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Remote Sensing for Management of Invasive Species

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: 28 June 2024 | Viewed by 2618

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Special Issue Editor

Dr. John Dymond

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Guest Editor

Manaaki Whenua—Landcare Research, Palmerston North 4472, New Zealand
Interests: environmental monitoring; remote sensing; environmental modelling

Special Issue Information

Dear Colleagues,

Invasive species have devastating effects on ecosystems and economies. They are alien plants, animals, or micro-organisms that are harmful to ecosystems through excessive success in distribution. Often, they are spread by human activities, intentionally or unintentionally, but climate change can also be responsible by giving certain species advantages, which makes them become aggressive and invasive. Spatial information on the status of invasion is essential for understanding drivers and guiding management response, such as prevention, eradication, or control. Remote sensing can be used to map and monitor the spread of invasive species and impact, but it has been challenging as invasive species and impacts are often difficult to detect from space. The advent of new data and methods is improving utility, especially integration with ground surveillance and policy response.

The aim of this Special Issue on “Remote Sensing for Management of Invasive Species” is to bring together recent advances in the field of remote sensing for application to invasive species management. Not only are new remotely sensed data and new analysis methods being used to map more accurate and cost-effective maps of invasive species and their environmental impacts, but new approaches are being developed for integrating remotely sensed data with ground data to provide response managers with more useful information. As such, the themes of this Special Issue will not only cover new remote sensing methods, but also how those methods can provide useful information for practical management of biological invasions.

Contributions focusing on the following themes are welcome for this Special Issue:

Mapping and monitoring invasive species;
Mapping and monitoring impact of invasive species;
Monitoring and predicting distribution of invasive species;
The use of remotely sensed information for helping manage response to biological invasions;
Integration of remotely sensed data with ground data to help manage biological invasions;
New data and methods for detection of invasive species.

Dr. John Dymond
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

invasive species and pathogens
remote sensing
machine learning
mapping invasive species
monitoring environmental impact
species distribution modelling
invasive species management
biocontrol
pest and weed control

Published Papers (3 papers)

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Research

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30 pages, 14644 KiB

Open AccessArticle

Integrating Artificial Intelligence and UAV-Acquired Multispectral Imagery for the Mapping of Invasive Plant Species in Complex Natural Environments

by Narmilan Amarasingam, Fernando Vanegas, Melissa Hele, Angus Warfield and Felipe Gonzalez

Remote Sens. 2024, 16(9), 1582; https://doi.org/10.3390/rs16091582 - 29 Apr 2024

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Abstract

The proliferation of invasive plant species poses a significant ecological threat, necessitating effective mapping strategies for control and conservation efforts. Existing studies employing unmanned aerial vehicles (UAVs) and multispectral (MS) sensors in complex natural environments have predominantly relied on classical machine learning (ML) models for mapping plant species in natural environments. However, a critical gap exists in the literature regarding the use of deep learning (DL) techniques that integrate MS data and vegetation indices (VIs) with different feature extraction techniques to map invasive species in complex natural environments. This research addresses this gap by focusing on mapping the distribution of the Broad-leaved pepper (BLP) along the coastal strip in the Sunshine Coast region of Southern Queensland in Australia. The methodology employs a dual approach, utilising classical ML models including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) in conjunction with the U-Net DL model. This comparative analysis allows for an in-depth evaluation of the performance and effectiveness of both classical ML and advanced DL techniques in mapping the distribution of BLP along the coastal strip. Results indicate that the DL U-Net model outperforms classical ML models, achieving a precision of 83%, recall of 81%, and F1–score of 82% for BLP classification during training and validation. The DL U-Net model attains a precision of 86%, recall of 76%, and F1–score of 81% for BLP classification, along with an Intersection over Union (IoU) of 68% on the separate test dataset not used for training. These findings contribute valuable insights to environmental conservation efforts, emphasising the significance of integrating MS data with DL techniques for the accurate mapping of invasive plant species. Full article

(This article belongs to the Special Issue Remote Sensing for Management of Invasive Species)

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56 pages, 143133 KiB

Open AccessArticle

Analysis and Quantification of the Distribution of Marabou (Dichrostachys cinerea (L.) Wight & Arn.) in Valle de los Ingenios, Cuba: A Remote Sensing Approach

by Eduardo Moreno, Encarnación Gonzalez, Reinaldo Alvarez and Julio Menendez

Remote Sens. 2024, 16(5), 752; https://doi.org/10.3390/rs16050752 - 21 Feb 2024

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Abstract

Cuba is struggling with a growing environmental problem: the uncontrolled spread of the allochthonous weed species marabou (Dichrostachys cinerea) throughout the country. Over the last 70 years, marabou has become a formidable invasive species that poses a threat to Cuban biodiversity and agricultural productivity. In this paper, we present a free and affordable method for regularly mapping the spatial distribution of the marabou based on the Google Earth Engine platform and ecological surveys. To test its accuracy, we develop an 18-year remote sensing analysis (2000–2018) of marabou dynamics using the Valle de los Ingenios, a Cuban UNESCO World Heritage Site, as an experimental model. Our spatial analysis reveals clear patterns of marabou distribution and highlights areas of concentrated growth. Temporal trends illustrate the aggressive nature of the species, identifying periods of expansion and decline. In addition, our system is able to detect specific, large-scale human interventions against the marabou plague in the area. The results highlight the urgent need for remedial strategies to maintain the fragile ecological balance in the region. Full article

(This article belongs to the Special Issue Remote Sensing for Management of Invasive Species)

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Other

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16 pages, 17976 KiB

Open AccessTechnical Note

Advanced Detection of Invasive Neophytes in Agricultural Landscapes: A Multisensory and Multiscale Remote Sensing Approach

by Florian Thürkow, Christopher Günter Lorenz, Marion Pause and Jens Birger

Remote Sens. 2024, 16(3), 500; https://doi.org/10.3390/rs16030500 - 28 Jan 2024

Viewed by 721

Abstract

The sustainable provision of ecological products and services, both natural and man-made, faces a substantial threat emanating from invasive plant species (IPS), which inflict considerable economic and ecological harm on a global scale. They are widely recognized as one of the primary drivers of global biodiversity decline and have become the focal point of an increasing number of studies. The integration of remote sensing (RS) and geographic information systems (GIS) plays a pivotal role in their detection and classification across a diverse range of research endeavors, emphasizing the critical significance of accounting for the phenological stages of the targeted species when endeavoring to accurately delineate their distribution and occurrences. This study is centered on this fundamental premise, as it endeavors to amass terrestrial data encompassing the phenological stages and spectral attributes of the specified IPS, with the overarching objective of ascertaining the most opportune time frames for their detection. Moreover, it involves the development and validation of a detection and classification algorithm, harnessing a diverse array of RS datasets, including satellite and unmanned aerial vehicle (UAV) imagery spanning the spectrum from RGB to multispectral and near-infrared (NIR). Taken together, our investigation underscores the advantages of employing an array of RS datasets in conjunction with the phenological stages, offering an economically efficient and adaptable solution for the detection and monitoring of invasive plant species. Such insights hold the potential to inform both present and future policymaking pertaining to the management of invasive species in agricultural and natural ecosystems. Full article

(This article belongs to the Special Issue Remote Sensing for Management of Invasive Species)

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