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

Tobias Jensen,Mohammad Seam Akbar,Philip Holm Petersen,Jamal Jokar Arsanjani,Frederik Seerup Hass

doi:10.3390/su12093544

Tobias Jensen, Mohammad Seam Akbar + Show 3 more

Open Access

https://doi.org/10.3390/su12093544

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Journal: Sustainability	Publication Date: Apr 27, 2020
Citations: 26	License type: CC BY 4.0

Affiliation: Aalborg University

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.

Highlights

Our finding shows that random forest, neural network and support vector machine classifiers outperformed
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
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

Summary

Introduction

An invasive species is defined by the Executive Order 13112 as “an alien species whose introduction does or is likely to cause economic or environmental harm or harm to human health” [1]. There is a substantial problem with invasive species in the U.S, which is causing a lot of harm both economically and environmentally [3]. Invasive species can have widespread impacts on societies ranging from reduced agricultural output and reducing populations of native species significantly due to invasive species having a competitive advantage in their new environment. Remote sensing technology and methods offer an opportunity to detect many different objects and entities on the ground and at sea, which includes different plant species. The methodology used to detect different types of plants is rooted in the target plant’s unique characteristics [6,7]

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