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  5. Canopy height Mapper: A google earth engine application for predicting global canopy heights combining GEDI with multi-source data.

Canopy height Mapper: A google earth engine application for predicting global canopy heights combining GEDI with multi-source data.

Resource type
Journal article
Creator (person)
Alvites, Cesar
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O'Sullivan, Hannah
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Francini, Saverio
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Marchetti, Marco
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Santopuoli, Giovanni
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more
Date published
November 18, 2024
Organisational unit
Science
Funder
Funder nameISNIAwards
European Commission, European Union
Establishing Urban Forest-based Solutions In Changing Cities (EUFORICC), the ForestWard Observatory to Secure Resilience of European Forests (FORWARDS) project. - National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.5 - Call for tender No.3277
Ministero dell’Istruzione, dell’Università e della Ricerca, Italy
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PRIN 2020 Research Project of National Relevance (protocol 2020E52THS - Missione 4 Component 2, “Dalla ricerca all'impresa”, Investment 1.4, Project CN00000033 - Project Code ECS0000038 – Project Title eINS Ecosystem of Innovation for Next Generation Sardinia – CUP F53C22000430001- Grant Assignment Decree No. 1056
Horizon 2020 Framework Programme, European Union
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The Systemic solutions for upscaling of urgent ecosystem restoration for forest related biodiversity and ecosystem services (SUPERB) project, grant number 101036849, call LC-GD-7-1-2020
European Forest Institute, Finland
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European Forest Institute's Network Fund (grant G-01-2021)
Natural Environment Research Council, United Kingdom
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Grant number NE/P012345/1
Journal title
Environmental Modelling & Software
Volume
183
Article number
106268
Publisher
Elsevier BV
Place of publication
Amsterdam, Netherlands
ISSN
1364-8152
eISSN
1873-6726
Date accepted
November 10, 2024
Official URL
https://doi.org/10.1016/j.envsoft.2024.106268
Related URL
https://linkinghub.elsevier.com/retrieve/pii/S1364815224003293
Rights statement
In Copyright
Licence
CC BY 4.0
DOI
10.1016/j.envsoft.2024.106268
Alternate identifier
S1364815224003293
106268
Keywords
Remote sensing
Sentinel
GEE web app
Local-to-country calibrated model
Forest monitoring
Machine learning
Additional information
IF = 4.6 (2024)
Managed by the British Library and supported by the AHRC

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