Unmanned Aerial Vehicle (UAV)-Based Mapping of <i>Acacia saligna</i> Invasion in the Mediterranean Coast
Remote Sensing (RS) is a useful tool for detecting and mapping Invasive Alien Plants (IAPs). IAPs mapping on dynamic and heterogeneous landscapes, using satellite RS data, is not always feasible. Unmanned aerial vehicles (UAV) with ultra-high spatial resolution data represent a promising tool for IA...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/17/3361 |
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| author | Flavio Marzialetti Ludovico Frate Walter De Simone Anna Rita Frattaroli Alicia Teresa Rosario Acosta Maria Laura Carranza |
| author_facet | Flavio Marzialetti Ludovico Frate Walter De Simone Anna Rita Frattaroli Alicia Teresa Rosario Acosta Maria Laura Carranza |
| author_sort | Flavio Marzialetti |
| collection | DOAJ |
| description | Remote Sensing (RS) is a useful tool for detecting and mapping Invasive Alien Plants (IAPs). IAPs mapping on dynamic and heterogeneous landscapes, using satellite RS data, is not always feasible. Unmanned aerial vehicles (UAV) with ultra-high spatial resolution data represent a promising tool for IAPs detection and mapping. This work develops an operational workflow for detecting and mapping <i>Acacia saligna</i> invasion along Mediterranean coastal dunes. In particular, it explores and tests the potential of RGB (Red, Green, Blue) and multispectral (Green, Red, Red Edge, Near Infra—Red) UAV images collected in pre-flowering and flowering phenological stages for detecting and mapping <i>A. saligna</i>. After ortho—mosaics generation, we derived from RGB images the DSM (Digital Surface Model) and HIS (Hue, Intensity, Saturation) variables, and we calculated the NDVI (Normalized Difference Vegetation Index). For classifying images of the two phenological stages we built a set of raster stacks which include different combination of variables. For image classification, we used the Geographic Object-Based Image Analysis techniques (GEOBIA) in combination with Random Forest (RF) classifier. All classifications derived from RS information (collected on pre-flowering and flowering stages and using different combinations of variables) produced <i>A. saligna</i> maps with acceptable accuracy values, with higher performances on classification derived from flowering period images, especially using DSM + HIS combination. The adopted approach resulted an efficient method for mapping and early detection of IAPs, also in complex environments offering a sound support to the prioritization of conservation and management actions claimed by the EU IAS Regulation 1143/2014. |
| first_indexed | 2024-03-10T08:04:44Z |
| format | Article |
| id | doaj.art-353a6053741c4963af6e5213c60ac777 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T08:04:44Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-353a6053741c4963af6e5213c60ac7772023-11-22T11:07:49ZengMDPI AGRemote Sensing2072-42922021-08-011317336110.3390/rs13173361Unmanned Aerial Vehicle (UAV)-Based Mapping of <i>Acacia saligna</i> Invasion in the Mediterranean CoastFlavio Marzialetti0Ludovico Frate1Walter De Simone2Anna Rita Frattaroli3Alicia Teresa Rosario Acosta4Maria Laura Carranza5Envix-Lab, Departement of Biosciences and Territory, Molise University, Contrada Fonte Lappone, 86090 Pesche, ItalyEnvix-Lab, Departement of Biosciences and Territory, Molise University, Contrada Fonte Lappone, 86090 Pesche, ItalyDepartment of Life, Health and Environmental Sciences, University of L’Aquila, Piazzale Salvatore Tommasi, 67100 L’Aquila, ItalyDepartment of Life, Health and Environmental Sciences, University of L’Aquila, Piazzale Salvatore Tommasi, 67100 L’Aquila, ItalyDepartment of Sciences, University of Roma Tre, Viale G. Marconi 446, 00146 Rome, ItalyEnvix-Lab, Departement of Biosciences and Territory, Molise University, Contrada Fonte Lappone, 86090 Pesche, ItalyRemote Sensing (RS) is a useful tool for detecting and mapping Invasive Alien Plants (IAPs). IAPs mapping on dynamic and heterogeneous landscapes, using satellite RS data, is not always feasible. Unmanned aerial vehicles (UAV) with ultra-high spatial resolution data represent a promising tool for IAPs detection and mapping. This work develops an operational workflow for detecting and mapping <i>Acacia saligna</i> invasion along Mediterranean coastal dunes. In particular, it explores and tests the potential of RGB (Red, Green, Blue) and multispectral (Green, Red, Red Edge, Near Infra—Red) UAV images collected in pre-flowering and flowering phenological stages for detecting and mapping <i>A. saligna</i>. After ortho—mosaics generation, we derived from RGB images the DSM (Digital Surface Model) and HIS (Hue, Intensity, Saturation) variables, and we calculated the NDVI (Normalized Difference Vegetation Index). For classifying images of the two phenological stages we built a set of raster stacks which include different combination of variables. For image classification, we used the Geographic Object-Based Image Analysis techniques (GEOBIA) in combination with Random Forest (RF) classifier. All classifications derived from RS information (collected on pre-flowering and flowering stages and using different combinations of variables) produced <i>A. saligna</i> maps with acceptable accuracy values, with higher performances on classification derived from flowering period images, especially using DSM + HIS combination. The adopted approach resulted an efficient method for mapping and early detection of IAPs, also in complex environments offering a sound support to the prioritization of conservation and management actions claimed by the EU IAS Regulation 1143/2014.https://www.mdpi.com/2072-4292/13/17/3361invasive plant speciescoastal dunesRGB and multispectral imagesspecies floweringdronesGEOBIA |
| spellingShingle | Flavio Marzialetti Ludovico Frate Walter De Simone Anna Rita Frattaroli Alicia Teresa Rosario Acosta Maria Laura Carranza Unmanned Aerial Vehicle (UAV)-Based Mapping of <i>Acacia saligna</i> Invasion in the Mediterranean Coast Remote Sensing invasive plant species coastal dunes RGB and multispectral images species flowering drones GEOBIA |
| title | Unmanned Aerial Vehicle (UAV)-Based Mapping of <i>Acacia saligna</i> Invasion in the Mediterranean Coast |
| title_full | Unmanned Aerial Vehicle (UAV)-Based Mapping of <i>Acacia saligna</i> Invasion in the Mediterranean Coast |
| title_fullStr | Unmanned Aerial Vehicle (UAV)-Based Mapping of <i>Acacia saligna</i> Invasion in the Mediterranean Coast |
| title_full_unstemmed | Unmanned Aerial Vehicle (UAV)-Based Mapping of <i>Acacia saligna</i> Invasion in the Mediterranean Coast |
| title_short | Unmanned Aerial Vehicle (UAV)-Based Mapping of <i>Acacia saligna</i> Invasion in the Mediterranean Coast |
| title_sort | unmanned aerial vehicle uav based mapping of i acacia saligna i invasion in the mediterranean coast |
| topic | invasive plant species coastal dunes RGB and multispectral images species flowering drones GEOBIA |
| url | https://www.mdpi.com/2072-4292/13/17/3361 |
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