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Agreement Analysis and Accuracy Assessment of Multiple Mangrove Datasets in Guangxi Beibu Gulf and Guangdong-Hong Kong-Macau Greater Bay, China, for 2000–2020

Authors :
Zhijie Xiao
Weiguo Jiang
Zhifeng Wu
Ziyan Ling
Yawen Deng
Ze Zhang
Kaifeng Peng
Source :
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 3438-3451 (2024)
Publication Year :
2024
Publisher :
IEEE, 2024.

Abstract

Accurate and reliable mangrove datasets are essential for the protection and management of mangrove ecosystems. Therefore, the evaluation of the current mangrove datasets and understanding the differences among them are critical. This study takes the Guangxi Beibu Gulf (GBG) and Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as the study areas and analyzes the agreement and accuracy of eight mangrove datasets from 2000 to 2020 using area comparison, spatial agreement analysis, and absolute accuracy evaluation. The results show that; 1) significant differences exist in mangrove area and spatial distribution among the different mangrove datasets, with the percentage of high agreement areas ranging from 10% to 42%. 2) The overall accuracy of the evaluated mangrove datasets ranges from 56.2% to 95.6%, and the classification accuracy of mangrove datasets in inland areas is lower than the overall level. 3) There are regional differences in the quality of mangrove datasets, with the agreement and accuracy of mangrove datasets in the GBG being greater than those in the GBA. 4) Fine-scale mangrove mapping based on high-resolution remote sensing images, such as Sentinel, and global mangrove mapping based on the Google Earth Engine (GEE) cloud platform should be emphasized in the future. The findings of this study can provide guidance for data users to select appropriate mangrove datasets and a reference for future mangrove mapping research.

Details

Language :
English
ISSN :
21511535
Volume :
17
Database :
Directory of Open Access Journals
Journal :
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.299ad07b34ae4bd79f6a5a8d4e175ff2
Document Type :
article
Full Text :
https://doi.org/10.1109/JSTARS.2024.3353251