Efficacy of multi-season Sentinel-2 imagery for compositional vegetation classification

• Published in: International journal of applied earth observation and geoinformation Vol. 85; p. 101980
• Main Authors: Macintyre, Paul, van Niekerk, Adriaan, Mucina, Ladislav
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-03-2020; Elsevier
• Subjects: Mapping; Multispectral; Phenology; Tasselled cap; Vegetation classification; Mapping; Tasselled cap; Multispectral; Phenology; Vegetation classification
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2019.101980

•Sentinel-2 imagery is effective at classifying compositional vegetation patterns.•Compositional patterns are finer than land use/land cover structures.•Multi-season Autumn/Spring feature set returns highest accuracy.•Highest overall accuracy from SVM (74%) and NN (72%) classifiers.•Sub-pixel ecological factors likely driving confusion.
Vegetation maps are essential tools for the conservation and management of landscapes as they contain essential information for informing conservation decisions. Traditionally, maps have been created using field-based approaches which, due to limitations in costs and time, restrict the size of the area for which they can be created and frequency at which they can be updated. With the increasing availability of satellite sensors providing multi-spectral imagery with high temporal frequency, new methods for efficient and accurate vegetation mapping have been developed. The objective of this study was to investigate to what extent multi-seasonal Sentinel-2 imagery can assist in mapping complex compositional classifications at fine spatial scales. We deliberately chose a challenging case study, namely a visually and structurally homogenous scrub vegetation (known as kwongan) of Western Australia. The classification scheme consists of 24 target classes and a random 60/40 split was used for model building and validation. We compared several multi-temporal (seasonal) feature sets, consisting of numerous combinations of spectral bands, vegetation indices as well as principal component and tasselled cap transformations, as input to four machine learning classifiers (Support Vector Machines; SVM, Nearest Neighbour; NN, Random Forests; RF, and Classification Trees; CT) to separate target classes. The results show that a multi-temporal feature set combining autumn and spring images sufficiently captured the phenological differences between the classes and produced the best results, with SVM (74%) and NN (72%) classifiers returning statistically superior results compared to RF (65%) and CT (50%). The SWIR spectral bands captured during spring, the greenness indices captured during spring and the tasselled cap transformations derived from the autumn image emerged as most informative, which suggests that ecological factors (e.g. shared species, patch dynamics) occurring at a sub-pixel level likely had the biggest impact on class confusion. However, despite these challenges, the results are auspicious and suggest that seasonal Sentinel-2 imagery has the potential to predict compositional vegetation classes with high accuracy. Further work is needed to determine whether these results are replicable in other vegetation types and regions.