Impact of ecological redundancy on the performance of machine learning classifiers in vegetation mapping
Date
2017
Journal Title
Journal ISSN
Volume Title
Publisher
Wiley Open Access
Abstract
Vegetation maps are models of the real vegetation patterns and are considered important
tools in conservation and management planning. Maps created through traditional methods can be expensive and time-consuming,
thus, new more efficient
approaches are needed. The prediction of vegetation patterns using machine learning
shows promise, but many factors may impact on its performance. One important
factor is the nature of the vegetation–environment relationship assessed and ecological
redundancy. We used two datasets with known ecological redundancy levels
(strength of the vegetation–environment relationship) to evaluate the performance
of four machine learning (ML) classifiers (classification trees, random forests, support
vector machines, and nearest neighbor). These models used climatic and soil variables
as environmental predictors with pretreatment of the datasets (principal component
analysis and feature selection) and involved three spatial scales. We show that
the ML classifiers produced more reliable results in regions where the vegetation–
environment relationship is stronger as opposed to regions characterized by redundant
vegetation patterns. The pretreatment of datasets and reduction in prediction
scale had a substantial influence on the predictive performance of the classifiers. The
use of ML classifiers to create potential vegetation maps shows promise as a more
efficient way of vegetation modeling. The difference in performance between areas
with poorly versus well-structured
vegetation–environment relationships shows that
some level of understanding of the ecology of the target region is required prior to
their application. Even in areas with poorly structured vegetation–environment relationships,
it is possible to improve classifier performance by either pretreating the
dataset or reducing the spatial scale of the predictions.
Description
CITATION: Macintyre, P. D., et al. 2018. Impact of ecological redundancy on the performance of machine learning classifiers in vegetation mapping. Ecology and Evolution, 8(13):6728-6737, doi:10.1002/ece3.4176.
The original publication is available at https://onlinelibrary.wiley.com
The original publication is available at https://onlinelibrary.wiley.com
Keywords
Vegetation mapping, Machine learning
Citation
Macintyre, P. D., et al. 2018. Impact of ecological redundancy on the performance of machine learning classifiers in vegetation mapping. Ecology and Evolution, 8(13):6728-6737, doi:10.1002/ece3.4176