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

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 19379

Special Issue Editors


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Guest Editor
Institute of Biology Biotechnology and Environmental Protection, University of Silesia in Katowice, 40-032 Katowice, Poland
Interests: biodiversity and ecosystem services; ecology and distribution of invasive species; nature conservation; remote sensing
Special Issues, Collections and Topics in MDPI journals

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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 Issues, Collections and Topics in MDPI journals

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 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

  • 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

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Published Papers (9 papers)

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Research

21 pages, 10024 KiB  
Article
Sentinel-2 versus PlanetScope Images for Goldenrod Invasive Plant Species Mapping
by Bogdan Zagajewski, Marcin Kluczek, Karolina Barbara Zdunek and David Holland
Remote Sens. 2024, 16(4), 636; https://doi.org/10.3390/rs16040636 - 08 Feb 2024
Viewed by 1022
Abstract
A proliferation of invasive species is displacing native species, occupying their habitats and degrading biodiversity. One of these is the invasive goldenrod (Solidago spp.), characterized by aggressive growth that results in habitat disruption as it outcompetes native plants. This invasiveness also leads [...] Read more.
A proliferation of invasive species is displacing native species, occupying their habitats and degrading biodiversity. One of these is the invasive goldenrod (Solidago spp.), characterized by aggressive growth that results in habitat disruption as it outcompetes native plants. This invasiveness also leads to altered soil composition through the release of allelopathic chemicals, complicating control efforts and making it challenging to maintain ecological balance in affected areas. The research goal was to develop methods that allow the analysis of changes in heterogeneous habitats with high accuracy and repeatability. For this reason, we used open source classifiers Support Vector Machine (SVM), Random Forest (RF), and satellite images of Sentinel-2 (free) and PlanetScope (commercial) to assess their potential in goldenrod classification. Due to the fact that invasions begin with invasion footholds, created by small patches of invasive, autochthonous plants and different land cover patterns (asphalt, concrete, buildings) forming heterogeneous areas, we based our studies on field-verified polygons, which allowed the selection of randomized pixels for the training and validation of iterative classifications. The results confirmed that the optimal solution is the use of multitemporal Sentinel-2 images and the RF classifier, as this combination gave F1-score accuracy of 0.92–0.95 for polygons dominated by goldenrod and 0.85–0.89 for heterogeneous areas where goldenrod was in the minority (mix class; smaller share of goldenrod in canopy than autochthonous plants). The mean decrease in the accuracy analysis (MDA), indicating an informativeness of individual spectral bands, showed that Sentinel-2 bands coastal aerosol, NIR, green, SWIR, and red were comparably important, while in the case of PlanetScope data, the NIR and red were definitely the most important, and remaining bands were less informative, and yellow (B5) did not contribute significant information even during the flowering period, when the plant was covered with intensely yellow perianth, and red-edge, coastal aerosol, or green II were much more important. The maximum RF classification values of Sentinel-2 and PlanetScope images for goldenrod are similar (F1-score > 0.9), but the medians are lower for PlanetScope data, especially with the SVM algorithm. Full article
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19 pages, 4574 KiB  
Article
Automated Hyperspectral Feature Selection and Classification of Wildlife Using Uncrewed Aerial Vehicles
by Daniel McCraine, Sathishkumar Samiappan, Leon Kohler, Timo Sullivan and David J. Will
Remote Sens. 2024, 16(2), 406; https://doi.org/10.3390/rs16020406 - 20 Jan 2024
Viewed by 962
Abstract
Timely and accurate detection and estimation of animal abundance is an important part of wildlife management. This is particularly true for invasive species where cost-effective tools are needed to enable landscape-scale surveillance and management responses, especially when targeting low-density populations residing in dense [...] Read more.
Timely and accurate detection and estimation of animal abundance is an important part of wildlife management. This is particularly true for invasive species where cost-effective tools are needed to enable landscape-scale surveillance and management responses, especially when targeting low-density populations residing in dense vegetation and under canopies. This research focused on investigating the feasibility and practicality of using uncrewed aerial systems (UAS) and hyperspectral imagery (HSI) to classify animals in the wild on a spectral—rather than spatial—basis, in the hopes of developing methods to accurately classify animal targets even when their form may be significantly obscured. We collected HSI of four species of large mammals reported as invasive species on islands: cow (Bos taurus), horse (Equus caballus), deer (Odocoileus virginianus), and goat (Capra hircus) from a small UAS. Our objectives of this study were to (a) create a hyperspectral library of the four mammal species, (b) study the efficacy of HSI for animal classification by only using the spectral information via statistical separation, (c) study the efficacy of sequential and deep learning neural networks to classify the HSI pixels, (d) simulate five-band multispectral data from HSI and study its effectiveness for automated supervised classification, and (e) assess the ability of using HSI for invasive wildlife detection. Image classification models using sequential neural networks and one-dimensional convolutional neural networks were developed and tested. The results showed that the information from HSI derived using dimensionality reduction techniques were sufficient to classify the four species with class F1 scores all above 0.85. The performances of some classifiers were capable of reaching an overall accuracy over 98%and class F1 scores above 0.75, thus using only spectra to classify animals to species from existing sensors is feasible. This study discovered various challenges associated with the use of HSI for animal detection, particularly intra-class and seasonal variations in spectral reflectance and the practicalities of collecting and analyzing HSI data over large meaningful areas within an operational context. To make the use of spectral data a practical tool for wildlife and invasive animal management, further research into spectral profiles under a variety of real-world conditions, optimization of sensor spectra selection, and the development of on-board real-time analytics are needed. Full article
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31 pages, 16924 KiB  
Article
Assessing the Current and Future Potential Distribution of Solanum rostratum Dunal in China Using Multisource Remote Sensing Data and Principal Component Analysis
by Tiecheng Huang, Tong Yang, Kun Wang and Wenjiang Huang
Remote Sens. 2024, 16(2), 271; https://doi.org/10.3390/rs16020271 - 10 Jan 2024
Viewed by 769
Abstract
Accurate information concerning the spatial distribution of invasive alien species’ habitats is essential for invasive species prevention and management, and ecological sustainability. Currently, nationwide identification of suitable habitats for the highly destructive and potentially invasive weed, Solanum rostratum Dunal (S. rostratum), [...] Read more.
Accurate information concerning the spatial distribution of invasive alien species’ habitats is essential for invasive species prevention and management, and ecological sustainability. Currently, nationwide identification of suitable habitats for the highly destructive and potentially invasive weed, Solanum rostratum Dunal (S. rostratum), poses a series of challenges. Simultaneously, research on potential future invasion areas and likely directions of spread has not received adequate attention. This study, based on species occurrence data and multi-dimensional environmental variables constructed from multi-source remote sensing data, utilized Principal Component Analysis (PCA) in combination with the Maxent model to effectively model the current and future potential habitat distribution of S. rostratum in China, while quantitatively assessing the various factors influencing its distribution. Research findings indicate that the current suitable habitat area of S. rostratum covers 1.3952 million km2, all of which is located in northern China. As the trend of climate warming persists, the potential habitat suitability range of S. rostratum is projected to shift southward and expand in the future; while still predominantly located in northern China, it will have varying degrees of expansion at different time frames. Notably, during the period from 2040 to 2061, under the SSP1-2.6 scenario, the habitat area exhibits the most significant increase, surpassing the current scenario by 19.23%. Furthermore, attribution analysis based on PCA inverse transformation reveals that a combination of soil, climate, spatial, humanistic, and topographic variables collectively influence the suitability of S. rostratum habitats, with soil factors, in particular, playing a dominant role and contributing up to 75.85%. This study identifies target areas for the management and control of S. rostratum, providing valuable insights into factor selection and variable screening methods in species distribution modeling (SDM). Full article
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16 pages, 4581 KiB  
Article
The Sky Is Not the Limit: Use of a Spray Drone for the Precise Application of Herbicide and Control of an Invasive Plant in Managed Wetlands
by John Y. Takekawa, Jason S. Hagani, Timothy J. Edmunds, Jesirae M. Collins, Steven C. Chappell and William H. Reynolds
Remote Sens. 2023, 15(15), 3845; https://doi.org/10.3390/rs15153845 - 02 Aug 2023
Cited by 1 | Viewed by 1453
Abstract
Controlling non-native plant invasions that reduce the quality of preferred wetland habitats is a challenge for many wetland managers. Herbicides may be used to control invasions, but it may be difficult to find effective application methods depending on the terrain. Manned aircraft cover [...] Read more.
Controlling non-native plant invasions that reduce the quality of preferred wetland habitats is a challenge for many wetland managers. Herbicides may be used to control invasions, but it may be difficult to find effective application methods depending on the terrain. Manned aircraft cover large patches, but aerial use is limited by high costs, weather conditions, and overspray concerns. Ground applications target smaller patches, but their effectiveness may be limited by accessibility, labor costs, and applicator health concerns. Considering these difficulties, unmanned aerial systems (UAS) have emerged as a viable alternative for more effectively treating plant invasions. We tested the use of a specialized UAS to control invasive perennial pepperweed (Lepidium latifolium) in Suisun Marsh in northern California, USA. This “spray drone” flew at an altitude of 2–3 m, a speed of 24 kmph, and applied herbicide with a swath width of 6 m. We applied herbicide with the spray drone to treat small patches before they expanded. To delineate invasive patch boundaries, we first flew a survey drone with a 4K resolution camera to detect emerging plants with color imagery and conduct an initial classification analysis. We subsequently visited areas with suspected invasive patches based on the classification, and observers manually confirmed the presence of the invasive species. We then flew the spray drone on transects to treat the patches and examined the results with post-treatment survey drone imagery and plots along ground transects. In total, we sprayed 14 ha of Lepidium across eight sites and found that 87% of the Lepidium was discernibly affected by our herbicide treatment. Furthermore, we measured the overspray, which was substantially less than that reported for other aerial application methods such as helicopter-spraying, and our estimated operational costs were lower. Our results indicated that applying remote-sensing imagery for the identification of invasive species patches and the use of a spray drone for treatment may be an effective means of controlling invasive plants with high precision at a reasonable cost. In the near future, a unified UAS system that both identifies invasive species and then treats them in a single pass should be a promising goal for early detection and rapid response in wetland management. Full article
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36 pages, 7321 KiB  
Article
Improving Machine Learning Classifications of Phragmites australis Using Object-Based Image Analysis
by Connor J. Anderson, Daniel Heins, Keith C. Pelletier and Joseph F. Knight
Remote Sens. 2023, 15(4), 989; https://doi.org/10.3390/rs15040989 - 10 Feb 2023
Cited by 5 | Viewed by 1959
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 [...] Read more.
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
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37 pages, 19283 KiB  
Article
Passive Acoustic Monitoring as a Tool to Investigate the Spatial Distribution of Invasive Alien Species
by 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 7 | Viewed by 3715
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, [...] Read more.
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
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23 pages, 6277 KiB  
Article
Assessment of Invasive and Weed Species by Hyperspectral Imagery in Agrocenoses Ecosystem
by Pavel A. Dmitriev, Boris L. Kozlovsky, Denis P. Kupriushkin, Anastasia A. Dmitrieva, Vishnu D. Rajput, Vasily A. Chokheli, Ekaterina P. Tarik, Olga A. Kapralova, Valeriy K. Tokhtar, Tatiana M. Minkina and Tatiana V. Varduni
Remote Sens. 2022, 14(10), 2442; https://doi.org/10.3390/rs14102442 - 19 May 2022
Cited by 9 | Viewed by 1902
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., [...] Read more.
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
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27 pages, 4999 KiB  
Article
Early Detection of Wild Rocket Tracheofusariosis Using Hyperspectral Image-Based Machine Learning
by 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 10 | Viewed by 2881
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 [...] Read more.
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
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22 pages, 6773 KiB  
Article
Comparison of Different Analytical Strategies for Classifying Invasive Wetland Vegetation in Imagery from Unpiloted Aerial Systems (UAS)
by Louis Will Jochems, Jodi Brandt, Andrew Monks, Megan Cattau, Nicholas Kolarik, Jason Tallant and Shane Lishawa
Remote Sens. 2021, 13(23), 4733; https://doi.org/10.3390/rs13234733 - 23 Nov 2021
Cited by 2 | Viewed by 2298
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 [...] Read more.
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
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