Spatial validation reveals poor predictive performance of large-scale ecological mapping models.

ABSTRACT: Mapping aboveground forest biomass is central for assessing the global carbon balance. However, current large-scale maps show strong disparities, despite good validation statistics of their underlying models. Here, we attribute this contradiction to a flaw in the validation methods, which ignore spatial autocorrelation (SAC) in data, leading to overoptimistic assessment of model predictive power. To illustrate this issue, we reproduce the approach of large-scale mapping studies using a massive forest inventory dataset of 11.8 million trees in central Africa to train and validate a random forest model based on multispectral and environmental variables. A standard nonspatial validation method suggests that the model predicts more than half of the forest biomass variation, while spatial valid

SUBMITTER: Ploton P 

PROVIDER: S-EPMC7486894 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature