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Remote Sensing 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: closed (15 January 2022) | Viewed by 27959

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Special Issue Editors

Dr. Sandra Eckert

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Guest Editor

Centre for Development and Environment, University of Bern, 3012 Bern, Switzerland
Interests: remote sensing applications for an improved understanding of coupled human–environment systems; time-series analysis for monitoring and assessing invasive plant species; land use intensification; land degradation; forest degradation and deforestation

Dr. Urs Schaffner

E-Mail Website
Guest Editor

CABI Switzerland, Rue des Grillons 1, 2800 Delémont, Switzerland
Interests: grassland ecology and management; environmental impact of invasive species; evolutionary ecology of invasive species; regulation of non-native species

Special Issue Information

Dear Colleagues,

Invasive species cause ecological, economic, and/or social impacts, and are key drivers of global change. Spatially explicit information is needed in order to quantify the impacts of invasive species, including their current distribution and invasion level, their potential distribution, as well as that of affected species, habitats, or ecosystem services. Where such information is robust, it can guide the design of management strategies and the allocation of resources to mitigate the negative impacts and/or reduce the further spread of invasive species.

Remote sensing data have long been applied to assess and monitor the state of and changes in natural systems as well as extremes affecting the land surface and its processes. Remote sensing is increasingly applied in invasive species research, as new data become available and researchers are finding different ways to link remotely sensed data to plot-level data collected on the ground, which are essential to understand the characteristics and impacts of the invasion.

The increasing availability of high spatial and temporal resolution optical as well as radar satellite data and novel approaches of cloud-computing-based (big data) analysis offer new opportunities to cost-effectively identify invasive species or differentiate them from other vegetation and assess their fractional cover. The continuous monitoring and control of invasive species spread that allows for the development and adaptation of effective management strategies, which are so important in invasive species management, seem possible.

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

Innovative approaches, algorithms, and data for
- The detection of invasive species and their separation from other vegetation covers (e.g., by including information on ecosystem or seasonal dynamics derived from remotely sensed data);
- The assessment of the fractional cover or relative abundance of invasive species;
- The assessment of environmental impacts of invasive species on local, landscape, or national scale;
- Tackling the limitations of spectral resolution;
Assessing environmental impacts of invasive species combining survey data with remotely sensed data;
Advancing spatially-explicit invasion management through remote sensing technology;
Spatio-temporal data analysis, machine learning, big data analytics, etc.;
Studies focusing on the transferability of remotely-sensed invasive species findings across temporal and spatial scales.

Dr. Sandra Eckert
Dr. Urs Schaffner
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

Biological invasions
Invasive species
Machine learning
Remote sensing (optical, radar, hyperspectral, fluorescence, VHR, UAV, etc.)
Time series analysis
Fractional vegetation cover
Species distribution and spread modelling
Environmental impacts
Spatially explicit invasive species management

Published Papers (8 papers)

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Research

18 pages, 22711 KiB

Open AccessArticle

Assessing the Dynamics of Plant Species Invasion in Eastern-Mediterranean Coastal Dunes Using Cellular Automata Modeling and Satellite Time-Series Analyses

by Giorgi Kozhoridze, Eyal Ben Dor and Marcelo Sternberg

Remote Sens. 2022, 14(4), 1014; https://doi.org/10.3390/rs14041014 - 19 Feb 2022

Cited by 7 | Viewed by 2211

Abstract

Biological invasion is a major contributor to local and global biodiversity loss, in particular in dune ecosystems. In this study we evaluated current and future cover expansion of the invasive plant species, Heterotheca subaxillaris, and Acacia saligna, in the Mediterranean coastal plain of Israel. This is the first effort to quantify current surface cover of the focal species in this area. We reconstructed plant cover for 1990–2020 using Landsat time series and modeled future potential expansion using cellular automata (CA) modeling. The overall accuracy of the results varied in the range 85–95% and the simulated plant growth using CA varied between 74% and 84%, for A. saligna and H. subaxillaris, respectively. The surface area covered by H. subaxillaris in 2020, 45 years since its introduction, was approximately 81 km². Acacia saligna covered an area of 74.6 km², while the vacant area available for potential spread of these two species was 630 km². Heterotheca subaxillaris showed a mean expansion rate of 107% per decade from 2000 to 2020, while the mean expansion rate of A. saligna was lower, ranging between 48% and 54% within the same time period. Furthermore, based on the plant expansion model simulation we estimated that A. saligna and H. subaxillaris will continue to spread by 60% per decade, on average, from 2020 to 2070, with a maximum growth rate of 80% per decade during 2040–2050. According to future expansion projections, the species will cover all open vacant areas by 2070 (95% of the total vacant area) and most areas will be shared by both species. Full article

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

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22 pages, 9958 KiB

Open AccessArticle

Detection of Invasive Black Locust (Robinia pseudoacacia) in Small Woody Features Using Spatiotemporal Compositing of Sentinel-2 Data

by Tomáš Rusňák, Andrej Halabuk, Ľuboš Halada, Hubert Hilbert and Katarína Gerhátová

Remote Sens. 2022, 14(4), 971; https://doi.org/10.3390/rs14040971 - 16 Feb 2022

Cited by 5 | Viewed by 2013

Abstract

Recognition of invasive species and their distribution is key for managing and protecting native species within both natural and man-made ecosystems. Small woody features (SWF) represent fragmented patches or narrow linear tree features that are of high importance in intensively utilized agricultural landscapes. Simultaneously, they frequently serve as expansion pathways for invasive species such as black locust. In this study, Sentinel-2 products, combined with spatiotemporal compositing approaches, are used to address the challenge of broad area black locust mapping at a high granularity. This is accomplished by conducting a comprehensive analysis of the classification performance of various compositing approaches and multitemporal classification settings throughout four vegetation seasons. The annual, seasonal (bi-monthly), and monthly median values of cloud-masked Sentinel-2 reflectance products are aggregated and stacked into varied time-series datasets per given year. The random forest algorithm is trained and output classification maps validated based on field-based reference datasets across Danubian lowlands (Slovakia). The main results of the study proved the usefulness of spatiotemporal compositing of Sentinel-2 products for mapping black locust in small woody features across wide area. In particular, temporally aggregated monthly composites stacked to seasonal time series datasets yielded consistently high overall accuracies ranging from 89.10% to 91.47% with balanced producer’s and user’s accuracies for each year’s annual series. We presume that a similar approach could be used for a broader scale species distribution mapping, assuming they are spectrally or phenologically distinctive, as is often the case for many invasive species. Full article

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

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19 pages, 8507 KiB

Open AccessArticle

Phenology and Spectral Unmixing-Based Invasive Kudzu Mapping: A Case Study in Knox County, Tennessee

by Ming Shen, Maofeng Tang and Yingkui Li

Remote Sens. 2021, 13(22), 4551; https://doi.org/10.3390/rs13224551 - 12 Nov 2021

Cited by 2 | Viewed by 2736

Abstract

As an invasive plant species, kudzu has been spreading rapidly in the Southeastern United States in recent years. Accurate mapping of kudzu is critical for effective invasion control and management. However, the remote detection of kudzu distribution using multispectral images is challenging because of the mixed reflectance and potential misclassification with other vegetation. We propose a three-step classification process to map kudzu in Knox County, Tennessee, using multispectral Sentinel-2 images and the integration of spectral unmixing analysis and phenological characteristics. This classification includes an initial linear unmixing process to produce an overestimated kudzu map, a phenological-based masking to reduce misclassification, and a nonlinear unmixing process to refine the classification. The initial linear unmixing provides high producer’s accuracy (PA) but low user’s accuracy (UA) due to misclassification with grasslands. The phenological-based masking increases the accuracy of the kudzu classification and reduces the domain for further processing. The nonlinear unmixing further refines the kudzu classification via the selection of an appropriate nonlinear model. The final kudzu classification for Knox County reaches relatively high accuracy, with UA, PA, Jaccard, and Kappa index values of 0.858, 0.907, 0.789, and 0.725, respectively. Our proposed method has potential for continuous monitoring of kudzu in large areas. Full article

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

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28 pages, 4764 KiB

Open AccessArticle

Mapping Invasive Phragmites australis Using Unoccupied Aircraft System Imagery, Canopy Height Models, and Synthetic Aperture Radar

by Connor J. Anderson, Daniel Heins, Keith C. Pelletier, Julia L. Bohnen and Joseph F. Knight

Remote Sens. 2021, 13(16), 3303; https://doi.org/10.3390/rs13163303 - 20 Aug 2021

Cited by 9 | Viewed by 2552

Abstract

Invasive plant species are an increasing worldwide threat both ecologically and financially. Knowing the location of these invasive plant infestations is the first step in their control. Surveying for invasive Phragmites australis is particularly challenging due to limited accessibility in wetland environments. Unoccupied aircraft systems (UAS) are a popular choice for invasive species management due to their ability to survey challenging environments and their high spatial and temporal resolution. This study tested the utility of three-band (i.e., red, green, and blue; RGB) UAS imagery for mapping Phragmites in the St. Louis River Estuary in Minnesota, U.S.A. and Saginaw Bay in Michigan, U.S.A. Iterative object-based image analysis techniques were used to identify two classes, Phragmites and Not Phragmites. Additionally, the effectiveness of canopy height models (CHMs) created from two data types, UAS imagery and commercial satellite stereo retrievals, and the RADARSAT-2 horizontal-horizontal (HH) polarization were tested for Phragmites identification. The highest overall classification accuracy of 90% was achieved when pairing the UAS imagery with a UAS-derived CHM. Producer’s accuracy for the Phragmites class ranged from 3 to 76%, and the user’s accuracies were above 90%. The Not Phragmites class had user’s and producer’s accuracies above 88%. Inclusion of the RADARSAT-2 HH polarization caused a slight reduction in classification accuracy. Commercial satellite stereo retrievals increased commission errors due to decreased spatial resolution and vertical accuracy. The lowest classification accuracy was seen when using only the RGB UAS imagery. UAS are promising for Phragmites identification, but the imagery should be used in conjunction with a CHM. Full article

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

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24 pages, 4149 KiB

Open AccessArticle

‘The Best of Two Worlds’—Combining Classifier Fusion and Ecological Models to Map and Explain Landscape Invasion by an Alien Shrub

by Nuno Mouta, Renato Silva, Silvana Pais, Joaquim M. Alonso, João F. Gonçalves, João Honrado and Joana R. Vicente

Remote Sens. 2021, 13(16), 3287; https://doi.org/10.3390/rs13163287 - 19 Aug 2021

Cited by 9 | Viewed by 2896

Abstract

The spread of invasive alien species promotes ecosystem structure and functioning changes, with detrimental effects on native biodiversity and ecosystem services, raising challenges for local management authorities. Predictions of invasion dynamics derived from modeling tools are often spatially coarse and therefore unsuitable for guiding local management. Accurate information on the occurrence of invasive plants and on the main factors that promote their spread is critical to define successful control strategies. For addressing this challenge, we developed a dual framework combining satellite image classification with predictive ecological modeling. By combining data from georeferenced invaded areas with multispectral imagery with 10-meter resolution from Sentinel-2 satellites, a map of areas invaded by the woody invasive Acacia longifolia in a municipality of northern Portugal was devised. Classifier fusion techniques were implemented through which eight statistical and machine-learning algorithms were ensembled to produce accurate maps of invaded areas. Through a Random Forest (RF) model, these maps were then used to explore the factors driving the landscape-level abundance of A. longifolia. RF models were based on explanatory variables describing hypothesized environmental drivers, including climate, topography/geomorphology, soil properties, fire disturbance, landscape composition, linear structures, and landscape spatial configuration. Satellite-based maps synoptically described the spatial patterns of invaded areas, with classifications attaining high accuracy values (True Skill Statistic, TSS: 0.895, Area Under the Receiver Operating Curve, ROC: 0.988, Kappa: 0.857). The predictive RF models highlighted the primary role of climate, followed by landscape composition and configuration, as the most important drivers explaining the species abundance at the landscape level. Our innovative dual framework—combining image classification and predictive ecological modeling—can guide decision-making processes regarding effective management of invasions by prioritizing the invaded areas and tackling the primary environmental and anthropogenic drivers of the species’ abundance and spread. Full article

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

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16 pages, 3078 KiB

Open AccessArticle

Monitoring Tamarix Changes Using WorldView-2 Satellite Imagery in Grand Canyon National Park, Arizona

by Nathaniel Bransky, Temuulen Sankey, Joel B. Sankey, Matthew Johnson and Levi Jamison

Remote Sens. 2021, 13(5), 958; https://doi.org/10.3390/rs13050958 - 4 Mar 2021

Cited by 3 | Viewed by 2955

Abstract

Remote sensing methods are commonly used to monitor the invasive riparian shrub tamarisk (Tamarix spp.) and its response to the northern tamarisk beetle (D. carinulata), a specialized herbivore introduced as a biocontrol agent to control tamarisk in the Southwest USA in 2001. We use a Spectral Angle Mapper (SAM) supervised classification method with WorldView-2 (2 m spatial resolution) multispectral images from May and August of 2019 to map healthy tamarisk, canopy dieback, and defoliated tamarisk over a 48 km segment of the Colorado River in the topographically complex Grand Canyon National Park, where coarse-resolution satellite images are of limited use. The classifications in May and August produced overall accuracies of 80.0% and 83.1%, respectively. Seasonal change detection between May and August 2019 indicated that 47.5% of the healthy tamarisk detected in May 2019 had been defoliated by August 2019 within the WorldView-2 image extent. When compared to a previously published tamarisk map from 2009, derived from multispectral aerial imagery, we found that 29.5% of healthy tamarisk canopy declined between 2009 and 2019. This implies that tamarisk beetle impacts are continuing to accumulate even though land managers have noted the presence of the beetles in this reach of the river for 7 years since 2012. Full article

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

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24 pages, 7821 KiB

Open AccessArticle

A Remote Sensing Method to Monitor Water, Aquatic Vegetation, and Invasive Water Hyacinth at National Extents

by Geethen Singh, Chevonne Reynolds, Marcus Byrne and Benjamin Rosman

Remote Sens. 2020, 12(24), 4021; https://doi.org/10.3390/rs12244021 - 9 Dec 2020

Cited by 32 | Viewed by 7078

Abstract

Diverse freshwater biological communities are threatened by invasive aquatic alien plant (IAAP) invasions and consequently, cost countries millions to manage. The effective management of these IAAP invasions necessitates their frequent and reliable monitoring across a broad extent and over a long-term. Here, we introduce and apply a monitoring approach that meet these criteria and is based on a three-stage hierarchical classification to firstly detect water, then aquatic vegetation and finally water hyacinth (Pontederia crassipes, previously Eichhornia crassipes), the most damaging IAAP species within many regions of the world. Our approach circumvents many challenges that restricted previous satellite-based water hyacinth monitoring attempts to smaller study areas. The method is executable on Google Earth Engine (GEE) extemporaneously and utilizes free, medium resolution (10–30 m) multispectral Earth Observation (EO) data from either Landsat-8 or Sentinel-2. The automated workflow employs a novel simple thresholding approach to obtain reliable boundaries for open-water, which are then used to limit the area for aquatic vegetation detection. Subsequently, a random forest modelling approach is used to discriminate water hyacinth from other detected aquatic vegetation using the eight most important variables. This study represents the first national scale EO-derived water hyacinth distribution map. Based on our model, it is estimated that this pervasive IAAP covered 417.74 km² across South Africa in 2013. Additionally, we show encouraging results for utilizing the automatically derived aquatic vegetation masks to fit and evaluate a convolutional neural network-based semantic segmentation model, removing the need for detection of surface water extents that may not always be available at the required spatio-temporal resolution or accuracy. The water hyacinth species discrimination has a 0.80, or greater, overall accuracy (0.93), F1-score (0.87) and Matthews correlation coefficient (0.80) based on 98 widely distributed field sites across South Africa. The results suggest that the introduced workflow is suitable for monitoring changes in the extent of open water, aquatic vegetation, and water hyacinth for individual waterbodies or across national extents. The GEE code can be accessed here. Full article

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

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24 pages, 3430 KiB

Open AccessArticle

Mapping an Invasive Plant Spartina alterniflora by Combining an Ensemble One-Class Classification Algorithm with a Phenological NDVI Time-Series Analysis Approach in Middle Coast of Jiangsu, China

by Xiang Liu, Huiyu Liu, Pawanjeet Datta, Julian Frey and Barbara Koch

Remote Sens. 2020, 12(24), 4010; https://doi.org/10.3390/rs12244010 - 8 Dec 2020

Cited by 14 | Viewed by 3104

Abstract

Spartina alterniflora (S. alterniflora) is one of the worst plant invaders in the coastal wetlands of China. Accurate and repeatable mapping of S. alterniflora invasion is essential to develop cost-effective management strategies for conserving native biodiversity. Traditional remote-sensing-based mapping methods require a lot of fieldwork for sample collection. Moreover, our ability to detect this invasive species is still limited because of poor spectral separability between S. alterniflora and its co-dominant native plants. Therefore, we proposed a novel scheme that uses an ensemble one-class classifier (EOCC) in combination with phenological Normalized Difference Vegetation Index (NDVI) time-series analysis (TSA) to detect S. alterniflora. We evaluated the performance of the EOCC algorithm in two scenarios, i.e., single-scene analysis (SSA) and NDVI-TSA in the core zones of Yancheng National Natural Reserve (YNNR). Meanwhile, a fully supervised classifier support vector machine (SVM) was tested in the two scenarios for comparison. With these scenarios, the crucial phenological stages and the advantage of phenological NDVI-TSA in S. alterniflora recognition were also investigated. Results indicated the EOCC using only positive training data performed similarly well with the SVM trained on complete training data in the YNNR. Moreover, the EOCC algorithm presented a more robust transferability with notably higher classification accuracy than the SVM when being transferred to a second site, without a second training. Furthermore, when combined with the phenological NDVI-TSA, the EOCC algorithm presented more balanced sensitivity–specificity result, showing slightly better transferability than it performed in the best phenological stage (i.e., senescence stage of November). The achieved results (overall accuracy (OA), Kappa, and true skill statistic (TSS) were 92.92%, 0.843, and 0.834 for the YNNR, and OA, Kappa, and TSS were 90.94%, 0.815, and 0.825 for transferability to the non-training site) suggest that our detection scheme has a high potential for the mapping of S. alterniflora across different areas, and the EOCC algorithm can be a viable alternative to traditional supervised classification method for invasive plant detection. Full article

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

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