From f10a4fecd879a0aad4cc966014aa0fd8670cddb3 Mon Sep 17 00:00:00 2001 From: Anthony Boyd <92742765+aboydnw@users.noreply.github.com> Date: Wed, 17 Jul 2024 13:58:21 -0700 Subject: [PATCH] Add exploration info + Launch new E&A page (#405) * Update CMIP-winter-median-pr.data.mdx * Update CMIP-winter-median-ta.data.mdx * Update FLDAS-soilmoisture-anomalies.data.mdx * Update aerosol-difference.data.mdx * Update bangladesh-landcover-2001-2020.data.mdx * Update barc-thomasfire.data.mdx * Update caldor-fire-characteristics-burn-severity.data.mdx * Update camp-fire-albedo-wsa-diff.data.mdx * Update camp-fire-lst-day-diff.data.mdx * Update camp-fire-lst-day-diff.data.mdx * Update camp-fire-lst-night-diff.data.mdx * Update camp-fire-ndvi-diff.data.mdx * Update camp-fire-ndvi-diff.data.mdx * Update camp-fire-nlcd.data.mdx * Update cmip6-tas.data.mdx * Update co2.data.mdx * Update conus-reach.data.mdx * Update conus-reach.data.mdx * Update conus-reach.data.mdx * Update conus-reach.data.mdx * Update conus-reach.data.mdx * Update conus-reach.data.mdx * Update conus-reach.data.mdx * Update conus-reach.data.mdx * Update disalexi-etsuppression.data.mdx * Update ecco-surface-height-change.data.mdx * Update emit-landfill.data.mdx * Update epa-agriculture.data.mdx * Update epa-coal-mines.data.mdx * Update epa-natural-gas-systems.data.mdx * Update epa-other.data.mdx * Update epa-petroleum-systems.data.mdx * Update epa-waste.data.mdx * Update fb_population.ej.data.mdx * Update fire.data.mdx * Update frp-max-thomasfire.data.mdx * Update geoglam.data.mdx * Update global-reanalysis-da.data.mdx * Update global-reanalysis-da.data.mdx * Update global-reanalysis-da.data.mdx * Update grdi-v1.data.mdx * Update hls-events.ej.data.mdx * Update is2sitmogr4.data.mdx * Update lahaina-fire.data.mdx * Update lis-etsuppression.data.mdx * Update lis-tvegsuppression.data.mdx * Update lis.da.trend.data.mdx * Update mo_npp_vgpm.data.mdx * Update modis-aerosol-dataset.data.mdx * Update mtbs-burn-severity.data.mdx * Update nceo_africa_2017.data.mdx * Update nighttime-lights.data.mdx * Update nighttime-lights.ej.data.mdx * Update nlcd-urbanization.data.mdx * Update no2.data.mdx * Update ps_blue_tarp_detections.ej.data.mdx * Update snow-projections-diff.data.mdx * Update snow-projections-median.data.mdx * Update so2.data.mdx * Update sport-lis.data.mdx * Update svi_household.ej.data.mdx * Update svi_housing.ej.data.mdx * Update svi_minority.ej.data.mdx * Update svi_overall.ej.data.mdx * Update svi_socioeconomic.ej.data.mdx * Update twsanomaly.data.mdx * Update twsnonstationarity.data.mdx * Update twstrend.data.mdx * Update urban-heating.data.mdx * Turn on new E&A page * Update cmip6-tas.data.mdx * Fix geoglam --------- Co-authored-by: Hanbyul Jo --- .env | 2 + datasets/CMIP-winter-median-pr.data.mdx | 13 +++++ datasets/CMIP-winter-median-ta.data.mdx | 13 +++++ .../FLDAS-soilmoisture-anomalies.data.mdx | 14 +++++ datasets/aerosol-difference.data.mdx | 11 ++-- .../bangladesh-landcover-2001-2020.data.mdx | 8 +++ datasets/barc-thomasfire.data.mdx | 9 ++++ ...ire-characteristics-burn-severity.data.mdx | 14 ++++- datasets/camp-fire-albedo-wsa-diff.data.mdx | 14 ++--- datasets/camp-fire-lst-day-diff.data.mdx | 14 ++--- datasets/camp-fire-lst-night-diff.data.mdx | 14 ++--- datasets/camp-fire-ndvi-diff.data.mdx | 14 ++--- datasets/camp-fire-nlcd.data.mdx | 13 +++-- datasets/cmip6-tas.data.mdx | 15 ++++++ datasets/co2.data.mdx | 13 +++++ datasets/conus-reach.data.mdx | 9 +++- datasets/disalexi-etsuppression.data.mdx | 13 +++++ datasets/ecco-surface-height-change.data.mdx | 9 +++- datasets/emit-landfill.data.mdx | 13 +++++ datasets/epa-agriculture.data.mdx | 38 +++++++++++++ datasets/epa-coal-mines.data.mdx | 19 ++++++- datasets/epa-natural-gas-systems.data.mdx | 29 +++++++++- datasets/epa-other.data.mdx | 39 +++++++++++++- datasets/epa-petroleum-systems.data.mdx | 14 ++++- datasets/epa-waste.data.mdx | 29 +++++++++- datasets/fb_population.ej.data.mdx | 8 +++ datasets/fire.data.mdx | 8 +++ datasets/frp-max-thomasfire.data.mdx | 14 ++++- datasets/geoglam.data.mdx | 8 +++ datasets/global-reanalysis-da.data.mdx | 50 ++++++++++++++++- datasets/grdi-v1.data.mdx | 53 ++++++++++++++++++- datasets/hls-events.ej.data.mdx | 15 ++++++ datasets/is2sitmogr4.data.mdx | 8 +++ datasets/lahaina-fire.data.mdx | 21 +++++++- datasets/lis-etsuppression.data.mdx | 14 +++++ datasets/lis-tvegsuppression.data.mdx | 14 +++++ datasets/lis.da.trend.data.mdx | 15 ++++++ datasets/mo_npp_vgpm.data.mdx | 8 +++ datasets/modis-aerosol-dataset.data.mdx | 8 +++ datasets/mtbs-burn-severity.data.mdx | 9 +++- datasets/nceo_africa_2017.data.mdx | 8 +++ datasets/nighttime-lights.data.mdx | 10 +++- datasets/nighttime-lights.ej.data.mdx | 8 +++ datasets/nlcd-urbanization.data.mdx | 9 +++- datasets/no2.data.mdx | 21 ++++++++ datasets/ps_blue_tarp_detections.ej.data.mdx | 21 ++++++++ datasets/snow-projections-diff.data.mdx | 15 ++++++ datasets/snow-projections-median.data.mdx | 15 ++++++ datasets/so2.data.mdx | 8 +++ datasets/sport-lis.data.mdx | 8 +++ datasets/svi_household.ej.data.mdx | 15 ++++++ datasets/svi_housing.ej.data.mdx | 15 ++++++ datasets/svi_minority.ej.data.mdx | 15 ++++++ datasets/svi_overall.ej.data.mdx | 15 ++++++ datasets/svi_socioeconomic.ej.data.mdx | 15 ++++++ datasets/twsanomaly.data.mdx | 9 ++++ datasets/twsnonstationarity.data.mdx | 8 +++ datasets/twstrend.data.mdx | 8 +++ datasets/urban-heating.data.mdx | 35 +++++++++++- 59 files changed, 855 insertions(+), 49 deletions(-) diff --git a/.env b/.env index 5d0390fe2..0aeb58068 100644 --- a/.env +++ b/.env @@ -25,3 +25,5 @@ GOOGLE_FORM = 'https://docs.google.com/forms/d/e/1FAIpQLSfGcd3FDsM3kQIOVKjzdPn4f # Google analytics tracking code GOOGLE_ANALYTICS_ID='G-CQ3WLED121' + +FEATURE_NEW_EXPLORATION = 'TRUE' diff --git a/datasets/CMIP-winter-median-pr.data.mdx b/datasets/CMIP-winter-median-pr.data.mdx index f33f41b99..c1efdc2b7 100644 --- a/datasets/CMIP-winter-median-pr.data.mdx +++ b/datasets/CMIP-winter-median-pr.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + Future changes to precipitation are expected to alter the volume and timing of snow water resources. Here, we present the projected percent-change to Western US cumulative winter precipitation at quarter-degree spatial resoutions across 20-year time periods between 2016 and 2095. Projections are averaged from an ensemble of 23 downscaled climate models from the CMIP6 NASA Earth Exchange Global Daily Downscaled Projections. layers: - id: CMIP245-winter-median-pr stacCol: CMIP245-winter-median-pr @@ -48,6 +51,11 @@ layers: - "#A0CBE4" - "#5EA4D1" - "#207BBD" + info: + source: NASA + spatialExtent: Regional + temporalResolution: Annual + unit: Percent Difference - id: CMIP585-winter-median-pr stacCol: CMIP585-winter-median-pr name: 'Percent-change to winter cumulative precipitation, SSP5-8.5' @@ -83,6 +91,11 @@ layers: - "#A0CBE4" - "#5EA4D1" - "#207BBD" + info: + source: NASA + spatialExtent: Regional + temporalResolution: Annual + unit: Percent Difference --- diff --git a/datasets/CMIP-winter-median-ta.data.mdx b/datasets/CMIP-winter-median-ta.data.mdx index 63dee81e7..5a1c52249 100644 --- a/datasets/CMIP-winter-median-ta.data.mdx +++ b/datasets/CMIP-winter-median-ta.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + Future changes to air temperature are expected to influence the phase of winter precipitation (snowfall or rainfall) and the timing and amount of snowmelt and streamflow. Here, we present the projected percent-change to Western US average winter temperature at quarter-degree spatial resoutions across 20-year time periods between 2016 and 2095. Projections are averaged from an ensemble of 23 downscaled climate models from the [CMIP6 NASA Earth Exchange Global Daily Downscaled Projections](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6). layers: - id: CMIP245-winter-median-ta stacCol: CMIP245-winter-median-ta @@ -49,6 +52,11 @@ layers: - "#F2B089" - "#DE6158" - "#CA171C" + info: + source: NASA + spatialExtent: Regional + temporalResolution: Annual + unit: Percent Difference - id: CMIP585-winter-median-ta stacCol: CMIP585-winter-median-ta name: 'SSP5-8.5, Change to winter average air temperature' @@ -85,6 +93,11 @@ layers: - "#F2B089" - "#DE6158" - "#CA171C" + info: + source: NASA + spatialExtent: Regional + temporalResolution: Annual + unit: Percent Difference --- diff --git a/datasets/FLDAS-soilmoisture-anomalies.data.mdx b/datasets/FLDAS-soilmoisture-anomalies.data.mdx index 870a4850f..6d35dc63f 100644 --- a/datasets/FLDAS-soilmoisture-anomalies.data.mdx +++ b/datasets/FLDAS-soilmoisture-anomalies.data.mdx @@ -14,6 +14,15 @@ taxonomy: - name: Source values: - NASA GES DISC +infoDescription: | + ::markdown + - **Temporal Extent:** January 1982 - June 2023 + - **Temporal Resolution:** Monthly + - **Spatial Extent:** Quasi-Global ( -180.0,-60.0,180.0,90.0) + - **Spatial Resolution:** 10 km x 10 km + - **Data Units:** Fraction Soil moisture anomaly (mm3/mm3) difference from 1982-2016 monthly mean + - **Data Type:** Research + - **Data Latency:** Monthly layers: - id: SoilMoi00_10cm_tavg stacCol: fldas-soil-moisture-anomalies @@ -49,6 +58,11 @@ layers: - "#d1e5f0" - "#4393c3" - "#053061" + info: + source: NASA + spatialExtent: Quasi-Global + temporalResolution: Monthly + unit: mm3/mm3 --- diff --git a/datasets/aerosol-difference.data.mdx b/datasets/aerosol-difference.data.mdx index ab54f0f31..7e82eed78 100644 --- a/datasets/aerosol-difference.data.mdx +++ b/datasets/aerosol-difference.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - Air Quality +infoDescription: | + ::markdown + This dataset comes from the two decadal COGs that displayed mean Aerosol Optical Depth for 2000-2009 and for 2010-2019. Those tiffs were subtracted to display the differences between the two decades. layers: - id: houston-aod-diff stacCol: houston-aod-diff @@ -47,9 +50,11 @@ layers: - "#fee090" - "#fc8d59" - "#d73027" - - - + info: + source: NASA + spatialExtent: Regional + temporalResolution: Annual + unit: Percent Difference --- diff --git a/datasets/bangladesh-landcover-2001-2020.data.mdx b/datasets/bangladesh-landcover-2001-2020.data.mdx index b4a92a63b..1ed510d48 100644 --- a/datasets/bangladesh-landcover-2001-2020.data.mdx +++ b/datasets/bangladesh-landcover-2001-2020.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + The annual land cover maps of 2001 and 2021 were captured using combined Moderate Resolution Imaging Spectroradiometer (MODIS) Annual Land Cover Type dataset (MCD12Q1 V6, dataset link: [https://lpdaac.usgs.gov/products/mcd12q1v006/](https://lpdaac.usgs.gov/products/mcd12q1v006/)). The actual data product provides global land cover types at yearly intervals (2001-2020) at 500 meters with six different types of land cover classification. Among six different schemes, The International Geosphere–Biosphere Programme (IGBP) land cover classification selected and further simplified to dominant land cover classes (water, urban, cropland, native vegetation) for two different years to illustrate the changes in land use and land cover of the country. layers: - id: bangladesh-landcover-2001-2020 stacCol: bangladesh-landcover-2001-2020 @@ -46,6 +49,11 @@ layers: ::js ({ dateFns, datetime, compareDatetime }) => { if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`; } + info: + source: NASA + spatialExtent: Bangladesh + temporalResolution: Annual + unit: Categorical --- diff --git a/datasets/barc-thomasfire.data.mdx b/datasets/barc-thomasfire.data.mdx index 5c951361e..83dfe414c 100644 --- a/datasets/barc-thomasfire.data.mdx +++ b/datasets/barc-thomasfire.data.mdx @@ -12,6 +12,10 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + Burn Area Reflectance Classification (BARC) from the Burned Area Emergency Response (BAER) program for the Thomas Fire of 2017. + layers: - id: barc-thomasfire stacCol: barc-thomasfire @@ -38,6 +42,11 @@ layers: label: "Moderate" - color: "#971d2b" label: "High" + info: + source: NASA + spatialExtent: Regional + temporalResolution: Annual + unit: Categorical --- diff --git a/datasets/caldor-fire-characteristics-burn-severity.data.mdx b/datasets/caldor-fire-characteristics-burn-severity.data.mdx index 49fc51d7b..e4cbec8d4 100644 --- a/datasets/caldor-fire-characteristics-burn-severity.data.mdx +++ b/datasets/caldor-fire-characteristics-burn-severity.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + This dataset describes the progression and active fire behavior of the 2021 Caldor Fire in California, as recorded by the algorithm detailed in https://www.nature.com/articles/s41597-022-01343-0. It includes an extra layer detailing the soil burn severity (SBS) conditions provided by the [Burned Area Emergency Response](https://burnseverity.cr.usgs.gov/baer/) team. layers: - id: caldor-fire-behavior stacCol: caldor-fire-behavior @@ -36,6 +39,11 @@ layers: - "#BB3754" - "#781D6D" - "#34095F" + info: + source: NASA + spatialExtent: Regional + temporalResolution: Annual + unit: Categorical - id: caldor-fire-burn-severity stacCol: caldor-fire-burn-severity name: Burn Severity @@ -59,7 +67,11 @@ layers: - "#BB3754" - "#781D6D" - "#34095F" - + info: + source: NASA + spatialExtent: Regional + temporalResolution: Annual + unit: Categorical --- diff --git a/datasets/camp-fire-albedo-wsa-diff.data.mdx b/datasets/camp-fire-albedo-wsa-diff.data.mdx index 121ef80c4..343d37803 100644 --- a/datasets/camp-fire-albedo-wsa-diff.data.mdx +++ b/datasets/camp-fire-albedo-wsa-diff.data.mdx @@ -12,8 +12,9 @@ taxonomy: - name: Topics values: - EIS - - +infoDescription: | + ::markdown + In order to examine how the fire event affected the changes in surface properties, we utilized the MODIS-derived Normalized Difference Vegetation Index (NDVI), albedo, and land surface temperature (LST) products for a six-year period centered on the Camp Fire event (2015-2022). We used these products which are available at 16-day intervals to compute monthly averaged spatial maps of NDVI, albedo, and LST. The monthly average spatial maps were then averaged over the areas affected by the Camp Fire to compute monthly mean values. This dataset is the Albedo WSA difference portion of that analysis. layers: - id: modis-albedo-wsa-diff-2015-2022 stacCol: campfire-albedo-wsa-diff @@ -42,10 +43,11 @@ layers: - "#fee090" - "#fc8d59" - "#d73027" - - - - + info: + source: NASA + spatialExtent: Regional + temporalResolution: Annual + unit: Percent Difference --- diff --git a/datasets/camp-fire-lst-day-diff.data.mdx b/datasets/camp-fire-lst-day-diff.data.mdx index bae42dc19..6baa80d77 100644 --- a/datasets/camp-fire-lst-day-diff.data.mdx +++ b/datasets/camp-fire-lst-day-diff.data.mdx @@ -12,8 +12,9 @@ taxonomy: - name: Topics values: - EIS - - +infoDescription: | + ::markdown + In order to examine how the fire event affected the changes in surface properties, we utilized the MODIS-derived Normalized Difference Vegetation Index (NDVI), albedo, and land surface temperature (LST) products for a six-year period centered on the Camp Fire event (2015-2022). We used these products which are available at 16-day intervals to compute monthly averaged spatial maps of NDVI, albedo, and LST. The monthly average spatial maps were then averaged over the areas affected by the Camp Fire to compute monthly mean values. This dataset is the LST Day difference portion of that analysis. layers: - id: modis-lst-day-diff-2015-2022 stacCol: campfire-lst-day-diff @@ -44,10 +45,11 @@ layers: - "#fee090" - "#fc8d59" - "#d73027" - - - - + info: + source: NASA + spatialExtent: Regional + temporalResolution: Annual + unit: Percent Difference --- diff --git a/datasets/camp-fire-lst-night-diff.data.mdx b/datasets/camp-fire-lst-night-diff.data.mdx index 4ead02a1b..8995f3d9a 100644 --- a/datasets/camp-fire-lst-night-diff.data.mdx +++ b/datasets/camp-fire-lst-night-diff.data.mdx @@ -12,8 +12,9 @@ taxonomy: - name: Topics values: - EIS - - +infoDescription: | + ::markdown + In order to examine how the fire event affected the changes in surface properties, we utilized the MODIS-derived Normalized Difference Vegetation Index (NDVI), albedo, and land surface temperature (LST) products for a six-year period centered on the Camp Fire event (2015-2022). We used these products which are available at 16-day intervals to compute monthly averaged spatial maps of NDVI, albedo, and LST. The monthly average spatial maps were then averaged over the areas affected by the Camp Fire to compute monthly mean values. This dataset is the LST Night difference portion of that analysis. layers: - id: modis-lst-night-diff-2015-2022 stacCol: campfire-lst-night-diff @@ -44,10 +45,11 @@ layers: - "#fee090" - "#fc8d59" - "#d73027" - - - - + info: + source: NASA + spatialExtent: Regional + temporalResolution: Annual + unit: Percent Difference --- diff --git a/datasets/camp-fire-ndvi-diff.data.mdx b/datasets/camp-fire-ndvi-diff.data.mdx index 97a6c69b5..5b7e3237e 100644 --- a/datasets/camp-fire-ndvi-diff.data.mdx +++ b/datasets/camp-fire-ndvi-diff.data.mdx @@ -12,8 +12,9 @@ taxonomy: - name: Topics values: - EIS - - +infoDescription: | + ::markdown + In order to examine how the fire event affected the changes in surface properties, we utilized the MODIS-derived Normalized Difference Vegetation Index (NDVI), albedo, and land surface temperature (LST) products for a six-year period centered on the Camp Fire event (2015-2022). We used these products which are available at 16-day intervals to compute monthly averaged spatial maps of NDVI, albedo, and LST. The monthly average spatial maps were then averaged over the areas affected by the Camp Fire to compute monthly mean values. This dataset is the NDVI difference portion of that analysis. layers: - id: modis-ndvi-diff-2015-2022 stacCol: campfire-ndvi-diff @@ -42,10 +43,11 @@ layers: - "#fee090" - "#fc8d59" - "#d73027" - - - - + info: + source: NASA + spatialExtent: Regional + temporalResolution: Annual + unit: Percent Difference --- diff --git a/datasets/camp-fire-nlcd.data.mdx b/datasets/camp-fire-nlcd.data.mdx index 76923c23f..b82e63c9d 100644 --- a/datasets/camp-fire-nlcd.data.mdx +++ b/datasets/camp-fire-nlcd.data.mdx @@ -12,8 +12,9 @@ taxonomy: - name: Topics values: - EIS - - +infoDescription: | + ::markdown + We utilized the National Land Cover Database (NLCD), which provides a classification of land cover categories at 30m spatial resolution over geographical locations within the Continental United States (CONUS). The NLCD is derived from Landsat satellite sensors data and is available at approximately three-year time intervals. We used the NLCD maps for the years 2016 and 2019 to examine changes in land cover type resulting from the Camp Fire event, to examine LULC before and after the Camp Fire. This analysis shows that the dominant vegetation cover type that was present within the region per-wildfire are evergreen forest and shrub/scrub cover, while post-wildfire are grasslands and herbaceous vegetation. layers: - id: campfire-nlcd stacCol: campfire-nlcd @@ -83,9 +84,11 @@ layers: compare: datasetId: campfire-nlcd layerId: campfire-nlcd - - - + info: + source: NASA + spatialExtent: Regional + temporalResolution: Annual + unit: Percent Difference --- diff --git a/datasets/cmip6-tas.data.mdx b/datasets/cmip6-tas.data.mdx index 272ed430c..18f9963b9 100644 --- a/datasets/cmip6-tas.data.mdx +++ b/datasets/cmip6-tas.data.mdx @@ -13,6 +13,16 @@ taxonomy: - name: Topics values: - Climate +infoDescription: | + ::markdown + * Format: [kerchunk (metadata)](https://fsspec.github.io/kerchunk/) for netCDF4 + * Spatial Coverage: 180° W to 180° E, 60° S to 90° N + * Temporal: 1950-01-01 to 1951-12-31 + * _As noted below, this dataset is a subset all available data. The full dataset includes data from 1950 to 2100._ + * Data Resolution: + * Latitude Resolution: 0.25 degrees (25 km) + * Longitude Resolution: 0.25 degrees (25 km) + * Temporal Resolution: daily layers: - id: combined_CMIP6_daily_GISS-E2-1-G_tas_kerchunk_DEMO stacCol: combined_CMIP6_daily_GISS-E2-1-G_tas_kerchunk_DEMO @@ -43,6 +53,11 @@ layers: - '#f2cbb7' - '#ee8468' - '#b40426' + info: + source: NASA + spatialExtent: Global + temporalResolution: Daily + unit: Kelvin --- diff --git a/datasets/co2.data.mdx b/datasets/co2.data.mdx index bf6711eb9..5050b4440 100644 --- a/datasets/co2.data.mdx +++ b/datasets/co2.data.mdx @@ -13,6 +13,9 @@ taxonomy: values: - Air Quality - EIS +infoDescription: | + ::markdown + The Impact of the COVID-19 Pandemic on Atmospheric CO2 layers: - id: co2-mean stacCol: co2-mean @@ -47,6 +50,11 @@ layers: - "#fee090" - "#fc8d59" - "#d73027" + info: + source: NASA + spatialExtent: Global + temporalResolution: Daily + unit: ppm - id: co2-diff stacCol: co2-diff name: Difference CO2 @@ -77,6 +85,11 @@ layers: - "#f39779" - "#db5c48" - "#b50021" + info: + source: NASA + spatialExtent: Global + temporalResolution: Daily + unit: ppm --- diff --git a/datasets/conus-reach.data.mdx b/datasets/conus-reach.data.mdx index 5ab22469b..a66c77dd1 100644 --- a/datasets/conus-reach.data.mdx +++ b/datasets/conus-reach.data.mdx @@ -13,6 +13,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + This dataset describes the Stream network across the Contiguous United States delineated using Soil and Water Assessment Tool layers: - id: conus-reach stacCol: conus-reach @@ -28,7 +31,11 @@ layers: - 1 - 1 nodata: 65535 - + info: + source: NASA + spatialExtent: Contiguous US + temporalResolution: Annual + unit: N/A --- diff --git a/datasets/disalexi-etsuppression.data.mdx b/datasets/disalexi-etsuppression.data.mdx index f71b6198d..d68c6d8b3 100644 --- a/datasets/disalexi-etsuppression.data.mdx +++ b/datasets/disalexi-etsuppression.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + Impact of fires on changes in evapotranspiration, obtained OpenET observations (DisALEXI model) for 2017-20 fires layers: - id: disalexi-etsuppression stacCol: disalexi-etsuppression @@ -46,6 +49,11 @@ layers: - "#e0f3f8" - "#74add1" - "#313695" + info: + source: NASA + spatialExtent: Regional + temporalResolution: Annual + unit: Difference - id: mtbs-burn-severity stacCol: mtbs-burn-severity name: MTBS Burn Severity @@ -70,6 +78,11 @@ layers: label: "Moderate" - color: "#971d2b" label: "High" + info: + source: NASA + spatialExtent: Regional + temporalResolution: Annual + unit: Categorical --- diff --git a/datasets/ecco-surface-height-change.data.mdx b/datasets/ecco-surface-height-change.data.mdx index 4cc9be96c..d2ed42f41 100644 --- a/datasets/ecco-surface-height-change.data.mdx +++ b/datasets/ecco-surface-height-change.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + Gridded global sea-surface height change from 1992 to 2017 from the Estimating the Circulation and Climate of the Ocean (ECCO) ocean state estimate. The dataset was calculated as the difference between the annual means over 2017 and 1992, from the 0.5 degree, gridded monthly mean data product available on [PO.DAAC](https://podaac.jpl.nasa.gov/dataset/ECCO_L4_SSH_05DEG_MONTHLY_V4R4). layers: - id: ecco-surface-height-change stacCol: ecco-surface-height-change @@ -34,7 +37,11 @@ layers: - "#EF8A62" - "#F7F7F7" - "#67A9CF" - + info: + source: NASA + spatialExtent: Global + temporalResolution: Annual + unit: meters --- diff --git a/datasets/emit-landfill.data.mdx b/datasets/emit-landfill.data.mdx index 8cb54a7b8..cb62a63ff 100644 --- a/datasets/emit-landfill.data.mdx +++ b/datasets/emit-landfill.data.mdx @@ -16,6 +16,14 @@ taxonomy: - name: Source values: - NASA EMIT +infoDescription: | + ::markdown + - **Temporal Extent:** June 22, and August 25, 2023 + - **Temporal Resolution:** Variable (based on ISS orbit, solar illumination, and target mask) + - **Spatial Extent:** Stockton, CA and Dallas, TX + - **Spatial Resolution:** 60 m + - **Data Units:** Parts per million-meter (ppm m) + - **Data Type:** Research layers: - id: landfill-emit stacCol: landfill-emit @@ -63,6 +71,11 @@ layers: - '#fdac33' - '#fdc527' - '#f8df25' + info: + source: NASA + spatialExtent: Stockton, CA and Dallas, TX + temporalResolution: Annual + unit: ppm m --- diff --git a/datasets/epa-agriculture.data.mdx b/datasets/epa-agriculture.data.mdx index bfa30c6c5..89f871f62 100644 --- a/datasets/epa-agriculture.data.mdx +++ b/datasets/epa-agriculture.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - EPA GHG +infoDescription: | + ::markdown + A team at Harvard University along with EPA and other coauthors developed a gridded inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 U.S. [EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) estimates for the year 2012, which presents national totals for different source types. The gridded inventory was developed using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types. layers: - id: epa-annual-emissions_4b_manure_management stacCol: EPA-annual-emissions_4B_Manure_Management @@ -42,6 +45,11 @@ layers: - "#f2ce00" - "#ef6a01" - "#cc0019" + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-monthly-emissions_4b_manure_management stacCol: EPA-monthly-emissions_4B_Manure_Management name: Manure Management (monthly) @@ -68,6 +76,11 @@ layers: - "#f2ce00" - "#ef6a01" - "#cc0019" + info: + source: EPA + spatialExtent: United States + temporalResolution: Monthly + unit: Mg 1/a km² - id: epa-annual-emissions_4c_rice_cultivation stacCol: EPA-annual-emissions_4C_Rice_Cultivation name: Rice Cultivation @@ -94,6 +107,11 @@ layers: - "#f2ce00" - "#ef6a01" - "#cc0019" + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-monthly-emissions_4c_rice_cultivation stacCol: EPA-monthly-emissions_4C_Rice_Cultivation name: Rice Cultivation (monthly) @@ -120,6 +138,11 @@ layers: - "#f2ce00" - "#ef6a01" - "#cc0019" + info: + source: EPA + spatialExtent: United States + temporalResolution: Monthly + unit: Mg 1/a km² - id: epa-annual-emissions_4a_enteric_fermentation stacCol: EPA-annual-emissions_4A_Enteric_Fermentation name: Enteric Fermentation @@ -148,6 +171,11 @@ layers: - "#f2ce00" - "#ef6a01" - "#cc0019" + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-annual-emissions_4f_field_burning stacCol: EPA-annual-emissions_4F_Field_Burning name: Field Burning @@ -174,6 +202,11 @@ layers: - "#f2ce00" - "#ef6a01" - "#cc0019" + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-monthly-emissions_4f_field_burning stacCol: EPA-monthly-emissions_4F_Field_Burning name: Field Burning (monthly) @@ -200,6 +233,11 @@ layers: - "#f2ce00" - "#ef6a01" - "#cc0019" + info: + source: EPA + spatialExtent: United States + temporalResolution: Monthly + unit: Mg 1/a km² --- diff --git a/datasets/epa-coal-mines.data.mdx b/datasets/epa-coal-mines.data.mdx index 79918a883..f867717a9 100644 --- a/datasets/epa-coal-mines.data.mdx +++ b/datasets/epa-coal-mines.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - EPA GHG +infoDescription: | + ::markdown + A team at Harvard University along with EPA and other coauthors developed a gridded inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 U.S. [EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) estimates for the year 2012, which presents national totals for different source types. The gridded inventory was developed using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types. layers: - id: epa-annual-emissions_1b1a_coal_mining_underground stacCol: EPA-annual-emissions_1B1a_Coal_Mining_Underground @@ -42,6 +45,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-annual-emissions_1b1a_coal_mining_surface stacCol: EPA-annual-emissions_1B1a_Coal_Mining_Surface name: Surface Coal Mines @@ -68,6 +76,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-annual-emissions_1b1a_abandoned_coal stacCol: EPA-annual-emissions_1B1a_Abandoned_Coal name: Abandoned Coal Mines @@ -94,7 +107,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' - + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² --- diff --git a/datasets/epa-natural-gas-systems.data.mdx b/datasets/epa-natural-gas-systems.data.mdx index 985f670cb..d241f778c 100644 --- a/datasets/epa-natural-gas-systems.data.mdx +++ b/datasets/epa-natural-gas-systems.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - EPA GHG +infoDescription: | + ::markdown + A team at Harvard University along with EPA and other coauthors developed a gridded inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 U.S. [EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) estimates for the year 2012, which presents national totals for different source types. The gridded inventory was developed using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types. layers: - id: epa-annual-emissions_1b2b_natural_gas_processing stacCol: EPA-annual-emissions_1B2b_Natural_Gas_Processing @@ -42,6 +45,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-annual-emissions_1b2b_natural_gas_production stacCol: EPA-annual-emissions_1B2b_Natural_Gas_Production name: Natural Gas Production @@ -68,6 +76,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-monthly-emissions_1b2b_natural_gas_production stacCol: EPA-monthly-emissions_1B2b_Natural_Gas_Production name: Natural Gas Production (monthly) @@ -96,6 +109,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Monthly + unit: Mg 1/a km² - id: epa-annual-emissions_1b2b_natural_gas_transmission stacCol: EPA-annual-emissions_1B2b_Natural_Gas_Transmission name: Natural Gas Transmission @@ -122,6 +140,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-annual-emissions_1b2b_natural_gas_distribution stacCol: EPA-annual-emissions_1B2b_Natural_Gas_Distribution name: Natural Gas Distribution @@ -148,7 +171,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' - + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² --- diff --git a/datasets/epa-other.data.mdx b/datasets/epa-other.data.mdx index 3ed980e90..69f9afab7 100644 --- a/datasets/epa-other.data.mdx +++ b/datasets/epa-other.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - EPA GHG +infoDescription: | + ::markdown + A team at Harvard University along with EPA and other coauthors developed a gridded inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 U.S. [EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) estimates for the year 2012, which presents national totals for different source types. The gridded inventory was developed using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types. layers: - id: epa-annual-emissions_2b5_petrochemical_production stacCol: EPA-annual-emissions_2B5_Petrochemical_Production @@ -42,6 +45,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-annual-emissions_2c2_ferroalloy_production stacCol: EPA-annual-emissions_2C2_Ferroalloy_Production name: Ferroalloy Production @@ -68,6 +76,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-annual-emissions_1a_combustion_mobile stacCol: EPA-annual-emissions_1A_Combustion_Mobile name: Mobile Combustion @@ -96,6 +109,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-annual-emissions_1a_combustion_stationary stacCol: EPA-annual-emissions_1A_Combustion_Stationary name: Stationary Combustion @@ -126,6 +144,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-monthly-emissions_1a_combustion_stationary stacCol: EPA-monthly-emissions_1A_Combustion_Stationary name: Stationary Combustion (monthly) @@ -156,6 +179,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Monthly + unit: Mg 1/a km² - id: epa-annual-emissions_5_forest_fires stacCol: EPA-annual-emissions_5_Forest_Fires name: Forest Fires @@ -182,6 +210,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-daily-emissions_5_forest_fires stacCol: EPA-daily-emissions_5_Forest_Fires name: Forest Fires (daily) @@ -208,7 +241,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' - + info: + source: EPA + spatialExtent: United States + temporalResolution: Daily + unit: Mg 1/a km² --- diff --git a/datasets/epa-petroleum-systems.data.mdx b/datasets/epa-petroleum-systems.data.mdx index 870a2e688..5273c0552 100644 --- a/datasets/epa-petroleum-systems.data.mdx +++ b/datasets/epa-petroleum-systems.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - EPA GHG +infoDescription: | + ::markdown + A team at Harvard University along with EPA and other coauthors developed a gridded inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 U.S. [EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) estimates for the year 2012, which presents national totals for different source types. The gridded inventory was developed using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types. layers: - id: epa-annual-emissions_1b2a_petroleum stacCol: EPA-annual-emissions_1B2a_Petroleum @@ -44,6 +47,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-monthly-emissions_1b2a_petroleum stacCol: EPA-monthly-emissions_1B2a_Petroleum name: Petroleum (monthly) @@ -72,7 +80,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' - + info: + source: EPA + spatialExtent: United States + temporalResolution: Monthly + unit: Mg 1/a km² --- diff --git a/datasets/epa-waste.data.mdx b/datasets/epa-waste.data.mdx index 6c7e1b96b..5e6fd8cab 100644 --- a/datasets/epa-waste.data.mdx +++ b/datasets/epa-waste.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - EPA GHG +infoDescription: | + ::markdown + A team at Harvard University along with EPA and other coauthors developed a gridded inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 U.S. [EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) estimates for the year 2012, which presents national totals for different source types. The gridded inventory was developed using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types. layers: - id: epa-annual-emissions_6b_wastewater_treatment_domestic stacCol: EPA-annual-emissions_6B_Wastewater_Treatment_Domestic @@ -42,6 +45,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-annual-emissions_6b_wastewater_treatment_industrial stacCol: EPA-annual-emissions_6B_Wastewater_Treatment_Industrial name: Industrial Wastewater Treatment @@ -70,6 +78,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-annual-emissions_6a_landfills_industrial stacCol: EPA-annual-emissions_6A_Landfills_Industrial name: Industrial Landfills @@ -98,6 +111,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-annual-emissions_6a_landfills_municipal stacCol: EPA-annual-emissions_6A_Landfills_Municipal name: Municipal Landfills @@ -126,6 +144,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² - id: epa-annual-emissions_6d_composting stacCol: EPA-annual-emissions_6D_Composting name: Composting @@ -152,7 +175,11 @@ layers: - '#f2ce00' - '#ef6a01' - '#cc0019' - + info: + source: EPA + spatialExtent: United States + temporalResolution: Annual + unit: Mg 1/a km² --- diff --git a/datasets/fb_population.ej.data.mdx b/datasets/fb_population.ej.data.mdx index 73a11905c..0417424b2 100644 --- a/datasets/fb_population.ej.data.mdx +++ b/datasets/fb_population.ej.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - Meta +infoDescription: | + ::markdown + In partnership with the Center for International Earth Science Information Network (CIESIN) at Columbia University, Meta [formerly known as Facebook] used census data and computer vision techniques (Convolutional Neural Networks) to identify buildings from publicly accessible mapping services to create population density datasets. These high-resolution maps estimate the number of individuals living within 30-meter grid tiles on a global scale. The population estimates are based on data from the Gridded Population of the World (GPWv4) data collection. layers: - id: facebook_population_density stacCol: facebook_population_density @@ -31,6 +34,11 @@ layers: rescale: - 0 - 69 + info: + source: Meta + spatialExtent: Global + temporalResolution: Annual + unit: Units of People per Square Meter legend: type: gradient min: "0" diff --git a/datasets/fire.data.mdx b/datasets/fire.data.mdx index 98ecdd94e..2f2a3e7d8 100644 --- a/datasets/fire.data.mdx +++ b/datasets/fire.data.mdx @@ -16,6 +16,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + Fire perimeter data is generated by the FEDs algorithm. The FEDs algorithm tracks fire movement and severity by ingesting observations from the VIIRS thermal sensors on the Suomi NPP and NOAA-20 satellites. This algorithm uses raw VIIRS observations to generate a polygon of the fire, locations of the active fire line, and estimates of fire mean Fire Radiative Power (FRP). The VIIRS sensors overpass at ~1:30 AM and PM local time, and provide estimates of fire evolution ~ every 12 hours. The data produced by this algorithm describe where fires are in space and how fires evolve through time. This CONUS-wide implementation of the FEDs algorithm is based on [Chen et al 2020’s algorithm for California.](https://www.nature.com/articles/s41597-022-01343-0) layers: - id: eis_fire_perimeter stacCol: eis_fire_perimeter @@ -25,6 +28,11 @@ layers: zoomExtent: - 5 - 20 + info: + source: NASA + spatialExtent: Contiguous United States + temporalResolution: Daily + unit: N/A --- diff --git a/datasets/frp-max-thomasfire.data.mdx b/datasets/frp-max-thomasfire.data.mdx index 40c93fdb7..0070893d6 100644 --- a/datasets/frp-max-thomasfire.data.mdx +++ b/datasets/frp-max-thomasfire.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + Maximum Fire Radiative Power recorded by the Suomi NPP VIIRS sensor per 12hr fire line segment for the Thomas Fire of 2017 layers: - id: frp-max-thomasfire stacCol: frp-max-thomasfire @@ -45,6 +48,11 @@ layers: - "#BB3754" - "#781D6D" - "#34095F" + info: + source: NASA + spatialExtent: Thomas Fire Area + temporalResolution: Annual + unit: Watts - id: barc-thomasfire stacCol: barc-thomasfire name: Burn Area Reflectance Classification for Thomas Fire @@ -70,7 +78,11 @@ layers: label: "Moderate" - color: "#971d2b" label: "High" - + info: + source: NASA + spatialExtent: Thomas Fire Area + temporalResolution: Annual + unit: Categorical --- diff --git a/datasets/geoglam.data.mdx b/datasets/geoglam.data.mdx index 234dfb7c9..46fcaa082 100644 --- a/datasets/geoglam.data.mdx +++ b/datasets/geoglam.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - GEOGLAM +infoDescription: | + ::markdown + The Group on Earth Observation's Global Agricultural Monitoring Initiative (GEOGLAM) Global Crop Monitor uses remote sensing data like global precipitation and soil moisture measurements to help reduce uncertainty, promote market transparency, and provide early warning for crop failures through multi-agency collaboration. layers: - id: geoglam stacCol: geoglam @@ -49,6 +52,11 @@ layers: label: "Out of season" - color: "#804115" label: "No data" + info: + source: GEOGLAM + spatialExtent: Global + temporalResolution: Monthly + unit: Categorical --- diff --git a/datasets/global-reanalysis-da.data.mdx b/datasets/global-reanalysis-da.data.mdx index 97cab95d5..f8d3e6625 100644 --- a/datasets/global-reanalysis-da.data.mdx +++ b/datasets/global-reanalysis-da.data.mdx @@ -13,6 +13,10 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + The reanalysis product is created using the [NASA Land Information System](https://lis.gsfc.nasa.gov/) modeling framework to merge land surface model simulations with observations from satellites through data assimilation. The team uses the Noah-MP land surface model and assimilates soil moisture from the European Space Agency’s Climate Change Initiative Program (ESA CCI), leaf area index from the Moderate Resolution Imaging Spectroradiometer (MODIS), and terrestrial water storage anomalies from the Gravity Recovery and Climate Experiment and the follow-on missions (GRACE/GRACE-FO). + layers: - id: lis-global-da-evap stacCol: lis-global-da-evap @@ -49,7 +53,11 @@ layers: - '#21918c' - '#5ec962' - '#fde725' - + info: + source: NASA + spatialExtent: Global + temporalResolution: Daily + unit: kg m-2 s-1 - id: lis-global-da-gpp stacCol: lis-global-da-gpp name: 'Gross Primary Productivity' @@ -85,6 +93,11 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA + spatialExtent: Global + temporalResolution: Daily + unit: g m-2 s-1 - id: lis-global-da-gws stacCol: lis-global-da-gws @@ -121,6 +134,11 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA + spatialExtent: Global + temporalResolution: Daily + unit: mm - id: lis-global-da-swe stacCol: lis-global-da-swe @@ -158,6 +176,11 @@ layers: - "#4A98C9" - "#2164AB" - "#0E316B" + info: + source: NASA + spatialExtent: Global + temporalResolution: Daily + unit: mm - id: lis-global-da-streamflow stacCol: lis-global-da-streamflow @@ -194,6 +217,11 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA + spatialExtent: Global + temporalResolution: Daily + unit: m3 s-1 - id: lis-global-da-qs stacCol: lis-global-da-qs @@ -230,6 +258,11 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA + spatialExtent: Global + temporalResolution: Daily + unit: kg m-2 s-1 - id: lis-global-da-qsb stacCol: lis-global-da-qsb @@ -266,6 +299,11 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA + spatialExtent: Global + temporalResolution: Daily + unit: kg m-2 s-1 - id: lis-global-da-tws stacCol: lis-global-da-tws @@ -302,6 +340,11 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA + spatialExtent: Global + temporalResolution: Daily + unit: mm - id: lis-global-da-totalprecip stacCol: lis-global-da-totalprecip @@ -339,6 +382,11 @@ layers: - "#4A98C9" - "#2164AB" - "#0E316B" + info: + source: NASA + spatialExtent: Global + temporalResolution: Daily + unit: kg m-2 s-1 --- diff --git a/datasets/grdi-v1.data.mdx b/datasets/grdi-v1.data.mdx index 4471310c6..fb2c7d426 100644 --- a/datasets/grdi-v1.data.mdx +++ b/datasets/grdi-v1.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - NASA CIESIN +infoDescription: | + ::markdown + The 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. GRDIv1 is built from sociodemographic and satellite data inputs that were spatially harmonized, indexed, and weighted into six main components 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. layers: - id: grdi-cdr-raster stacCol: grdi-cdr-raster @@ -39,6 +42,12 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA CIESIN + spatialExtent: Global + temporalResolution: Annual + unit: Ratio + - id: grdi-filled-missing-values-count stacCol: grdi-filled-missing-values-count name: GRDI Constituent Inputs @@ -62,6 +71,12 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA CIESIN + spatialExtent: Global + temporalResolution: Annual + unit: Ratio + - id: grdi-imr-raster stacCol: grdi-imr-raster name: GRDI Infant Mortality Rate @@ -85,6 +100,12 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA CIESIN + spatialExtent: Global + temporalResolution: Annual + unit: Ratio + - id: grdi-shdi-raster stacCol: grdi-shdi-raster name: GRDI Subnational Human Development Index @@ -108,6 +129,12 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA CIESIN + spatialExtent: Global + temporalResolution: Annual + unit: Ratio + - id: grdi-v1-built stacCol: grdi-v1-built name: GRDI v1 built-up area @@ -131,6 +158,12 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA CIESIN + spatialExtent: Global + temporalResolution: Annual + unit: Ratio + - id: grdi-v1-raster stacCol: grdi-v1-raster name: GRDI v1 raster @@ -154,6 +187,12 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA CIESIN + spatialExtent: Global + temporalResolution: Annual + unit: Ratio + - id: grdi-vnl-raster stacCol: grdi-vnl-raster name: GRDI VNL Constituent raster @@ -177,6 +216,12 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA CIESIN + spatialExtent: Global + temporalResolution: Annual + unit: Ratio + - id: grdi-vnl-slope-raster stacCol: grdi-vnl-slope-raster name: GRDI VNL Slope Constituent raster @@ -200,6 +245,12 @@ layers: - '#21918c' - '#5ec962' - '#fde725' + info: + source: NASA CIESIN + spatialExtent: Global + temporalResolution: Annual + unit: Ratio + --- @@ -302,4 +353,4 @@ layers: - World Bank. (2020). Poverty and Shared Prosperity 2020: Reversals of Fortune - Frequently Asked Questions. World Bank. https://www.worldbank.org/en/research/brief/poverty-and-shared-prosperity-2020-reversals-of-fortune-frequently-asked-questions - \ No newline at end of file + diff --git a/datasets/hls-events.ej.data.mdx b/datasets/hls-events.ej.data.mdx index 806d687fb..fdd6da615 100644 --- a/datasets/hls-events.ej.data.mdx +++ b/datasets/hls-events.ej.data.mdx @@ -19,6 +19,9 @@ taxonomy: - name: Topics values: - Environmental Justice +infoDescription: | + ::markdown + Input data from Landsat 8/9 and Sentinel-2A/B is reprojected and Sentinel-2 data adjusted so that the output data products, HLSL30 (Landsat-derived) and HLSS30 (Sentinel-2-derived) can be used interchangeably. The harmonization of the Optical Land Imager (OLI) on Landsat 8/9 and Multispectral Imager (MSI) on Sentinel-2A/B increases the time series density of plot-scale observations such that data is available every 2-4 days over a given location. layers: - id: hls-l30-002-ej stacCol: hls-l30-002-ej-reprocessed @@ -41,6 +44,12 @@ layers: ::js ({ dateFns, datetime, compareDatetime }) => { return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`; } + info: + source: NASA + spatialExtent: Puerto Rico + temporalResolution: Daily + unit: N/A + - id: hls-s30-002-ej stacCol: hls-s30-002-ej-reprocessed name: HLS Sentinel-2 SWIR @@ -55,6 +64,12 @@ layers: - B12 - B8A - B04 + info: + source: NASA + spatialExtent: Puerto Rico + temporalResolution: Daily + unit: N/A + --- diff --git a/datasets/is2sitmogr4.data.mdx b/datasets/is2sitmogr4.data.mdx index 0cf7cd7ca..6150da084 100644 --- a/datasets/is2sitmogr4.data.mdx +++ b/datasets/is2sitmogr4.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + This data set reports monthly, gridded winter sea ice thickness across the Arctic Ocean. Sea ice thickness is estimated using ATLAS/ICESat-2 L3A Sea Ice Freeboard (ATL10), Version 5 data and NASA Eulerian Snow On Sea Ice Model (NESOSIM) snow loading. layers: - id: IS2SITMOGR4-cog stacCol: IS2SITMOGR4-cog @@ -38,6 +41,11 @@ layers: - '#cc4778' - '#f89540' - '#f89540' + info: + source: NASA + spatialExtent: Polar + temporalResolution: Monthly + unit: Meters --- diff --git a/datasets/lahaina-fire.data.mdx b/datasets/lahaina-fire.data.mdx index 3bcf2f45d..b12527eb9 100644 --- a/datasets/lahaina-fire.data.mdx +++ b/datasets/lahaina-fire.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - UAH +infoDescription: | + ::markdown + On August 8th, 2023, a devastating wildfire rapidly spread through the city of Lahaina, Hawai’i, which is located on the island of Maui and home to over 13,000 residents. This destructive wildfire was initially ignited by a downed powerline on Lahainaluna Road and was later fueled by intense wind gusts that persisted throughout the day. The National Weather Service recorded wind gusts as high as 67 mph in the area, contributing to the rapid spread of the wildfire across much of Lahaina during the afternoon hours of August 8th. layers: - id: hls-bais2-v2 stacCol: hls-bais2-v2 @@ -48,6 +51,11 @@ layers: - "#fee090" - "#f46d43" - "#a50026" + info: + source: NASA + spatialExtent: Hawaii + temporalResolution: Annual + unit: N/A - id: hls-swir-falsecolor-composite stacCol: hls-swir-falsecolor-composite @@ -69,6 +77,11 @@ layers: ::js ({ dateFns, datetime, compareDatetime }) => { return `${dateFns.format(datetime, 'DD LLL yyyy')}`; } + info: + source: NASA + spatialExtent: Hawaii + temporalResolution: Annual + unit: N/A - id: landsat-nighttime-thermal stacCol: landsat-nighttime-thermal @@ -106,7 +119,11 @@ layers: - '#52076c' - '#f57c16' - '#f7cf39' - + info: + source: NASA + spatialExtent: Hawaii + temporalResolution: Annual + unit: N/A --- @@ -204,4 +221,4 @@ Environmental Aspects: When interpreting the data, it is essential to consider t * [The Devastating August 8th, 2023 Lahaina, Hawai'i Wildfire](https://www.earthdata.nasa.gov/dashboard/stories/lahaina-fire) - \ No newline at end of file + diff --git a/datasets/lis-etsuppression.data.mdx b/datasets/lis-etsuppression.data.mdx index 6b8990943..9b252466c 100644 --- a/datasets/lis-etsuppression.data.mdx +++ b/datasets/lis-etsuppression.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + Change in ET for 2020 fires using model outputs from Land Information System (LIS) framework that synthesizes multiple remote sensing observations within the Noah-MP land surface model. Change is calculated as the difference of ET in the immediate post-fire water year from that in the immediate pre-fire water year. The difference is normalized by pre-fire ET and negative values denote vegetation disturbance induced by fire or by a climatological anomaly resulting in the decline in ET. layers: - id: lis-etsuppression stacCol: lis-etsuppression @@ -46,6 +49,12 @@ layers: - "#e0f3f8" - "#74add1" - "#313695" + info: + source: NASA + spatialExtent: Western United States + temporalResolution: Annual + unit: Percentage Diff + - id: mtbs-burn-severity stacCol: mtbs-burn-severity name: MTBS Burn Severity @@ -70,6 +79,11 @@ layers: label: "Moderate" - color: "#971d2b" label: "High" + info: + source: NASA + spatialExtent: Western United States + temporalResolution: Annual + unit: Categorical --- diff --git a/datasets/lis-tvegsuppression.data.mdx b/datasets/lis-tvegsuppression.data.mdx index 3f9b6a388..61d747ade 100644 --- a/datasets/lis-tvegsuppression.data.mdx +++ b/datasets/lis-tvegsuppression.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + Change in vegetation transpiration for 2020 fires using model outputs from Land Information System (LIS) framework that synthesizes multiple remote sensing observations within the Noah-MP land surface model. Change is calculated as the difference of transpiration in the immediate post-fire water year from that in the immediate pre-fire water year. The difference is normalized by pre-fire transpiration and negative values denote vegetation disturbance induced by fire or by a climatological anomaly resulting in the decline in transpiration. layers: - id: lis-tvegsuppression stacCol: lis-tvegsuppression @@ -46,6 +49,12 @@ layers: - "#e0f3f8" - "#74add1" - "#313695" + info: + source: NASA + spatialExtent: Western United States + temporalResolution: Annual + unit: Percent Diff + - id: mtbs-burn-severity stacCol: mtbs-burn-severity name: MTBS Burn Severity @@ -70,6 +79,11 @@ layers: label: "Moderate" - color: "#971d2b" label: "High" + info: + source: NASA + spatialExtent: Western United States + temporalResolution: Annual + unit: Categorical --- diff --git a/datasets/lis.da.trend.data.mdx b/datasets/lis.da.trend.data.mdx index 633d242c6..ac951bb4c 100644 --- a/datasets/lis.da.trend.data.mdx +++ b/datasets/lis.da.trend.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + Realistic estimates of water and energy cycle variables are necessary for accurate understanding of earth system processes. We develop a 10 km global reanalysis product of water, energy, and carbon fluxes by assimilating satellite observed surface soil moisture, leaf area index, and terrestrial water storage anomalies into a land surface model within NASA Land Information System Framework. We applied a seasonal and trend decomposition algorithm to get the trend estimates for terrestrial water storage and gross primary production. The method can better help to deal with [nonstationarities](https://github.com/Earth-Information-System/sea-level-and-coastal-risk/blob/main/AMS_2023_Wanshu_Nie_for_VEDA_Discovery.pdf) and seasonal shifts and provide a more robust estimate of trends. layers: - id: lis-global-da-tws-trend stacCol: lis-global-da-tws-trend @@ -49,6 +52,12 @@ layers: - "#e0f3f8" - "#74add1" - "#313695" + info: + source: NASA + spatialExtent: Global + temporalResolution: Annual + unit: mm/yr + - id: lis-global-da-gpp-trend stacCol: lis-global-da-gpp-trend name: 'LIS DA GPP Trend' @@ -78,6 +87,12 @@ layers: - "#e0f3f8" - "#74add1" - "#313695" + info: + source: NASA + spatialExtent: Global + temporalResolution: Annual + unit: mm/yr + --- diff --git a/datasets/mo_npp_vgpm.data.mdx b/datasets/mo_npp_vgpm.data.mdx index c50d1a4d4..74f2cc720 100644 --- a/datasets/mo_npp_vgpm.data.mdx +++ b/datasets/mo_npp_vgpm.data.mdx @@ -19,6 +19,9 @@ taxonomy: - name: Topics values: - Water Quality +infoDescription: | + ::markdown + Find information at the [Ocean Productivity website](https://sites.science.oregonstate.edu/ocean.productivity/index.php) layers: - id: MO_NPP_npp_vgpm stacCol: MO_NPP_npp_vgpm @@ -44,6 +47,11 @@ layers: - "#ffff00" - "#fa0000" - "#800000" + info: + source: Oregon State University + spatialExtent: Global + temporalResolution: Monthly + unit: Mg C/m²/day --- diff --git a/datasets/modis-aerosol-dataset.data.mdx b/datasets/modis-aerosol-dataset.data.mdx index 008098964..366aaa002 100644 --- a/datasets/modis-aerosol-dataset.data.mdx +++ b/datasets/modis-aerosol-dataset.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - Air Quality +infoDescription: | + ::markdown + The MCD19A2 product represents a dataset that offers insights into aerosol optical thickness over land surfaces, grounded in the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. Originating from both the Terra and Aqua MODIS satellites, this dataset is remarkable for its fusion of information from multiple satellite platforms. Generated daily, the data has a high spatial resolution of 1 km per pixel, allowing detailed observiations. layers: - id: houston-aod stacCol: houston-aod @@ -43,6 +46,11 @@ layers: compare: datasetId: houston-aod layerId: houston-aod + info: + source: NASA + spatialExtent: Houston, Texas + temporalResolution: Annual + unit: Unitless --- diff --git a/datasets/mtbs-burn-severity.data.mdx b/datasets/mtbs-burn-severity.data.mdx index 58b95bf62..69dd05639 100644 --- a/datasets/mtbs-burn-severity.data.mdx +++ b/datasets/mtbs-burn-severity.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + MTBS is an interagency program whose goal is to consistently map the burn severity and extent of large fires across all lands of the United States from 1984 to present. This includes all fires 1000 acres or greater in the western United States and 500 acres or greater in the eastern Unites States. The extent of coverage includes the continental U.S., Alaska, Hawaii and Puerto Rico. layers: - id: mtbs-burn-severity stacCol: mtbs-burn-severity @@ -44,7 +47,11 @@ layers: label: "Moderate" - color: "#971d2b" label: "High" - + info: + source: Interagency + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Categorical --- diff --git a/datasets/nceo_africa_2017.data.mdx b/datasets/nceo_africa_2017.data.mdx index aaa06cf9f..b6fa95599 100644 --- a/datasets/nceo_africa_2017.data.mdx +++ b/datasets/nceo_africa_2017.data.mdx @@ -19,6 +19,9 @@ taxonomy: - name: Topics values: - Biomass +infoDescription: | + ::markdown + The NCEO Africa Aboveground Woody Biomass (AGB) map for the year 2017 at 100 m spatial resolution was developed using a combination of LiDAR, Synthetic Aperture Radar (SAR) and optical based data. This product was developed by the UK’s National Centre for Earth Observation (NCEO) through the Carbon Cycle and Official Development Assistance (ODA) programmes. For more information see [CEOS biomass](https://ceos.org/gst/biomass.html). layers: - id: nceo_africa_2017 stacCol: nceo_africa_2017 @@ -48,6 +51,11 @@ layers: - '#1f567b' - '#080e74' - '#000000' + info: + source: UK + spatialExtent: Africa + temporalResolution: Annual + unit: N/A --- diff --git a/datasets/nighttime-lights.data.mdx b/datasets/nighttime-lights.data.mdx index b820b1dec..24b9aaebf 100644 --- a/datasets/nighttime-lights.data.mdx +++ b/datasets/nighttime-lights.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - Covid 19 +infoDescription: | + ::markdown + Nightlights data are collected by the [Visible Infrared Radiometer Suite (VIIRS) Day/Night Band (DNB)](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/viirs/) on the Suomi-National Polar-Orbiting Partnership (Suomi-NPP) platform, a joint National Oceanic and Atmospheric Administration (NOAA) and NASA satellite. The images are produced by [NASA’s Black Marble](https://blackmarble.gsfc.nasa.gov/) products suite. All data are calibrated daily, corrected, and validated with ground measurements for science-ready analysis. layers: - id: nightlights-hd-monthly stacCol: nightlights-hd-monthly @@ -44,6 +47,11 @@ layers: - '#52076c' - '#f57c16' - '#f7cf39' + info: + source: NOAA & NASA + spatialExtent: Global + temporalResolution: Monthly + unit: N/A --- @@ -121,4 +129,4 @@ Black Marble data courtesy of [Universities Space Research Association (USRA) Ea * [NASA’s Black Marble](https://blackmarble.gsfc.nasa.gov/) * [Suomi National Polar-orbiting Partnership (Suomi NPP)](https://www.nasa.gov/mission_pages/NPP/main/index.html) - \ No newline at end of file + diff --git a/datasets/nighttime-lights.ej.data.mdx b/datasets/nighttime-lights.ej.data.mdx index 03aa4ad4f..df0e19464 100644 --- a/datasets/nighttime-lights.ej.data.mdx +++ b/datasets/nighttime-lights.ej.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - Environmental Justice +infoDescription: | + ::markdown + Nightlights data are collected by the [Visible Infrared Radiometer Suite (VIIRS) Day/Night Band (DNB)](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/viirs/) on the Suomi-National Polar-Orbiting Partnership (Suomi-NPP) platform, a joint National Oceanic and Atmospheric Administration (NOAA) and NASA satellite. The images are produced by [NASA’s Black Marble](https://blackmarble.gsfc.nasa.gov/) products suite. All data are calibrated daily, corrected, and validated with ground measurements for science-ready analysis. layers: - id: nightlights-hd-1band stacCol: nightlights-hd-1band @@ -30,6 +33,11 @@ layers: compare: datasetId: nighttime-lights-ej layerId: nightlights-hd-1band + info: + source: NOAA & NASA + spatialExtent: Puerto Rico + temporalResolution: Monthly + unit: N/A --- diff --git a/datasets/nlcd-urbanization.data.mdx b/datasets/nlcd-urbanization.data.mdx index d2ea062ca..7709431e8 100644 --- a/datasets/nlcd-urbanization.data.mdx +++ b/datasets/nlcd-urbanization.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + The National Land Cover Database (NLCD) stands as a paramount dataset offering an in-depth overview of the land cover characteristics in the United States. Spearheaded by the Earth Resources Observation and Science (EROS) Center, this database is renewed every two to three years to provide updated and accurate data for the nation. layers: - id: houston-urbanization stacCol: houston-urbanization @@ -35,7 +38,11 @@ layers: label: No Data - color: "#d73027" label: Urbanization - + info: + source: EROS + spatialExtent: Houston + temporalResolution: Annual + unit: Binary --- diff --git a/datasets/no2.data.mdx b/datasets/no2.data.mdx index 732fb4b4e..a77167464 100644 --- a/datasets/no2.data.mdx +++ b/datasets/no2.data.mdx @@ -21,6 +21,9 @@ taxonomy: values: - Air Quality - Covid 19 +infoDescription: | + ::markdown + OMI, which launched in 2004, preceded TROPOMI, which launched in 2017. While TROPOMI provides higher resolution information, the longer OMI data record provides context for the TROPOMI observations. layers: - id: no2-monthly stacCol: no2-monthly @@ -55,6 +58,12 @@ layers: - "#E4EEF3" - "#FDDCC9" - "#DD7059" + info: + source: NASA + spatialExtent: Global + temporalResolution: Monthly + unit: N/A + - id: no2-monthly-diff stacCol: no2-monthly-diff name: Nitrogen Dioxide (monthly difference) @@ -87,6 +96,12 @@ layers: - "#E4EEF3" - "#FDDCC9" - "#DD7059" + info: + source: NASA + spatialExtent: Global + temporalResolution: Monthly + unit: N/A + - id: OMI_trno2-COG stacCol: OMI_trno2-COG name: Nitrogen Dioxide Total and Tropospheric Column (NASA OMI/Aura) @@ -111,6 +126,12 @@ layers: - '#fdd1bf' - '#e02d26' - '#67000c' + info: + source: NASA + spatialExtent: Global + temporalResolution: Monthly + unit: N/A + --- diff --git a/datasets/ps_blue_tarp_detections.ej.data.mdx b/datasets/ps_blue_tarp_detections.ej.data.mdx index 15bf42b05..5e78ae97a 100644 --- a/datasets/ps_blue_tarp_detections.ej.data.mdx +++ b/datasets/ps_blue_tarp_detections.ej.data.mdx @@ -12,6 +12,15 @@ taxonomy: - name: Topics values: - Environmental Justice +infoDescription: | + ::markdown + Planetscope provides 3-band RGB imagery at 3-meter ground resolution which + can support building-scale analysis of the land surface. In the aftermath of + natural disasters associated with high wind speeds, homes with damaged roofs + typically are covered with blue tarps to protect the interior of the home + from further damage. Using machine learning, blue tarps can be detected from + the Planetscope imagery using pre-event cloud free images to detect blue + pixels and potential impacts after a natural disaster. layers: - id: blue-tarp-detection stacCol: blue-tarp-detection @@ -28,6 +37,12 @@ layers: rescale: - 0 - 400 + info: + source: NASA + spatialExtent: Puerto Rico + temporalResolution: Sub-Annual + unit: N/A + - id: blue-tarp-planetscope stacCol: blue-tarp-planetscope name: Planetscope input RGB imagery used for blue tarp detection @@ -42,6 +57,12 @@ layers: ::js ({ dateFns, datetime, compareDatetime }) => { if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`; } + info: + source: NASA + spatialExtent: Puerto Rico + temporalResolution: Sub-Annual + unit: N/A + --- diff --git a/datasets/snow-projections-diff.data.mdx b/datasets/snow-projections-diff.data.mdx index ff86c62b5..c95d895e7 100644 --- a/datasets/snow-projections-diff.data.mdx +++ b/datasets/snow-projections-diff.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + Snow water equivalent (SWE) is defined as the amount of water in the snow. Here, we present the projected percent-change to projected snow in future periods, relative to the historical period (1995 - 2014). layers: - id: snow-projections-diff-scenario-245 stacCol: snow-projections-diff-245 @@ -45,6 +48,12 @@ layers: - "#D1E5F0" - "#4393C3" - "#0D2F60" + info: + source: NASA + spatialExtent: Western United States + temporalResolution: Annual + unit: Percent Diff + - id: snow-projections-diff-scenario-585 stacCol: snow-projections-diff-585 name: 'SWE Losses, SSP5-8.5' @@ -77,6 +86,12 @@ layers: - "#D1E5F0" - "#4393C3" - "#0D2F60" + info: + source: NASA + spatialExtent: Western United States + temporalResolution: Annual + unit: Percent Diff + --- diff --git a/datasets/snow-projections-median.data.mdx b/datasets/snow-projections-median.data.mdx index 7a32de071..43c923e91 100644 --- a/datasets/snow-projections-median.data.mdx +++ b/datasets/snow-projections-median.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + Snow water equivalent (SWE) is defined as the amount of water in the snow. It is expressed as a height (in millimeters), representative of the height of water that would exist if snow was only in a liquid state. layers: - id: snow-projections-median-scenario-245 stacCol: snow-projections-median-245 @@ -46,6 +49,12 @@ layers: - "#4A98C9" - "#2164AB" - "#0E316B" + info: + source: NASA + spatialExtent: Western United States + temporalResolution: Annual + unit: mm + - id: snow-projections-median-scenario-585 stacCol: snow-projections-median-585 name: 'SWE, SSP5-8.5' @@ -79,6 +88,12 @@ layers: - "#4A98C9" - "#2164AB" - "#0E316B" + info: + source: NASA + spatialExtent: Western United States + temporalResolution: Annual + unit: mm + --- diff --git a/datasets/so2.data.mdx b/datasets/so2.data.mdx index 51d40103b..f1b82c179 100644 --- a/datasets/so2.data.mdx +++ b/datasets/so2.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - Air Quality +infoDescription: | + ::markdown + The OMI Sulfur Dioxide (SO2) Total Column layer indicates the column density of sulfur dioxide and is measured in Dobson Units (DU). Sulfur Dioxide and Aerosol Index products are used to monitor volcanic clouds and detect pre-eruptive volcanic degassing globally. This information is used by the Volcanic Ash Advisory Centers in advisories to airlines for operational decision layers: - id: OMSO2PCA-COG stacCol: OMSO2PCA-COG @@ -47,6 +50,11 @@ layers: - "#fee090" - "#f46d43" - "#a50026" + info: + source: NASA + spatialExtent: Global + temporalResolution: Annual + unit: N/A ---
diff --git a/datasets/sport-lis.data.mdx b/datasets/sport-lis.data.mdx index 9bf949d9a..1e123c041 100644 --- a/datasets/sport-lis.data.mdx +++ b/datasets/sport-lis.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + The NASA Land Information System (LIS) is a high-performance land surface modeling and data assimilation system used to characterize land surface states and fluxes by integrating satellite-derived datasets, ground-based observations, and model re-analyses. The NASA SPoRT Center at MSFC developed a real-time configuration of the LIS (“SPoRT-LIS”), which is designed for use in experimental operations by domestic and international users. SPoRT-LIS is an observations-driven, historical and real-time modeling setup that runs the Noah land surface model over a full CONUS domain. It provides soil moisture estimates at approximately 3-km horizontal grid spacing over a 2-meter-deep soil column and has been validated for regional applications. layers: - sourceParams: resampling: bilinear @@ -47,6 +50,11 @@ layers: ::js ({ dateFns, datetime, compareDatetime }) => { if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'MMM yyyy')} VS ${dateFns.format(compareDatetime, 'MMM yyyy')}`; } + info: + source: NASA + spatialExtent: Contiguous United States + temporalResolution: Sub-Annual + unit: cm --- diff --git a/datasets/svi_household.ej.data.mdx b/datasets/svi_household.ej.data.mdx index 0fe82da17..72220faf4 100644 --- a/datasets/svi_household.ej.data.mdx +++ b/datasets/svi_household.ej.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - ATSDR +infoDescription: | + ::markdown + The Household Composition & Disability Score (HCDS) is one of the four themes used in determining a community’s social vulnerability. This dataset can be used to create a community evacuation plan accounting for individuals who have special needs, the elderly, and/or families with young children. In the event of a disaster, this data can also help responders determine the number of emergency personnel required for special household cases (accessibility assistance), the type of supplies needed based on age, and the amount of supplies, food, and other restorative resources needed¹. The HCDS SVI Grid is part of the U.S. Census Grids collection, and displays the Center for Disease Control & Prevention (CDC) SVI score. Funding for the final development, processing and dissemination of this data set by the Socioeconomic Data and Applications Center (SEDAC)was provided under the U.S. National Aeronautics and Space Administration (NASA)². layers: - id: social-vulnerability-index-household stacCol: social-vulnerability-index-household @@ -49,6 +52,12 @@ layers: - "#f3701b" - "#c54102" - "#7f2704" + info: + source: NASA + spatialExtent: United States + temporalResolution: Annual + unit: Percentile Ranking + - id: social-vulnerability-index-household-nopop stacCol: social-vulnerability-index-household-nopop name: Household and Disability Score (No Pop) @@ -82,6 +91,12 @@ layers: - "#f3701b" - "#c54102" - "#7f2704" + info: + source: NASA + spatialExtent: United States + temporalResolution: Annual + unit: Percentile Ranking + --- diff --git a/datasets/svi_housing.ej.data.mdx b/datasets/svi_housing.ej.data.mdx index 8ae33bb5b..f2e9fdb7e 100644 --- a/datasets/svi_housing.ej.data.mdx +++ b/datasets/svi_housing.ej.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - ATSDR +infoDescription: | + ::markdown + The Housing Type & Transportation Score (HTTS) is one of the four themes used in determining a community’s social vulnerability, examining it against housing structure/type and vehicle access. As with the other SVI thematic areas, in the event of a disaster, or to better prepare for one, this dataset can help emergency personnel create an evacuation plan for individuals without vehicles, allocate emergency preparedness funding by community need, and identify areas in need of emergency shelters¹. It can also be used for local governments to identify areas needing more robust public transportation, areas of overcrowding, and local housing vulnerability. The HTTS SVI Grid is part of the U.S. Census Grids collection, and displays the Center for Disease Control & Prevention (CDC) SVI score. Funding for the final development, processing and dissemination of this data set by the Socioeconomic Data and Applications Center (SEDAC)was provided under the U.S. National Aeronautics and Space Administration (NASA)². layers: - id: social-vulnerability-index-housing stacCol: social-vulnerability-index-housing @@ -53,6 +56,12 @@ layers: - "#4a98c9" - "#1764ab" - "#08306b" + info: + source: NASA + spatialExtent: United States + temporalResolution: Annual + unit: Percentile Ranking + - id: social-vulnerability-index-housing-nopop stacCol: social-vulnerability-index-housing-nopop name: Housing Type and Transportation Score - Masked for No Population @@ -86,6 +95,12 @@ layers: - "#4a98c9" - "#1764ab" - "#08306b" + info: + source: NASA + spatialExtent: United States + temporalResolution: Annual + unit: Percentile Ranking + --- diff --git a/datasets/svi_minority.ej.data.mdx b/datasets/svi_minority.ej.data.mdx index 5ab3dd35a..b1477f83d 100644 --- a/datasets/svi_minority.ej.data.mdx +++ b/datasets/svi_minority.ej.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - ATSDR +infoDescription: | + ::markdown + The Minority Status & Language Score (MSLS), as with the other SVI thematic areas, is used to calculate a community’s social vulnerability. This data set can be used to prepare emergency plans for communities with lower English-proficiency levels¹, and has helped contribute to efforts such as the Minority Health SVI and its related Dashboard. The Minority Health SVI is an extension of the CDC/ATSDR Social Vulnerability Index (CDC/ATSDR SVI), which is a database that helps emergency response planners and public health officials identify, map, and plan support for communities that will most likely need support before, during, and after a public health emergency². The MSLS SVI Grid is part of the U.S. Census Grids collection, and displays the Center for Disease Control & Prevention (CDC) SVI score. Funding for the final development, processing and dissemination of this data set by the Socioeconomic Data and Applications Center (SEDAC) was provided under the U.S. National Aeronautics and Space Administration (NASA)³. layers: - id: social-vulnerability-index-minority stacCol: social-vulnerability-index-minority @@ -49,6 +52,12 @@ layers: - "#8683bd" - "#61409b" - "#3f007d" + info: + source: NASA + spatialExtent: United States + temporalResolution: Annual + unit: Percentile Ranking + - id: social-vulnerability-index-minority-nopop stacCol: social-vulnerability-index-minority-nopop name: Minority Status and Language Score - Masked for No Population @@ -82,6 +91,12 @@ layers: - "#8683bd" - "#61409b" - "#3f007d" + info: + source: NASA + spatialExtent: United States + temporalResolution: Annual + unit: Percentile Ranking + --- diff --git a/datasets/svi_overall.ej.data.mdx b/datasets/svi_overall.ej.data.mdx index e7fd873f9..3322f42d3 100644 --- a/datasets/svi_overall.ej.data.mdx +++ b/datasets/svi_overall.ej.data.mdx @@ -22,6 +22,9 @@ taxonomy: - name: Source values: - ATSDR +infoDescription: | + ::markdown + The SVI Overall Score provides the overall, summed social vulnerability score for a given tract. The Overall Score SVI Grid is part of the U.S. Census Grids collection, and displays the Center for Disease Control & Prevention (CDC) SVI score. Funding for the final development, processing and dissemination of this data set by the Socioeconomic Data and Applications Center (SEDAC) was provided under the U.S. National Aeronautics and Space Administration (NASA)¹. layers: - id: social-vulnerability-index-overall stacCol: social-vulnerability-index-overall @@ -56,6 +59,12 @@ layers: - "#2498c1" - "#234da0" - "#081d58" + info: + source: NASA + spatialExtent: United States + temporalResolution: Annual + unit: Percentile Ranking + - id: social-vulnerability-index-overall-nopop stacCol: social-vulnerability-index-overall-nopop name: Overall (NoPop) Social Vulnerability - Percentile Ranking @@ -89,6 +98,12 @@ layers: - "#2498c1" - "#234da0" - "#081d58" + info: + source: NASA + spatialExtent: United States + temporalResolution: Annual + unit: Percentile Ranking + --- diff --git a/datasets/svi_socioeconomic.ej.data.mdx b/datasets/svi_socioeconomic.ej.data.mdx index 0e429a907..2903c2be8 100644 --- a/datasets/svi_socioeconomic.ej.data.mdx +++ b/datasets/svi_socioeconomic.ej.data.mdx @@ -15,6 +15,9 @@ taxonomy: - name: Source values: - ATSDR +infoDescription: | + ::markdown + The Economic Status Score, like the three other themes, is used in observing a community’s social vulnerability. As with other SVI scores, the economic status score can help local officials and teams identify communities that will need continued support to recover following an emergency or natural disaster¹. The Economic Status SVI Grid is part of the U.S. Census Grids collection, and displays the Center for Disease Control & Prevention (CDC) SVI score. Funding for the final development, processing and dissemination of this data set by the Socioeconomic Data and Applications Center (SEDAC) was provided under the U.S. National Aeronautics and Space Administration (NASA)². layers: - id: social-vulnerability-index-socioeconomic stacCol: social-vulnerability-index-socioeconomic @@ -49,6 +52,12 @@ layers: - "#4bb062" - "#157f3b" - "#00441b" + info: + source: NASA + spatialExtent: United States + temporalResolution: Annual + unit: Percentile Ranking + - id: social-vulnerability-index-socioeconomic-nopop stacCol: social-vulnerability-index-socioeconomic-nopop name: Socioeconomic (No Pop) Vulnerability Score @@ -82,6 +91,12 @@ layers: - "#4bb062" - "#157f3b" - "#00441b" + info: + source: NASA + spatialExtent: United States + temporalResolution: Annual + unit: Percentile Ranking + --- diff --git a/datasets/twsanomaly.data.mdx b/datasets/twsanomaly.data.mdx index 5d0ead4f1..51bfc86f9 100644 --- a/datasets/twsanomaly.data.mdx +++ b/datasets/twsanomaly.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + Terrestrial water storage (TWS) is defined as the summation of all water on the land surface and in the subsurface. It includes surface soil moisture, root zone soil moisture, groundwater, snow, ice, water stored in the vegetation, river and lake water. layers: - id: lis-tws-anomaly stacCol: lis-tws-anomaly @@ -47,6 +50,12 @@ layers: - "#e0f3f8" - "#74add1" - "#313695" + info: + source: NASA + spatialExtent: Global + temporalResolution: Daily + unit: N/A + --- diff --git a/datasets/twsnonstationarity.data.mdx b/datasets/twsnonstationarity.data.mdx index c6f8e466f..d9286260e 100644 --- a/datasets/twsnonstationarity.data.mdx +++ b/datasets/twsnonstationarity.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + Terrestrial water storage (TWS) is defined as the summation of all water on the land surface and in the subsurface. It includes surface soil moisture, root zone soil moisture, groundwater, snow, ice, water stored in the vegetation, river and lake water. layers: - id: lis-tws-nonstationarity-index stacCol: lis-tws-nonstationarity-index @@ -40,6 +43,11 @@ layers: - "#e0f3f8" - "#74add1" - "#313695" + info: + source: NASA + spatialExtent: Global + temporalResolution: Annual + unit: N/A --- diff --git a/datasets/twstrend.data.mdx b/datasets/twstrend.data.mdx index 8b93c0c09..1f91d2eb8 100644 --- a/datasets/twstrend.data.mdx +++ b/datasets/twstrend.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - EIS +infoDescription: | + ::markdown + Terrestrial water storage (TWS) is defined as the summation of all water on the land surface and in the subsurface. It includes surface soil moisture, root zone soil moisture, groundwater, snow, ice, water stored in the vegetation, river and lake water. layers: - id: lis-tws-trend stacCol: lis-tws-trend @@ -40,6 +43,11 @@ layers: - "#e0f3f8" - "#74add1" - "#313695" + info: + source: NASA + spatialExtent: Global + temporalResolution: Annual + unit: N/A --- diff --git a/datasets/urban-heating.data.mdx b/datasets/urban-heating.data.mdx index ad0f99b21..d773d73c4 100644 --- a/datasets/urban-heating.data.mdx +++ b/datasets/urban-heating.data.mdx @@ -8,6 +8,9 @@ media: author: name: Arto Marttinen url: https://unsplash.com/photos/6xh7H5tWj9c +infoDescription: | + ::markdown + Terra MODIS has been instrumental in capturing LST data. This platform, orbiting Earth, scans our planet in multiple spectral bands, allowing for a detailed analysis of LST values. The data periods 2000-20009 and 2010-2019 form this satellite have been particularly enlightening, revealing distinct shifts in Houston’s urban heat profile. layers: - sourceParams: resampling: bilinear @@ -42,6 +45,12 @@ layers: ::js ({ dateFns, datetime, compareDatetime }) => { if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; } + info: + source: NASA + spatialExtent: Houston + temporalResolution: Annual + unit: N/A + - sourceParams: resampling: bilinear bidx: 1 @@ -75,6 +84,12 @@ layers: ::js ({ dateFns, datetime, compareDatetime }) => { if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; } + info: + source: NASA + spatialExtent: Houston + temporalResolution: Annual + unit: N/A + - sourceParams: resampling: bilinear bidx: 1 @@ -108,6 +123,12 @@ layers: ::js ({ dateFns, datetime, compareDatetime }) => { if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; } + info: + source: NASA + spatialExtent: Houston + temporalResolution: Annual + unit: N/A + - sourceParams: resampling: bilinear bidx: 1 @@ -181,6 +202,12 @@ layers: ::js ({ dateFns, datetime, compareDatetime }) => { if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; } + info: + source: NASA + spatialExtent: Houston + temporalResolution: Annual + unit: Categorical + - sourceParams: resampling: bilinear bidx: 1 @@ -203,6 +230,12 @@ layers: ::js ({ dateFns, datetime, compareDatetime }) => { if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; } + info: + source: NASA + spatialExtent: Houston + temporalResolution: Annual + unit: N/A + --- @@ -236,4 +269,4 @@ layers: Houston’s LST data, meticulously captured by Terra MODIS, serves as a crucial pointer for urban planners, environmentalists, and policymakers. By understanding the nexus of urban heat, infrastructure, and socio-economics, we can shape urban features that are not only sustainable but also equitable. As Houston continues its urban journey, armed with this data, it has the potential to redefine urban resilience in the face of escalating heat challenges. - \ No newline at end of file +