Monitoring Invasive Plant Species Using Hyperspectral Remote Sensing Data

Levente Papp,János Mészáros,László Pásztor,Boudewijn Van Leeuwen,József Szatmári,Péter Szilassi,Mátyás Árvai,Zalán Tobak

doi:10.3390/land10010029

Abstract

The species richness and biodiversity of vegetation in Hungary are increasingly threatened by invasive plant species brought in from other continents and foreign ecosystems. These invasive plant species have spread aggressively in the natural and semi-natural habitats of Europe. Common milkweed (Asclepias syriaca) is one of the species that pose the greatest ecological menace. Therefore, the primary purpose of the present study is to map and monitor the spread of common milkweed, the most common invasive plant species in Europe. Furthermore, the possibilities to detect and validate this special invasive plant by analyzing hyperspectral remote sensing data were investigated. In combination with field reference data, high-resolution hyperspectral aerial images acquired by an unmanned aerial vehicle (UAV) platform in 138 spectral bands in areas infected by common milkweed were examined. Then, support vector machine (SVM) and artificial neural network (ANN) classification algorithms were applied to the highly accurate field reference data. As a result, common milkweed individuals were distinguished in hyperspectral images, achieving an overall accuracy of 92.95% in the case of supervised SVM classification. Using the ANN model, an overall accuracy of 99.61% was achieved. To evaluate the proposed approach, two experimental tests were conducted, and in both cases, we managed to distinguish the individual specimens within the large variety of spreading invasive species in a study area of 2 ha, based on centimeter spatial resolution hyperspectral UAV imagery.

Highlights

At present, nature conservation is encountering a major challenge to preserve natural habitats and biodiversity
We investigated the areas infected with common milkweed, weed, analyzing data acquired by a platform, and processed them considering the analyzing data acquired by a unmanned aerial vehicle (UAV) platform, and processed them considering the ground ground reference data
Different Machine Learning (ML) algorithms were compared in terms of the task of Machine Learning (ML) algorithms were compared in terms of automatically and delineating common milkweed plants basedofonthe thetask automatically recognizing and delineating common milkweed plants based on the orthomosaic

Summary

Introduction

Nature conservation is encountering a major challenge to preserve natural habitats and biodiversity. One of the most critical threats to biodiversity is the spread of invasive species that are non-native to a specific location. They spread aggressively and rapidly, covering large areas and adversely affecting native species and habitats. The importance of this topic has been highlighted by the United Nations (UN). The spread of invasive species is a serious issue in Hungary and Central Europe and at global level, being one of the major threats to biodiversity [6,7]

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