Dense neural network outperforms other machine learning models for scaling-up lichen cover maps in Eastern Canada.

ABSTRACT: Lichen mapping is vital for caribou management plans and sustainable land conservation. Previous studies have used random forest, dense neural network, and convolutional neural network models for mapping lichen coverage. However, to date, it is not clear how these models rank in this task. In this study, these machine learning models were evaluated on their ability to predict lichen percent coverage in Sentinel-2 imagery in Québec and Labrador, Canada. The models were trained on 10-m resolution lichen coverage (%) maps created from 20 drone surveys collected in July 2019 and 2022. The dense neural network achieved a higher accuracy than the other two, with a reported mean absolute error of 5.2% and an R2 of 0.76. By comparison, the random forest model returned a mean absolute error of 5.5%

SUBMITTER: Richardson G 

PROVIDER: S-EPMC10659193 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature