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

Gorgens, Eric Bastos; Nunes, Matheus Henrique; Jackson, Tobias; Coomes, David; Keller, Michael; Reis, Cristiano Rodrigues; Valbuena, Rubén; Rosette, Jacqueline; Almeida, Danilo Roberti Alves; Gimenez, Bruno; Cantinho, Roberta; Motta, Alline Zagnolli; Assis, Mauro; Pereira, Francisca Rocha de Souza; Spanner, Gustavo; Higuchi, Niro; Ometto, Jean Pierre

The 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.

Funding was provided by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Brasil (CAPES; Finance Code 001); Conselho Nacional de Desenvolvi­mento Científico e Tecnológico (Processes 403297/2016-8 and 301661/2019-7); Amazon Fund (grant 14.2.0929.1); Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM); Instituto Nacional de Pesquisas Espaciais (INPE); São Paulo Research Foundation (#2018/21338-3 and #2019/14697-0); INCT-Madeiras da Amazônia and Next Generation Ecosystem Experiments-Tropics (NGEE-Tropics), as part of DOE's Terrestrial Ecosystem Science Program – Contract No. DE-AC02-05CH11231; National Academy of Sciences and US Agency for International Development (grant AID-OAA-A-11-00012); Royal Society University Research Fellowship (URF\R\191014).
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