Using Landsat Time Series to Understand How Management and Disturbances Influence the Expansion of an Invasive Tree

de, Nuno Cesar; Carvalho, Sabrina; Castro, Paula M.; Marchante, Elizabete; Marchante, Hélia

doi:10.1109/jstars.2017.2673761

Cited by 22 publications

(10 citation statements)

References 28 publications

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“…is easily identifiable both during the flowering season due to the bloom of yellow flowers as well as during the fruitification when its seed pods begin to brown and remain on the branches. It can develop as a shrub (often up to 4 m in height), when occurring in primary dunes, and as small tree (up to 8 m in height) when occurring more inland, also below the canopy of other species (e.g., maritime pines) ( Cesar de Sa et al, 2017 ; Invasoras, 2017 ). During fieldwork we observed a high variability in flower patterns amongst plants depending on its morphology (tree or shrub) and on the position in the dune system ( Figures 1B,C ).…”

Section: Methodsmentioning

confidence: 99%

“…80 km of the central-northern coast of Portugal ( Figure 2 and Table 1 ). Sites were selected in order to include the most common coastal habitats invaded by A. longifolia : (1) several plant communities in sand dunes (SD), including primary dunes, interdunes and secondary dunes ( Cesar de Sa et al, 2017 ); maritime pine occurs in these areas but generally does not dominate (SD1, SD2, SD3, SD4, SD5); and (2) pine forests plantations (hereafter pine forests, PF), where maritime pine dominates and A. longifolia occurs in the understory (PF1, PF2).…”

Section: Methodsmentioning

confidence: 99%

“…Acacia longifolia (Andrews) Willd. (Sydney Golden Wattle; Fabaceae ) is one of several Australian Acacia species that are invasive in many regions around the world ( Rejmánek and Richardson, 2013 ) including in southern Europe where it has proliferated mostly on coastal areas ( Marchante et al, 2014 ; Cesar de Sa et al, 2017 ). This leguminous shrub/small tree has a long track of diverse ecological impacts in coastal systems ( Marchante et al, 2003 , 2008 ), where it replaces native herbaceous and shrub dune communities and proliferates under maritime pine forests.…”

Section: Introductionmentioning

confidence: 99%

“…Ultimately, we expect that monitoring the loss of flowering can be used to continuously monitor the establishment of the BCA. The use of remotely sensed data has rapidly increased and has nowadays a wide range of applications in many different fields, with potential to become a key tool for ecological research and conservation ( Horning et al, 2010 ; Gonzalez et al, 2016 ) including for the study of invasive alien plants ( Cesar de Sa et al, 2017 ; Weisberg et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

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Mapping the Flowering of an Invasive Plant Using Unmanned Aerial Vehicles: Is There Potential for Biocontrol Monitoring?

et al. 2018

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Invasion by alien species is a worldwide phenomenon with negative consequences at both natural and production areas. Acacia longifolia is an invasive shrub/small tree well known for its negative ecological impacts in several places around the world. The recent introduction of a biocontrol agent (Trichilogaster acaciaelongifoliae), an Australian bud-galling wasp which decreases flowering of A. longifolia, in Portugal, demands the development of a cost-efficient method to monitor its establishment. We tested how unmanned aerial vehicles (UAV) can be used to map A. longifolia flowering. Our core assumption is as the population of the biocontrol agent increases, its impacts on the reduction of A. longifolia flowering will be increasingly visible. Additionally, we tested if there is a simple linear correlation between the number of flowers of A. longifolia counted in field and the area covered by flowers in the UAV imagery. UAV imagery was acquired over seven coastal areas including frontal dunes, interior sand dunes and pine forests considering two phenological stages: peak and off-peak flowering season. The number of flowers of A. longifolia was counted, in a minimum of 60 1 m2 quadrats per study area. For each study area, flower presence/absence maps were obtained using supervised Random Forest. The correlation between the number of flowers and the area covered by flowering plants could then be tested. The flowering of A. longifolia was mapped using UAV mounted with RGB and CIR Cannon IXUS/ELPH cameras (Overall Accuracy > 0.96; Cohen’s Kappa > 0.85) varying according to habitat type and flowering season. The correlation between the number of flowers counted and the area covered by flowering was weak (r2 between 0.0134 and 0.156). This is probably explained, at least partially, by the high variability of A. longifolia in what regards flowering morphology and distribution. The very high accuracy of our approach to map A. longifolia flowering proved to be cost efficient and replicable, showing great potential for detecting the future decrease in flowering promoted by the biocontrol agent. The attempt to provide a low-cost method to estimate A. longifolia flower productivity using UAV failed, but it provided valuable insights on the future steps.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mapping the Flowering of an Invasive Plant Using Unmanned Aerial Vehicles: Is There Potential for Biocontrol Monitoring?

et al. 2018

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“…A range of satellite products have been used in studies monitoring exotic plant invasions. These range from studies using moderate resolution sensors such as Landsat (spatial resolution = 30 m) [53][54][55], to higher spatial resolution sensors such as WorldView 2 (spatial resolution = 0.5 m) [56] and Pleaides 1A (spatial resolution = 0.5 m) [57,58]. Higher resolution imagery collected from conventional manned aircraft has been widely used for invasive plant detection.…”

Section: Introductionmentioning

confidence: 99%

Early Detection of Invasive Exotic Trees Using UAV and Manned Aircraft Multispectral and LiDAR Data

et al. 2019

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Exotic conifers can provide significant ecosystem services, but in some environments, they have become invasive and threaten indigenous ecosystems. In New Zealand, this phenomenon is of considerable concern as the area occupied by invasive exotic trees is large and increasing rapidly. Remote sensing methods offer a potential means of identifying and monitoring land infested by these trees, enabling managers to efficiently allocate resources for their control. In this study, we sought to develop methods for remote detection of exotic invasive trees, namely Pinus sylvestris and P. ponderosa. Critically, the study aimed to detect these species prior to the onset of maturity and coning as this is important for preventing further spread. In the study environment in New Zealand’s South Island, these species reach maturity and begin bearing cones at a young age. As such, detection of these smaller individuals requires specialist methods and very high-resolution remote sensing data. We examined the efficacy of classifiers developed using two machine learning algorithms with multispectral and laser scanning data collected from two platforms—manned aircraft and unmanned aerial vehicles (UAV). The study focused on a localized conifer invasion originating from a multi-species pine shelter belt in a grassland environment. This environment provided a useful means of defining the detection thresholds of the methods and technologies employed. An extensive field dataset including over 17,000 trees (height range = 1 cm to 476 cm) was used as an independent validation dataset for the detection methods developed. We found that data from both platforms and using both logistic regression and random forests for classification provided highly accurate (kappa < 0.996 ) detection of invasive conifers. Our analysis showed that the data from both UAV and manned aircraft was useful for detecting trees down to 1 m in height and therefore shorter than 99.3% of the coning individuals in the study dataset. We also explored the relative contribution of both multispectral and airborne laser scanning (ALS) data in the detection of invasive trees through fitting classification models with different combinations of predictors and found that the most useful models included data from both sensors. However, the combination of ALS and multispectral data did not significantly improve classification accuracy. We believe that this was due to the simplistic vegetation and terrain structure in the study site that resulted in uncomplicated separability of invasive conifers from other vegetation. This study provides valuable new knowledge of the efficacy of detecting invasive conifers prior to the onset of coning using high-resolution data from UAV and manned aircraft. This will be an important tool in managing the spread of these important invasive plants.

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A novel invasive plant detection approach using time series images from unmanned aerial systems based on convolutional and recurrent neural networks

Guo

Yun

Rothfus

et al. 2022

Neural Comput & Applic

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Using Landsat Time Series to Understand How Management and Disturbances Influence the Expansion of an Invasive Tree

Cited by 22 publications

References 28 publications

Mapping the Flowering of an Invasive Plant Using Unmanned Aerial Vehicles: Is There Potential for Biocontrol Monitoring?

Mapping the Flowering of an Invasive Plant Using Unmanned Aerial Vehicles: Is There Potential for Biocontrol Monitoring?

Early Detection of Invasive Exotic Trees Using UAV and Manned Aircraft Multispectral and LiDAR Data

A novel invasive plant detection approach using time series images from unmanned aerial systems based on convolutional and recurrent neural networks

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