2.3 Remotely-sensed data as model predictors
As a cloud-based platform, Google Earth Engine (GEE) provides easy access to an extensive catalog of satellite imagery and other geospatial data for scientific, business and government users (Gorelick et al., 2017). We obtained a combination of topographic, climatic, and vegetation derived variables with pixel sizes of 1000 m (Supplementary Table S1) for the period of 2009 to 2014 from GEE to assemble a nation-wide geospatial dataset to use as predictors in tree height and tree density models.
Datasets included WorldClim V1; a set of bioclimatic variables derived from the monthly temperature and rainfall (Hijmans, 2005); time-series analysis of Landsat images from the Hansen Global Forest Change v1.8 (2000-2020) dataset (Hansen et al., 2013); 4-day composite dataset from Moderate Resolution Imaging Spectro-radiometer (MODIS) sensors with fraction of photosynthetic active radiation and leaf area index at 500-m resolution (Myneni,  Ranga et al., 2015) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Database (2000-2008) (Hulley et al., 2009, 2012, 2015; Hulley & Hook, 2008, 2009, 2011; NASA JPL, 2014) (Supporting Information, Table S1). All covariates were resampled to 1000 m. The resampling was done with conventional bilinear interpolation as implemented in GEE. Data available from Zenodo under the name “Nationwide geospatial dataset of environmental covariates at 1km resolution in Mexico” (https://doi.org/10.5281/zenodo.7130164) (Barreras & Guevara, 2022).
We reduced the number of potential predictor layers to 6, through a culling process guided by an analysis of the correlation between each potential predictor and the target variables (tree height and density). We ranked the variables based on the magnitude of their correlation coefficients. The intent of this data reduction step was to improve the efficiency of our modeling framework. These univariate correlation results are only included to give a sense of the directionality of the relationships with target variables, but do not suggest causality.