Performances of machine learning algorithms for mapping fractional cover of an invasive plant species in a dryland ecosystem.

ABSTRACT: In recent years, an increasing number of distribution maps of invasive alien plant species (IAPS) have been published using different machine learning algorithms (MLAs). However, for designing spatially explicit management strategies, distribution maps should include information on the local cover/abundance of the IAPS. This study compares the performances of five MLAs: gradient boosting machine in two different implementations, random forest, support vector machine and deep learning neural network, one ensemble model and a generalized linear model; thereby identifying the best-performing ones in mapping the fractional cover/abundance and distribution of IPAS, in this case called Prosopis juliflora (SW. DC.). Field level Prosopis cover and spatial datasets of seventeen biophysical and anth

SUBMITTER: Shiferaw H 

PROVIDER: S-EPMC6405495 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature