Spatial predictions of tree density and tree height across Mexico´s
forests using ensemble learning and forest inventory data (2009-2014)
Abstract
The National Forestry Commission of Mexico continuously monitors forest
structure within the country’s continental territory by the
implementation of the National Forest and Soils Inventory (INFyS). Due
to the challenges involved in collecting data exclusively from field
surveys, there are spatial information gaps for important forest
attributes. This can produce bias or increase uncertainty when
generating estimates required to support forest management decisions.
Our objective is to predict the spatial distribution of tree height and
tree density in all Mexican forests. We performed wall-to-wall spatial
predictions of both attributes in 1-km grids, using ensemble machine
learning across each forest type in Mexico. Predictor variables include
remote sensing imagery and other geospatial data (e.g., vegetation
indexes, surface temperature). Training data is from the 2009-2014 cycle
(n>26,000 sampling plots). Spatial cross validation
suggested that the model had a better performance when predicting tree
height r2=0.4 [0.15,0.55] (mean[min, max]) than for tree density
r2=0.2[0.10,0.31]. Maximum values of tree height were for coniferous
forests, coniferous-broadleaf forests and cloud mountain forest
(~36 m, 30 m and 21 m, respectively). Tropical forests
had maximum values of tree density (~1370 trees/ha),
followed by tropical dry forest (1006 trees/ha) and coniferous forest
(988 trees/ha). Although most forests had relatively low values of
uncertainty, e.g., values <40%, arid and semiarid ecosystems
had high uncertainty in both tree height and tree density predictions,
e.g., values >60%. The applied open science approach we
present is easily replicable and scalable, thus it is helpful to assist
in the decision-making and future of the National Forest and Soils
Inventory. This work highlights the need for technical capabilities
aimed to use and resignify all the effort done by the Mexican Forestry
Commission in implementing the INFyS.