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Remote Sensing for Natural Resources    2025, Vol. 37 Issue (2) : 265-273     DOI: 10.6046/zrzyyg.2023345
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Classification of wetland plant communities in the Yellow River Delta based on GEE and multisource remote sensing data
ZHANG Nianqiu1,2(), MAO Dehua1,3(), FENG Kaidong1,2, ZHEN Jianing1, XIANG Hengxing1, REN Yongxing1
1. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China
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Abstract  

Accurately identifying plant communities in coastal wetlands is critical for strengthening the ecological quality monitoring and enhancing the ecosystem functions of coastal wetlands. With the Yellow River Delta as the study area, this study constructed a feature vector set including phenological, optical, red-edge, and radar features based on Sentinel-1/2 image data using the Google Earth Engine (GEE) platform. It classified the wetland plant communities in the Yellow River Delta in 2021 using the random forest algorithm. Moreover, it explored the effects of phenological features in classification. The results reveal an overall classification accuracy of 97.91 % and a Kappa coefficient of 0.97. In 2021, the distribution areas of Phragmites australis, Suaeda glauca, Spartina alterniflora, and Tamarix chinensis were 49.91 km2, 39.91 km2, 79.36 km2, and 20.86 km2, respectively. The phenological features of typical plant communities in the Yellow River Delta wetlands were effectively extracted based on the normalized difference vegetation index (NDVI) time-series fitting curves. The highly distinguishable features included the maximum value date, base value, growth amplitude, beginning-of-season growth rate, and end-of-season decline rate. Compared to other feature variables, phenological features contributed more significantly to the overall classification accuracy, suggesting their prominent role in classification. The results of this study provide a methodological reference and scientific basis for the plant community monitoring and ecological conservation of coastal wetlands in the Yellow River Delta.
Keywords GEE      Sentinel-1/2 images      phenological features      wetland plant community      Yellow River Delta     
ZTFLH:  TP79  
Issue Date: 09 May 2025
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Nianqiu ZHANG
Dehua MAO
Kaidong FENG
Jianing ZHEN
Hengxing XIANG
Yongxing REN
Cite this article:   
Nianqiu ZHANG,Dehua MAO,Kaidong FENG, et al. Classification of wetland plant communities in the Yellow River Delta based on GEE and multisource remote sensing data[J]. Remote Sensing for Natural Resources, 2025, 37(2): 265-273.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023345     OR     https://www.gtzyyg.com/EN/Y2025/V37/I2/265
Fig.1  Location of study area
数据
名称		 传感器
类型		 影像获取时间 空间分
辨率/m		 重访
周期/d
Sentinel-1 SAR-C 2021-05-01—2021-10-31 10 6
Sentinel-2 MSI 2021-01-01—2021-12-31 10~60 2~5
Tab.1  Information of satellite remote sensing data
类型 芦苇 碱蓬 互花米草 柽柳 非植被 总计
训练样本 111 39 131 37 42 360
验证样本 36 23 53 19 13 144
合计 147 62 184 56 55 504
Tab.2  Number of samples for each plant species and non-vegetation (个)
Fig.2  Illustration of the NDVI fitted curve based on the two-term Fourier function
特征变量 特征简称 特征说明
光谱波段 B B1,B2,B3,B4,B8,B9,B11,B12
植被指数 NDVI (B8-B4)/(B8+B4)
EVI 2.5[(B8-B4)/(B8+6B4-7.5B2+1)]
RVI B8/B4
水体指数 NDWI (B3-B8)/(B3+B8)
mNDWI (B3-B11)/(B3+B11)
LSWI (B8-B11)/(B8+B11)
红边特征 Bre B5,B6,B7,B8A
NDVIre1 (B8A-B5)/(B8A+B5)
NDVIre2 (B8A-B6)/(B8A+B6)
NDre1 (B6-B5)/(B6+B5)
NDre2 (B7-B5)/(B7+B5)
CIre B7/B5-1
雷达特征 VV 垂直发射和垂直接收的极化波
VH 垂直发射和水平接收的极化波
SAR_sum VV+VH
SAR_diff VV-VH
SAR_NDVI (VV-VH)/(VV+VH)
物候特征 SOS NDVI从左最小值向右增加到季节振幅的20%对应的日期
EOS NDVI从右最小值向左增加到季节振幅的20%对应的日期
LOS 从生长季开始到生长季结束持续的时间
MOS 拟合函数在季节中达到最大值对应的日期
BV 拟合函数的最小值
SA 拟合函数最大值与基准值之差
IRS (最大值-SOS对应的NDVI)/(MOS-SOS)
DRS (最大值-EOS对应的NDVI)/(EOS-MOS)
Tab.3  Description of the feature set
方案 特征组合
方案1 传统光学特征
方案2 传统光学特征+红边特征
方案3 传统光学特征+红边特征+雷达特征
方案4 传统光学特征+红边特征+雷达特征+物候特征
Tab.4  Information of classification model
Fig.3  Comparison of growth season NDVI and time of different plant species
Fig.4  Comparison of phenological features index of different plant species
Fig.5  Relationship between classification accuracy and number of feature combinations
比例 物候特征 传统光
学特征		 红边特征 雷达特征
特征变量数量占比 38.5 38.5 15.4 7.7
特征变量重要性总
和占比		 35.9 40.3 14.7 9.0
Tab.5  Comparison of importance of different features (%)
Fig.6  Classification and area of wetland plant communities in the Yellow River Delta in 2021
类型 芦苇 碱蓬 互花米草 柽柳 非植被 UA/%
芦苇 36 0 0 0 0 100.00
碱蓬 0 23 0 0 0 100.00
互花米草 0 0 52 1 0 98.11
柽柳 0 0 0 19 0 100.00
非植被 0 2 0 0 11 84.62
PA/% 100.00 92.00 100.00 95.00 100.00 —
OA: 97.91% Kappa系数: 0.972 2
Tab.6  The confusion matrix of classification of YRD wetland plant species
Fig.7  Accuracy comparisons of classification results of each scheme
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