Access the full text.
Sign up today, get DeepDyve free for 14 days.
References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.
Mapping vegetation communities requires considerable investment in field data collection, analysis and interpretation. The methods for data collection and analysis can significantly affect field time and the accuracy of the classifications. We test the ability of field data subsets and data pre-treatments to reproduce an intuitively derived vegetation classification within the Australian tropical savanna biome. The data subsets include all strata, upper strata, ground strata, and tree basal area. A range of multivariate techniques were used to describe patterns in the datasets as they related to the a priori vegetation classification. We tested the degree of floristic correlation among the data subsets and the extent to which several data transformations (square root, fourth root, presence or absence) improved the level of agreement between the numerically and the intuitively derived mapping units. Our results implied high redundancy in sampling both basal area and upper strata species cover, and the ground stratum was poorly correlated with the upper stratum. Across all statistical tests, the groups derived from analysis of square root-transformed upper stratum cover data were closely aligned with the expert classification. We propose that a numerical approach using an optimal dataset will produce a meaningful classification for vegetation mapping in poorly known Australian tropical savanna.
Australian Journal of Botany – CSIRO Publishing
Published: Aug 23, 2021
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.