Progress in Geography, 28(2): 271-279
  20. 20.
    Liu H, Gong P, Wang J, Clinton N, Bai Y Q and Liang S L. 2020. Annual dynamics of global land cover and its long-term changes from 1982 to 2015. Earth System Science Data, 12(2): 1217-1243
  21. 21.
    Liu H Y, Lü X G and Zhang S K. 2004. Landscape biodiversity of wetlands and their changes in 50 years in watersheds of the Sanjiang Plain. Acta Ecologica Sinica, 24(7): 1472-1479
  22. 22.
    Liu L, Zang S Y, Shao T T, Wei J H and Song K S. 2015. Characterization of lake morphology in China using remote sensing and GIS. Remote Sensing For Land Resource, 27(3): 92-98
  23. 23.
    Liu S L, Mei B Q, Zhang J X, Peng B Y and Peng P B. 2018. Current status of biodiversity and conservation strategy for West Dongting Lake Wetland. Wetland Science and Management, 14(2): 16-19
  24. 24.
    Ma Z W and Zhang M X. 2015. Prospective on development trends of international wetland conservation and management from the 12th meeting of the conference of the parties of the convention on wetlands. Wetland Science, 13(5): 523-527
  25. 25.
    Mahdavi S, Salehi B, Granger J, Amani M, Brisco B and Huang W M. 2018. Remote sensing for wetland classification: a comprehensive review. GIScience and Remote Sensing, 55(5): 623-658
  26. 26.
    Mao D H, Wang Z M, Du B J, Li L, Tian Y L, Jia M M, Zeng Y, Song K S, Jiang M and Wang Y Q. 2020. National wetland mapping in China: a new product resulting from object-based and hierarchical classification of Landsat 8 OLI images. ISPRS Journal of Photogrammetry and Remote Sensing, 164: 11-25
  27. 27.
    Ning X G, Chang W T, Wang H, Zhang H C and Zhu Q D. 2022. Extraction of marsh wetland in Heilongjiang Basin based on GEE and multi-source remote sensing data. National Remote Sensing Bulletin, 26(2): 386-396
  28. 28.
    Peng K F, Jiang W G, Hou P, Ling Z Y, Niu Z G, Mao D H and Huang Z. 2021. Dense wetland sample production at large scale by combining multi-source thematic datasets and visual interpretation. National Remote Sensing Bulletin
  29. 29.
    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
  30. 30.
    Wang A Q, Zhou D M and Gong H L. 2012. A research on method of wetland vegetation identification and classification based on radar backscatter characteristics. Remote Sensing Information, (2): 15-19
  31. 31.
    Wang C H, Peng Y L and Wang Y. 2010. Plant diversity of wetland communities in the Changde City, China. Pratacultural Science, 27(12): 96-101
  32. 32.
    Wang H, Liu M X, Zhao Y W and Wen Y L. 2017. International wetland city accreditation and suggestions for its implementation in China. World Forestry Research, 30(6): 6-11
  33. 33.
    Xiong J. 2019. Making wetland ecology sustainable and beautiful--Exploration and practice of Changde City in building an international wetland City. Forestry and Ecology, (2): 6-7
  34. 34.
    Yang L, Wang L C, Yu D Q, Yao R, Li C, He Q H, Wang S Q and Wang L Z. 2020. Four decades of wetland changes in Dongting Lake using Landsat observations during 1978-2018. Journal of Hydrology, 587: 124954
  35. 35.
    You J, Zhang H Q, Chen Y F, Liu H and Gao Z H. 2016. Comparative study of Dongting lake wetland information extraction based on GF-4 Satellite image. Spacecraft Recovery and Remote Sensing, 37(4): 116-122
  36. 36.
    Zhang G Q. 2019. China lake dataset (1960s-2020). A Big Earth Data Platform for Three Poles
  37. 37.
    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
  38. 38.
    Zhang X, Liu L Y, Chen X D, Gao Y, Xie S and Mi J. 2021. GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery. Earth System Science Data, 13(6): 2753-2776
  39. 39.
    Zhao X Y, Tian B, Niu Y, Chen C P and Zhou Y X. 2022. Classification of coastal salt marsh based on Sentinel-1 time series backscattering characteristics: the case of the Yangtze River delta. National Remote Sensing Bulletin, 26(4): 672-682
  40. 40.
    Zhao Z Y, Anand R and Wang M. 2019. Maximum relevance and minimum redundancy feature selection methods for a marketing machine learning platform//2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA). Washington: IEEE: 442-452
  41. 41.
    Zhu X. 2014. Geography of Hunan Province. Beijing: Beijing Normal University Press

Lire l'article complet

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website