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Classification scheme for mapping wetland herbaceous plant communities using time series Sentinel-1 and Sentinel-2 data

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang 330022, China

    School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China

    Nanchang Base of International Centre on Space Technologies for Natural and Cultural Heritage under the Auspices of UNESCO,Nanchang 330022, China

  • Email：zhanglinu@126.com
  • Introduction：张琍，研究方向为流域及水环境遥感。E-mail： zhanglinu@126.com
ZHANG Li125,  
  • Affiliation：

    College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China

LUO Wenting3,  
  • Affiliation：

    Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang 330022, China

    School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China

ZHANG Haohuan12,  
  • Affiliation：

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

YIN Xiuwan4,  
  • Affiliation：

    College of Oceanography and Space Informatics, China University of Petroleum(East China), Qingdao 266580, China

LI Bin6

resumen

Plant communities play an important role in wetland elements and are vulnerable to human activities and climate change. Wetland plant community classification and mapping provide scientific important data support for wetland ecological monitoring and evaluation. This study aims to develop a classification scheme suitable for the wetland plant communities in the Poyang Lake wetland.Taking Poyang Lake National Nature Reserve as the research area and on the basis of the monthly Sentinel-1 and Sentinel-2 time-series data in 2019, this study extracts five types of image feature parameters, including water and vegetation index group, red edge index group, texture feature group, spectral feature group, and polarization radar backscatter group, with a total of 240 feature indexes, and uses Random Forest (RF), Support Vector Machine (SVM), and Deep Neural Network (DNN) algorithms for classification to explore a set of optimal feature combinations and a suitable classification scheme for wetland vegetation mapping in Poyang Lake.In conclusion, A classification scheme for wetland plant communities in the Poyang Lake wetland was proposed in this study using multi time-series Sentinel-2 and Sentinel-1 data. The optimal acquisition time periods of satellite data are in January, April, August, September, October, and December. The optimal image feature group can be red edge index group or water and vegetation index group for feature selection. The classification algorithm can select deep learning or RF algorithm to classify wetland plant communities according to the requirements. This classification scheme can effectively improve the accuracy of wetland vegetation mapping in the Poyang Lake and provide scientific and technical solutions for decision-making departments.Results show the following(1) Compared with radar data, the extraction accuracy of optical data is remarkably better than that of radar data in wetland plant community classification and mapping. Radar data can be used as a supplement to optical data when optical data are insufficient. (2) Screening the importance of each image feature of Sentinel-2 helps improve the classification accuracy. The preferred time periods are mainly distributed in January, May, August, September, October, and December. (3) Five groups of unitary image features are selected to classify separately, and the classification accuracy is as follows: red edge index group > water and vegetation index group > spectral feature group > radar polarization data group > texture feature group. (4) Comparing the combined image feature groups with the unitary image feature groups reveals that the combined image feature group is not necessarily helpful to improve the classification accuracy. The classification accuracy is as follows: red edge index group > water vegetation index group > combined image feature group. Among them, the overall accuracy of the classification scheme using the red edge index group and random forest method is 0.81, and the Kappa coefficient is 0.76. (4) By comparing the three classification algorithms, the classification accuracy is ranked as follows: DNN > RF > SVM. The overall accuracy of the deep learning method does not greatly improve, that is, only 2% higher than the RF algorithm. Thus, the DNN and machine learning method (RF) can be used as optimization algorithms.

palabra clave

remote sensing;Poyang Lake;wetland vegetation Mapping;Image features selection;random forest algorithm;Deep Neural Networks (DNN);Multi-temporal optical and SAR data

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