Published September 18, 2020 | Version v1
Dataset Open

L4B - Maximum tree height map for the Brazilian Amazon

  • 1. Universidade Federal dos Vales do Jequitinhonha e Mucuri
  • 2. University of Helsinki
  • 3. University of Cambridge
  • 4. United States Forest Service
  • 5. Universidade de São Paulo
  • 6. Bangor University
  • 7. Swansea University
  • 8. Instituto Nacional de Pesquisas da Amazônia
  • 9. Universidade de Brasília
  • 10. Instituto Nacional de Pesquisas Espaciais


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 %). 


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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Journal article: 10.1111/gcb.15423 (DOI)


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