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Special Issue "Remote Sensing of Invasive Alien Species—towards Effective Monitoring and Management"

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

Deadline for manuscript submissions: 31 October 2023 | Viewed by 8719

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Special Issue Editors

Prof. Dr. Barbara Tokarska-Guzik

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Guest Editor

Research Team of Botany and Nature Protection, Institute of Biology, Biotechnology and Environmental Protection, Faculty of Natural Sciences, University of Silesia in Katowice, Jagiellońska 28, 40-032 Katowice, Poland
Interests: ecology of invasions; plant geography; nature conservation
Special Issues, Collections and Topics in MDPI journals

Dr. Sylwia Szporak-Wasilewska

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Guest Editor

Warsaw University of Life Sciences—SGGW, Nowoursynowska 166, 02-787 Warsaw, Poland
Interests: environmental science; ecological engineering; geoinformatics; remote sensing; hydrological modeling

Special Issue Information

Dear colleagues,

Globalization through increased trade, transport, travel, and tourism inevitably increases the intentional or accidental introduction of various organisms to new environments. Invasive alien species (IAS) are recognized as one of the most important threats to biodiversity on a worldwide scale, and the problem has been addressed in many international and national documents.

Aichi Target 9 of the Strategic Plan for Biodiversity 2011-2020 under the Convention of Biological Diversity and Regulation (EU) 1143/2014 on invasive alien species (the IAS Regulation), fulfilling Action 16 of Target 5 of the EU 2020 Biodiversity Strategy, as well as national strategies on IAS, have outlined the essential goals that require research and education, practical actions, and organizational and legal solutions.

One of the crucial tasks is to develop objective and effective methods and tools to identify and map the distribution of IAS in various spatial and temporal scales. Remote sensing technologies in this field have advanced rapidly in recent years and are increasingly adopted in various fields of science and sectors of the economy (e.g., nature conservation, forestry, agriculture, water management, spatial planning).

This Special Issue aims to collect recent research results and experiences related to their implementation in practice. It is dedicated to the use of remote data acquisition technologies concerned with the detection, mapping, and monitoring of invasive alien species (IAS), enabling their widespread use in further research and in various sectors of the economy. We also encourage you to share research that did not deliver the expected results and to discuss the limitations of the methodology used, which will allow us to develop the most effective, transferable, and least costly procedures.

Topics include but are not limited to:

Influence of spatial and spectral resolution on the quality of invasive species detection—practical constraints and possible opportunities;
Effectiveness of data fusion in IAS identification;
Classification algorithms utilised in IAS identification;
Spectral discrimination of IAS;
Invasive species detection: weaknesses and strengths of remote sensing methods;
Distribution mapping of IAS and tracking their spread at different spatial scales;
Invasion monitoring with remote sensing for the development of the national, subnational or site-based observation and monitoring systems for IAS;
Early detection of IAS: new methods and variables relevant to use for rapid response;
Risk assessment of IAS through remote sensing;
Detection of landscape dynamics caused by IAS by the integration of multiple complementary tools;
Multitemporal analysis for IAS mapping;
Multiscale assessment of the occurrence and diversity of invasive plant species;
Management planning for protected areas and administrative units with the use of remote sensing and integrated tools;
Invasive species control programmes: the use of remote sensing in planning the control/eradication and evaluation of effects.

Prof. Dr. Barbara Tokarska-Guzik
Dr. Sylwia Szporak-Wasilewska
Guest Editors

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 2500 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

Spatial and spectral resolutions
Data fusion
Classification algorithms
Spectral discrimination
Invasive species
Distribution mapping
Invasion monitoring
Early detection
Risk assessment
Landscape dynamics
Multitemporal analysis
Multiscale assessment
Management planning
Invasive species control programs

Published Papers (5 papers)

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Research

Open AccessArticle

Improving Machine Learning Classifications of Phragmites australis Using Object-Based Image Analysis

and

Remote Sens. 2023, 15(4), 989; https://doi.org/10.3390/rs15040989 - 10 Feb 2023

Viewed by 751

Abstract

Uncrewed aircraft systems (UASs) are a popular tool when surveilling for invasive alien plants due to their high spatial and temporal resolution. This study investigated the efficacy of a UAS equipped with a three-band (i.e., red, green, blue; RGB) sensor to identify invasive Phragmites australis in multiple Minnesota wetlands using object-based image analysis (OBIA) and machine learning (ML) algorithms: artificial neural network (ANN), random forest (RF), and support vector machine (SVM). The addition of a post-ML classification OBIA workflow was tested to determine if ML classifications can be improved using OBIA techniques. Results from each ML algorithm were compared across study sites both with and without the post-ML OBIA workflow. ANN was identified as the best classifier when not incorporating a post-ML OBIA workflow with a classification accuracy of 88%. Each of the three ML algorithms achieved a classification accuracy of 91% when incorporating the post-ML OBIA workflow. Results from this study suggest that a post-ML OBIA workflow can increase the ability of ML algorithms to accurately identify invasive Phragmites australis and should be used when possible. Additionally, the decision of which ML algorithm to use for Phragmites mapping becomes less critical with the addition of a post-ML OBIA workflow. Full article

(This article belongs to the Special Issue Remote Sensing of Invasive Alien Species—towards Effective Monitoring and Management)

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Open AccessArticle

Passive Acoustic Monitoring as a Tool to Investigate the Spatial Distribution of Invasive Alien Species

José W. Ribeiro, Jr.

Kristopher Harmon

Gabriel Augusto Leite

Tomaz Nascimento de Melo

Jack LeBien

and

Marconi Campos-Cerqueira

Remote Sens. 2022, 14(18), 4565; https://doi.org/10.3390/rs14184565 - 13 Sep 2022

Cited by 3 | Viewed by 1945

Abstract

Invasive alien species (IAS) are a threat to biodiversity and ecosystem function worldwide. Unfortunately, researchers, agencies, and other management groups face the unresolved challenge of effectively detecting and monitoring IAS at large spatial and temporal scales. To improve the detection of soniferous IAS, we introduced a pipeline for large-scale passive acoustic monitoring (PAM). Our main goal was to illustrate how PAM can be used to rapidly provide baseline information on soniferous IAS. To that aim, we collected acoustic data across Puerto Rico from March to June 2021 and used single-species occupancy models to investigate species distribution of species in the archipelago and to assess the peak of vocal activity. Overall, we detected 16 IAS (10 birds, 3 mammals, and 3 frogs) and 79 native species in an extensive data set with 1,773,287 1-min recordings. Avian activity peaked early in the morning (between 5 a.m. and 7 a.m.), while amphibians peaked between 1 a.m. and 5 a.m. Occupancy probability for IAS in Puerto Rico ranged from 0.002 to 0.67. In general, elevation and forest cover older than 54 years were negatively associated with IAS occupancy, corroborating our expectation that IAS occurrence is related to high levels of human disturbance and present higher occupancy probabilities in places characterized by more intense human activities. The work presented here demonstrates that PAM is a workable solution for monitoring vocally active IAS over a large area and provides a reproducible workflow that can be extended to allow for continued monitoring over longer timeframes. Full article

(This article belongs to the Special Issue Remote Sensing of Invasive Alien Species—towards Effective Monitoring and Management)

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Open AccessArticle

Assessment of Invasive and Weed Species by Hyperspectral Imagery in Agrocenoses Ecosystem

Pavel A. Dmitriev

Boris L. Kozlovsky

Denis P. Kupriushkin

Anastasia A. Dmitrieva

and

Remote Sens. 2022, 14(10), 2442; https://doi.org/10.3390/rs14102442 - 19 May 2022

Cited by 4 | Viewed by 1111

Abstract

The present study aimed to investigate the possibility of using hyperspectral imaging data to identify the invasive and weed species in agrocenoses ecosystem. The most common weeds in grain agrocenoses, i.e., Ambrosia artemisiifolia L., Euphorbia seguieriana Neck., Atriplex tatarica L., Glycyrrhiza glabra L., Setaria pumila (Poir.) Roem. and Schult, served as objects. The population of weeds, especially Ambrosia artemisiifolia is invasive for the selected region of study. Therefore, the shooting of objects was carried out with a hyperspectral camera, Cubert UHD185, and the values of 100 spectral channels were obtained from hyperspectral images. The values of 80 vegetation indices (VIs) were calculated. The material was processed using mathematical statistics (analysis of variance, t-test) and search methods of data analysis (principal component analysis, decision tree, and random forest). Using statistical methods, the simultaneous use of several VIs differentiated between species more deliberately and precisely. The combination of VIs Derivative index (D1), Chlorophyll content index (Datt3), and Pigment specific normalized difference (PSND) can be used for weeds identification. Using the decision tree method, VIs established a good division of weeds into groups; (1) perennial rhizomatous weeds (Euphorbia seguieriana, and Glycyrrhiza glabra), and (2) annual weeds (A. artemisiifolia, A. tatarica, and S. pumila); These Vis are Chlorophyll index (CI), D1, and Datt3. Using the random forest method, the VIs that have the greatest impact on Mean Decrease Accuracy and Mean Decrease Gini are D1, Datt3, PSND, and Double Peak Index (DPI). The use of spectral channel values for the identification of plant species using the principal component analysis, decision tree, and random forest methods showed worse results than when using VIs. A great similarity of the results was obtained with the help of statistical and search methods of data analysis. Full article

(This article belongs to the Special Issue Remote Sensing of Invasive Alien Species—towards Effective Monitoring and Management)

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Open AccessArticle

Early Detection of Wild Rocket Tracheofusariosis Using Hyperspectral Image-Based Machine Learning

Catello Pane

Gelsomina Manganiello

Nicola Nicastro

and

Francesco Carotenuto

Remote Sens. 2022, 14(1), 84; https://doi.org/10.3390/rs14010084 - 24 Dec 2021

Cited by 4 | Viewed by 1995

Abstract

Fusarium oxysporum f. sp. raphani is responsible for wilting wild rocket (Diplotaxis tenuifolia L. [D.C.]). A machine learning model based on hyperspectral data was constructed to monitor disease progression. Thus, pathogenesis after artificial inoculation was monitored over a 15-day period by symptom assessment, qPCR pathogen quantification, and hyperspectral imaging. The host colonization by a pathogen evolved accordingly with symptoms as confirmed by qPCR. Spectral data showed differences as early as 5-day post infection and 12 hypespectral vegetation indices were selected to follow disease development. The hyperspectral dataset was used to feed the XGBoost machine learning algorithm with the aim of developing a model that discriminates between healthy and infected plants during the time. The multiple cross-prediction strategy of the pixel-level models was able to detect hyperspectral disease profiles with an average accuracy of 0.8. For healthy pixel detection, the mean Precision value was 0.78, the Recall was 0.88, and the F1 Score was 0.82. For infected pixel detection, the average evaluation metrics were Precision: 0.73, Recall: 0.57, and F1 Score: 0.63. Machine learning paves the way for automatic early detection of infected plants, even a few days after infection. Full article

(This article belongs to the Special Issue Remote Sensing of Invasive Alien Species—towards Effective Monitoring and Management)

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Graphical abstract

Open AccessArticle

Comparison of Different Analytical Strategies for Classifying Invasive Wetland Vegetation in Imagery from Unpiloted Aerial Systems (UAS)

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Remote Sens. 2021, 13(23), 4733; https://doi.org/10.3390/rs13234733 - 23 Nov 2021

Viewed by 1487

Abstract

Detecting newly established invasive plants is key to prevent further spread. Traditional field surveys are challenging and often insufficient to identify the presence and extent of invasions. This is particularly true for wetland ecosystems because of difficult access, and because floating and submergent plants may go undetected in the understory of emergent plants. Unpiloted aerial systems (UAS) have the potential to revolutionize how we monitor invasive vegetation in wetlands, but key components of the data collection and analysis workflow have not been defined. In this study, we conducted a rigorous comparison of different methodologies for mapping invasive Emergent (Typha × glauca (cattail)), Floating (Hydrocharis morsus-ranae (European frogbit)), and Submergent species (Chara spp. and Elodea canadensis) using the machine learning classifier, random forest, in a Great Lakes wetland. We compared accuracies using (a) different spatial resolutions (11 cm pixels vs. 3 cm pixels), (b) two classification approaches (pixel- vs. object-based), and (c) including structural measurements (e.g., surface/canopy height models and rugosity as textural metrics). Surprisingly, the coarser resolution (11 cm) data yielded the highest overall accuracy (OA) of 81.4%, 2.5% higher than the best performing model of the finer (3 cm) resolution data. Similarly, the Mean Area Under the Receiving Operations Characteristics Curve (AUROC) and F1 Score from the 11 cm data yielded 15.2%, and 6.5% higher scores, respectively, than those in the 3 cm data. At each spatial resolution, the top performing models were from pixel-based approaches and included surface model data over those with canopy height or multispectral data alone. Overall, high-resolution maps generated from UAS classifications will enable early detection and control of invasive plants. Our workflow is likely applicable to other wetland ecosystems threatened by invasive plants throughout the globe. Full article

(This article belongs to the Special Issue Remote Sensing of Invasive Alien Species—towards Effective Monitoring and Management)

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