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Full catalog/CMS_Global_Mangrove_Loss_1768
CMS_Global_Mangrove_Loss_1768·v1·dataset

Where mangrove forests were lost worldwide (2000-2016)

Global Mangrove Loss Extent, Land Cover Change, and Loss Drivers, 2000-2016
biosphere NASA ORNL_CLOUD Level 4 multiple
In plain English

What it measures. Maps showing where the world's mangrove forests were lost between 2000 and 2016, broken into three periods, along with what drove each loss - whether farming, aquaculture, settlement, erosion, or extreme weather.

How it's made. Built from Landsat satellite imagery using a vegetation-greenness signal to detect loss, then a machine-learning (Random Forest) method combined with global land-use data to identify the cause of each loss.

How & where you'd use it. Useful for understanding mangrove decline across 39 nations and the human and climate pressures behind it, supporting coastal conservation and restoration.

What's measured

BIOSPHERE › VEGETATION › VEGETATION COVERBIOSPHERE › ECOSYSTEMS › MARINE ECOSYSTEMS › ESTUARYLAND SURFACE › EROSION/SEDIMENTATION › EROSIONBIOSPHERE › VEGETATION › VEGETATION INDEX › NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI)OCEANS › COASTAL PROCESSES › EROSIONLAND SURFACE › LAND USE/LAND COVER › LAND RESOURCESOCEANS › COASTAL PROCESSES › MANGROVESBIOSPHERE › ECOSYSTEMS › MARINE ECOSYSTEMS › COASTALBIOSPHERE › ECOSYSTEMS › ANTHROPOGENIC/HUMAN INFLUENCED ECOSYSTEMS

Coverage & cadence

  • Time span2000-01-01 → 2016-12-31
  • Measured byLANDSAT-7 (ETM+) · LANDSAT-8 (OLI) · COMPUTERS (Computer) · LANDSAT-5 (TM)
  • Processing levelLevel 4
  • Spatial extent-94.5607, -58.4496, 164.691, 27.0432
  • Formatsmultiple
  • StatusCOMPLETE

What you can do with it

  • Map vegetation, forests and biomass
  • Monitor ecosystem productivity and carbon
  • Support habitat and biodiversity studies
Official description

This dataset provides estimates of the extent of mangrove loss, land cover change, and its anthropogenic or climatic drivers in three time periods: 2000-2005, 2005-2010, and 2010-2016. Landsat-based Normalized Difference Vegetation Index (NDVI) anomalies were used to determine loss extent in each period. The drivers of mangrove loss were determined by examining land cover changes using a random forest machine learning technique that considered change from mangrove to wet soil, dry soil, and water at each loss pixel. A series of decision trees used several global-scale land-use datasets to identify the ultimate driver of the mangrove loss. Loss drivers include commodity production (agriculture, aquaculture), settlement, erosion, extreme climatic events, and non-productive conversion. Maps of loss extent per period, mangrove land cover changes, and loss drivers are provided for each of 39 mangrove holding nations.

Get the data

cms_global_mangrove_loss_1768_access.py
import earthaccess
earthaccess.login(strategy="netrc")          # free Earthdata Login

results = earthaccess.search_data(
    short_name="CMS_Global_Mangrove_Loss_1768",
    version="1",
    bounding_box=(-122.5, 37.2, -121.8, 37.9),  # your area (W,S,E,N)
    temporal=("2024-01-01", "2024-12-31"),       # your dates
)
files = earthaccess.open(results)   # stream straight from ORNL_CLOUD
Browsing CMR needs no login. Downloading or streaming bytes needs a free Earthdata Login + the earthaccess package.