Using remote sensing data within an optimal spatiotemporal model for invasive plant management: the case of Ailanthus altissima in the Alta Murgia National Park
Abstract We tackle the problem of coupling a spatiotemporal model for simulating the spread and control of an invasive alien species with data coming from image processing and expert knowledge. In this study, we implement a spatially explicit optimal control model based on a reaction–diffusion equat...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-41607-2 |
| _version_ | 1827634792129101824 |
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| author | Christopher M. Baker Palma Blonda Francesca Casella Fasma Diele Carmela Marangi Angela Martiradonna Francesco Montomoli Nick Pepper Cristiano Tamborrino Cristina Tarantino |
| author_facet | Christopher M. Baker Palma Blonda Francesca Casella Fasma Diele Carmela Marangi Angela Martiradonna Francesco Montomoli Nick Pepper Cristiano Tamborrino Cristina Tarantino |
| author_sort | Christopher M. Baker |
| collection | DOAJ |
| description | Abstract We tackle the problem of coupling a spatiotemporal model for simulating the spread and control of an invasive alien species with data coming from image processing and expert knowledge. In this study, we implement a spatially explicit optimal control model based on a reaction–diffusion equation which includes an Holling II type functional response term for modeling the density control rate. The model takes into account the budget constraint related to the control program and searches for the optimal effort allocation for the minimization of the invasive alien species density. Remote sensing and expert knowledge have been assimilated in the model to estimate the initial species distribution and its habitat suitability, empirically extracted by a land cover map of the study area. The approach has been applied to the plant species Ailanthus altissima (Mill.) Swingle within the Alta Murgia National Park. This area is one of the Natura 2000 sites under the study of the ongoing National Biodiversity Future Center (NBFC) funded by the Italian National Recovery and Resilience Plan (NRRP), and pilot site of the finished H2020 project ECOPOTENTIAL, which aimed at the integration of modeling tools and Earth Observations for a sustainable management of protected areas. Both the initial density map and the land cover map have been generated by using very high resolution satellite images and validated by means of ground truth data provided by the EU Life Alta Murgia Project (LIFE12 BIO/IT/000213), a project aimed at the eradication of A. altissima in the Alta Murgia National Park. |
| first_indexed | 2024-03-09T15:19:13Z |
| format | Article |
| id | doaj.art-19c990a8da4549139252c00efcf17a8e |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:19:13Z |
| publishDate | 2023-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-19c990a8da4549139252c00efcf17a8e2023-11-26T12:54:32ZengNature PortfolioScientific Reports2045-23222023-09-0113111410.1038/s41598-023-41607-2Using remote sensing data within an optimal spatiotemporal model for invasive plant management: the case of Ailanthus altissima in the Alta Murgia National ParkChristopher M. Baker0Palma Blonda1Francesca Casella2Fasma Diele3Carmela Marangi4Angela Martiradonna5Francesco Montomoli6Nick Pepper7Cristiano Tamborrino8Cristina Tarantino9School of Mathematics and Statistics, The University of MelbourneInstitute of Atmospheric Pollution Research, National Research Council (CNR)Institute of Sciences of Food Production, National Research Council (CNR)Istituto per le Applicazioni del Calcolo M. Picone, National Research Council (CNR)Istituto per le Applicazioni del Calcolo M. Picone, National Research Council (CNR)Istituto per le Applicazioni del Calcolo M. Picone, National Research Council (CNR)Department of Aeronautics, Imperial College LondonThe Alan Turing Institute, The British LibraryDepartment of Computer Science, University of BariInstitute of Atmospheric Pollution Research, National Research Council (CNR)Abstract We tackle the problem of coupling a spatiotemporal model for simulating the spread and control of an invasive alien species with data coming from image processing and expert knowledge. In this study, we implement a spatially explicit optimal control model based on a reaction–diffusion equation which includes an Holling II type functional response term for modeling the density control rate. The model takes into account the budget constraint related to the control program and searches for the optimal effort allocation for the minimization of the invasive alien species density. Remote sensing and expert knowledge have been assimilated in the model to estimate the initial species distribution and its habitat suitability, empirically extracted by a land cover map of the study area. The approach has been applied to the plant species Ailanthus altissima (Mill.) Swingle within the Alta Murgia National Park. This area is one of the Natura 2000 sites under the study of the ongoing National Biodiversity Future Center (NBFC) funded by the Italian National Recovery and Resilience Plan (NRRP), and pilot site of the finished H2020 project ECOPOTENTIAL, which aimed at the integration of modeling tools and Earth Observations for a sustainable management of protected areas. Both the initial density map and the land cover map have been generated by using very high resolution satellite images and validated by means of ground truth data provided by the EU Life Alta Murgia Project (LIFE12 BIO/IT/000213), a project aimed at the eradication of A. altissima in the Alta Murgia National Park.https://doi.org/10.1038/s41598-023-41607-2 |
| spellingShingle | Christopher M. Baker Palma Blonda Francesca Casella Fasma Diele Carmela Marangi Angela Martiradonna Francesco Montomoli Nick Pepper Cristiano Tamborrino Cristina Tarantino Using remote sensing data within an optimal spatiotemporal model for invasive plant management: the case of Ailanthus altissima in the Alta Murgia National Park Scientific Reports |
| title | Using remote sensing data within an optimal spatiotemporal model for invasive plant management: the case of Ailanthus altissima in the Alta Murgia National Park |
| title_full | Using remote sensing data within an optimal spatiotemporal model for invasive plant management: the case of Ailanthus altissima in the Alta Murgia National Park |
| title_fullStr | Using remote sensing data within an optimal spatiotemporal model for invasive plant management: the case of Ailanthus altissima in the Alta Murgia National Park |
| title_full_unstemmed | Using remote sensing data within an optimal spatiotemporal model for invasive plant management: the case of Ailanthus altissima in the Alta Murgia National Park |
| title_short | Using remote sensing data within an optimal spatiotemporal model for invasive plant management: the case of Ailanthus altissima in the Alta Murgia National Park |
| title_sort | using remote sensing data within an optimal spatiotemporal model for invasive plant management the case of ailanthus altissima in the alta murgia national park |
| url | https://doi.org/10.1038/s41598-023-41607-2 |
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