Dataset Open Access
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
Maximum tree height distribution estimated by the Random Forest model based on the environmental variables. To explore the influence and importance of the environmental variables for development in tree height, we employed Random Forest modeling, which consists of generating a large number of regression trees, each constructed considering a random data subset. The regression trees are used to identify the best sequence to split the solution space to estimate the output. Were considered 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 %).