Remote sensing monitoring and comparison of classification algorithms for wetland vegetation phenology: a case study of the Yellow River delta

Zedong Liu; Yanfang Ming; Chunxiu Liu

doi:10.1117/12.3007420

30 August 2023 Remote sensing monitoring and comparison of classification algorithms for wetland vegetation phenology: a case study of the Yellow River delta

Zedong Liu, Yanfang Ming, Chunxiu Liu

Author Affiliations +

Proceedings Volume 12797, Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023); 127972O (2023) https://doi.org/10.1117/12.3007420
Event: 2nd International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023), 2023, Qingdao, China

Abstract

This study aims to investigate the wetland vegetation phenology in the Yellow River Delta using remote sensing data from the Sentinel-2 satellite and four classification algorithms: Support Vector Machine (SVM), Classification And Regression Tree (CART), Gradient Tree Boost (GTB), and Random Forest (RF). By constructing feature sets and analyzing time series curves, the phenological information of wetland vegetation, including Spartina alterniflora (SA), Suaeda salsa (SS), Phragmites australis (PA), and Willow (WW), was explored and the classification accuracy of different algorithms was compared. The results demonstrate distinct phenological characteristics of wetland vegetation in the Yellow River Delta, with RF algorithm showing excellent performance in accurately extracting large areas of SA and achieving good results in mixed vegetation areas. The invasion of SA poses a significant threat to native vegetation, gradually occupying their growth space. This study provides scientific decision support for the ecological restoration and conservation of wetland vegetation in the Yellow River Delta.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Zedong Liu, Yanfang Ming, and Chunxiu Liu "Remote sensing monitoring and comparison of classification algorithms for wetland vegetation phenology: a case study of the Yellow River delta", Proc. SPIE 12797, Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023), 127972O (30 August 2023); https://doi.org/10.1117/12.3007420

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KEYWORDS

Vegetation

Phenology

Remote sensing

Environmental monitoring

Image classification

Tunable filters

Decision trees

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