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Automated generation of consistent annual maximum NDVI on coal bases with a new algorithm
Jun Li1,2, Tingting Qin1, Chengye Zhang3,4
1College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing, 100083, Beijing, China.
Scientific Data
|June 26, 2024
Summary
A new algorithm, Auto-NDVIcb, accurately generates annual maximum NDVI for China's coal mining regions. This publicly available dataset monitors vegetation changes and aids in developing conservation policies.
Area of Science:
- Environmental Science
- Remote Sensing
- Geospatial Analysis
Background:
- Coal mining impacts global energy security and necessitates balancing resource extraction with vegetation conservation.
- Existing annual maximum NDVI data present limitations, including low values, temporal inconsistency, and mosaic line artifacts.
- Monitoring vegetation changes in coal mining areas is crucial for environmental management.
Purpose of the Study:
- To develop an automated algorithm (Auto-NDVIcb) for generating accurate annual maximum NDVI data for China's coal bases.
- To release a publicly accessible dataset of annual maximum NDVI for China's coal bases from 2013 to 2022.
- To provide a tool for continuous monitoring of vegetation dynamics influenced by coal mining.
Main Methods:
- Developed the Auto-NDVIcb algorithm within the Google Earth Engine platform.
- Validated the algorithm's accuracy using Root Mean Square Error (RMSE) across 14 coal bases.
- Generated and released an annual maximum NDVI dataset for 14 Chinese coal bases (2013-2022).
Main Results:
- The Auto-NDVIcb algorithm achieved an average RMSE of 0.087, demonstrating high accuracy.
- A comprehensive annual maximum NDVI dataset for China's coal bases (2013-2022) has been created and released.
- The dataset is designed for fast, automatic online updates, ensuring current monitoring capabilities.
Conclusions:
- The Auto-NDVIcb algorithm provides a reliable method for assessing vegetation status in coal mining regions.
- The publicly released dataset supports continuous monitoring of vegetation changes and degradation mechanisms.
- This data is vital for informing effective vegetation protection policies in China's coal mining areas.
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