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Published on: September 12, 2017
A 30-meter resolution global land productivity dynamics dataset from 2013 to 2022.
Xiaosong Li1,2, Tong Shen3,4, Cesar Luis Garcia5,6
1International Research Center of Big Data for Sustainable Development Goals, Beijing, China.
This study introduces the first global 30-meter land productivity dynamics (LPD) dataset. This high-resolution data supports monitoring land degradation neutrality (SDG 15.3) and enables targeted interventions.
Area of Science:
- Environmental Science
- Remote Sensing
- Geospatial Analysis
Background:
- Land degradation is a critical global environmental issue.
- The United Nations' Sustainable Development Goal 15.3 (SDG 15.3) aims for Land Degradation Neutrality (LDN).
- Current global land productivity dynamics (LPD) products lack the fine spatial resolution necessary for effective SDG 15.3 reporting and localized interventions.
Purpose of the Study:
- To generate a high-resolution global land productivity dynamics (LPD) dataset.
- To overcome challenges in processing high-resolution satellite data for global applications.
- To provide crucial data for monitoring progress towards Land Degradation Neutrality (SDG 15.3).
Main Methods:
- Utilized Google Earth Engine for large-scale data processing.
- Employed Landsat-8 and MODIS imagery to create a 30-meter NDVI dataset using gap-filling and Savitzky-Golay filtering.
- Applied advanced spatiotemporal filtering and the FAO-WOCAT methodology to generate the global 30-meter LPD product from 2013 to 2022.
Main Results:
- Successfully generated the first global 30-meter land productivity dynamics (LPD) dataset.
- The dataset covers the period from 2013 to 2022.
- The generated LPD product was validated against multiple existing datasets.
Conclusions:
- The new 30-meter global LPD dataset significantly enhances capabilities for SDG 15.3 monitoring.
- This high-resolution data is essential for fine-scale analysis of land productivity trends.
- The findings support improved global efforts in combating land degradation.
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