Global Mangrove Loss Drivers and Extent from 2000 to 2016

Maps detail the extent of mangrove loss and its primary drivers across 39 nations for three periods between 2000 and 2016. The dataset was produced by NASA using Landsat-based NDVI anomalies and a random forest machine learning model to classify land cover changes and assign loss drivers. It covers anthropogenic causes like commodity production and settlement, as well as natural drivers such as erosion and extreme climatic events.

Use Cases

• Quantifying national-scale mangrove loss extent for conservation reporting and policy assessment.
• Analyzing transitions from mangrove to wet soil, dry soil, or water to understand degradation processes.
• Attributing loss to specific drivers like agriculture, aquaculture, settlement, or erosion for targeted intervention planning.
• Modeling long-term coastal ecosystem vulnerability by examining loss trends across the 2000-2016 period.
• Validating global land-use change models using the dataset's machine learning-derived driver classifications.

Strengths

• Covers a 16-year time series across three distinct analysis periods (2000-2005, 2005-2010, 2010 -2016).
• Applies a consistent, peer-reviewed methodology (random forest ML on Landsat NDVI) across 39 mangrove-holding nations.
• Distinguishes between multiple specific loss drivers, including commodity production, settlement, and climatic events.

Limitations

• Key technical details are unspecified: row count, column names, and exact file size are unknown from all sources.
• Conflicting last-updated dates exist between platforms (2016 vs. 2026), creating uncertainty about version currency.

Provenance

Source

National Aeronautics and Space Administration (NASA)

Collection Method

Landsat-based NDVI anomaly analysis combined with random forest machine learning classification of land cover changes.

Time Range

2000-2016

Freshness

2026-03-13 02:13:08.354436

Geography

39 mangrove-holding nations globally