Current and future global distribution of potential biomes under climate change scenarios
Probability and uncertainty maps showing the potential current and future natural vegetation on a global scale under three different climate change scenarios (RCP 2.6, RCP 4.5 and RCP 8.5) predicted using ensemble machine learning. Current (2022 - 2023) conditions are calculated on historical long t...
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ftunivwagenin:oai:library.wur.nl:wurpubs/614917 2024-02-04T09:53:52+01:00 Current and future global distribution of potential biomes under climate change scenarios Bonannella, Carmelo Hengl, Tomislav Leal Parente, Leandro de Bruin, Sytze 2023 text/html https://research.wur.nl/en/datasets/current-and-future-global-distribution-of-potential-biomes-under- https://doi.org/10.5281/zenodo.7822868 unknown OpenGeoHub Foundation https://edepot.wur.nl/631226 https://research.wur.nl/en/datasets/current-and-future-global-distribution-of-potential-biomes-under- doi:10.5281/zenodo.7822868 info:eu-repo/semantics/openAccess Wageningen University & Research biomes climate change scenario ensemble machine learning global rcp info:eu-repo/semantics/other info:eu-repo/semantics/publishedVersion 2023 ftunivwagenin https://doi.org/10.5281/zenodo.7822868 2024-01-10T23:13:20Z Probability and uncertainty maps showing the potential current and future natural vegetation on a global scale under three different climate change scenarios (RCP 2.6, RCP 4.5 and RCP 8.5) predicted using ensemble machine learning. Current (2022 - 2023) conditions are calculated on historical long term averages (1979 - 2013), while future projections cover two different epochs: 2040 - 2060 and 2061 - 2080. Files are named according to the following naming convention, e.g.: biomes_graminoid.and.forb.tundra.rcp85_p_1km_a_20610101_20801231_go_epsg.4326_v20230410 with the following fields: generic theme: biomes, variable name: graminoid.and.forb.tundra.rcp85, variable type, e.g. probability ("p"), hard class ("c"), model deviation ("md") spatial resolution: 1km, depth reference, e.g. below ("b"), above ("a") ground or at surface ("s"), begin time (YYYYMMDD): 20610101, end time: 20801231, bounding box, e.g. global land without Antarctica ("go"), EPSG code: epsg.4326, version code, e.g. creation date: v20230410. We provide probability and hard class layers using a revised classification system of the BIOME 6000 project explained in the work of Hengl et al. (2018). The 20 classes from this classification system have then been aggregated in 6 biome classes following the IUCN Global Ecosystem Typology classification system. For probability layers, the uncertainty (model deviation: md) is calculated as the standard deviation of the predicted values of the base learners of the ensemble model. The higher the standard deviation the more uncertain the model is regarding the right value to assign to the pixel. For hard class layers the uncertainty is calculated using the margin of victory (Calderón-Loor et al., 2021) defined as the difference between the first and the second highest class probability value in a given pixel. High values would be measures of low uncertainty, while low values would indicate a high uncertainty. It is highly recommended to use the md layers to properly interpret the results of the map. Styling ... Other/Unknown Material Antarc* Antarctica Wageningen UR (University & Research Centre): Digital Library Calderón ENVELOPE(-57.967,-57.967,-63.300,-63.300) |
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Open Polar |
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Wageningen UR (University & Research Centre): Digital Library |
op_collection_id |
ftunivwagenin |
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topic |
biomes climate change scenario ensemble machine learning global rcp |
spellingShingle |
biomes climate change scenario ensemble machine learning global rcp Bonannella, Carmelo Hengl, Tomislav Leal Parente, Leandro de Bruin, Sytze Current and future global distribution of potential biomes under climate change scenarios |
topic_facet |
biomes climate change scenario ensemble machine learning global rcp |
description |
Probability and uncertainty maps showing the potential current and future natural vegetation on a global scale under three different climate change scenarios (RCP 2.6, RCP 4.5 and RCP 8.5) predicted using ensemble machine learning. Current (2022 - 2023) conditions are calculated on historical long term averages (1979 - 2013), while future projections cover two different epochs: 2040 - 2060 and 2061 - 2080. Files are named according to the following naming convention, e.g.: biomes_graminoid.and.forb.tundra.rcp85_p_1km_a_20610101_20801231_go_epsg.4326_v20230410 with the following fields: generic theme: biomes, variable name: graminoid.and.forb.tundra.rcp85, variable type, e.g. probability ("p"), hard class ("c"), model deviation ("md") spatial resolution: 1km, depth reference, e.g. below ("b"), above ("a") ground or at surface ("s"), begin time (YYYYMMDD): 20610101, end time: 20801231, bounding box, e.g. global land without Antarctica ("go"), EPSG code: epsg.4326, version code, e.g. creation date: v20230410. We provide probability and hard class layers using a revised classification system of the BIOME 6000 project explained in the work of Hengl et al. (2018). The 20 classes from this classification system have then been aggregated in 6 biome classes following the IUCN Global Ecosystem Typology classification system. For probability layers, the uncertainty (model deviation: md) is calculated as the standard deviation of the predicted values of the base learners of the ensemble model. The higher the standard deviation the more uncertain the model is regarding the right value to assign to the pixel. For hard class layers the uncertainty is calculated using the margin of victory (Calderón-Loor et al., 2021) defined as the difference between the first and the second highest class probability value in a given pixel. High values would be measures of low uncertainty, while low values would indicate a high uncertainty. It is highly recommended to use the md layers to properly interpret the results of the map. Styling ... |
format |
Other/Unknown Material |
author |
Bonannella, Carmelo Hengl, Tomislav Leal Parente, Leandro de Bruin, Sytze |
author_facet |
Bonannella, Carmelo Hengl, Tomislav Leal Parente, Leandro de Bruin, Sytze |
author_sort |
Bonannella, Carmelo |
title |
Current and future global distribution of potential biomes under climate change scenarios |
title_short |
Current and future global distribution of potential biomes under climate change scenarios |
title_full |
Current and future global distribution of potential biomes under climate change scenarios |
title_fullStr |
Current and future global distribution of potential biomes under climate change scenarios |
title_full_unstemmed |
Current and future global distribution of potential biomes under climate change scenarios |
title_sort |
current and future global distribution of potential biomes under climate change scenarios |
publisher |
OpenGeoHub Foundation |
publishDate |
2023 |
url |
https://research.wur.nl/en/datasets/current-and-future-global-distribution-of-potential-biomes-under- https://doi.org/10.5281/zenodo.7822868 |
long_lat |
ENVELOPE(-57.967,-57.967,-63.300,-63.300) |
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Calderón |
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Calderón |
genre |
Antarc* Antarctica |
genre_facet |
Antarc* Antarctica |
op_relation |
https://edepot.wur.nl/631226 https://research.wur.nl/en/datasets/current-and-future-global-distribution-of-potential-biomes-under- doi:10.5281/zenodo.7822868 |
op_rights |
info:eu-repo/semantics/openAccess Wageningen University & Research |
op_doi |
https://doi.org/10.5281/zenodo.7822868 |
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1789968792606998528 |