Coastal Dune Invaders: Integrative Mapping of Carpobrotus sp. pl. (Aizoaceae) Using UAVs

Innangi, Michele; Marzialetti, Flavio; Febbraro, Mirko Di; Acosta, Alicia Teresa Rosario; Simone, Walter De; Frate, Ludovico; Finizio, Michele; Perna, Priscila Villalobos Villalobos; Carranza, María Laura

doi:10.3390/rs15020503

Cited by 10 publications

(7 citation statements)

References 82 publications

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“…Furthermore, this limited number of countries constrained the number of studied IAP using RS platforms providing a partial picture of the invasions on a global scale. A large number of IAP impinging coastal ecosystems worldwide are still needed of specific RS research and applications [52,90,91], albeit the research in this field is progressing as shown by promising results in 2022 [92][93][94][95].…”

Section: Discussionmentioning

confidence: 99%

Remote Sensing and Invasive Plants in Coastal Ecosystems: What We Know So Far and Future Prospects

Perna

Febbraro

Carranza

et al. 2023

Land

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Coastal environments are highly threatened by invasive alien plants (IAP), and Remote Sensing (RS) may offer a sound support for IAP detection and mapping. There is still a need for an overview of the progress and extent of RS applications on invaded coasts that can help the development of better RS procedures to support IAP management. We conducted a systematic literature review of 68 research papers implementing, recommending, or discussing RS tools for IAP mapping in coastal environments, published from 2000 to 2021. According to this review, most research was done in China and USA, with Sporobolus (17.3%) being the better studied genus. The number of studies increased at an accelerated rate from 2015 onwards, coinciding with the transition from RS for IAP detection to RS for invasion modeling. The most used platforms in the 2000s were aircraft, with satellites that increased from 2005 and unmanned aerial vehicles after 2014. Frequentist inference was the most adopted classification approach in the 2000s, as machine learning increased after 2009. RS applications vary with coastal ecosystem types and across countries. RS has a huge potential to further improve IAP monitoring. The extension of RS to all coasts of the world requires advanced applications that bring together current and future Earth observation data.

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Section: Discussionmentioning

confidence: 99%

Remote Sensing and Invasive Plants in Coastal Ecosystems: What We Know So Far and Future Prospects

Perna

Febbraro

Carranza

et al. 2023

Land

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“…This choice was made based on the belief that flowers could potentially influence the classification outcomes and biomass estimation via vegetation indices. However, a recent study [48], which involved the classification of C. edulis during the flowering season, revealed that flowers do not pose a significant impact on image classification results.…”

Section: Estimation Uncertaintiesmentioning

confidence: 99%

Using Remote Sensing Multispectral Imagery for Invasive Species Quantification: The Effect of Image Resolution on Area and Biomass Estimation

Meyer,

Gonçalves,

Bio

2024

Remote Sensing

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This study assesses the applicability of different-resolution multispectral remote sensing images for mapping and estimating the aboveground biomass (AGB) of Carpobrotus edulis, a prominent invasive species in European coastal areas. This study was carried out on the Cávado estuary sand spit (Portugal). The performance of three sets of multispectral images with different Ground Sample Distances (GSDs) were compared: 2.5 cm, 5 cm, and 10 cm. The images were classified using the supervised classification algorithm random forest and later improved by applying a sieve filter. Samples of C. edulis were also collected, dried, and weighed to estimate the AGB using the relationship between the dry weight (DW) and vegetation indices (VIs). The resulting regression models were evaluated based on their coefficient of determination (R2), Normalised Root Mean Square Error (NRMSE), p-value, Akaike information criterion (AIC), and the Bayesian information criterion (BIC). The results show that the three tested image resolutions allow for constructing reliable coverage maps of C. edulis, with overall accuracy values of 89%, 85%, and 88% for the classification of the 2.5 cm, 5 cm, and 10 cm GSD images, respectively. The best-performing VI-DW regression models achieved R2 = 0.87 and NRMSE = 0.09 for the 2.5 cm resolution; R2 = 0.77 and NRMSE = 0.12 for the 5 cm resolution; and R2 = 0.64 and NRMSE = 0.15 for the 10 cm resolution. The C. edulis area and total AGB were 3441.10 m2 and 28,327.1 kg (with an AGB relative error (RE) = 0.08) for the 2.5 cm resolution; 3070.04 m2 and 29,170.8 kg (AGB RE = 0.08) for the 5 cm resolution; and 2305.06 m2 and 22,135.7 kg (AGB RE = 0.11) for the 10 cm resolution. Spatial and model differences were analysed in detail to determine their causes. Final analyses suggest that multispectral imagery of up to 5 cm GSD is adequate for estimating C. edulis distribution and biomass.

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“…Most of these papers (73%) monitored vegetation cover over time (e.g., [160]), whereas about half of the papers (60%) compared different methods (e.g., [161]) and 42% retrieved this parameter from several remote data (e.g., [123]). Moreover, three papers combined remote data with models (e.g., [162]) or GIS (e.g., [163]).…”

Section: Vegetation Covermentioning

confidence: 99%

“…Most of these papers (79%) monitored vegetation species over time (e.g., [164]), whereas about half of the papers (58%) compared different methods or products to obtain results with the lowest error (e.g., [165]) and 53% retrieved this parameter from several remote data (e.g., [161]). Moreover, one paper combined GIS and remote sensing techniques [166].…”

Section: Vegetation Speciesmentioning

confidence: 99%

“…The eligible papers that mapped and/or monitored vegetation species. Coastal vegetation (1) Coastal vulnerability assessment Sentinel-2 (10-20-60 m) No -- [166] Coastal vegetation (1) Habitat GaoFen2 (0.8-3.2 m) No --[559] Coastal vegetation (1) Habitat Google Earth image No --[164] Coastal vegetation (1) Habitat Landsat (15-30 m) No --[424,559] Coastal vegetation (1) Habitat RapidEye (5 m) No -- [424] Coastal vegetation (1) Wetland Biodiversity Estimation ZiYuan1-02D-HIS (30 m) (2) No - [467] -Coastal vegetation (1) Wetland Biodiversity Estimation ZiYuan1-02D-MSI (10 m) No -[467] -Invasive species (1) Coastal vulnerability assessment Landsat (15-30 m) No [55] - [413] Invasive species (1) Invasive alien species Landsat (15-30 m) No --[520] Invasive species (1) Invasive alien species Sentinel-2 (10-20-60 m) No --[473,520] Invasive species (1) Invasive alien species UAV-MSI No [161] --Invasive species (1) Invasive alien species UAV No [161] --Invasive species (1) LU/LC change Google Earth image No --[437] Invasive species (1) LU/LC change Landsat (15-30 m) No --[437] Invasive species (1) Mangrove ecosystems ALOS-2 No -[365] -Invasive species (1) Mangrove ecosystems Landsat (15-30 m) No -[365] -Invasive species (1) Mangrove ecosystems SPOT (~10-20 m) No -[365] -Invasive species (1) Tidal flat Sentinel-2 (10-20-60 m) No -[453] -Riparian species (1) Invasive alien species Landsat (15-30 m) No -[432] -Salt marsh species (1) Habitat Sentinel-1 (~10 m) No --[560] Salt marsh species (1) Habitat Sentinel-2 (10-20-60 m) No --[428,560] Salt marsh species (1) Habitat UAV-Lidar No -[142] -Salt marsh species (1) Habitat UAV-MSI No -[142] -Wetland species (1) Habitat Sentinel-1 (~10 m) No …”

Section: Parametermentioning

confidence: 99%

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Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review

Cavalli

2024

Remote Sensing

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Since 1971, remote sensing techniques have been used to map and monitor phenomena and parameters of the coastal zone. However, updated reviews have only considered one phenomenon, parameter, remote data source, platform, or geographic region. No review has offered an updated overview of coastal phenomena and parameters that can be accurately mapped and monitored with remote data. This systematic review was performed to achieve this purpose. A total of 15,141 papers published from January 2021 to June 2023 were identified. The 1475 most cited papers were screened, and 502 eligible papers were included. The Web of Science and Scopus databases were searched using all possible combinations between two groups of keywords: all geographical names in coastal areas and all remote data and platforms. The systematic review demonstrated that, to date, many coastal phenomena (103) and parameters (39) can be mapped and monitored using remote data (e.g., coastline and land use and land cover changes, climate change, and coastal urban sprawl). Moreover, the authors validated 91% of the retrieved parameters, retrieved from remote data 39 parameters that were mapped or monitored 1158 times (88% of the parameters were combined together with other parameters), monitored 75% of the parameters over time, and retrieved 69% of the parameters from several remote data and compared the results with each other and with available products. They obtained 48% of the parameters using different methods, and their results were compared with each other and with available products. They combined 17% of the parameters that were retrieved with GIS and model techniques. In conclusion, the authors addressed the requirements needed to more effectively analyze coastal phenomena and parameters employing integrated approaches: they retrieved the parameters from different remote data, merged different data and parameters, compared different methods, and combined different techniques.

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Coastal Dune Invaders: Integrative Mapping of Carpobrotus sp. pl. (Aizoaceae) Using UAVs

Cited by 10 publications

References 82 publications

Remote Sensing and Invasive Plants in Coastal Ecosystems: What We Know So Far and Future Prospects

Remote Sensing and Invasive Plants in Coastal Ecosystems: What We Know So Far and Future Prospects

Using Remote Sensing Multispectral Imagery for Invasive Species Quantification: The Effect of Image Resolution on Area and Biomass Estimation

Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review

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