Refined remote sensing classification of Yancheng coastal wetland considering tide-level changes and vegetation phenological characteristics on the GEE platform

Gu Rong; Zhang Dong; Qian Linfeng; Lv Lin; Chen Yanyan; Yu Lingcheng

doi:10.12284/hyxb2024030

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Gu Rong，Zhang Dong，Qian Linfeng, et al. Refined remote sensing classification of Yancheng coastal wetland considering tide-level changes and vegetation phenological characteristics on the GEE platform[J]. Haiyang Xuebao，2024, 46(x)：1–13 doi: 10.12284/hyxb2024030

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Refined remote sensing classification of Yancheng coastal wetland considering tide-level changes and vegetation phenological characteristics on the GEE platform

doi: 10.12284/hyxb2024030

1.
College of Marine Science and Engineering, Nanjing Normal University, Nanjing 210023, China
2.
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
3.
Sea Area Use Dynamic Surveillant and Monitoring Center of Jiangsu Province, Nanjing 210017, China

Received Date: 2023-07-31
Rev Recd Date: 2023-11-22

Available Online: 2024-03-06

Abstract

Abstract

Coastal wetlands have important economic and ecological value. Rapid and accurate monitoring of the status of coastal wetlands is of great significance for the protection and management of coastal wetland resources. Due to factors such as the variability of the tide-level changes, similarity of vegetation spectra, and frequent cloud cover, remote sensing monitoring of coastal wetlands faced certain challenges. In this paper, we proposed a multi-technology coupled remote sensing classification method of coastal wetlands that considers tide-level changes and vegetation phenological characteristics. Based on the Google Earth Engine (GEE) platform, the Fmask (Function of mask) algorithm was first performed for cloud testing and cloud removal processing. Then, the S-G (Savitzky-Golay) filtering algorithm was used to reconstruct NDVI time series data and extract vegetation phenological characteristic parameters. In this phase, the random forest algorithm was applied for the classification of four vegetation types namely Phragmites australi (PA), Suaeda salsa (SS), Spartina alterniflora (SA), and Imperata cylindrical (IC). Finally, the Maximum Spectral Index Composite (MSIC) algorithm was used to generate composite images of the highest and lowest tide levels. The tidal flats and seawater were precisely extracted using the Otsu algorithm based on these two composite images. Combining these feature types, the refined remote sensing classification of coastal wetlands was ideally obtained. The results showed that start-of-season time, end-of-season time, length of season, base value, amplitude, and small seasonal integral were the six key vegetation phenological characteristic parameters for distinguishing different types of coastal wetland vegetation. Applying this method to classify coastal wetlands on the Yancheng coast, the overall classification accuracy was 96.50%, and the Kappa coefficient reached 0.957 1. Among the wetland vegetation, the highest user accuracy was 96.59% for SA, followed by PA and SS, and the lowest was 93.55% for IC. Compared with object-oriented methods, our method can extract the complete range of tidal flats, and the overall accuracy is improved by 10.25%, reflecting the potential application of vegetation phenological characteristics in remote sensing monitoring of dynamic changes in coastal wetlands.
- Google Earth Engine,
- tide-level,
- vegetation phenological characteristics,
- cloud testing,
- Savitzky-Golay filtering algorithm,
- Maximum Spectral Index Composite

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