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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ftzenodo:oai:zenodo.org:7520814 2023-07-16T03:52:58+02:00 Current and future global distribution of potential biomes under climate change scenarios Bonannella, Carmelo Hengl, Tomislav Leal Parente, Leandro de Bruin, Sytze 2023-01-10 https://zenodo.org/record/7520814 https://doi.org/10.5281/zenodo.7520814 eng eng info:eu-repo/grantAgreement/EC/HE/101059548/ doi:10.5281/zenodo.7520813 https://zenodo.org/communities/oemc-project https://zenodo.org/record/7520814 https://doi.org/10.5281/zenodo.7520814 oai:zenodo.org:7520814 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode ensemble machine learning climate change scenario rcp biomes global info:eu-repo/semantics/other dataset 2023 ftzenodo https://doi.org/10.5281/zenodo.752081410.5281/zenodo.7520813 2023-06-27T23:02:30Z 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_v20230110 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: v20230110. 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 ... Dataset Antarc* Antarctica Zenodo Calderón ENVELOPE(-57.967,-57.967,-63.300,-63.300) |
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English |
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ensemble machine learning climate change scenario rcp biomes global |
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ensemble machine learning climate change scenario rcp biomes global Bonannella, Carmelo Hengl, Tomislav Leal Parente, Leandro de Bruin, Sytze Current and future global distribution of potential biomes under climate change scenarios |
topic_facet |
ensemble machine learning climate change scenario rcp biomes global |
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_v20230110 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: v20230110. 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 |
Dataset |
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 |
publishDate |
2023 |
url |
https://zenodo.org/record/7520814 https://doi.org/10.5281/zenodo.7520814 |
long_lat |
ENVELOPE(-57.967,-57.967,-63.300,-63.300) |
geographic |
Calderón |
geographic_facet |
Calderón |
genre |
Antarc* Antarctica |
genre_facet |
Antarc* Antarctica |
op_relation |
info:eu-repo/grantAgreement/EC/HE/101059548/ doi:10.5281/zenodo.7520813 https://zenodo.org/communities/oemc-project https://zenodo.org/record/7520814 https://doi.org/10.5281/zenodo.7520814 oai:zenodo.org:7520814 |
op_rights |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode |
op_doi |
https://doi.org/10.5281/zenodo.752081410.5281/zenodo.7520813 |
_version_ |
1771547944312373248 |