A method for extracting water surface and hydrophytic vegetation from ICESat-2 data in wetlands

The Ice, Cloud, and Land Elevation Satellite-2 provides a great opportunity to measure water surface and hydrophytic vegetation in complex wetlands. Obtaining reliable signal photons from ICESat-2 data in wetlands is challenging because there are many types of noise photons, such as specular return photons, after-pulse photons, and noise photons caused by sunlight. In addition, the high photon density difference between the water and hydrophytic vegetation makes it difficult to find accurate hydrophytic vegetation photons. Therefore, this research aims to propose a method to obtain high-accuracy signal photons and classify water body photons and hydrophytic vegetation photons in complex wetlands. First, we introduced the modified elevation histogram statistics vector-based (MEHSV) method to filter out noise photons caused by sunlight. The MEHSV method was developed to retain sparse canopy photons. Therefore, the MEHSV method can retain sparse hydrophytic vegetation photons. Second, peak analysis of the elevation histogram statistics removed the specular return photons and after-pulse photons caused by the water surface. Finally, the manually labeled photons and reference water surface level data were used to assess the proposed method. The filtering results showed that the F value of the proposed method achieved 0.99. Compared with other reference methods, the proposed method both preserved hydrophytic vegetation photons being misrecognized and removed all types of noise photons effectively. The water photons and hydrophytic vegetation photons were distinguished accurately. Additionally, the accuracy of water surface level (R 2 = 0.97, and RMSE = 0.84 m) witnessed the good performance of the proposed method.

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Publication Details

Journal
ISPRS Open Journal of Photogrammetry and Remote Sensing
Published
2025-08-01
DOI
https://doi.org/10.1016/j.ophoto.2025.100097
Citations
1
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
0.80
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article

A method for extracting water surface and hydrophytic vegetation from ICESat-2 data in wetlands

Shijuan Gao, Rong Zhao, Yi Li, David Li et al.
1 citations
ISPRS Open Journal of Photogrammetry and Remote Sensing
Remote Sensing and LiDAR Applications
0.80
article

A method for extracting water surface and hydrophytic vegetation from ICESat-2 data in wetlands

Shijuan Gao, Rong Zhao, Yi Li, David Li, Kun Zhang
article en
1 citations

Abstract

The Ice, Cloud, and Land Elevation Satellite-2 provides a great opportunity to measure water surface and hydrophytic vegetation in complex wetlands. Obtaining reliable signal photons from ICESat-2 data in wetlands is challenging because there are many types of noise photons, such as specular return photons, after-pulse photons, and noise photons caused by sunlight. In addition, the high photon density difference between the water and hydrophytic vegetation makes it difficult to find accurate hydrophytic vegetation photons. Therefore, this research aims to propose a method to obtain high-accuracy signal photons and classify water body photons and hydrophytic vegetation photons in complex wetlands. First, we introduced the modified elevation histogram statistics vector-based (MEHSV) method to filter out noise photons caused by sunlight. The MEHSV method was developed to retain sparse canopy photons. Therefore, the MEHSV method can retain sparse hydrophytic vegetation photons. Second, peak analysis of the elevation histogram statistics removed the specular return photons and after-pulse photons caused by the water surface. Finally, the manually labeled photons and reference water surface level data were used to assess the proposed method. The filtering results showed that the F value of the proposed method achieved 0.99. Compared with other reference methods, the proposed method both preserved hydrophytic vegetation photons being misrecognized and removed all types of noise photons effectively. The water photons and hydrophytic vegetation photons were distinguished accurately. Additionally, the accuracy of water surface level (R 2 = 0.97, and RMSE = 0.84 m) witnessed the good performance of the proposed method.

ISPRS Open Journal of Photogrammetry and Remote SensingVol. 17
Central South University of Forestry and Technology (CN), Central South University (CN), Beijing Municipal Ecological and Environmental Monitoring Center (CN)
Clean water and sanitation
Openalex Percentile: Top 26%
Remote Sensing and LiDAR Applications
0.80
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