Code and Data Availability of the manuscript: Mapping tree diversity in the tropical forest region of Chocó-Colombia

Code and Data Availability of the manuscript: Mapping tree diversity in the tropical forest region of Chocó-Colombia

In revision at Environmental Research letters.

J. Camilo Fagua1*, Patrick Jantz1, Patrick Burns1, Richard Massey1, Jeiner Y. Buitrago2, Sassan Saatchi3

and Scott J. Goetz1

1 Global Earth Observation & Dynamics of Ecosystems Lab (GEODE), School of Informatics, Computing, and Cyber Systems (SICCS), Northern Arizona University, Flagstaff, Arizona, US.

2 Department of Agronomy, Faculty of Agricultural Sciences, Universidad Nacional de Colombia. Bogota (Colombia).

3 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

*Corresponding author: J. Camilo Fagua, School of Informatics, Computing, and Cyber Systems (Building #90) 1295 S. Knoles Dr. Flagstaff, AZ 86011. Email: [e-mail]

1) The files “0_Forest_Structure_maps_in_tif” and “1_Forest_Structure_maps_in_tif” contain seven maps of forest structural metrics for the Chocó region of Colombia in the raster format .tif

(a) Canopy height (CH) map: CHOCO_MAP_CH

(b) 50th percentile of heights (z_50) map: CHOCO_MAP_z_50

(c) Vertical Canopy Heterogeneity (VCH) map:  CHOCO_MAP_VCH

(d) Standard deviation of normalized heights (z_SD) map: CHOCO_MAP_z_SD

(e) Horizontal standard deviation of canopy height (CH_ SD):  CHOCO_MAP_CH_SD

(f) Horizontal heterogeneity of canopy height (CH_H): CHOCO_MAP_CH_H

(g) Rumple Index of canopy height (R_INDEX): CHOCO_MAP_R_INDEX

2) The file “2_Diversity_maps_in_tif” contains five maps of forest structural metrics for the Chocó region of Colombia in the raster format .tif

(a) Species richness (Richness): MAPS_MEAN_Richness

(b) Shannon’s entropy index (ENS-Shannon): MAPS_MEAN_ENS_shannon

(c) Simpson’s concentration index (ENS-Simpson): MAPS_MEAN_ENS_Simpson

(d) Shannon diversity index (H): MAPS_MEAN_Shannon

(e) Simpson diversity index (D): MAPS_MEAN_Simpson

3) The file “3_LiDAR_processing” contents data and R codes to estimate forest structural metrics from ALS-LiDAR data (.las) in an Amazon moist forest.