From 3b2bf666dd9875b034b2368f013c147e82e84485 Mon Sep 17 00:00:00 2001 From: tamaramegan Date: Tue, 7 Sep 2021 11:20:40 +0200 Subject: [PATCH] updated 4 files staging to prod --- ...prod_staging_match_13f4e065-b579-41f0-938d-b97c9dd54ce2.json | 2 +- ...prod_staging_match_7c06a7a2-4c47-4ecb-a2a5-1c76eaf2db65.json | 2 +- ...prod_staging_match_b2f00f99-46ed-43e6-a7a1-a5809d9369d4.json | 2 +- ...prod_staging_match_e488781e-2b7a-4e49-ab5d-682f646363f1.json | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/ResourceWatch/dataset_sync_files/RW_prod_staging_match_13f4e065-b579-41f0-938d-b97c9dd54ce2.json b/ResourceWatch/dataset_sync_files/RW_prod_staging_match_13f4e065-b579-41f0-938d-b97c9dd54ce2.json index 112eecf..2e332b1 100644 --- a/ResourceWatch/dataset_sync_files/RW_prod_staging_match_13f4e065-b579-41f0-938d-b97c9dd54ce2.json +++ b/ResourceWatch/dataset_sync_files/RW_prod_staging_match_13f4e065-b579-41f0-938d-b97c9dd54ce2.json @@ -1 +1 @@ -[{"type": "dataset", "prodId": "13f4e065-b579-41f0-938d-b97c9dd54ce2", "stagingId": "e16d6b2d-7084-4891-878c-ae47fc49da52"}, {"type": "vocabulary", "prodId": null, "stagingId": [{"id": "knowledge_graph", "type": "vocabulary", "attributes": {"tags": ["geospatial", "global", "raster", "historical", "soil", "erosion", "water", "flood", "agriculture"], "name": "knowledge_graph", "application": "rw"}}]}, {"type": "layer", "prodId": "555d1d25-3112-430f-9fd5-5b00aa94b613", "stagingId": "6ab77e36-df9d-4047-8641-ca49922522b5"}, {"type": "layer", "prodId": "503f1001-e69c-4111-8ef6-5d34fa451e94", "stagingId": "6a7c59b9-2e79-42f4-9caf-6b1bf068ae09"}, {"type": "layer", "prodId": "065e6200-1670-4832-b0ae-70b7852a9875", "stagingId": "f37d8246-ddb6-4dbb-a9db-715fa889a88b"}, {"type": "layer", "prodId": "e6eafefd-bb28-429e-9fff-1d6205f5d5b2", "stagingId": "70b7071b-2c3a-425e-9ba2-51cab97fc681"}, {"type": "layer", "prodId": "5c79ccac-2c31-4399-9a54-ae964feb7419", "stagingId": "5ad82c6b-b5a4-47c5-8417-e768eff56790"}, {"type": "widget", "prodId": "fd292bc0-be35-4fc9-8dc1-8485ea0360b8", "stagingId": "6e26a922-3c1b-40cf-8fe2-8059b25e7261"}, {"type": "widget", "prodId": "a72e4432-dd8f-43b1-a33c-b0d61432d6f1", "stagingId": "7f18260c-aada-4dd3-baf1-a51af52947db"}, {"type": "metadata", "prodId": "60eca114b66636001aa44c11", "stagingId": [{"id": "611147d2cd40d0001afca550", "type": "metadata", "attributes": {"dataset": "e16d6b2d-7084-4891-878c-ae47fc49da52", "application": "rw", "resource": {"id": "7f18260c-aada-4dd3-baf1-a51af52947db", "type": "widget"}, "language": "en", "info": {"caption": "The Soil Erosion Prevalence dataset, produced by the World Agroforestry Centre, provides predictions for the percentage of area with soil erosion across the global tropics (between the parallels of 40\u00b0 south and 40\u00b0 north). The authors predicted soil erosion for the years 2002, 2007, 2012, 2017, and 2020 from satellite imagery at 500 m resolution using machine learning algorithms trained with field observations. ", "widgetLinks": [{"link": "https://www.mdpi.com/2072-4292/11/15/1800", "name": "Learn more"}]}, "createdAt": "2021-08-09T15:20:50.370Z", "updatedAt": "2021-08-09T15:52:27.938Z", "status": "published"}}]}, {"type": "metadata", "prodId": "60d64348173c43001ad6b8d1", "stagingId": [{"id": "60d645b3cb6c5c001a6546c3", "type": "metadata", "attributes": {"dataset": "e16d6b2d-7084-4891-878c-ae47fc49da52", "application": "rw", "resource": {"id": "e16d6b2d-7084-4891-878c-ae47fc49da52", "type": "dataset"}, "language": "en", "name": "Soil Erosion", "description": "### Overview \n \nThe Soil Erosion Prevalence dataset, produced by the World Agroforestry Centre (ICRAF), provides predictions for the percentage of area with soil erosion across the global tropics (between the parallels of 40\u00b0 south and 40\u00b0 north). The authors predicted soil erosion for the years 2002, 2007, 2012, 2017, and 2020 from satellite imagery at 500 m resolution using machine learning algorithms trained with field observations. \n\n \n \nSoil is eroding more quickly than its being formed, contributing to the degradation of millions of hectares of land globally. The nutrient rich layer of soil on the surface, called topsoil, is vulnerable to wind and water erosion and its loss is accelerated by modifications in land use. Erosion of this critical layer of soil comes at a [major economic and environmental cost](https://www.wri.org/insights/causes-and-effects-soil-erosion-and-how-prevent-it). It causes [billions](https://www.sciencedirect.com/science/article/pii/S0264837718319343) of dollars of losses due to decreased soil fertility, reduced crop yields, and increased water usage. The eroded soil can be carried into rivers and streams. This creates a heavy layer of sediment which is carried downstream. This process clogs waterways, prevents smooth water flow, and may eventually lead to flooding. Other environmental costs include loss of productivity and biodiversity, decreased resilience of marine and terrestrial ecosystems, and increased vulnerability to climate change and food insecurity. \n\n \n \nEstimates of both spatial and temporal dynamics of soil erosion are needed to better track the occurrence and severity of erosion in landscapes over time. The main objectives of this dataset are to provide rapid assessments of soil erosion for spatially distributed monitoring as well as assess changes in soil erosion prevalence over time. The spatial assessments of erosion provide estimates of land degradation hotspots and can be combined with other indicators of ecosystem health, including social factors, to better assess and identify drivers of land degradation and target land management interventions to reverse degradation.\n \n \n### Methodology \n \nThe authors used a combination of systematically collected field observations of erosion and remote sensing to predict the spatial and temporal distribution of soil erosion prevalence at moderate spatial resolution (500 m) for the years 2002, 2007, 2012, 2017, and 2020. The model was trained and tested using field data collected via [the Land Degradation Surveillance Framework](http://landscapeportal.org/blog/2015/03/25/the-land-degradation-surveillance-framework-ldsf) (LDSF). As part of the LDSF, sample sites are divided into plots and subplots. Erosion prevalence was scored at the plot level by summing up the number of subplots with visible signs of erosion, with 0 being no observed erosion and 4 being erosion observed in all four subplots. The authors took a cut-off at three subplots or more (>50%) to represent \u201csevere\u201d soil erosion. Erosion prevalence was modeled using a decision-tree approach known as [Random Forests](https://www.jstatsoft.org/article/view/v077i01/v77i01.pdf) to generate a classification model for soil erosion prevalence using imagery from the [Moderate Resolution Imaging Spectroradiomer](https://modis.gsfc.nasa.gov/data/) (MODIS) platform. Annual composites were matched to the years of LDSF field data collection and fitted to annual composite reflectance data for 2002, 2007, 2012, and 2017. Soil erosion prevalence is displayed on the maps as the percentage of area (of a pixel) predicted to be eroded. Model accuracy for the detection of erosion was about 86% and accuracy for non-detection was about 91%.\n\n \n \nFor the full documentation, please see the source [methodology](https://www.mdpi.com/2072-4292/11/15/1800).\n \n \n### Additional Information \n \nFor access to additional information, click on the \u201cLearn more\u201d button. \n \n### Visualizing the Data \n \nOur team reformatted this dataset before displaying it on Resource Watch. See the documentation on how Resource Watch retrieved the data on [Github](https://github.com/resource-watch/data-pre-processing/tree/master/wat_070_rw0_soil_erosion). \n \n### Disclaimer \n \nExcerpts of this description page were taken from the source metadata. Resource Watch shows only a subset of the dataset. For access to the full dataset and additional information, click on the \u201cLearn more\u201d button.", "source": "ICRAF", "info": {"rwId": "wat.070.rw0", "data_type": "Raster", "name": "Soil Erosion", "sources": [{"source-name": "", "id": 0, "source-description": "World Agroforestry Centre (ICRAF)\n"}], "technical_title": "Predicted Soil Erosion Prevalence", "functions": "Predicted percentage of eroded area for the years 2002, 2007, 2012, 2017, and 2020", "cautions": "- There is no calibration/validation data for the US, Europe and Australia, although these regions are included in the maps.\n \n \n- Hyperarid areas have been masked out (excluded) based on TRMM rainfall data.\n \n \n- The drivers of soil erosion, which include both social and ecological factors can be challenging to detect remotely.\n", "citation": "V\u00e5gen T-G, Winowiecki LA. Predicting the Spatial Distribution and Severity of Soil Erosion in the Global Tropics using Satellite Remote Sensing. Remote Sensing. 2019; 11(15):1800. https://doi.org/10.3390/rs11151800. Accessed through Resource Watch, (date). [www.resourcewatch.org](https://www.resourcewatch.org).", "license": "Restrictions Apply", "license_link": null, "geographic_coverage": "40\u00b0S\u2013 40\u00b0N", "spatial_resolution": "500 m", "date_of_content": "2002, 2007, 2012, 2017, 2020", "frequency_of_updates": "Unknown", "learn_more_link": "https://www.mdpi.com/2072-4292/11/15/1800", "data_download_link": "http://wri-public-data.s3.amazonaws.com/resourcewatch/raster/wat_070_rw0_soil_erosion.zip", "data_download_original_link": "https://www.mdpi.com/2072-4292/11/15/1800"}, "createdAt": "2021-06-25T21:08:03.264Z", "updatedAt": "2021-08-09T15:52:29.537Z", "status": "published"}}]}] \ No newline at end of file +[{"prodId": "13f4e065-b579-41f0-938d-b97c9dd54ce2", "stagingId": "e16d6b2d-7084-4891-878c-ae47fc49da52", "type": "dataset"}, {"prodId": null, "stagingId": [{"attributes": {"application": "rw", "name": "knowledge_graph", "tags": ["geospatial", "global", "raster", "historical", "soil", "erosion", "water", "flood", "agriculture"]}, "id": "knowledge_graph", "type": "vocabulary"}], "type": "vocabulary"}, {"prodId": "555d1d25-3112-430f-9fd5-5b00aa94b613", "stagingId": "6ab77e36-df9d-4047-8641-ca49922522b5", "type": "layer"}, {"prodId": "503f1001-e69c-4111-8ef6-5d34fa451e94", "stagingId": "6a7c59b9-2e79-42f4-9caf-6b1bf068ae09", "type": "layer"}, {"prodId": "065e6200-1670-4832-b0ae-70b7852a9875", "stagingId": "f37d8246-ddb6-4dbb-a9db-715fa889a88b", "type": "layer"}, {"prodId": "e6eafefd-bb28-429e-9fff-1d6205f5d5b2", "stagingId": "70b7071b-2c3a-425e-9ba2-51cab97fc681", "type": "layer"}, {"prodId": "5c79ccac-2c31-4399-9a54-ae964feb7419", "stagingId": "5ad82c6b-b5a4-47c5-8417-e768eff56790", "type": "layer"}, {"prodId": "fd292bc0-be35-4fc9-8dc1-8485ea0360b8", "stagingId": "6e26a922-3c1b-40cf-8fe2-8059b25e7261", "type": "widget"}, {"prodId": "dbd066c0-a1c0-4e0c-9eec-9294b96b49ef", "stagingId": "c5237807-c4f4-4ec1-8d6a-3b5931679eba", "type": "widget"}, {"prodId": "a72e4432-dd8f-43b1-a33c-b0d61432d6f1", "stagingId": "7f18260c-aada-4dd3-baf1-a51af52947db", "type": "widget"}, {"prodId": "60eca114b66636001aa44c11", "stagingId": [{"attributes": {"application": "rw", "createdAt": "2021-08-09T15:20:50.370Z", "dataset": "e16d6b2d-7084-4891-878c-ae47fc49da52", "info": {"caption": "The Soil Erosion Prevalence dataset, produced by the World Agroforestry Centre, provides predictions for the percentage of area with soil erosion across the global tropics (between the parallels of 40\u00b0 south and 40\u00b0 north). The authors predicted soil erosion for the years 2002, 2007, 2012, 2017, and 2020 from satellite imagery at 500 m resolution using machine learning algorithms trained with field observations. ", "widgetLinks": [{"link": "https://www.mdpi.com/2072-4292/11/15/1800", "name": "Learn more"}]}, "language": "en", "resource": {"id": "7f18260c-aada-4dd3-baf1-a51af52947db", "type": "widget"}, "status": "published", "updatedAt": "2021-09-07T09:11:07.303Z"}, "id": "611147d2cd40d0001afca550", "type": "metadata"}], "type": "metadata"}, {"prodId": "60d64348173c43001ad6b8d1", "stagingId": [{"attributes": {"application": "rw", "createdAt": "2021-06-25T21:08:03.264Z", "dataset": "e16d6b2d-7084-4891-878c-ae47fc49da52", "description": "### Overview \n \nThe Soil Erosion Prevalence dataset, produced by the World Agroforestry Centre (ICRAF), provides predictions for the percentage of area with soil erosion across the global tropics (between the parallels of 40\u00b0 south and 40\u00b0 north). The authors predicted soil erosion for the years 2002, 2007, 2012, 2017, and 2020 from satellite imagery at 500 m resolution using machine learning algorithms trained with field observations. \n\n \n \nSoil is eroding more quickly than its being formed, contributing to the degradation of millions of hectares of land globally. The nutrient rich layer of soil on the surface, called topsoil, is vulnerable to wind and water erosion and its loss is accelerated by modifications in land use. Erosion of this critical layer of soil comes at a [major economic and environmental cost](https://www.wri.org/insights/causes-and-effects-soil-erosion-and-how-prevent-it). It causes [billions](https://www.sciencedirect.com/science/article/pii/S0264837718319343) of dollars of losses due to decreased soil fertility, reduced crop yields, and increased water usage. The eroded soil can be carried into rivers and streams. This creates a heavy layer of sediment which is carried downstream. This process clogs waterways, prevents smooth water flow, and may eventually lead to flooding. Other environmental costs include loss of productivity and biodiversity, decreased resilience of marine and terrestrial ecosystems, and increased vulnerability to climate change and food insecurity. \n\n \n \nEstimates of both spatial and temporal dynamics of soil erosion are needed to better track the occurrence and severity of erosion in landscapes over time. The main objectives of this dataset are to provide rapid assessments of soil erosion for spatially distributed monitoring as well as assess changes in soil erosion prevalence over time. The spatial assessments of erosion provide estimates of land degradation hotspots and can be combined with other indicators of ecosystem health, including social factors, to better assess and identify drivers of land degradation and target land management interventions to reverse degradation.\n \n \n### Methodology \n \nThe authors used a combination of systematically collected field observations of erosion and remote sensing to predict the spatial and temporal distribution of soil erosion prevalence at moderate spatial resolution (500 m) for the years 2002, 2007, 2012, 2017, and 2020. The model was trained and tested using field data collected via [the Land Degradation Surveillance Framework](http://landscapeportal.org/blog/2015/03/25/the-land-degradation-surveillance-framework-ldsf) (LDSF). As part of the LDSF, sample sites are divided into plots and subplots. Erosion prevalence was scored at the plot level by summing up the number of subplots with visible signs of erosion, with 0 being no observed erosion and 4 being erosion observed in all four subplots. The authors took a cut-off at three subplots or more (>50%) to represent \u201csevere\u201d soil erosion. Erosion prevalence was modeled using a decision-tree approach known as [Random Forests](https://www.jstatsoft.org/article/view/v077i01/v77i01.pdf) to generate a classification model for soil erosion prevalence using imagery from the [Moderate Resolution Imaging Spectroradiomer](https://modis.gsfc.nasa.gov/data/) (MODIS) platform. Annual composites were matched to the years of LDSF field data collection and fitted to annual composite reflectance data for 2002, 2007, 2012, and 2017. Soil erosion prevalence is displayed on the maps as the percentage of area (of a pixel) predicted to be eroded. Model accuracy for the detection of erosion was about 86% and accuracy for non-detection was about 91%.\n\n \n \nFor the full documentation, please see the source [methodology](https://www.mdpi.com/2072-4292/11/15/1800).\n \n \n### Additional Information \n \nFor access to additional information, click on the \u201cLearn more\u201d button. \n \n### Visualizing the Data \n \nOur team reformatted this dataset before displaying it on Resource Watch. See the documentation on how Resource Watch retrieved the data on [Github](https://github.com/resource-watch/data-pre-processing/tree/master/wat_070_rw0_soil_erosion). \n \n### Disclaimer \n \nExcerpts of this description page were taken from the source metadata. Resource Watch shows only a subset of the dataset. For access to the full dataset and additional information, click on the \u201cLearn more\u201d button.", "info": {"cautions": "- There is no calibration/validation data for the US, Europe and Australia, although these regions are included in the maps.\n \n \n- Hyperarid areas have been masked out (excluded) based on TRMM rainfall data.\n \n \n- The drivers of soil erosion, which include both social and ecological factors can be challenging to detect remotely.\n", "citation": "V\u00e5gen T-G, Winowiecki LA. Predicting the Spatial Distribution and Severity of Soil Erosion in the Global Tropics using Satellite Remote Sensing. Remote Sensing. 2019; 11(15):1800. https://doi.org/10.3390/rs11151800. Accessed through Resource Watch, (date). 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The aragonite saturation state (\u03a9arag) is commonly used to track ocean acidification because it is a measure of carbonate ion concentration, which indicates the availability of the calcium carbonate that is widely used by marine calcifiers, from lobsters to clams to starfish. Aragonite itself is the building block of stony corals that are the primary framework and habitat builders of coral reef ecosystems. Corals and other calcifiers are most likely to survive and reproduce when the aragonite saturation state is greater than 4. Saturation above 3.5 is adequate; above 3 is considered marginal, with growth difficult. When the saturation state falls below 3, these organisms are in danger. Below 1, reefs, shells, and other aragonite structures begin to dissolve entirely.\n\n \n \nThese projections are based on future greenhouse gas emission rates determined by the Intergovernmental Panel on Climate Change\u2019s (IPCC\u2019s) Representative Concentration Pathway (RCP) 8.5. The RCP 8.5 scenario presumes no decrease in greenhouse gas emission rates within the 21st century. More information on these scenarios can be found [here](http://tntcat.iiasa.ac.at:8787/RcpDb/dsd?Action=htmlpage&page=about). The projections have a spatial resolution of 1 degree, and cover the entire globe, although the significance of carbonate ion concentrations outside of areas containing warm-water coral reefs is beyond the scope of this discussion. \n\n \n \nThe dataset was created by the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data and Information Service (NESDIS) as part of their Coral Reef Watch program. It was created as a way to both incite and inform discussions about the effects of climate change on coral reefs. These may be dramatic: the predicted acidification during the 21st century would be a greater change than possibly at any time in the last 300 million years.\n\n \n \nCoral reefs are among the most biologically rich and productive ecosystems on earth and provide valuable ecosystem benefits to millions of people in coastal areas. They are, however, widely threatened by both local threats (such as overfishing, pollution, and direct physical damage) and the global threats of ocean warming and acidification. These have already caused widespread damage to most of the world\u2019s coral reefs, resulting in reduced areas of living coral, increased algal cover, diminished species diversity, and lower fish abundance, as well as degraded economic and social benefits to many coastal communities.\n \n \n### Methodology \n \nEnsembles of climate models were used to generate projections of the year when severe coral bleaching events will start to occur annually, and of changes in \u03a9arag. The models were used to generate projected monthly data for the following variables: sea surface temperature (SST), surface pressure of CO\u2082, pH, and salinity. Each model independently generated monthly data for the listed variables, and these model outputs were then adjusted to the mean and annual cycle of observations of SST based on the [OISST V2 1982\u20102005 climatology](https://www.ncdc.noaa.gov/oisst/optimum-interpolation-sea-surface-temperature-oisst-v20). \n\n \n \nProjections were generated for all four RCP scenarios (known as RCP 2.6, 4.5, 6.0, and 8.5); those corresponding to RCP 8.5 were produced using 9 fully coupled models in the [Coupled Model Intercomparison Project 5 (CMIP5)](https://esgf-node.llnl.gov/projects/cmip5/). For each model, \u03a9arag was computed independently on the basis of those four key variables, as forecast by that same model, by adopting the routines of the Matlab program [CO2SYS](https://cdiac.ess-dive.lbl.gov/ftp/oceans/co2sys/CO2SYS_calc_MATLAB_v1.1/). Finally, the calculated \u03a9arag projections from all models were combined, as averaging over multiple models can reduce errors and generally increases the accuracy, skill, and consistency of model forecasts.\n\n \n \nAll modeled data were remapped to a 1\u00ba resolution. Some model outputs were reduced to a sub-set of only reef locations. These were obtained from the United Nations Environmental Program-World Conservation Monitoring Center\u2019s [Millennium Coral Reef Mapping Project Seascape](http://imars.usf.edu/MC/). A cell was counted as a reef cell if it contained any tropical coral reefs according to the original Seascape database.\n\n \n \nFor the full documentation, please see the source [methodology](https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.12394).\n \n \n### Additional Information \n \nResource Watch shows only a subset of the dataset. \u03a9arag is projected annually, but shown on Resource Watch only at decadal intervals. The full dataset also includes projections of coral bleaching and reductions in calcification, as well as additional ocean acidification projections, under four climate scenarios (RCP 2.6, 4.5, 6.0, 8.5). For access to the full dataset and additional information, click on the \u201cLearn more\u201d button. \n \n### Visualizing the Data \n \nOur team reformatted this dataset before displaying it on Resource Watch. See the documentation on how Resource Watch processed the data on [Github](https://github.com/resource-watch/data-pre-processing/tree/master/ocn_006_projected_ocean_acidification). \n \n### Disclaimer \n \nExcerpts of this description page were taken from the source metadata.", "source": "NOAA", "info": {"rwId": "ocn.006a.rw0", "data_type": "Raster", "name": "Projected Ocean Acidification", "sources": [{"source-name": "", "id": 0, "source-description": "National Oceanic and Atmospheric Administration (NOAA)"}], "technical_title": "Projections of Coral Bleaching and Ocean Acidification for Coral Reef Areas", "functions": "Projected level of ocean acidification, as indicated by carbonate ion concentration", "cautions": "- Data and images hosted on the STAR webservers are not official NOAA operational products, and are provided only as examples for experimental use by remote sensing researchers, experienced meteorologists, or oceanographers. Although STAR provides \"operational\" data for some products, the STAR website primarily hosts examples of ongoing experimental product development. STAR pages and data sources may therefore be interrupted, changed, or canceled at any time without notice. STAR websites are not managed to meet operational data standards of uptime or redundant operations.\n \n \n - Climate model resolution is very coarse. A 1\u00ba grid cell can contain many individual coral reefs. Each reef can in turn be highly diverse in terms of the communities present, geomorphology, and level of stress from human activities. All of these will determine the local spatial and temporal patterns of impacts from acidification and thermal stress. \n \n - The models describe surface and oceanic waters only and thus do not resolve near reef and near-coastal processes such as upwelling that can influence \u03a9arag and sea temperatures. Also, the models do not reflect the temporal (diurnal, weekly) variability in \u03a9arag on reefs. There are considerable biases in the representation of the annual cycle in the models.\n \n \n - There are the standard issues with the use of climate model data, in that all models used have uncertainties and a varying capacity to project trends in key drivers of climate in the tropics such as the El Ni\u00f1o Southern Oscillation. ", "citation": "van Hooidonk R, Maynard J, Manzello D, Planes S (2014) Opposite latitudinal gradients in projected ocean acidification and bleaching impacts on coral reefs. Global Change Biology, doi: 10.1111/gcb.12394. Accessed through Resource Watch, (date). [www.resourcewatch.org](https://www.resourcewatch.org).", "license": "Public domain", "license_link": "https://coralreefwatch.noaa.gov/satellite/docs/recommendations_crw_citation.php", "geographic_coverage": "Global", "spatial_resolution": "1\u00b0", "date_of_content": "2006-2099", "frequency_of_updates": "Unknown", "learn_more_link": "https://coralreefwatch.noaa.gov/climate/projections/piccc_oa_and_bleaching/index.php", "data_download_link": null, "data_download_original_link": "https://coralreefwatch.noaa.gov/climate/projections/piccc_oa_and_bleaching/index.php"}, "createdAt": "2021-06-09T18:02:48.835Z", "updatedAt": "2021-06-09T18:02:48.835Z", "status": "published"}}]}] \ No newline at end of file +[{"prodId": "7c06a7a2-4c47-4ecb-a2a5-1c76eaf2db65", "stagingId": "e935f438-b508-40af-b13c-e3b7f6d8651c", "type": "dataset"}, {"prodId": null, "stagingId": [{"attributes": {"application": "rw", "name": "knowledge_graph", "tags": ["geospatial", "future", "acidification", "coral_reef", "climate_change", "biodiversity", "vulnerability", "ocean", "raster", "annual", "historical"]}, "id": "knowledge_graph", "type": "vocabulary"}], "type": "vocabulary"}, {"prodId": "e21383ae-40e9-4bde-a4b0-49a970a1947c", "stagingId": "b63fade8-5c64-46a4-a40f-fa8f72e935cb", "type": "layer"}, {"prodId": "2d6faf70-e0dd-41d8-aaf0-475528af5f70", "stagingId": "e57bb5d1-307d-4bc1-a5f8-b828bfa90240", "type": "layer"}, {"prodId": "f632cb5c-b8c5-48ac-be7c-4205eb61fffe", "stagingId": "7eb5eaa3-c23c-4474-a5bd-c0ba63210e74", "type": "layer"}, {"prodId": "52061b34-97e9-4b60-8d67-7ca9c13c22d2", "stagingId": "2d5b79ae-32f7-4bef-ad38-ae9dcc6188db", "type": "layer"}, {"prodId": "e4434119-0800-4b75-b5d3-9b82f8e2d691", "stagingId": "5f83034d-7b60-4d13-b44c-988dbfd60efd", "type": "layer"}, {"prodId": "6a452b02-84d5-4c31-a978-9c84bcda302d", "stagingId": "fb40902f-a45b-47ce-8c0b-854655e06a02", "type": "layer"}, {"prodId": "1b810cf4-0025-4865-a741-793488362a10", "stagingId": "ad42d30f-5abe-4061-826a-a6631379e006", "type": "layer"}, {"prodId": "0e742239-a069-4ae4-9757-8b947a095dce", "stagingId": "fd6630c8-444a-4d11-80f6-7979badc6e79", "type": "layer"}, {"prodId": "65380403-a565-48f5-ab43-6e92933f5d0e", "stagingId": "a336800d-2854-43f8-b762-20d84e335fc7", "type": "layer"}, {"prodId": "8a49b28b-becf-451f-8c81-610e78e1226d", "stagingId": "a3a68186-9806-427b-bbec-60a079c087b5", "type": "layer"}, {"prodId": "2390ebbf-49ed-4510-84ac-d0a81b73f480", "stagingId": "745d3c3d-7cb5-47f6-804d-0c6878c78747", "type": "widget"}, {"prodId": "5fceb7346810d3001ae5d168", "stagingId": [{"attributes": {"application": "rw", "createdAt": "2021-06-09T18:02:48.367Z", "dataset": "e935f438-b508-40af-b13c-e3b7f6d8651c", "info": {"caption": "NOAA", "widgetLinks": [{"link": "https://resourcewatch.org/data/explore/7c06a7a2-4c47-4ecb-a2a5-1c76eaf2db65?section=Discover&zoom=3&lat=0&lng=0&pitch=0&bearing=0&basemap=dark&labels=light&layers=%255B%257B%2522dataset%2522%253A%25227c06a7a2-4c47-4ecb-a2a5-1c76eaf2db65%2522%252C%2522opacity%2522%253A1%252C%2522layer%2522%253A%25221b810cf4-0025-4865-a741-793488362a10%2522%257D%255D&page=1&sort=most-viewed&sortDirection=-1", "name": "Coral Reef Watch"}, {"link": "https://coralreefwatch.noaa.gov/climate/projections/piccc_oa_and_bleaching/index.php", "name": "NOAA"}]}, "language": "en", "resource": {"id": "745d3c3d-7cb5-47f6-804d-0c6878c78747", "type": "widget"}, "status": "published", "updatedAt": "2021-09-07T08:37:06.230Z"}, "id": "60c10248da9cef001a648b75", "type": "metadata"}], "type": "metadata"}, {"prodId": "11121aac-8f74-41c5-ad75-e0cc7f9c7b1d", "stagingId": "c506e664-b226-476a-9326-85c62ac19412", "type": "widget"}, {"prodId": "bb31b474-a6d9-41f5-b3dc-e05035aa8315", "stagingId": "e6fb58ca-9414-4967-a4b2-796d86d2fd96", "type": "widget"}, {"prodId": "5faeeb4d7fe3c8001be2f27c", "stagingId": [{"attributes": {"application": "rw", "createdAt": "2021-06-09T18:02:47.071Z", "dataset": "e935f438-b508-40af-b13c-e3b7f6d8651c", "info": {"caption": ""}, "language": "en", "resource": {"id": "e6fb58ca-9414-4967-a4b2-796d86d2fd96", "type": "widget"}, "status": "published", "updatedAt": "2021-09-07T08:37:09.533Z"}, "id": "60c10247da9cef001a648b74", "type": "metadata"}], "type": "metadata"}, {"prodId": "05fae357-b898-4f28-9d0f-727acfab8c24", "stagingId": "7d246f6e-1334-4637-9740-51b461f77c38", "type": "widget"}, {"prodId": "5fb4187111ff7a001abdbb0d", "stagingId": [{"attributes": {"application": "rw", "createdAt": "2021-06-09T18:02:48.835Z", "dataset": "e935f438-b508-40af-b13c-e3b7f6d8651c", "description": "### Overview \n \nThis dataset shows projections of aragonite saturation in the world\u2019s oceans from 2006-2099. The aragonite saturation state (\u03a9arag) is commonly used to track ocean acidification because it is a measure of carbonate ion concentration, which indicates the availability of the calcium carbonate that is widely used by marine calcifiers, from lobsters to clams to starfish. Aragonite itself is the building block of stony corals that are the primary framework and habitat builders of coral reef ecosystems. Corals and other calcifiers are most likely to survive and reproduce when the aragonite saturation state is greater than 4. Saturation above 3.5 is adequate; above 3 is considered marginal, with growth difficult. When the saturation state falls below 3, these organisms are in danger. Below 1, reefs, shells, and other aragonite structures begin to dissolve entirely.\n\n \n \nThese projections are based on future greenhouse gas emission rates determined by the Intergovernmental Panel on Climate Change\u2019s (IPCC\u2019s) Representative Concentration Pathway (RCP) 8.5. The RCP 8.5 scenario presumes no decrease in greenhouse gas emission rates within the 21st century. More information on these scenarios can be found [here](http://tntcat.iiasa.ac.at:8787/RcpDb/dsd?Action=htmlpage&page=about). The projections have a spatial resolution of 1 degree, and cover the entire globe, although the significance of carbonate ion concentrations outside of areas containing warm-water coral reefs is beyond the scope of this discussion. \n\n \n \nThe dataset was created by the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data and Information Service (NESDIS) as part of their Coral Reef Watch program. It was created as a way to both incite and inform discussions about the effects of climate change on coral reefs. These may be dramatic: the predicted acidification during the 21st century would be a greater change than possibly at any time in the last 300 million years.\n\n \n \nCoral reefs are among the most biologically rich and productive ecosystems on earth and provide valuable ecosystem benefits to millions of people in coastal areas. They are, however, widely threatened by both local threats (such as overfishing, pollution, and direct physical damage) and the global threats of ocean warming and acidification. These have already caused widespread damage to most of the world\u2019s coral reefs, resulting in reduced areas of living coral, increased algal cover, diminished species diversity, and lower fish abundance, as well as degraded economic and social benefits to many coastal communities.\n \n \n### Methodology \n \nEnsembles of climate models were used to generate projections of the year when severe coral bleaching events will start to occur annually, and of changes in \u03a9arag. The models were used to generate projected monthly data for the following variables: sea surface temperature (SST), surface pressure of CO\u2082, pH, and salinity. Each model independently generated monthly data for the listed variables, and these model outputs were then adjusted to the mean and annual cycle of observations of SST based on the [OISST V2 1982\u20102005 climatology](https://www.ncdc.noaa.gov/oisst/optimum-interpolation-sea-surface-temperature-oisst-v20). \n\n \n \nProjections were generated for all four RCP scenarios (known as RCP 2.6, 4.5, 6.0, and 8.5); those corresponding to RCP 8.5 were produced using 9 fully coupled models in the [Coupled Model Intercomparison Project 5 (CMIP5)](https://esgf-node.llnl.gov/projects/cmip5/). For each model, \u03a9arag was computed independently on the basis of those four key variables, as forecast by that same model, by adopting the routines of the Matlab program [CO2SYS](https://cdiac.ess-dive.lbl.gov/ftp/oceans/co2sys/CO2SYS_calc_MATLAB_v1.1/). Finally, the calculated \u03a9arag projections from all models were combined, as averaging over multiple models can reduce errors and generally increases the accuracy, skill, and consistency of model forecasts.\n\n \n \nAll modeled data were remapped to a 1\u00ba resolution. Some model outputs were reduced to a sub-set of only reef locations. These were obtained from the United Nations Environmental Program-World Conservation Monitoring Center\u2019s [Millennium Coral Reef Mapping Project Seascape](http://imars.usf.edu/MC/). A cell was counted as a reef cell if it contained any tropical coral reefs according to the original Seascape database.\n\n \n \nFor the full documentation, please see the source [methodology](https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.12394).\n \n \n### Additional Information \n \nResource Watch shows only a subset of the dataset. \u03a9arag is projected annually, but shown on Resource Watch only at decadal intervals. The full dataset also includes projections of coral bleaching and reductions in calcification, as well as additional ocean acidification projections, under four climate scenarios (RCP 2.6, 4.5, 6.0, 8.5). For access to the full dataset and additional information, click on the \u201cLearn more\u201d button. \n \n### Visualizing the Data \n \nOur team reformatted this dataset before displaying it on Resource Watch. See the documentation on how Resource Watch processed the data on [Github](https://github.com/resource-watch/data-pre-processing/tree/master/ocn_006_projected_ocean_acidification). \n \n### Disclaimer \n \nExcerpts of this description page were taken from the source metadata.", "info": {"cautions": "- Data and images hosted on the STAR webservers are not official NOAA operational products, and are provided only as examples for experimental use by remote sensing researchers, experienced meteorologists, or oceanographers. Although STAR provides \"operational\" data for some products, the STAR website primarily hosts examples of ongoing experimental product development. STAR pages and data sources may therefore be interrupted, changed, or canceled at any time without notice. STAR websites are not managed to meet operational data standards of uptime or redundant operations.\n \n \n - Climate model resolution is very coarse. A 1\u00ba grid cell can contain many individual coral reefs. Each reef can in turn be highly diverse in terms of the communities present, geomorphology, and level of stress from human activities. All of these will determine the local spatial and temporal patterns of impacts from acidification and thermal stress. \n \n - The models describe surface and oceanic waters only and thus do not resolve near reef and near-coastal processes such as upwelling that can influence \u03a9arag and sea temperatures. Also, the models do not reflect the temporal (diurnal, weekly) variability in \u03a9arag on reefs. There are considerable biases in the representation of the annual cycle in the models.\n \n \n - There are the standard issues with the use of climate model data, in that all models used have uncertainties and a varying capacity to project trends in key drivers of climate in the tropics such as the El Ni\u00f1o Southern Oscillation. ", "citation": "van Hooidonk R, Maynard J, Manzello D, Planes S (2014) Opposite latitudinal gradients in projected ocean acidification and bleaching impacts on coral reefs. Global Change Biology, doi: 10.1111/gcb.12394. Accessed through Resource Watch, (date). [www.resourcewatch.org](https://www.resourcewatch.org).", "data_download_link": "https://wri-projects.s3.amazonaws.com/resourcewatch/raster/ocn_006_projected_ocean_acidification.zip", "data_download_original_link": "https://coralreefwatch.noaa.gov/climate/projections/piccc_oa_and_bleaching/index.php", "data_type": "Raster", "date_of_content": "2006-2099", "frequency_of_updates": "Unknown", "functions": "Projected level of ocean acidification, as indicated by carbonate ion concentration", "geographic_coverage": "Global", "learn_more_link": "https://coralreefwatch.noaa.gov/climate/projections/piccc_oa_and_bleaching/index.php", "license": "Public domain", "license_link": "https://coralreefwatch.noaa.gov/satellite/docs/recommendations_crw_citation.php", "name": "Projected Ocean Acidification", "rwId": "ocn.006a.rw0", "sources": [{"id": 0, "source-description": "National Oceanic and Atmospheric Administration (NOAA)", "source-name": ""}], "spatial_resolution": "1\u00b0", "technical_title": "Projections of Coral Bleaching and Ocean Acidification for Coral Reef Areas"}, "language": "en", "name": "Projected Ocean Acidification", "resource": {"id": "e935f438-b508-40af-b13c-e3b7f6d8651c", "type": "dataset"}, "source": "NOAA", "status": "published", "updatedAt": "2021-09-07T08:37:11.461Z"}, "id": "60c10248da9cef001a648b76", "type": "metadata"}], "type": "metadata"}] \ No newline at end of file diff --git a/ResourceWatch/dataset_sync_files/RW_prod_staging_match_b2f00f99-46ed-43e6-a7a1-a5809d9369d4.json b/ResourceWatch/dataset_sync_files/RW_prod_staging_match_b2f00f99-46ed-43e6-a7a1-a5809d9369d4.json index 3f3813b..925a99f 100644 --- a/ResourceWatch/dataset_sync_files/RW_prod_staging_match_b2f00f99-46ed-43e6-a7a1-a5809d9369d4.json +++ b/ResourceWatch/dataset_sync_files/RW_prod_staging_match_b2f00f99-46ed-43e6-a7a1-a5809d9369d4.json @@ -1 +1 @@ -[{"type": "dataset", "prodId": "b2f00f99-46ed-43e6-a7a1-a5809d9369d4", "stagingId": "f7bbc39e-81ab-4d91-be37-c12e33cd8436"}, {"type": "vocabulary", "prodId": null, "stagingId": [{"id": "knowledge_graph", "type": "vocabulary", "attributes": {"tags": ["geospatial", "global", "annual", "raster", "society", "land_use", "land_cover", "forest", "wetland", "cropland", "urbanization", "water", "deforestation", "urban_expansion"], "name": "knowledge_graph", "application": "rw"}}]}, {"type": "layer", "prodId": "fb0a373e-398e-433c-ac8e-8fedc966b9d0", "stagingId": "fe6315f7-e208-441d-9193-9dee6499b349"}, {"type": "layer", "prodId": "23a821b6-a185-4f5d-8896-1ff581142719", "stagingId": "ca0ff237-0ae4-487c-aa5d-e5478d3ea7a9"}, {"type": "layer", "prodId": "fb9636ca-32d5-4814-8e00-f212dc321b0e", "stagingId": "7a8fe1c7-8ada-4a00-96de-342a0b3bdc25"}, {"type": "layer", "prodId": "536ebdcf-d024-4f5c-8333-b633d6492485", "stagingId": "ac080ce6-7c65-4225-8815-011971027f80"}, {"type": "layer", "prodId": "36dcfc44-b263-4973-8dd2-699435ece1f2", "stagingId": "f1a8d9d4-c607-4b42-9689-adb39a84a0ce"}, {"type": "widget", "prodId": "d89aefac-32eb-4fbf-bce4-902f4a2dab7e", "stagingId": "8b3c8460-d676-438a-b57f-ad71f8e573c0"}, {"type": "metadata", "prodId": "609c1965b1b3c1001af41423", "stagingId": [{"id": "60c0baee577805001a26c031", "type": "metadata", "attributes": {"dataset": "f7bbc39e-81ab-4d91-be37-c12e33cd8436", "application": "rw", "resource": {"id": "8b3c8460-d676-438a-b57f-ad71f8e573c0", "type": "widget"}, "language": "en", "info": {"caption": "Main Land Cover Classification for 2019, according to UN-FAO LCCS scheme with 13 classes (forest classes collapsed into open/closed forests; Ocean and Unknown classifications not shown)", "widgetLinks": [{"link": "https://land.copernicus.eu/global/products/lc", "name": "Copernicus Land Cover Product"}, {"link": "https://lcviewer.vito.be/2019", "name": "Copernicus Land Cover Data Viewer"}]}, "createdAt": "2021-06-09T12:58:22.290Z", "updatedAt": "2021-06-25T20:50:42.467Z", "status": "published"}}]}, {"type": "widget", "prodId": "6010a72c-3b62-4076-abd7-f4921f6f4605", "stagingId": "ab571ea8-c86f-482f-8758-9d37511a20ee"}, {"type": "widget", "prodId": "b8e454be-9a0f-4a8f-8e92-3d7b0fd6423f", "stagingId": "f3ab5375-b278-4ad2-8311-a8652d34e222"}, {"type": "widget", "prodId": "a0663e71-5645-4bbe-9801-e91914c0a167", "stagingId": "1f1057e8-fc5c-4954-8add-1e5d35ba561d"}, {"type": "widget", "prodId": "0e8df4ea-d191-4c46-a626-268a4f23d18a", "stagingId": "edc144f2-ab22-4775-beac-1515eb581b26"}, {"type": "widget", "prodId": "bd2e07f0-f62a-42df-83e6-65a3ebcdbc29", "stagingId": "ee131e0e-71da-421c-bbac-ea398f4030cf"}, {"type": "widget", "prodId": "15b5ec89-0ea6-4cd4-beac-7caceb80e42c", "stagingId": "d7828256-5a99-4033-9583-fe35d36a41fd"}, {"type": "metadata", "prodId": "60aebebbb1b3c1001af4142a", "stagingId": [{"id": "60c0baee577805001a26c032", "type": "metadata", "attributes": {"dataset": "f7bbc39e-81ab-4d91-be37-c12e33cd8436", "application": "rw", "resource": {"id": "f7bbc39e-81ab-4d91-be37-c12e33cd8436", "type": "dataset"}, "language": "en", "name": "Global Land Cover", "description": "### Overview \n \nThe Copernicus Global Land Service (CGLS) delivers a global Land Cover product at 100-meter spatial resolution (CGLS-LC100) on an annual basis from 2015-2019. Each pixel value corresponds to a different type (class) of physical coverage of the Earth's surface, e.g. forests, grasslands, croplands, lakes, wetlands. The classifications follow the United Nations Food and Agriculture Organization (UN-FAO) Land Cover Classification System (LCCS) scheme. Resource watch displays 11 discrete LCCS classifications, with all forests grouped into open or closed classes.\n\n \n \nLand cover plays a major role in the climate and biogeochemistry of the Earth system. Understanding patterns in land use and transitions of land cover classes over time is crucial to understand, monitor, and manage a number of processes at global level. Therefore, accurate and spatially detailed maps on land cover and land use are relevant to a broad range of issues including deforestation, desertification, urbanization, land degradation, loss of biodiversity and ecosystem functions, water resource management, agriculture and food security, urban and regional development, and climate change and are critical to make informed policy development, planning, and resource management decisions. \n\n \n \nThe Copernicus Global Land Service (CGLS) is a component of the Land Monitoring Core Service (LMCS) of Copernicus, the European flagship programme on Earth Observation. The Global Land Service systematically produces a series of qualified bio-geophysical products on the status and evolution of the land surface, at global scale and at mid to low spatial resolution, complemented by the constitution of long term time series. The products are used to monitor the vegetation, the water cycle, the energy budget and the terrestrial cryosphere. \n \n### Methodology \n \nFor the global land cover 100-meter resolution data, the main inputs are PROBA-V satellite observations, organized into millions of Sentinel-2 equivalent tiles of 110x110-kilometers.\n\n \n \nThese satellite observations are put through an algorithm, that includes:\n \n \n- pre-processing, sensor specific data cleaning, and outlier screening \n \n \n- data fusion between the 5-day 100-meter resolution and daily 300-meter resolution data;\n \n \n- extraction of 183 metrics, including the base reflectances, vegetation indicators, time series harmonics and descriptive statistics;\n \n \n- the use of 168K training points, collected at 10-meter resolution, from GeoWIKI\u2019s crowd-sourcing, for year 2015\n \n \n- the use of well-established, external datasets for the shoreline masking, ecological regionalisation, built-up (urban) cover, permanent and seasonal water cover, arctic vegetation, weather and topography; supervised classification and regression.\n\n \n \nThe classified metrics are calculated over a three year period (epoch), in three processing modes:\n \n \n- base maps for epoch 2015, that serves as reference for the classifier and regression models,\n \n \n- consolidated maps (epochs 2016, 2017 and 2018) with full years of prior and pastor data and \n \n \n- the near-real time (nrt, 2019) maps with one year prior and only three months pastor data.\n\n \n \nThe final maps are validated using an independent set of 21,700 validation points, also sourced from Geo-WIKI.\n\n \n \nFor the full documentation, please see the source [user manual](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.4.pdf).\n \n \n### Additional Information \n \nResource Watch shows 11 of the 23 discrete LCCS classifications from the CGLS Land Cover Product. The 12 original forest classes are shown as two classes, open and closed. The ocean and unknown classes are not displayed. \n\n \n \nHistorical data on global land cover from 1992 to 2019 can be found on Resource Watch [here](https://resourcewatch.org/data/explore/soc068brw2-Global-Land-Cover-IPCC-Classification).\n\n \n \nThe products are provided as single-layer, internally compressed GeoTIFF files, per 20x20 degree on the [Land Cover viewer](https://lcviewer.vito.be/), as global files on [Zenodo](https://zenodo.org/communities/copernicus-land-cover?page=1&size=20), and analyzed on [Google Earth Engine](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V-C3_Global).\n\n \n \nThe annual LC product also includes data for cover fractions (%) for the 10 base classes, an indicator to distinguish forest types, an indicator for the input Data Density, and a change confidence layer (after 2015) are available in the data from the provider. The Land Cover viewer also shows annual maps of change occurrence and of change processes.\n\n \n \nFor access to the full dataset and additional information, click on the \u201cLearn more\u201d button.\n\n\n \n \nThe products are provided as single-layer, internally compressed GeoTIFF files, per 20x20 degree on the [Land Cover viewer](https://lcviewer.vito.be/), as global files on [Zenodo](https://zenodo.org/communities/copernicus-land-cover?page=1&size=20), and analyzed on [Google Earth Engine](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V-C3_Global).\n\n \n \n### Visualizing the Data \n \nSee the documentation on how Resource Watch retrieved the data on [Github](https://github.com/resource-watch/data-pre-processing/tree/master/soc_104_rw0_global_land_cover).\n \n \n### Disclaimer \n \nExcerpts of this description page were taken from the source metadata. Resource Watch shows only a subset of the dataset. For access to the full dataset and additional information, click on the \u201cLearn more\u201d button.", "source": "Copernicus Global Land Service (CGLS)", "info": {"rwId": "soc.104.rw0", "data_type": "Raster", "name": "Global Land Cover ", "sources": [{"source-name": "", "id": 0, "source-description": "Copernicus Global Land Service (CGLS)"}], "technical_title": "Global Land Cover", "functions": "Discrete land cover from 2015 to 2019 according to the UN-FAO Land Cover Classification System (LCCS)\n", "cautions": "- Due to the usage of optical remote sensing data, classifications in areas with high yearly cloud cover can have lower classification accuracies. \n \n \n- Fires and more specifically burned areas were not yet taken into account and therefore could lead to misclassifications. \n \n \n- Reduced accuracy in certain areas due to issues inherited from the external datasets (built-up & water classes, biome clusters) and their scaling, for instance in the mapping of very small villages or large houses and buildings that are incorrectly mapped in location or size;\n \n \n- Reduced accuracy in certain areas due to highly fragmented landscapes, in particular mixed areas with very small cropland fields (less < 0.5 ha), that are very difficult to map because of the resolution of 100 m (e.g. Nigeria, Ghana).\n \n \n- For a full list of known limitations, click \u201clearn more\u201d or refer to the [technical documentation](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.4.pdf)\n", "citation": "Buchhorn, M. ; Lesiv, M. ; Tsendbazar, N. - E. ; Herold, M. ; Bertels, L. ; Smets, B. Copernicus Global Land Cover Layers \u2014 Collection 2. Remote Sensing 2020, 12, Volume 108, 1044. DOI 10.3390/rs12061044. Accessed through Resource Watch, (date). [www.resourcewatch.org](https://www.resourcewatch.org).", "license": "Public domain", "license_link": "https://cds.climate.copernicus.eu/api/v2/terms/static/Copernicus-Global-Land-product-licence.pdf", "geographic_coverage": "Global", "spatial_resolution": "100 m", "date_of_content": "2015-2019", "frequency_of_updates": "Annual", "learn_more_link": "https://lcviewer.vito.be/about ", "data_download_link": null, "data_download_original_link": "https://lcviewer.vito.be/about"}, "createdAt": "2021-06-09T12:58:22.877Z", "updatedAt": "2021-06-25T20:50:50.823Z", "status": "published"}}]}] \ No newline at end of file +[{"prodId": "b2f00f99-46ed-43e6-a7a1-a5809d9369d4", "stagingId": "f7bbc39e-81ab-4d91-be37-c12e33cd8436", "type": "dataset"}, {"prodId": null, "stagingId": [{"attributes": {"application": "rw", "name": "knowledge_graph", "tags": ["geospatial", "global", "annual", "raster", "society", "land_use", "land_cover", "forest", "wetland", "cropland", "urbanization", "water", "deforestation", "urban_expansion"]}, "id": "knowledge_graph", "type": "vocabulary"}], "type": "vocabulary"}, {"prodId": "23a821b6-a185-4f5d-8896-1ff581142719", "stagingId": "ca0ff237-0ae4-487c-aa5d-e5478d3ea7a9", "type": "layer"}, {"prodId": "fb9636ca-32d5-4814-8e00-f212dc321b0e", "stagingId": "7a8fe1c7-8ada-4a00-96de-342a0b3bdc25", "type": "layer"}, {"prodId": "536ebdcf-d024-4f5c-8333-b633d6492485", "stagingId": "ac080ce6-7c65-4225-8815-011971027f80", "type": "layer"}, {"prodId": "36dcfc44-b263-4973-8dd2-699435ece1f2", "stagingId": "f1a8d9d4-c607-4b42-9689-adb39a84a0ce", "type": "layer"}, {"prodId": "fb0a373e-398e-433c-ac8e-8fedc966b9d0", "stagingId": "fe6315f7-e208-441d-9193-9dee6499b349", "type": "layer"}, {"prodId": "47fd4798-173a-4798-b3e8-48e00bcb2d11", "stagingId": "c1a226c5-e6c7-4b92-b89b-4b25c0f72e49", "type": "layer"}, {"prodId": "4cbefaf9-ded4-4f4c-b69e-f77282647f92", "stagingId": "0c39cff8-be87-4089-bf1b-2697889da7bc", "type": "layer"}, {"prodId": "2efb5809-f029-451d-9327-78a3f09ba71a", "stagingId": "b216ed0b-b662-491b-b542-8a73514076a7", "type": "layer"}, {"prodId": "5cd6c389-4cd7-4954-b197-2ffdd1d97777", "stagingId": "dc617d83-a397-42cd-984e-74e605934720", "type": "layer"}, {"prodId": "77908dc5-3a77-406f-9d18-18764cff777b", "stagingId": "c50f5e1b-ca09-4ba1-99d4-a0114fba03ae", "type": "layer"}, {"prodId": "d89aefac-32eb-4fbf-bce4-902f4a2dab7e", "stagingId": "8b3c8460-d676-438a-b57f-ad71f8e573c0", "type": "widget"}, {"prodId": "609c1965b1b3c1001af41423", "stagingId": [{"attributes": {"application": "rw", "createdAt": "2021-06-09T12:58:22.290Z", "dataset": "f7bbc39e-81ab-4d91-be37-c12e33cd8436", "info": {"caption": "Main Land Cover Classification for 2019, according to UN-FAO LCCS scheme with 13 classes (forest classes collapsed into open/closed forests; Ocean and Unknown classifications not shown)", "widgetLinks": [{"link": "https://land.copernicus.eu/global/products/lc", "name": "Copernicus Land Cover Product"}, {"link": "https://lcviewer.vito.be/2019", "name": "Copernicus Land Cover Data Viewer"}]}, "language": "en", "resource": {"id": "8b3c8460-d676-438a-b57f-ad71f8e573c0", "type": "widget"}, "status": "published", "updatedAt": "2021-09-07T08:57:55.271Z"}, "id": "60c0baee577805001a26c031", "type": "metadata"}], "type": "metadata"}, {"prodId": "70bef7d4-a4ec-47d3-a340-590ba0b06ebb", "stagingId": "9fd45aa9-e5e1-4088-92ba-7e962accfb1e", "type": "widget"}, {"prodId": "61370fd1d32fb1001aad8221", "stagingId": [{"attributes": {"application": "rw", "createdAt": "2021-09-07T08:57:56.409Z", "dataset": "f7bbc39e-81ab-4d91-be37-c12e33cd8436", "info": {"caption": "Main Land Cover Classification for 2019, according to UN-FAO LCCS scheme with 13 classes (forest classes collapsed into open/closed forests; Ocean and Unknown classifications not shown)", "widgetLinks": [{"link": "https://land.copernicus.eu/global/products/lc", "name": "Copernicus Land Cover Product"}, {"link": "https://lcviewer.vito.be/2019", "name": "Copernicus Land Cover Data Viewer"}]}, "language": "en", "resource": {"id": "9fd45aa9-e5e1-4088-92ba-7e962accfb1e", "type": "widget"}, "status": "published", "updatedAt": "2021-09-07T08:57:56.409Z"}, "id": "61372994cbd8f4001a73805e", "type": "metadata"}], "type": "metadata"}, {"prodId": "c5300590-f68b-44bc-b21a-e013c710f0b7", "stagingId": "a41e5675-55f5-4350-a7fb-8dee5436c1c2", "type": "widget"}, {"prodId": "72c28e9a-658e-4953-b8a7-499f755b6dff", "stagingId": "5a20cc3f-f0ac-44ad-a7dd-2ea583565eaf", "type": "widget"}, {"prodId": "a4f58ef9-f982-4b28-b1c5-6c2013fe0546", "stagingId": "6428a24f-661e-4095-ac12-600c01b88952", "type": "widget"}, {"prodId": "6010a72c-3b62-4076-abd7-f4921f6f4605", "stagingId": "ab571ea8-c86f-482f-8758-9d37511a20ee", "type": "widget"}, {"prodId": "b8e454be-9a0f-4a8f-8e92-3d7b0fd6423f", "stagingId": "f3ab5375-b278-4ad2-8311-a8652d34e222", "type": "widget"}, {"prodId": "a0663e71-5645-4bbe-9801-e91914c0a167", "stagingId": "1f1057e8-fc5c-4954-8add-1e5d35ba561d", "type": "widget"}, {"prodId": "0e8df4ea-d191-4c46-a626-268a4f23d18a", "stagingId": "edc144f2-ab22-4775-beac-1515eb581b26", "type": "widget"}, {"prodId": "bd2e07f0-f62a-42df-83e6-65a3ebcdbc29", "stagingId": "ee131e0e-71da-421c-bbac-ea398f4030cf", "type": "widget"}, {"prodId": "15b5ec89-0ea6-4cd4-beac-7caceb80e42c", "stagingId": "d7828256-5a99-4033-9583-fe35d36a41fd", "type": "widget"}, {"prodId": "0dc11aa6-72cd-42ea-82d2-c6cc5cd2bfd9", "stagingId": "77815b51-79ad-4d47-9d58-75550a488812", "type": "widget"}, {"prodId": "a75355ed-5899-4610-a1ee-510a6d536bfb", "stagingId": "8741c87c-f53e-4681-ae0a-0dd2927d727e", "type": "widget"}, {"prodId": "84240f27-ee4d-49b9-a55c-2e0e0232cd18", "stagingId": "56f3473a-c3e4-4642-b6aa-d85a88104c8a", "type": "widget"}, {"prodId": "8335b82f-3548-4d1c-8a09-cca21c636e3d", "stagingId": "ea62c0f3-1e6e-4309-a6ef-b15966881a68", "type": "widget"}, {"prodId": "60aebebbb1b3c1001af4142a", "stagingId": [{"attributes": {"application": "rw", "createdAt": "2021-06-09T12:58:22.877Z", "dataset": "f7bbc39e-81ab-4d91-be37-c12e33cd8436", "description": "### Overview \n \nThe Copernicus Global Land Service (CGLS) delivers a global Land Cover product at 100-meter spatial resolution (CGLS-LC100) on an annual basis from 2015-2019. Each pixel value corresponds to a different type (class) of physical coverage of the Earth's surface, e.g. forests, grasslands, croplands, lakes, wetlands. The classifications follow the United Nations Food and Agriculture Organization (UN-FAO) Land Cover Classification System (LCCS) scheme. Resource watch displays 11 discrete LCCS classifications, with all forests grouped into open or closed classes.\n\n \n \nLand cover plays a major role in the climate and biogeochemistry of the Earth system. Understanding patterns in land use and transitions of land cover classes over time is crucial to understand, monitor, and manage a number of processes at global level. Therefore, accurate and spatially detailed maps on land cover and land use are relevant to a broad range of issues including deforestation, desertification, urbanization, land degradation, loss of biodiversity and ecosystem functions, water resource management, agriculture and food security, urban and regional development, and climate change and are critical to make informed policy development, planning, and resource management decisions. \n\n \n \nThe Copernicus Global Land Service (CGLS) is a component of the Land Monitoring Core Service (LMCS) of Copernicus, the European flagship programme on Earth Observation. The Global Land Service systematically produces a series of qualified bio-geophysical products on the status and evolution of the land surface, at global scale and at mid to low spatial resolution, complemented by the constitution of long term time series. The products are used to monitor the vegetation, the water cycle, the energy budget and the terrestrial cryosphere. \n \n### Methodology \n \nFor the global land cover 100-meter resolution data, the main inputs are PROBA-V satellite observations, organized into millions of Sentinel-2 equivalent tiles of 110x110-kilometers.\n\n \n \nThese satellite observations are put through an algorithm, that includes:\n \n \n- pre-processing, sensor specific data cleaning, and outlier screening \n \n \n- data fusion between the 5-day 100-meter resolution and daily 300-meter resolution data;\n \n \n- extraction of 183 metrics, including the base reflectances, vegetation indicators, time series harmonics and descriptive statistics;\n \n \n- the use of 168K training points, collected at 10-meter resolution, from GeoWIKI\u2019s crowd-sourcing, for year 2015\n \n \n- the use of well-established, external datasets for the shoreline masking, ecological regionalisation, built-up (urban) cover, permanent and seasonal water cover, arctic vegetation, weather and topography; supervised classification and regression.\n\n \n \nThe classified metrics are calculated over a three year period (epoch), in three processing modes:\n \n \n- base maps for epoch 2015, that serves as reference for the classifier and regression models,\n \n \n- consolidated maps (epochs 2016, 2017 and 2018) with full years of prior and pastor data and \n \n \n- the near-real time (nrt, 2019) maps with one year prior and only three months pastor data.\n\n \n \nThe final maps are validated using an independent set of 21,700 validation points, also sourced from Geo-WIKI.\n\n \n \nFor the full documentation, please see the source [user manual](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.4.pdf).\n \n \n### Additional Information \n \nResource Watch shows 11 of the 23 discrete LCCS classifications from the CGLS Land Cover Product. The 12 original forest classes are shown as two classes, open and closed. The ocean and unknown classes are not displayed. \n\n \n \nHistorical data on global land cover from 1992 to 2019 can be found on Resource Watch [here](https://resourcewatch.org/data/explore/soc068brw2-Global-Land-Cover-IPCC-Classification).\n\n \n \nThe products are provided as single-layer, internally compressed GeoTIFF files, per 20x20 degree on the [Land Cover viewer](https://lcviewer.vito.be/), as global files on [Zenodo](https://zenodo.org/communities/copernicus-land-cover?page=1&size=20), and analyzed on [Google Earth Engine](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V-C3_Global).\n\n \n \nThe annual LC product also includes data for cover fractions (%) for the 10 base classes, an indicator to distinguish forest types, an indicator for the input Data Density, and a change confidence layer (after 2015) are available in the data from the provider. The Land Cover viewer also shows annual maps of change occurrence and of change processes.\n\n \n \nFor access to the full dataset and additional information, click on the \u201cLearn more\u201d button.\n\n\n \n \nThe products are provided as single-layer, internally compressed GeoTIFF files, per 20x20 degree on the [Land Cover viewer](https://lcviewer.vito.be/), as global files on [Zenodo](https://zenodo.org/communities/copernicus-land-cover?page=1&size=20), and analyzed on [Google Earth Engine](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V-C3_Global).\n\n \n \n### Visualizing the Data \n \nSee the documentation on how Resource Watch retrieved the data on [Github](https://github.com/resource-watch/data-pre-processing/tree/master/soc_104_rw0_global_land_cover).\n \n \n### Disclaimer \n \nExcerpts of this description page were taken from the source metadata. Resource Watch shows only a subset of the dataset. For access to the full dataset and additional information, click on the \u201cLearn more\u201d button.", "info": {"cautions": "- Due to the usage of optical remote sensing data, classifications in areas with high yearly cloud cover can have lower classification accuracies. \n \n \n- Fires and more specifically burned areas were not yet taken into account and therefore could lead to misclassifications. \n \n \n- Reduced accuracy in certain areas due to issues inherited from the external datasets (built-up & water classes, biome clusters) and their scaling, for instance in the mapping of very small villages or large houses and buildings that are incorrectly mapped in location or size;\n \n \n- Reduced accuracy in certain areas due to highly fragmented landscapes, in particular mixed areas with very small cropland fields (less < 0.5 ha), that are very difficult to map because of the resolution of 100 m (e.g. Nigeria, Ghana).\n \n \n- For a full list of known limitations, click \u201clearn more\u201d or refer to the [technical documentation](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LC100m-V3_I3.4.pdf)\n", "citation": "Buchhorn, M. ; Lesiv, M. ; Tsendbazar, N. - E. ; Herold, M. ; Bertels, L. ; Smets, B. Copernicus Global Land Cover Layers \u2014 Collection 2. Remote Sensing 2020, 12, Volume 108, 1044. DOI 10.3390/rs12061044. Accessed through Resource Watch, (date). [www.resourcewatch.org](https://www.resourcewatch.org).", "data_download_link": null, "data_download_original_link": "https://lcviewer.vito.be/about", "data_type": "Raster", "date_of_content": "2015-2019", "frequency_of_updates": "Annual", "functions": "Discrete land cover from 2015 to 2019 according to the UN-FAO Land Cover Classification System (LCCS)\n", "geographic_coverage": "Global", "learn_more_link": "https://lcviewer.vito.be/about ", "license": "Public domain", "license_link": "https://cds.climate.copernicus.eu/api/v2/terms/static/Copernicus-Global-Land-product-licence.pdf", "name": "Global Land Cover ", "rwId": "soc.104.rw0", "sources": [{"id": 0, "source-description": "Copernicus Global Land Service (CGLS)", "source-name": ""}], "spatial_resolution": "100 m", "technical_title": "Global Land Cover"}, "language": "en", "name": "Global Land Cover", "resource": {"id": "f7bbc39e-81ab-4d91-be37-c12e33cd8436", "type": "dataset"}, "source": "Copernicus Global Land Service (CGLS)", "status": "published", "updatedAt": "2021-09-07T08:58:10.712Z"}, "id": "60c0baee577805001a26c032", "type": "metadata"}], "type": "metadata"}] \ No newline at end of file diff --git a/ResourceWatch/dataset_sync_files/RW_prod_staging_match_e488781e-2b7a-4e49-ab5d-682f646363f1.json b/ResourceWatch/dataset_sync_files/RW_prod_staging_match_e488781e-2b7a-4e49-ab5d-682f646363f1.json index a293fb6..792dc47 100644 --- a/ResourceWatch/dataset_sync_files/RW_prod_staging_match_e488781e-2b7a-4e49-ab5d-682f646363f1.json +++ b/ResourceWatch/dataset_sync_files/RW_prod_staging_match_e488781e-2b7a-4e49-ab5d-682f646363f1.json @@ -1 +1 @@ -[{"type": "dataset", "prodId": "e488781e-2b7a-4e49-ab5d-682f646363f1", "stagingId": "10c26b23-b7cf-48fc-b5bf-dc3284fb8b1e"}, {"type": "layer", "prodId": "2b1f729e-f778-449d-b076-a6d1ae92c9de", "stagingId": "52c01177-2aeb-400c-9171-eefef5529c66"}] \ No newline at end of file +[{"prodId": "e488781e-2b7a-4e49-ab5d-682f646363f1", "stagingId": "10c26b23-b7cf-48fc-b5bf-dc3284fb8b1e", "type": "dataset"}, {"prodId": "2b1f729e-f778-449d-b076-a6d1ae92c9de", "stagingId": "52c01177-2aeb-400c-9171-eefef5529c66", "type": "layer"}, {"prodId": "83d41130-909a-44a8-8a15-b17ece5fb924", "stagingId": "fddf8913-a4e6-44ca-87c5-2c4e138d3f06", "type": "widget"}] \ No newline at end of file