Geomatics and Environmental Engineering, vol. 17, no. 1

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Vol. 17 No. 1 (2023): Geomatics and Environmental Engineering

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Submitted
May 18, 2022
Accepted
Oct 5, 2022
Published
Dec 11, 2022
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This work is licensed under a Creative Commons Attribution 4.0 International License.

Integrating Vegetation Indices and Spectral Features for Vegetation Mapping from Multispectral Satellite Imagery Using AdaBoost and Random Forest Machine Learning Classifiers

https://doi.org/10.7494/geom.2023.17.1.57
Rashmi Saini
Department of CSE, G. B. Pant Institute of Engineering and Technology, Pauri Garhwal, India
https://orcid.org/0000-0002-9802-7089

Corresponding Author(s) : Rashmi Saini

2rashmisaini@gmail.com

Geomatics and Environmental Engineering, Vol. 17 No. 1 (2023): Geomatics and Environmental Engineering

Abstract

Vegetation mapping is an active research area in the domain of remote sensing. This study proposes a methodology for the mapping of vegetation by integrating several vegetation indices along with original spectral bands. The Land Use Land Cover classification was performed by two powerful Machine Learning techniques, namely Random Forest and AdaBoost. The Random Forest algorithm works on the concept of building multiple decision trees for the final prediction. The other Machine Learning technique selected for the classification is AdaBoost (adaptive boosting), converts a set of weak learners into strong learners. Here, multispectral satellite data of Dehradun, India, was utilised. The results demonstrate an increase of 3.87% and 4.32% after inclusion of selected vegetation indices by Random Forest and AdaBoost respectively. An Overall Accuracy (OA) of 91.23% (kappa value of 0.89) and 88.59% (kappa value of 0.86) was obtained by means of the Random Forest and AdaBoost classifiers respectively. Although Random Forest achieved greater OA as compared to AdaBoost, interestingly AdaBoost provided better class-specific accuracy for the Shrubland class compared to Random Forest. Furthermore, this study also evaluated the importance of each individual feature used in the classification. Results demonstrated that the NDRE, GNDVI, and RTVIcore vegetation indices, and spectral bands (NIR, and Red-Edge), obtained higher importance scores.

Keywords

ensemble classifiers Machine Learning Random Forest AdaBoost vegetation mapping vegetation indices
Saini, R. (2022). Integrating Vegetation Indices and Spectral Features for Vegetation Mapping from Multispectral Satellite Imagery Using AdaBoost and Random Forest Machine Learning Classifiers. Geomatics and Environmental Engineering, 17(1), 57–74. https://doi.org/10.7494/geom.2023.17.1.57
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