Random Forest trained to estimate Amazon maximum height based on enviromental factors

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dc.contributor.affiliationUniversidade Federal dos Vales do Jequitinhonha e Mucuri - Gorgens, Eric Bastos
dc.contributor.affiliationUniversity of Helsinki - Nunes, Matheus Henrique
dc.contributor.affiliationUniversity of Cambridge - Jackson, Tobias
dc.contributor.affiliationUniversity of Cambridge - Coomes, David
dc.contributor.affiliationUnited States Forest Service - Keller, Michael
dc.contributor.affiliationUniversidade de São Paulo - Reis, Cristiano Rodrigues
dc.contributor.affiliationBangor University - Valbuena, Rubén
dc.contributor.affiliationSwansea University - Rosette, Jacqueline
dc.contributor.affiliationUniversidade de São Paulo - Almeida, Danilo Roberti Alves
dc.contributor.affiliationInstituto Nacional de Pesquisas da Amazônia - Gimenez, Bruno
dc.contributor.affiliationUniversidade de Brasília - Cantinho, Roberta
dc.contributor.affiliationUniversidade Federal dos Vales do Jequitinhonha e Mucuri - Motta, Alline Zagnolli
dc.contributor.affiliationInstituto Nacional de Pesquisas Espaciais - Assis, Mauro
dc.contributor.affiliationInstituto Nacional de Pesquisas Espaciais - Pereira, Francisca Rocha de Souza
dc.contributor.affiliationInstituto Nacional de Pesquisas da Amazônia - Spanner, Gustavo
dc.contributor.affiliationInstituto Nacional de Pesquisas da Amazônia - Higuchi, Niro
dc.contributor.affiliationInstituto Nacional de Pesquisas Espaciais - Ometto, Jean Pierre
dc.contributor.authorGorgens, Eric Bastos
dc.contributor.authorNunes, Matheus Henrique
dc.contributor.authorJackson, Tobias
dc.contributor.authorCoomes, David
dc.contributor.authorKeller, Michael
dc.contributor.authorReis, Cristiano Rodrigues
dc.contributor.authorValbuena, Rubén
dc.contributor.authorRosette, Jacqueline
dc.contributor.authorAlmeida, Danilo Roberti Alves
dc.contributor.authorGimenez, Bruno
dc.contributor.authorCantinho, Roberta
dc.contributor.authorMotta, Alline Zagnolli
dc.contributor.authorAssis, Mauro
dc.contributor.authorPereira, Francisca Rocha de Souza
dc.contributor.authorSpanner, Gustavo
dc.contributor.authorHiguchi, Niro
dc.contributor.authorOmetto, Jean Pierre
dc.date.accessioned2025-03-24T15:23:33Z
dc.date.issued2020-10-01
dc.date.issued2020-10-01
dc.descriptionThe Random Forest model obtained MAE = 3.62 m, RMSE  = 4.92 m, and R² = 0.735. we initially considered a total of 18 environmental variables: (1) fraction of absorbed photosynthetically active radiation (FAPAR; in %); (2) elevation above sea level (Elevation; in m);  (3) the component of the horizontal wind towards east, i.e. zonal velocity (u-speed ; in m s-1); (4) the component of the horizontal wind towards north, i.e. meridional velocity (v-speed ; in m s-1); (5) the number of days not affected by cloud cover (clear days; in days yr-1); (6) the number of days with precipitation above 20 mm (days > 20mm; in days yr-1 ); (7) the number of months with precipitation below 100 mm (months < 100mm; in months yr-1 ) ; (8) lightning frequency (flashes rate); (9) annual precipitation (in mm); (10) potential evapotranspiration (in mm); (11) coefficient of variation of precipitation (precipitation seasonality; in %); (12) amount of precipitation on the wettest month (precip. wettest; in mm); (13) amount of precipitation on the driest month (precip. driest; in mm); (14) mean annual temperature (in °C); (15)  standard deviation of temperature (temp. seasonality; in °C); (16) annual maximum temperature (in °C); (17) soil clay content (in %); and (18) soil water content (in %). Among the initial 18 environmental variables, two of them (precipitation on driest month and months < 100mm) were excluded due to high correlation (> 0.80) to other independent variables.
dc.identifierhttps://doi.org/10.5281/zenodo.4061838
dc.identifier.urihttps://hydatakatalogi-test-24.it.helsinki.fi/handle/123456789/11240
dc.rightsOpen
dc.rights.licensecc-by-4.0
dc.subjectmachine learning
dc.subjecttree height
dc.subjectamazon
dc.titleRandom Forest trained to estimate Amazon maximum height based on enviromental factors
dc.typesoftware
dc.typesoftware

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