Extending the observational record to provide new insights into invasive alien species in a coastal dune environment of New Zealand

Thomas, Zoë; Turney, Chris S. M.; Palmer, Jonathan; Lloydd, S.; Klaricich, J.N.L.; Hogg, Alan G.

doi:10.1016/j.apgeog.2018.07.006

Cited by 7 publications

(5 citation statements)

References 60 publications

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“…Consequently, very high spatial resolution imagery captured by UAVs became practical in natural resource management to monitor invasive plant species in several different ecosystems [31][32][33]. Several recent studies [22,[31][32][33][34][35][36] have proved that the use of UAV-borne remote sensing is an effective method to classify vegetation. Pande-Chhetri [37] used UAV data to classify wetland vegetation with pixel-based and hierarchical object-based classification approaches.…”

Section: Introductionmentioning

confidence: 99%

Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers

et al. 2019

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Unmanned aerial vehicles (UAV) are increasingly used for spatiotemporal monitoring of invasive plants in coastal wetlands. Early identification of invasive species is necessary in planning, restoring, and managing wetlands. This study assessed the effectiveness of UAV technology to identify invasive Phragmites australis in the Old Woman Creek (OWC) estuary using machine learning (ML) algorithms: Neural network (NN), support vector machine (SVM), and k-nearest neighbor (kNN). The ML algorithms were compared with the parametric maximum likelihood classifier (MLC) using pixel- and object-based methods. Pixel-based NN was identified as the best classifier with an overall accuracy of 94.80% and the lowest error of omission of 1.59%, the outcome desirable for effective eradication of Phragmites. The results were reached combining Sequoia multispectral imagery (green, red, red edge, and near-infrared bands) combined with the canopy height model (CHM) acquired in the mid-growing season and normalized difference vegetation index (NDVI) acquired later in the season. The sensitivity analysis, using various vegetation indices, image texture, CHM, and principal components (PC), demonstrated the impact of various feature layers on the classifiers. The study emphasizes the necessity of a suitable sampling and cross-validation methods, as well as the importance of optimum classification parameters.

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Section: Introductionmentioning

confidence: 99%

Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers

et al. 2019

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“…The still limited use of RS data on coastal ecosystems invaded by IAP may be due to the interaction of several aspects, such as the limited extent and fast ecological dynamic of coastal landscapes that require an accurate study of detailed spatial data registered with high frequency. Indeed, mapping coastal ecosystems characterized by tiny mosaics occurring on linear and narrow strips (usually less than 500 m) between sea and inland systems [41], requires the use of sensors with ultra-high spatial resolution regardless of the differences among satellites [42,43], aircraft [44][45][46], and UAVs [47][48][49][50]. The use of RS for the detection, mapping and modelling of invasive plants becomes even more difficult due to the characteristics of invasion processes that often occur on very small patches of IAP [51][52][53] interspersed with open areas such as bare sand or water [54].…”

Section: Discussionmentioning

confidence: 99%

“…[42,75,76]. UAV derived occurrence data improve the detection and mapping of IAP in complex areas with low invasion degree, filling the gap between the continuous field spectral values and those of the coarser multispectral satellite images [49,[75][76][77]. As for SAR images, while they offer a sound support for delineating coastline, mapping sea level, wind direction and ground displacements, their potential for detecting and mapping single plant species is very limited [78][79][80][81].…”

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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“…Increasing human occupation near the coastline has caused the fragmentation and degradation of these ecosystems [8,9]. In terms of vegetation, this degradation is also often associated with the introduction and spread of invasive alien species (IAS) as a means of stabilizing/retaining dune sands [10,11].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Foredune Dynamics in Response to Invasive Vegetation Control Actions

Bastos,

Taborda,

Andrade

et al. 2024

Remote Sensing

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Efforts to control the spread of invasive alien species (IAS) in coastal dunes are essential to protect biodiversity and maintain the integrity of the ecosystem. However, the timing and extent of these control measures often do not consider their potential impact on dune morphodynamics. This study investigated how IAS control measures can affect sand dune mobility. Research involved monitoring short-term morphological and vegetation changes using close-range remote sensing along with historical wind data. Findings revealed that changes in vegetation cover significantly impacted dune system evolution, notably increasing sand mobility when IAS vegetation was removed. Considering the seasonal wind regime, dominated by offshore winds in the summer, removing vegetation during this period can promote seaward sediment transport, potentially resulting in sediment loss to the beach. These findings highlight the importance of understanding sediment mobility patterns when planning vegetation control actions within dune restoration strategies.

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Extending the observational record to provide new insights into invasive alien species in a coastal dune environment of New Zealand

Cited by 7 publications

References 60 publications

Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers

Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers

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

Short-Term Foredune Dynamics in Response to Invasive Vegetation Control Actions

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