Classification of urban tree species using LiDAR data and WorldView-2 satellite imagery in a heterogeneous environment

Abstract

Feature complexity and heterogeneity of urban areas pose a challenge for tree species classification. This study examined the effectiveness of the integrated Worldview-2 (WV-2) bands, vegetation indices and normalized Digital Surface Model (nDSM) dataset in mapping common urban tree species and other land use and land cover (LULC) types using Random Forest (RF) and Support Vector Machine (SVM) algorithms. The study also ranked the importance of nDSM, WV-2 bands and vegetation indices. The results indicate that the integrated dataset was effective as shown by high classification accuracies of 97% for the RF and 94% for SVM classifiers. The nDSM was the top-ranked variable with high Mean Decrease in Accuracy (MDA) and Mean Decrease in Gini (MDG) scores of 0.98 and 0.61, respectively. This research provides information to municipalities on the methods and data that can be used for the sustainable management of urban tree species.

Keywords:

• Urban tree species
• LiDAR
• Normalized Digital Surface Model (nDSM)
• WorldView-2
• Machine learning algorithms

Acknowledgements

We thank the municipality of Johannesburg’s Corporate Geo-Informatics, Development Planning and Urban Management (DP&UM) department for the LiDAR data and the WorldView-2 imagery. We also thank the anonymous reviewers for their valuable comments, which significantly improved our paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from the municipality of Johannesburg’s Corporate Geo-Informatics, Development Planning and Urban Management (DP&UM) department. Restrictions apply to the availability of these data, which were used under license for this study.