Run zonal statistics on tree cover loss, GLAD alerts, or arbitrary contextual layers defined in our data lake.
See a gfw-data-api for how to access through analysis API.
All layers should be referred to by their standard data lake column name: or is for boolean layers.
See the data API for a full list of registered layers.
| Parameter | Type | Description | Example |
|---|---|---|---|
| geostore_id (required) | String | A valid geostore ID containing the GeoJSON for the geometry of interest (see further specification in Limitations and assumtions |
cb64960710fd11925df3fda6a2005ca9 |
| group_by | [String] | Rasters with categorical pixel values to group by. | umd_tree_cover_loss__year, umd_glad_alerts, tsc_tree_cover_loss_drivers__type |
| filters | [String] | Rasters to apply as a mask. Pixels with NoData values will be filtered out of the final result. For umd_tree_cover_density_2000/2010, you can put a threshold number as the data type, and it will apply a filter for that threshold | is__umd_regional_primary_forest_2001, umd_tree_cover_density_2000__30 |
| sum | [String] | Pixel values will be summed based on intersection with group_by layers. If there are no group_by layers, all pixel values will be summed to a single number. Pixel value must be numerical. This field can also include area__ha or alert__count, which will give the pixel count or area. | area__ha, whrc_aboveground_co2_emissions__Mg |
| start | Date | Filters date group_by columns to this start date. Must be a year or a YYYY-MM-DD formatted date. | 2015, 2015-02-04 |
| end | Date | Same format as 'start'. Must come after start. | 2016 , 2016-02-10 |
Request:
{
"geometry": {
"type": "Polygon",
"coordinates": [[[9, 4.1], [9.1, 4.1], [9.1, 4.2], [9, 4.2], [9, 4.1]]],
},
"group_by": ["umd_tree_cover_loss__year"],
"filters": [
"is__umd_regional_primary_forest_2001",
"umd_tree_cover_density_2000__30",
],
"sum": ["area__ha", "whrc_aboveground_co2_emissions__Mg"],
}Response:
{
"status":"success",
"data":[
{
"umd_tree_cover_loss__year":2001,
"area__ha":9.894410216509604,
"whrc_aboveground_co2_emissions__Mg":3560.875476837158
},
{
"umd_tree_cover_loss__year":2002,
"area__ha":40.0378459923877,
"whrc_aboveground_co2_emissions__Mg":14713.026161193848
},
{
"umd_tree_cover_loss__year":2003,
"area__ha":6.442871768889975,
"whrc_aboveground_co2_emissions__Mg":2568.1107501983643
},
{
"umd_tree_cover_loss__year":2005,
"area__ha":3.2214358844449875,
"whrc_aboveground_co2_emissions__Mg":1274.5636539459229
},
{
"umd_tree_cover_loss__year":2006,
"area__ha":22.01314521037408,
"whrc_aboveground_co2_emissions__Mg":8167.388116836548
},
{
"umd_tree_cover_loss__year":2007,
"area__ha":0.23010256317464195,
"whrc_aboveground_co2_emissions__Mg":136.68091201782227
},
{
"umd_tree_cover_loss__year":2008,
"area__ha":3.7583418651858187,
"whrc_aboveground_co2_emissions__Mg":1579.5646076202393
},
{
"umd_tree_cover_loss__year":2009,
"area__ha":0.7670085439154732,
"whrc_aboveground_co2_emissions__Mg":226.95782279968262
},
{
"umd_tree_cover_loss__year":2010,
"area__ha":108.37830725525636,
"whrc_aboveground_co2_emissions__Mg":41855.43841171265
},
{
"umd_tree_cover_loss__year":2011,
"area__ha":12.88574353777995,
"whrc_aboveground_co2_emissions__Mg":4887.8897132873535
},
{
"umd_tree_cover_loss__year":2012,
"area__ha":0.07670085439154732,
"whrc_aboveground_co2_emissions__Mg":23.061389923095703
},
{
"umd_tree_cover_loss__year":2013,
"area__ha":1.6107179422224938,
"whrc_aboveground_co2_emissions__Mg":601.4241733551025
},
{
"umd_tree_cover_loss__year":2014,
"area__ha":54.30420490921551,
"whrc_aboveground_co2_emissions__Mg":22433.24832725525
},
{
"umd_tree_cover_loss__year":2015,
"area__ha":0.3068034175661893,
"whrc_aboveground_co2_emissions__Mg":119.5254955291748
},
{
"umd_tree_cover_loss__year":2016,
"area__ha":5.752564079366049,
"whrc_aboveground_co2_emissions__Mg":2075.9469604492188
},
{
"umd_tree_cover_loss__year":2017,
"area__ha":24.774375968469784,
"whrc_aboveground_co2_emissions__Mg":9848.338472366333
},
{
"umd_tree_cover_loss__year":2018,
"area__ha":29.75993150392036,
"whrc_aboveground_co2_emissions__Mg":11987.563570022583
},
{
"umd_tree_cover_loss__year":2019,
"area__ha":27.382205017782393,
"whrc_aboveground_co2_emissions__Mg":10558.882364273071
}
]
}https://staging-data-api.globalforestwatch.org/analysis
https://data-api.globalforestwatch.org/analysisGFW raster tiles are organized in 10 x 10 Degree grids and have a pixel size of 0.00025 x 0.00025 degree. They are saved as Cloud Optimized TIFFs with 400 x 400 pixels blocks.
Because we can scale up parallel processing with lambda, size of the geometry shouldn't be an issue unless getting to massive scales (> 1 billion ha). But each lambda has in-memory cap of 3 GB, so currently only so many rasters can be loaded into memory at once. The limit depends on the size of the raster values (e.g. binary is way less memory than float), but generally max 4 or 5 raster layers is a good rule of thumb.
Use terraform:
./scripts/infra plan
./scripts/infra apply
Runtime: Python 3.7
Handler: lambda_function.lambda_handler
The GFW data lake is now in production, so this service will soon point to that instead of just test layers. Once it does, all data lake layers should be available for analysis. This currently includes:
- aqueduct_baseline_water_stress
- aqueduct_erosion_risk
- birdlife_alliance_for_zero_extinction_site
- birdlife_endemic_bird_areas
- birdlife_key_biodiversity_area
- bra_biomes
- esa_land_cover_2015
- gfw_aboveground_carbon_stock_2000
- gfw_aboveground_carbon_stock_in_emissions_year
- gfw_aboveground_carbon_stock_in_emissions_year__biomass_swap
- gfw_aboveground_carbon_stock_in_emissions_year__legal_amazon_loss
- gfw_aboveground_carbon_stock_in_emissions_year__no_primary_gain
- gfw_aboveground_carbon_stock_in_emissions_year__us_removals
- gfw_belowground_carbon_stock_2000
- gfw_belowground_carbon_stock_in_emissions_year
- gfw_deadwood_carbon_stock_2000
- gfw_deadwood_carbon_stock_in_emissions_year
- gfw_forest_age_category
- gfw_gross_annual_removals_biomass
- gfw_gross_cumul_removals_co2
- gfw_gross_cumul_removals_co2__biomass_swap
- gfw_gross_cumul_removals_co2__legal_amazon_loss
- gfw_gross_cumul_removals_co2__maxgain
- gfw_gross_cumul_removals_co2__no_primary_gain
- gfw_gross_cumul_removals_co2__us_removals
- gfw_gross_emissions_co2e_co2_only
- gfw_gross_emissions_co2e_co2_only__biomass_swap
- gfw_gross_emissions_co2e_co2_only__convert_to_grassland
- gfw_gross_emissions_co2e_co2_only__legal_amazon_loss
- gfw_gross_emissions_co2e_co2_only__no_primary_gain
- gfw_gross_emissions_co2e_co2_only__no_shifting_ag
- gfw_gross_emissions_co2e_co2_only__soil_only
- gfw_gross_emissions_co2e_co2_only__us_removals
- gfw_gross_emissions_co2e_non_co2
- gfw_gross_emissions_co2e_non_co2__biomass_swap
- gfw_gross_emissions_co2e_non_co2__convert_to_grassland
- gfw_gross_emissions_co2e_non_co2__legal_amazon_loss
- gfw_gross_emissions_co2e_non_co2__no_primary_gain
- gfw_gross_emissions_co2e_non_co2__no_shifting_ag
- gfw_gross_emissions_co2e_non_co2__soil_only
- gfw_gross_emissions_co2e_non_co2__us_removals
- gfw_intact_or_primary_forest_2000
- gfw_land_rights
- gfw_litter_carbon_stock_2000
- gfw_litter_carbon_stock_in_emissions_year
- gfw_managed_forests
- gfw_mining
- gfw_net_flux_co2e
- gfw_net_flux_co2e__biomass_swap
- gfw_net_flux_co2e__convert_to_grassland
- gfw_net_flux_co2e__legal_amazon_loss
- gfw_net_flux_co2e__maxgain
- gfw_net_flux_co2e__no_primary_gain
- gfw_net_flux_co2e__no_shifting_ag
- gfw_net_flux_co2e__us_removals
- gfw_oil_gas
- gfw_oil_palm
- gfw_peatlands
- gfw_peatlands__flux
- gfw_pixel_area
- gfw_plantations
- gfw_resource_rights
- gfw_soil_carbon_stock_2000
- gfw_soil_carbon_stock_in_emissions_year
- gfw_soil_carbon_stock_in_emissions_year__biomass_swap
- gfw_soil_carbon_stock_in_emissions_year__legal_amazon_loss
- gfw_soil_carbon_stock_in_emissions_year__no_primary_gain
- gfw_soil_carbon_stock_in_emissions_year__us_removals
- gfw_tiger_landscapes
- gfw_total_carbon_stock_2000
- gfw_total_carbon_stock_in_emissions_year
- gfw_wood_fiber
- gmw_mangroves_1996
- gmw_mangroves_2016
- idn_forest_area
- idn_forest_moratorium
- idn_land_cover_2017
- idn_primary_forest_2000
- ifl_intact_forest_landscapes
- jpl_mangrove_aboveground_biomass_stock_2000
- jpl_tropics_abovegroundbiomass_2000
- landmark_land_rights
- mapbox_river_basins
- mex_forest_zoning
- mex_payment_ecosystem_services
- mex_protected_areas
- per_forest_concessions
- per_permanent_production_forests
- per_protected_areas
- rspo_oil_palm
- tnc_urban_water_intake
- tsc_tree_cover_loss_drivers
- umd_regional_primary_forest_2001
- umd_tree_cover_density_2000
- umd_tree_cover_density_2010
- umd_tree_cover_gain
- umd_tree_cover_loss
- usfs_fia_regions
- wdpa_protected_areas
- whrc_aboveground_biomass_stock_2000
- wwf_eco_regions
These alerts are currently unsupported because we don't rasterize these layers. Instead, we store all enriched points in an document dataset. You can do on-the-fly analysis for these via SQL. (TBD: do we want to just forward that through here so there's only one endpoint?)