Employing Machine Learning for Detection of Invasive Species using Sentinel-2 and AVIRIS Data: The Case of Kudzu in the United States

Tobias Jensen, Frederik Seerup Hass, Mohammad Seam Akbar, Philip Holm Petersen and Jamal Jokar Arsanjani
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Tobias Jensen: Department of Planning, Geography and Surveying, Aalborg University Copenhagen, A.C Meyers Vænge 15, 2450 Copenhagen, Denmark
Frederik Seerup Hass: Department of Planning, Geography and Surveying, Aalborg University Copenhagen, A.C Meyers Vænge 15, 2450 Copenhagen, Denmark
Mohammad Seam Akbar: Department of Planning, Geography and Surveying, Aalborg University Copenhagen, A.C Meyers Vænge 15, 2450 Copenhagen, Denmark
Philip Holm Petersen: Department of Planning, Geography and Surveying, Aalborg University Copenhagen, A.C Meyers Vænge 15, 2450 Copenhagen, Denmark
Jamal Jokar Arsanjani: Department of Planning, Geography and Surveying, Aalborg University Copenhagen, A.C Meyers Vænge 15, 2450 Copenhagen, Denmark

Sustainability, 2020, vol. 12, issue 9, 1-16

Abstract: Invasive plants are causing massive economic and environmental troubles for our societies worldwide. The aim of this study is to employ a set of machine learning classifiers for detecting invasive plant species using remote sensing data. The target species is Kudzu vine, which mostly grows in the south-eastern states of the US and quickly outcompetes other plants, making it a relevant and threatening species to consider. Our study area is Atlanta, Georgia and the surrounding area. Five different algorithms: Boosted Logistic Regression (BLR), Naive Bayes (NB), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM) were tested with the aim of testing their performance and identifying the most optimal one. Furthermore, the influence of temporal, spectral and spatial resolution in detecting Kudzu was also tested and reviewed. Our finding shows that random forest, neural network and support vector machine classifiers outperformed. While the achieved internal accuracies were about 97%, an external validation conducted over an expanded area of interest resulted in 79.5% accuracy. Furthermore, the study indicates that high accuracy classification can be achieved using multispectral Sentinel-2 imagery and can be improved while integrating with airborne visible/infrared imaging spectrometer (AVIRIS) hyperspectral data. Finally, this study indicates that dimensionality reduction methods such as principal component analysis (PCA) should be applied cautiously to the hyperspectral AVIRIS data to preserve its utility. The applied approach and the utilized set of methods can be of interest for detecting other kinds of invasive species as part of fulfilling UN sustainable development goals, particularly number 12: responsible consumption and production, 13: climate action, and 15: life on land.

Keywords: machine learning; invasive species; sentinel; classification; hyperspectral imagery; principal component analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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