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Automated generation of consistent annual maximum NDVI on coal bases with a new algorithm

Authors :
Jun Li
Tingting Qin
Chengye Zhang
Yicong Zhang
Yaping Zhang
Haitao Shi
Yihao Yang
Source :
Scientific Data, Vol 11, Iss 1, Pp 1-16 (2024)
Publication Year :
2024
Publisher :
Nature Portfolio, 2024.

Abstract

Abstract Coal is one of the most important fossil energy sources and is ensuring global energy security. Annual maximum NDVI (Normalized Difference Vegetation Index) data is an important indicator for the research in balancing coal mining and vegetation conservation. However, the existing annual maximum NDVI data displayed lower values with temporally inconsistent and a noticeable mosaic line. Here we propose an algorithm for automatically generating the annual maximum NDVI of China’s coal bases in Google Earth Engine called: Auto-NDVIcb. The accuracy of the Auto-NDVIcb algorithm has been verified with an average RMSE of 0.087 for the 14 coal bases from 2013 to 2022. Based on the proposed Auto-NDVIcb algorithm, an annual maximum NDVI dataset for all 14 coal bases in China from 2013 to 2022 was publicly released. This dataset can be fast and automatically updated online. Hence, the public dataset will continuously serve to monitor the vegetation change induced by coal mining, exploring the mechanism of vegetation degradation, and providing scientific data for developing vegetation protection policies in coal mines.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20524463
Volume :
11
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Scientific Data
Publication Type :
Academic Journal
Accession number :
edsdoj.2149545d87154b37adcca33c5909fd29
Document Type :
article
Full Text :
https://doi.org/10.1038/s41597-024-03543-2