Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
Advances in Remote Sensing Applications for the Detection of Biological...
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advances in Remote Sensing Applications for the Detection of Biological Invasions

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 December 2018) | Viewed by 52241

Special Issue Editors

Prof. Dr. Duccio Rocchini

E-Mail Website
Guest Editor
Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, via Irnerio 42, 40126 Bologna, Italy
Interests: biodiversity estimate; computational ecology; ecological informatics; remote sensing; spatial ecology
Special Issues, Collections and Topics in MDPI journals
Dr. Matteo Marcantonio

E-Mail Website
Guest Editor
Department of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of California, Davis, CA 95616, USA
Interests: invasive mosquito ecology; species dispersal theory and application; invasive species dynamics; remote sensing; species distribution modelling; functional ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biological invasions are a threat for biodiversity, economics and human health. Tools that enable early detection or forecasting of biological invasions as well as characterizing current invasive populations, contribute to the preservation of biological diversity and to human health (e.g., considering the invasive species vector of pathogens, emerging pathogenic species, etc.).

The application of remote sensing is at the frontiers of biological invasion research. Indeed, remote sensing is an invaluable tool that, coupled with traditional scientific data collection, modeling, and analysis, is contributing to more reliable detection, forecasting, and mitigation of invasive species populations.

On the one hand, the data produced by remote sensing technologies is stimulating innovative methods to predict or track invasive species. On the other hand, the effective exploitation and application of such remote sensing datasets in space and over historical trajectories, lags behind their potential contribution and benefits to the study of biological invasions. We call for papers that fill this gap, and advance or stimulate research in remote sensing applications in biological invasion studies, with special interest in applications accounting for spatial variability across scales, biological invasion forecasting, and threats to biodiversity, human health and economics.

Prof. Duccio Rocchini
Dr. Matteo Marcantonio
Guest Editors

Related References

  1. Carter,A.; Lucas, K.L.; Blossom, G.A.; Lassitter, C.L.; Holiday, D.M.; Mooneyhan, D.S.; et al. remote sensing and mapping of Tamarisk along the Colorado River, USA: A comparative use of summer-acquired hyperion, Thematic Mapper and QuickBird Data. Remote Sens. 2009, 1, 318–329.
  2. Marcantonio, M.; Metz, M.; Baldacchino, F; Arnoldi, D.; Montarsi, F.; Capelli, G.; Carlin, S.; Neteler, M.; Rizzoli, A. First assessment of potential distribution and dispersal capacity of the emerging invasive mosquito Aedes koreicus in Northeast Italy. Vectors 2016, doi:10.1186/s13071-016-1340-9.
  3. Rocchini, D.; Andreo, V.; Forster, M.; Garzon-Lopez, C.X.; Gutierrez, A.P.; Gillespie, T.W.; Hauffe, H.C.; He, K.S.; Kleinschmit, B.; Mairota, P.; et al. Potential of remote sensing to predict species invasions: A modelling perspective. Phys. Geogr. 2015, 39, 283–309.
  4. Gutierrez, A.P.; Ponti, L.; Dalton, D.T. Analysis of the invasiveness of spotted wing Drosophila (Drosophila suzukii) in North America, Europe, and the Mediterranean Basin. Invasions. 2016, doi:10.1007/s10530-016-1255-6.

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
  • Remote sensing
  • Human health
  • Biodiversity
  • Spatial ecology

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

24 pages, 7066 KiB  
Article
Spatial Evolution of Prosopis Invasion and its Effects on LULC and Livelihoods in Baringo, Kenya
by Purity Rima Mbaabu, Wai-Tim Ng, Urs Schaffner, Maina Gichaba, Daniel Olago, Simon Choge, Silas Oriaso and Sandra Eckert
Remote Sens. 2019, 11(10), 1217; https://doi.org/10.3390/rs11101217 - 22 May 2019
Cited by 46 | Viewed by 9366
Abstract
Woody alien plant species have been deliberately introduced globally in many arid and semi-arid regions, as they can provide services and goods to the rural poor. However, some of these alien trees and shrubs have become invasive over time, with important impacts on [...] Read more.
Woody alien plant species have been deliberately introduced globally in many arid and semi-arid regions, as they can provide services and goods to the rural poor. However, some of these alien trees and shrubs have become invasive over time, with important impacts on biodiversity, ecosystem services, and human well-being. Prosopis was introduced in Baringo County, Kenya, in the 1980s, but since then, it has spread rapidly from the original plantations to new areas. To assess land-use and land-cover (LULC) changes and dynamics in Baringo, we used a combination of dry and wet season Landsat satellite data acquired over a seven-year time interval between 1988–2016, and performed a supervised Random Forest classification. For each time interval, we calculated the extent of Prosopis invasion, rates of spread, gains and losses of specific LULC classes, and the relative importance of Prosopis invasion on LULC changes. The overall accuracy and kappa coefficients of the LULC classifications ranged between 98.1–98.5% and 0.93–0.96, respectively. We found that Prosopis coverage increased from 882 ha in 1988 to 18,792 ha in 2016. The highest negative changes in LULC classes were found for grasslands (−6252 ha; −86%), irrigated cropland (−849 ha; −57%), Vachellia tortilis-dominated vegetation (−3602 ha; −42%), and rainfed cropland (−1432 ha; −37%). Prosopis invasion alone directly accounted for over 30% of these negative changes, suggesting that Prosopis invasion is a key driver of the observed LULC changes in Baringo County. Although the management of Prosopis by utilization has been promoted in Baringo for 10–15 years, the spread of Prosopis has not stopped or slowed down. This suggests that Prosopis management in Baringo and other invaded areas in East Africa needs to be based on a more integrated approach. Full article
(This article belongs to the Special Issue Advances in Remote Sensing Applications for the Detection of Biological Invasions)
►▼ Show Figures

Graphical abstract

12 pages, 1613 KiB  
Article
Strength in Numbers: Combining Multi-Source Remotely Sensed Data to Model Plant Invasions in Coastal Dune Ecosystems
by Marco Malavasi, Vojtěch Barták, Tommaso Jucker, Alicia Teresa Rosario Acosta, Maria Laura Carranza and Manuele Bazzichetto
Remote Sens. 2019, 11(3), 275; https://doi.org/10.3390/rs11030275 - 30 Jan 2019
Cited by 8 | Viewed by 3448
Abstract
A common feature of most theories of invasion ecology is that the extent and intensity of invasions is driven by a combination of drivers, which can be grouped into three main factors: propagule pressure (P), abiotic drivers (A) and biotic interactions (B). However, [...] Read more.
A common feature of most theories of invasion ecology is that the extent and intensity of invasions is driven by a combination of drivers, which can be grouped into three main factors: propagule pressure (P), abiotic drivers (A) and biotic interactions (B). However, teasing apart the relative contribution of P, A and B on Invasive Alien Species (IAS) distributions is typically hampered by a lack of data. We focused on Mediterranean coastal dunes as a model system to test the ability of a combination of multi-source Remote Sensing (RS) data to characterize the distribution of five IAS. Using generalized linear models, we explored and ranked correlates of P, A and B derived from high-resolution optical imagery and three-dimensional (3D) topographic models obtained from LiDAR, along two coastal systems in Central Italy (Lazio and Molise Regions). Predictors from all three factors contributed significantly to explaining the presence of IAS, but their relative importance varied among the two Regions, supporting previous studies suggesting that invasion is a context-dependent process. The use of RS data allowed us to characterize the distribution of IAS across broad, regional scales and to identify coastal sectors that are most likely to be invaded in the future. Full article
(This article belongs to the Special Issue Advances in Remote Sensing Applications for the Detection of Biological Invasions)
►▼ Show Figures

Graphical abstract

14 pages, 6779 KiB  
Article
Xf-Rovim. A Field Robot to Detect Olive Trees Infected by Xylella Fastidiosa Using Proximal Sensing
by Beatriz Rey, Nuria Aleixos, Sergio Cubero and José Blasco
Remote Sens. 2019, 11(3), 221; https://doi.org/10.3390/rs11030221 - 22 Jan 2019
Cited by 32 | Viewed by 6986
Abstract
The use of remote sensing to map the distribution of plant diseases has evolved considerably over the last three decades and can be performed at different scales, depending on the area to be monitored, as well as the spatial and spectral resolution required. [...] Read more.
The use of remote sensing to map the distribution of plant diseases has evolved considerably over the last three decades and can be performed at different scales, depending on the area to be monitored, as well as the spatial and spectral resolution required. This work describes the development of a small low-cost field robot (Remotely Operated Vehicle for Infection Monitoring in orchards, XF-ROVIM), which is intended to be a flexible solution for early detection of Xylella fastidiosa (X. fastidiosa) in olive groves at plant to leaf level. The robot is remotely driven and fitted with different sensing equipment to capture thermal, spectral and structural information about the plants. Taking into account the height of the olive trees inspected, the design includes a platform that can raise the cameras to adapt the height of the sensors to a maximum of 200 cm. The robot was tested in an olive grove (4 ha) potentially infected by X. fastidiosa in the region of Apulia, southern Italy. The tests were focused on investigating the reliability of the mechanical and electronic solutions developed as well as the capability of the sensors to obtain accurate data. The four sides of all trees in the crop were inspected by travelling along the rows in both directions, showing that it could be easily adaptable to other crops. XF-ROVIM was capable of inspecting the whole field continuously, capturing geolocated spectral information and the structure of the trees for later comparison with the in situ observations. Full article
(This article belongs to the Special Issue Advances in Remote Sensing Applications for the Detection of Biological Invasions)
►▼ Show Figures

Graphical abstract

15 pages, 1805 KiB  
Article
Using Single- and Multi-Date UAV and Satellite Imagery to Accurately Monitor Invasive Knotweed Species
by François-Marie Martin, Jana Müllerová, Laurent Borgniet, Fanny Dommanget, Vincent Breton and André Evette
Remote Sens. 2018, 10(10), 1662; https://doi.org/10.3390/rs10101662 - 20 Oct 2018
Cited by 59 | Viewed by 7008
Abstract
Understanding the spatial dynamics of invasive alien plants is a growing concern for many scientists and land managers hoping to effectively tackle invasions or mitigate their impacts. Consequently, there is an urgent need for the development of efficient tools for large scale mapping [...] Read more.
Understanding the spatial dynamics of invasive alien plants is a growing concern for many scientists and land managers hoping to effectively tackle invasions or mitigate their impacts. Consequently, there is an urgent need for the development of efficient tools for large scale mapping of invasive plant populations and the monitoring of colonization fronts. Remote sensing using very high resolution satellite and Unmanned Aerial Vehicle (UAV) imagery is increasingly considered for such purposes. Here, we assessed the potential of several single- and multi-date indices derived from satellite and UAV imagery (i.e., UAV-generated Canopy Height Models—CHMs; and Bi-Temporal Band Ratios—BTBRs) for the detection and mapping of the highly problematic Asian knotweeds (Fallopia japonica; Fallopia × bohemica) in two different landscapes (i.e., open vs. highly heterogeneous areas). The idea was to develop a simple classification procedure using the Random Forest classifier in eCognition, usable in various contexts and requiring little training to be used by non-experts. We also rationalized errors of omission by applying simple “buffer” boundaries around knotweed predictions to know if heterogeneity across multi-date images could lead to unfairly harsh accuracy assessment and, therefore, ill-advised decisions. Although our “crisp” satellite results were rather average, our UAV classifications achieved high detection accuracies. Multi-date spectral indices and CHMs consistently improved classification results of both datasets. To the best of our knowledge, it was the first time that UAV-generated CHMs were used to map invasive plants and their use substantially facilitated knotweed detection in heterogeneous vegetation contexts. Additionally, the “buffer” boundary results showed detection rates often exceeding 90–95% for both satellite and UAV images, suggesting that classical accuracy assessments were overly conservative. Considering these results, it seems that knotweed can be satisfactorily mapped and monitored via remote sensing with moderate time and money investment but that the choice of the most appropriate method will depend on the landscape context and the spatial scale of the invaded area. Full article
(This article belongs to the Special Issue Advances in Remote Sensing Applications for the Detection of Biological Invasions)
►▼ Show Figures

Graphical abstract

18 pages, 3355 KiB  
Article
Hyperspectral Measurement of Seasonal Variation in the Coverage and Impacts of an Invasive Grass in an Experimental Setting
by Yuxi Guo, Sarah Graves, S. Luke Flory and Stephanie Bohlman
Remote Sens. 2018, 10(5), 784; https://doi.org/10.3390/rs10050784 - 18 May 2018
Cited by 6 | Viewed by 4721
Abstract
Hyperspectral remote sensing can be a powerful tool for detecting invasive species and their impact across large spatial scales. However, remote sensing studies of invasives rarely occur across multiple seasons, although the properties of invasives often change seasonally. This may limit the detection [...] Read more.
Hyperspectral remote sensing can be a powerful tool for detecting invasive species and their impact across large spatial scales. However, remote sensing studies of invasives rarely occur across multiple seasons, although the properties of invasives often change seasonally. This may limit the detection of invasives using remote sensing through time. We evaluated the ability of hyperspectral measurements to quantify the coverage of a plant invader and its impact on senesced plant coverage and canopy equivalent water thickness (EWT) across seasons. A portable spectroradiometer was used to collect data in a field experiment where uninvaded plant communities were experimentally invaded by cogongrass, a non-native perennial grass, or maintained as an uninvaded reference. Vegetation canopy characteristics, including senesced plant material, the ratio of live to senesced plants, and canopy EWT varied across the seasons and showed different temporal patterns between the invaded and reference plots. Partial least square regression (PLSR) models based on a single season had a limited predictive ability for data from a different season. Models trained with data from multiple seasons successfully predicted invasive plant coverage and vegetation characteristics across multiple seasons and years. Our results suggest that if seasonal variation is accounted for, the hyperspectral measurement of invaders and their effects on uninvaded vegetation may be scaled up to quantify effects at landscape scales using airborne imaging spectrometers. Full article
(This article belongs to the Special Issue Advances in Remote Sensing Applications for the Detection of Biological Invasions)
►▼ Show Figures

Graphical abstract

5400 KiB  
Article
Mapping of the Invasive Species Hakea sericea Using Unmanned Aerial Vehicle (UAV) and WorldView-2 Imagery and an Object-Oriented Approach
by Flor Alvarez-Taboada, Claudio Paredes and Julia Julián-Pelaz
Remote Sens. 2017, 9(9), 913; https://doi.org/10.3390/rs9090913 - 1 Sep 2017
Cited by 88 | Viewed by 12388
Abstract
Invasive plants are non-native species that establish and spread in their new location, generating a negative impact on the local ecosystem and representing one of the most important causes of the extinction of local species. The first step for the control of invasion [...] Read more.
Invasive plants are non-native species that establish and spread in their new location, generating a negative impact on the local ecosystem and representing one of the most important causes of the extinction of local species. The first step for the control of invasion should be directed at understanding and quantification of their location, extent and evolution, namely the monitoring of the phenomenon. In this sense, the techniques and methods of remote sensing can be very useful. The aim of this paper was to identify and quantify the areas covered by the invasive plant Hakea sericea using high spatial resolution images obtained from aerial platforms (Unmanned Aerial Vehicle: UAV/drone) and orbital platforms (WorldView-2: WV2), following an object-oriented image analysis approach. The results showed that both data were suitable. WV2reached user and producer accuracies greater than 93% (Estimate of Kappa (KHAT): 0.95), while the classifications with the UAV orthophotographs obtained accuracies higher than 75% (KHAT: 0.51). The most suitable data to use as input consisted of using all of the multispectral bands that were available for each image. The addition of textural features did not increase the accuracies for the Hakea sericea class, but it did for the general classification using WV2. Full article
(This article belongs to the Special Issue Advances in Remote Sensing Applications for the Detection of Biological Invasions)
►▼ Show Figures

Graphical abstract

5369 KiB  
Article
Mapping of Aedes albopictus Abundance at a Local Scale in Italy
by Frédéric Baldacchino, Matteo Marcantonio, Mattia Manica, Giovanni Marini, Roberto Zorer, Luca Delucchi, Daniele Arnoldi, Fabrizio Montarsi, Gioia Capelli, Annapaola Rizzoli and Roberto Rosà
Remote Sens. 2017, 9(7), 749; https://doi.org/10.3390/rs9070749 - 21 Jul 2017
Cited by 20 | Viewed by 6984
Abstract
Given the growing risk of arbovirus outbreaks in Europe, there is a clear need to better describe the distribution of invasive mosquito species such as Aedes albopictus. Current challenges consist in simulating Ae. albopictus abundance, rather than its presence, and mapping its [...] Read more.
Given the growing risk of arbovirus outbreaks in Europe, there is a clear need to better describe the distribution of invasive mosquito species such as Aedes albopictus. Current challenges consist in simulating Ae. albopictus abundance, rather than its presence, and mapping its simulated abundance at a local scale to better assess the transmission risk of mosquito-borne pathogens and optimize mosquito control strategy. During 2014–2015, we sampled adult mosquitoes using 72 BG-Sentinel traps per year in the provinces of Belluno and Trento, Italy. We found that the sum of Ae. albopictus females collected during eight trap nights from June to September was positively related to the mean temperature of the warmest quarter and the percentage of artificial areas in a 250 m buffer around the sampling locations. Maps of Ae. albopictus abundance simulated from the most parsimonious model in the study area showed the largest populations in highly artificial areas with the highest summer temperatures, but with a high uncertainty due to the variability of the trapping collections. Vector abundance maps at a local scale should be promoted to support stakeholders and policy-makers in optimizing vector surveillance and control. Full article
(This article belongs to the Special Issue Advances in Remote Sensing Applications for the Detection of Biological Invasions)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop