Combining Airborne Laser Scanning and Aerial Imagery Enhances Echo Classification for Invasive Conifer Detection

Dash, Jonathan P.; Pearse, Grant D.; Watt, Michael S.; Paul, Thomas

doi:10.3390/rs9020156

Cited by 16 publications

(16 citation statements)

References 58 publications

(98 reference statements)

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“…The mean decrease in accuracy (MDA) importance measure is calculated as the normalised difference between OOB accuracy of the original observations to randomly permuted variables [75][76][77]. RF is a well-regarded machine learning tool that has the capacity to identify complex and non-linear relationships in the fitting dataset and offers high classification accuracy [75,77,78].…”

Section: Discussionmentioning

confidence: 99%

UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health

2018

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The development of methods that can accurately detect physiological stress in forest trees caused by biotic or abiotic factors is vital for ensuring productive forest systems that can meet the demands of the Earth’s population. The emergence of new sensors and platforms presents opportunities to augment traditional practices by combining remotely-sensed data products to provide enhanced information on forest condition. We tested the sensitivity of multispectral imagery collected from time-series unmanned aerial vehicle (UAV) and satellite imagery to detect herbicide-induced stress in a carefully controlled experiment carried out in a mature Pinus radiata D. Don plantation. The results revealed that both data sources were sensitive to physiological stress in the study trees. The UAV data were more sensitive to changes at a finer spatial resolution and could detect stress down to the level of individual trees. The satellite data tested could only detect physiological stress in clusters of four or more trees. Resampling the UAV imagery to the same spatial resolution as the satellite imagery revealed that the differences in sensitivity were not solely the result of spatial resolution. Instead, vegetation indices suited to the sensor characteristics of each platform were required to optimise the detection of physiological stress from each data source. Our results define both the spatial detection threshold and the optimum vegetation indices required to implement monitoring of this forest type. A comparison between time-series datasets of different spectral indices showed that the two sensors are compatible and can be used to deliver an enhanced method for monitoring physiological stress in forest trees at various scales. We found that the higher resolution UAV imagery was more sensitive to fine-scale instances of herbicide induced physiological stress than the RapidEye imagery. Although less sensitive to smaller phenomena the satellite imagery was found to be very useful for observing trends in physiological stress over larger areas.

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Section: Discussionmentioning

confidence: 99%

UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health

2018

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“…The area affected by invasive exotic conifers is thought to cover approximately 2 million ha and is believed to be increasing at a rate of 6% annually (Anon 2011;McAlpine et al 2016). The area covered by these wildings has resulted in an economic and ecological cost that is increasingly deemed to be unacceptable by New Zealand society (Dash et al 2017a).…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral VNIR-spectroscopy and imagery as a tool for monitoring herbicide damage in wilding conifers

et al. 2019

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Wilding conifers, the naturally regenerated scions of exotic conifer species, are a major ecological and economic issue in New Zealand. Hyperspectral imagery could provide a useful means of monitoring the efficacy of wilding control measures but little research has examined this possibility. Data were collected from an experiment, that included thirty-nine young Pinus contorta (Douglas) trees allocated to a control and two herbicide treatments (triclopyr butoxyethyl ester and diquat dibromide monohydrate). The objectives of this research were to examine the temporal impacts of herbicide on needle discolouration, spectral properties and tree physiology and to develop models to predict photosynthetic parameters from hyperspectral data. In accordance with obvious discolouration on the first day after treatment, values of net photosynthesis rate (A), electron transport rate (ETR), stomatal conductance (G) and optimal quantum yield of photosystem II (F v /F m) declined rapidly for plants treated with diquat. Discolouration following application of triclopyr was not evident before the second week, and values of A, ETR, G and F v /F m declined slower than those of diquat. Of the tested narrow-band spectral indices calculated from needle spectra photochemical reflectance index (PRI) exhibited the strongest correlation with the four photosynthetic parameters and R 2 values between PRI and A, F v / F m , ETR and G were, respectively, 0.53, 0.46, 0.68 and 0.29. Models that used raw spectra as inputs showed that sparse partial least squares (SPLS) outperformed the three other approaches tested (PLS, Support Vector Machines, Elastic Net) and R 2 values using this approach for A, F v /F m , ETR and G were, respectively, 0.64, 0.62, 0.65 and 0.42.

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“…The most commonly used classifier RF (Breiman, ) was used in 33% of studies. This algorithm has been shown to be highly flexible and capable of producing accurate results in many domains (Dash, Marshall, & Rawley, ; Dash, Pearse, et al, ; Mellor, Haywood, Stone, & Jones, ). The next most popular algorithm (SVM) was used in 17% of studies.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Effective management must be supported by appropriate methods for detection and monitoring (Richardson & Rejmánek, ). Traditional methods including observer‐based surveys are expensive, can be error prone and are difficult challenging terrain (Dash, Pearse, Watt, & Paul, ). As a result, new modes of detection and monitoring are required.…”

Section: Introductionmentioning

confidence: 99%

Taking a closer look at invasive alien plant research: A review of the current state, opportunities, and future directions for UAVs

et al. 2019

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The development and proliferation of unmanned aerial vehicles (UAV) in recent years presents a new data collection opportunity for invasive alien plant (IAP) research. The flexibility and cost‐efficiency of these craft offers a valuable solution where high‐spatial or high‐temporal resolution remotely sensed data are required. In this paper, we review all published studies using UAV for remote data collection in IAP research. We have systematically identified the taxonomy and habitat characteristics of the system studied, classified the UAV configuration, analytical methods and the limitations of each study. We used this synthesis to identify research gaps, suggest directions for future research, and identify opportunities for practical application of the technology.

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Combining Airborne Laser Scanning and Aerial Imagery Enhances Echo Classification for Invasive Conifer Detection

Cited by 16 publications

References 58 publications

UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health

UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health

Hyperspectral VNIR-spectroscopy and imagery as a tool for monitoring herbicide damage in wilding conifers

Taking a closer look at invasive alien plant research: A review of the current state, opportunities, and future directions for UAVs

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