diff --git a/catalogs/trilateral.json b/catalogs/trilateral.json index c0da16ed82..15aba9b6ac 100644 --- a/catalogs/trilateral.json +++ b/catalogs/trilateral.json @@ -1,151 +1,153 @@ { - "id": "trilateral", - "title": "Earth Observing Dashboard", - "description": "A Tri-Agency Dashboard by NASA, ESA, JAXA", - "endpoint": "https://eurodatacube.github.io/eodash-catalog/trilateral/", - "geodb_default_form": "https://santilland.github.io/process_example/definitions/geodbform.json", - "geodb_default_vega": "https://raw.githubusercontent.com/eurodatacube/eodash-assets/refs/heads/main/defaults/geodb_vega_definition.json", - "default_xcube_process": { - "JsonForm": "https://raw.githubusercontent.com/eurodatacube/eodash-assets/refs/heads/main/defaults/xcube_form.json", - "VegaDefinition": "https://raw.githubusercontent.com/eurodatacube/eodash-assets/refs/heads/main/defaults/xcube_vega.json", - "EndPoints": [ - { - "Identifier": "xcube_statistics", - "Method": "POST", - "Body": "https://raw.githubusercontent.com/eurodatacube/eodash-assets/refs/heads/main/defaults/xcube_body.json", - "Type": "application/json" - }] - }, - "assets_endpoint": "https://raw.githubusercontent.com/eurodatacube/eodash-assets/main/collections", - "default_base_layers": "layers/baselayers", - "default_overlay_layers": "layers/overlays", - "collections": [ - "E9_car_containers", - "N1_NO2", - "N1_methane_monitoring", - "N1_CO", - "N1_SO2", - "N1_NO2_monthly", - "N1_NO2_diff_monthly", - "N1_NO2_city_trilateral", - "N2_CO2_mean", - "N2_CO2_diff", - "facebook_population_density", - "N9_OMI_trno2-COG", - "NPP_ocean_primary_productivity", - "NPPN_net_primary_production", - "SITI_IS2SITMOGR4-cog", - "nceo_africa_2017", - "grdi-v1-built", - "grdi-v1-raster", - "grdi-shdi-raster", - "grdi-vnl-slope-raster", - "grdi-vnl-raster", - "grdi-filled-missing-values-count", - "grdi-imr-raster", - "grdi-cdr-raster", - "N10_OMSO2PCA-COG", - "SIE_sea_ice_thickness_envisat", - "SIC_sea_ice_thickness_cryosat", - "NASAPopulation", - "WSF_world_settlement_footprint_ind", - "N12_sea_ice_concentration_antarctic", - "N12_1_sea_ice_concentration_arctic", - "E10e_ndvi", - "N11_ocean_primary_productivity", - "SMC_soil_moisture_anomaly", - "PRC_precipitation_anomaly", - "PRCG_precipitation", - "E10a1_agricultural_production_productive_area", - "E10a2_agricultural_production_area", - "E10a3_agricultural_production_area_change", - "E10a6_harvested_parcels_evolution", - "E10a8_winter_cereals", - "E10c_rice_planting", - "N2_greenhouse_gases", - "VITS_vegetation_index_timeseries", - "SMCTS_soil_moisture_timeseries", - "PRCTS_precipitation_timeseries", - "LWE_lake_water_extent", - "LWL_lake_water_level", - "NLK_lakes", - "Lakes_S2L2A", - "Lakes_ALOS2", - "Lakes_Sentinel1", - "Lakes_SWT", - "Lakes_WQ_TC_water_quality", - "Lakes_WQ_TURB_water_turbidity", - "SMCG_soil_moisture_content", - "SIF_solar_induced_chlorophyll_fluorescence", - "N5_nightlights", - "N6_geoglam", - "CDS7_windu_ERA5-SingleLevel_100m_GLOBAL", - "CDS8_windv_ERA5-SingleLevel_100m_GLOBAL", - "N3b_water_quality_tsm_chart", - "N3b_water_quality_chl_chart", - "N3a2_chl_concentration_tri_esa", - "N3a2_chl_concentration_tri_jaxa", - "N3a2_chl_concentration_tri_nasa", - "N3a2_total_suspended_matter_tri_esa", - "N3a2_total_suspended_matter_tri_jaxa", - "N3a2_total_suspended_matter_tri_nasa", - "N3a2_sea_surface_temperature", - "N1_NO2_jaxa", - "N2_CO2_jaxa_gosat", - "GHS_BUILT-S-R2023A", - "RECCAP2_1_AGC_LVOD_amazonia_methods_mean_crop", - "RECCAP2_2_AGC_LVOD_amazonia_smooth_max_crop", - "RECCAP2_3_AGC_LVOD_amazonia_smooth_mean_crop", - "RECCAP2_4_AGC_LVOD_amazonia_trend_mean_crop", - "RECCAP2_5_SF_biomass_growth", - "RECCAP2_6_deforested_biomass", - "RECCAP2_7_degraded_biomass", - "RECCAP2_8_edge_biomass_change", - "RECCAP2_9_intact_biomass_change_methods_mean", - "RECCAP2_10_intact_biomass_change_smooth_max", - "RECCAP2_11_intact_biomass_change_smooth_mean", - "RECCAP2_12_intact_biomass_change_trend_mean", - "GGI_CO2", - "GGI_N2O", - "GGI_CH4", - "ESDC_gross_primary_productivity", - "ESDC_kndvi", - "ESDC_net_ecosystem_exchange", - "GG_Google_Mobility_Data_trilateral", - "E13c_shipping_activity", - "E13b_parked_airplanes", - "CV_Covid_19_cases_trilateral", - "OW_Covid_19_vaccinations_trilateral", - "FNF_Palsar", - "Modis_SNPP_2023", - "ADD_West_Antarctica_S1", - "ADD_Landsat_L2_Antarctica", - "ADD_Melt_Duration", - "ADD_Melt_Onset", - "ADD_Melt_Season_End", - "ADD_Meltmap", - "ESDL_Hydrology_SM", - "ESDL_Hydrology_Precipitation", - "MCD_S14Amazonas_detections", - "EPA_Field_burning_Monthly", - "EPA_Forest_fire_Methane_Daily", - "EPA_Forest_fire_Methane_Yearly", - "HLS_NDVI", - "LIS_Global_DA_Evap", - "NTLU_JAXA_Nighttimelevel_Urban", - "NTLR_JAXA_Nighttimelevel_Rural", - "4D_Greenland_Duration", - "4D_Greenland_Melt_Onset", - "4D_Greenland_Melt_Season_End", - "4D_Greenland_Meltmap", - "ENSST_by_GCOM-W-AMSR_JAXA", - "SLSTR1_Sentinel-3-SLSTR-L2-LST", - "SSLC1_Seasonal_S1_AMP_hv_interferometric_coherence", - "SSLC2_Seasonal_S1_COH12_hh_interferometric_coherence", - "OHC300_Ocean_Heat_Content_upper_300m", - "NELST_ECOSTRESS_LST", - "alos2_floods", - "SMOS_ocean_salinity", - "SMOS_soil_moisture" + "id": "trilateral", + "title": "Earth Observing Dashboard", + "description": "A Tri-Agency Dashboard by NASA, ESA, JAXA", + "endpoint": "https://eurodatacube.github.io/eodash-catalog/trilateral/", + "geodb_default_form": "https://santilland.github.io/process_example/definitions/geodbform.json", + "geodb_default_vega": "https://raw.githubusercontent.com/eurodatacube/eodash-assets/refs/heads/main/defaults/geodb_vega_definition.json", + "default_xcube_process": { + "JsonForm": "https://raw.githubusercontent.com/eurodatacube/eodash-assets/refs/heads/main/defaults/xcube_form.json", + "VegaDefinition": "https://raw.githubusercontent.com/eurodatacube/eodash-assets/refs/heads/main/defaults/xcube_vega.json", + "EndPoints": [ + { + "Identifier": "xcube_statistics", + "Method": "POST", + "Body": "https://raw.githubusercontent.com/eurodatacube/eodash-assets/refs/heads/main/defaults/xcube_body.json", + "Type": "application/json" + } ] + }, + "assets_endpoint": "https://raw.githubusercontent.com/eurodatacube/eodash-assets/main/collections", + "default_base_layers": "layers/baselayers", + "default_overlay_layers": "layers/overlays", + "collections": [ + "E9_car_containers", + "N1_NO2", + "N1_methane_monitoring", + "N1_CO", + "N1_SO2", + "N1_NO2_monthly", + "N1_NO2_diff_monthly", + "N1_NO2_city_trilateral", + "N2_CO2_mean", + "N2_CO2_diff", + "facebook_population_density", + "N9_OMI_trno2-COG", + "NPP_ocean_primary_productivity", + "NPPN_net_primary_production", + "SITI_IS2SITMOGR4-cog", + "nceo_africa_2017", + "grdi-v1-built", + "grdi-v1-raster", + "grdi-shdi-raster", + "grdi-vnl-slope-raster", + "grdi-vnl-raster", + "grdi-filled-missing-values-count", + "grdi-imr-raster", + "grdi-cdr-raster", + "N10_OMSO2PCA-COG", + "SIE_sea_ice_thickness_envisat", + "SIC_sea_ice_thickness_cryosat", + "NASAPopulation", + "WSF_world_settlement_footprint_ind", + "N12_sea_ice_concentration_antarctic", + "N12_1_sea_ice_concentration_arctic", + "E10e_ndvi", + "N11_ocean_primary_productivity", + "SMC_soil_moisture_anomaly", + "PRC_precipitation_anomaly", + "PRCG_precipitation", + "E10a1_agricultural_production_productive_area", + "E10a2_agricultural_production_area", + "E10a3_agricultural_production_area_change", + "E10a6_harvested_parcels_evolution", + "E10a8_winter_cereals", + "E10c_rice_planting", + "N2_greenhouse_gases", + "VITS_vegetation_index_timeseries", + "SMCTS_soil_moisture_timeseries", + "PRCTS_precipitation_timeseries", + "LWE_lake_water_extent", + "LWL_lake_water_level", + "NLK_lakes", + "Lakes_S2L2A", + "Lakes_ALOS2", + "Lakes_Sentinel1", + "Lakes_SWT", + "Lakes_WQ_TC_water_quality", + "Lakes_WQ_TURB_water_turbidity", + "SMCG_soil_moisture_content", + "SIF_solar_induced_chlorophyll_fluorescence", + "N5_nightlights", + "N6_geoglam", + "CDS7_windu_ERA5-SingleLevel_100m_GLOBAL", + "CDS8_windv_ERA5-SingleLevel_100m_GLOBAL", + "N3b_water_quality_tsm_chart", + "N3b_water_quality_chl_chart", + "N3a2_chl_concentration_tri_esa", + "N3a2_chl_concentration_tri_jaxa", + "N3a2_chl_concentration_tri_nasa", + "N3a2_total_suspended_matter_tri_esa", + "N3a2_total_suspended_matter_tri_jaxa", + "N3a2_total_suspended_matter_tri_nasa", + "N3a2_sea_surface_temperature", + "N1_NO2_jaxa", + "N2_CO2_jaxa_gosat", + "GHS_BUILT-S-R2023A", + "RECCAP2_1_AGC_LVOD_amazonia_methods_mean_crop", + "RECCAP2_2_AGC_LVOD_amazonia_smooth_max_crop", + "RECCAP2_3_AGC_LVOD_amazonia_smooth_mean_crop", + "RECCAP2_4_AGC_LVOD_amazonia_trend_mean_crop", + "RECCAP2_5_SF_biomass_growth", + "RECCAP2_6_deforested_biomass", + "RECCAP2_7_degraded_biomass", + "RECCAP2_8_edge_biomass_change", + "RECCAP2_9_intact_biomass_change_methods_mean", + "RECCAP2_10_intact_biomass_change_smooth_max", + "RECCAP2_11_intact_biomass_change_smooth_mean", + "RECCAP2_12_intact_biomass_change_trend_mean", + "GGI_CO2", + "GGI_N2O", + "GGI_CH4", + "ESDC_gross_primary_productivity", + "ESDC_kndvi", + "ESDC_net_ecosystem_exchange", + "GG_Google_Mobility_Data_trilateral", + "E13c_shipping_activity", + "E13b_parked_airplanes", + "CV_Covid_19_cases_trilateral", + "OW_Covid_19_vaccinations_trilateral", + "FNF_Palsar", + "Modis_SNPP_2023", + "ADD_West_Antarctica_S1", + "ADD_Landsat_L2_Antarctica", + "ADD_Melt_Duration", + "ADD_Melt_Onset", + "ADD_Melt_Season_End", + "ADD_Meltmap", + "ESDL_Hydrology_SM", + "ESDL_Hydrology_Precipitation", + "MCD_S14Amazonas_detections", + "EPA_Field_burning_Monthly", + "EPA_Forest_fire_Methane_Daily", + "EPA_Forest_fire_Methane_Yearly", + "HLS_NDVI", + "LIS_Global_DA_Evap", + "NTLU_JAXA_Nighttimelevel_Urban", + "NTLR_JAXA_Nighttimelevel_Rural", + "4D_Greenland_Duration", + "4D_Greenland_Melt_Onset", + "4D_Greenland_Melt_Season_End", + "4D_Greenland_Meltmap", + "ENSST_by_GCOM-W-AMSR_JAXA", + "SLSTR1_Sentinel-3-SLSTR-L2-LST", + "SSLC1_Seasonal_S1_AMP_hv_interferometric_coherence", + "SSLC2_Seasonal_S1_COH12_hh_interferometric_coherence", + "OHC300_Ocean_Heat_Content_upper_300m", + "NELST_ECOSTRESS_LST", + "alos2_floods", + "SMOS_ocean_salinity", + "SMOS_soil_moisture", + "grdi" + ] } diff --git a/collections/grdi.json b/collections/grdi.json new file mode 100644 index 0000000000..67b8ff892d --- /dev/null +++ b/collections/grdi.json @@ -0,0 +1,20 @@ +{ + "Name": "GRDIv1", + "EodashIdentifier": "GRDIv1", + "Title": "Global Gridded Relative Deprivation Index (GRDI)", + "Description": "# Global Gridded Relative Deprivation Index \nThe **Global Gridded Relative Deprivation Index (GRDI)**, Version 1 (GRDIv1) dataset characterizes the relative levels of multidimensional deprivation and poverty in each 30 arc-second (~1 km) pixel, where a **value of 100 represents the highest level of deprivation and a value of 0 the lowest**.\n\n### Creation of GRDIv1:\nGRDIv1 is built from sociodemographic and satellite data inputs that were spatially harmonized, indexed, and weighted into **six main components** (described bellow) to produce the final index raster. Inputs were selected from the best-available data that either continuously vary across space or have at least administrative level 1 (provincial/state) resolution, and which have global spatial coverage. GRDIv1 has six input components, or dimensions, that are combined to determine the degree of relative deprivation:\n\n* **1) The child dependency ratio (CDR):** defined as the ratio between the population of children (ages 0 to 14) to the working-age population (age 15 to 64), where a higher ratio implies a higher dependency on the working population (UN DESA 2006). CDR is intreperted as a dimension where higher dependency ratios are generally associated with younger age structures, implying higher relative deprivation.\n* **2) Infant mortality rates (IMRs):** Is defined as the number of deaths in children under 1 year of age per 1,000 live births in the same year, are a common indicator of population health (Reidpath & Allotey, 2003; Schell et al., 2007). Higher IMRs imply higher deprivation.\n* **3) The Subnational Human Development Index (SHDI):** attempts to assess human well-being through a combination of “three dimensions: education, health, and standard of living (Smits & Permanyer, 2019)”. Lower SHDIs imply higher deprivation.\n* **4) Global rural populations:** are more likely to experience a higher degree of multidimensional poverty when compared to urban populations, other things being equal (Castañeda et al., 2018; Laborde Debucquet & Martin, 2018; Lee & Kind, 2021; UN DESA, 2021; UNDP & OPHI, 2020). Therefore, low values of the ratio of built-up area to non-built up area (BUILT) imply higher deprivation.\n* **5) Intensity of nighttime lights:** are closely associated with anthropogenic activities, economic output, and infrastructure development (Elvidge et al., 2007; Ghosh et al., 2013; Lu et al., 2021; Small et al., 2013). Low values of nighttime lights for the year 2020 (VIIRS Night Lights (**VNL**) 2020) imply higher deprivation.\n* **6) Linear regression from annual VNL data between 2012 and 2020 (VNL slope)**: higher values (increasing brightness) imply decreasing deprivation and lower values (decreasing brightness) imply increasing deprivation. \n\n### GRDIv1 Citation\nCenter For International Earth Science Information Network-CIESIN-Columbia University. (2022). Global Gridded Relative Deprivation Index (GRDI), Version 1 (Version 1.00) [Data set]. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/3XXE-AP97 ", + "Resources": [ + { + "Name": "VEDA", + "EndPoint": "https://openveda.cloud/api/stac/", + "Type": "cog", + "CollectionId": "grdi-v1-raster", + "Bidx": 1, + "Rescale": [ + 0, + 100 + ], + "ColormapName": "viridis" + } + ] +}