Spatial predictions of tree density and tree height across Mexico forests using ensemble learning and forest inventory data.

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 da

SUBMITTER: Barreras A 

PROVIDER: S-EPMC10200803 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature