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CC BY V4.0, Open Access
Journal article
Impact of ecological redundancy on the performance of machine learning classifiers in vegetation mapping
Ecology and evolution, Vol.8(13), pp.6728-6737
2018
PMCID: PMC6053567
PMID: 30038769
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.
Details
- Title
- Impact of ecological redundancy on the performance of machine learning classifiers in vegetation mapping
- Authors/Creators
- P. D. Macintyre (Author) - Univ Western Australia, Sch Biol Sci, Crawley, WA, AustraliaA. van Niekerk - Stellenbosch Univ, Ctr Geog Anal, Stellenbosch, South AfricaM. P. Dobrowolski - Univ Western Australia, Sch Biol Sci, Crawley, WA, AustraliaJ. L. TsakalosL. Mucina - Univ Western Australia, Sch Biol Sci, Crawley, WA, Australia
- Publication Details
- Ecology and evolution, Vol.8(13), pp.6728-6737
- Publisher
- John Wiley & Sons Ltd.
- Identifiers
- 991005548969307891
- Copyright
- © 2018 The Authors.
- Murdoch Affiliation
- Harry Butler Institute; Centre for Terrestrial Ecosystem Science and Sustainability
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.169 Remote Sensing
- 4.169.91 Vegetation Mapping
- Web Of Science research areas
- Ecology
- Evolutionary Biology
- ESI research areas
- Environment/Ecology