A rapid and accurate method of mapping invasive Tamarix genotypes using Sentinel-2 images

Solomon Wakshom Newete; Samalesu Mayonde; Thabiso Kekana; Elhadi Adam

doi:10.7717/peerj.15027

A rapid and accurate method of mapping invasive Tamarix genotypes using Sentinel-2 images

PeerJ. 2023 Apr 17:11:e15027. doi: 10.7717/peerj.15027. eCollection 2023.

Authors

Solomon Wakshom Newete^#^{1

2}, Samalesu Mayonde^#², Thabiso Kekana^{3

4}, Elhadi Adam³

Affiliations

¹ Geoinformatics Division, Agricultural Research Council-Natural Resources and Engineering, Pretoria, Gauteng, South Africa.
² Animal Plant and Environmental Sciences, University of the Witwatersrand, Johannesburg, Gauteng, South Africa.
³ School of Geography, Archeology and Environmental Studies, University of the Witwatersrand, Johannesburg, Gauteng, South Africa.
⁴ Engineer Terrain Intelligence Regime, South African Army, Thaba, Tshwane, South Africa.

^# Contributed equally.

Abstract

Background: The management of invasive Tamarix genotypes depends on reliable and accurate information of their extent and distribution. This study investigated the utility of the multispectral Sentinel-2 imageries to map infestations of the invasive Tamarix along three riparian ecosystems in the Western Cape Province of South Africa.

Methods: The Sentinel-2 image was acquired from the GloVis website (http://glovis.usgs.gov/). Random forest (RF) and support vector machine (SVM) algorithms were used to classify and estimate the spatial distribution of invasive Tamarix genotypes and other land-cover types in three riparian zones viz. the Leeu, Swart and Olifants rivers. A total of 888 reference points comprising of actual 86 GPS points and additional 802 points digitized using the Google Earth Pro free software were used to ground-truth the Sentinel-2 image classification.

Results: The results showed the random forest classification produced an overall accuracy of 87.83% (with kappa value of 0.85), while SVM achieved an overall accuracy of 86.31% with kappa value of 0.83. The classification results revealed that the Tamarix invasion was more rampant along the Olifants River near De Rust with a spatial distribution of 913.39 and 857.74 ha based on the RF and SVM classifiers, respectively followed by the Swart River with Tamarix coverage of 420.06 ha and 715.46 hectares, respectively. The smallest extent of Tamarix invasion with only 113.52 and 74.27 hectares for SVM and RF, respectively was found in the Leeu River. Considering the overall accuracy of 85% as the lowest benchmark for a robust classification, the results obtained in this study suggests that the SVM and RF classification of the Sentinel-2 imageries were effective and suitable to map invasive Tamarix genotypes and discriminate them from other land-cover types.

Keywords: Alien invasive plants; Image classifications; Olifants river; Overal accuracies; Random forest; Spatial distribution; Support vector machine; Tamaricaceae.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Ecosystem*
Rivers
Software
Tamaricaceae*

Associated data

figshare/10.6084/m9.figshare.20509650.v1

Grants and funding

This work was supported by the National Research Foundation-South Africa to Solomon Newete (NRF Grant No: 114345). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.