Mapping of riparian invasive species with supervised classification of Unmanned Aerial System (UAS) imagery

Michez, Adrien; Piégay, Hervé; Lisein, Jonathan; Claessens, Hugues; Lejeune, Philippe

doi:10.1016/j.jag.2015.06.014

Cited by 115 publications

(134 citation statements)

References 36 publications

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“…UAS therefore offer the potential to overcome these limitations and have been applied to monitor a disparate range of habitats and locations, including tropical forests, riparian forests, dryland ecosystems, boreal forests, and peatlands. Pioneering researchers have been using UAS to monitor attributes such as plant population [107,108]; biodiversity and species richness [109,110]; plant species invasion [111]; restoration ecology [112]; disturbances [113]; phenology [114]; pest infestation in forests [115,116]; and land cover change [117].…”

Section: Monitoring Of Natural Ecosystemsmentioning

confidence: 99%

On the Use of Unmanned Aerial Systems for Environmental Monitoring

et al. 2018

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Environmental monitoring plays a central role in diagnosing climate and management impacts on natural and agricultural systems; enhancing the understanding of hydrological processes; optimizing the allocation and distribution of water resources; and assessing, forecasting, and even preventing natural disasters. Nowadays, most monitoring and data collection systems are based upon a combination of ground-based measurements, manned airborne sensors, and satellite observations. These data are utilized in describing both small-and large-scale processes, but have spatiotemporal constraints inherent to each respective collection system. Bridging the unique spatial and temporal divides that limit current monitoring platforms is key to improving our understanding of environmental systems. In this context, Unmanned Aerial Systems (UAS) have considerable potential to radically improve environmental monitoring. UAS-mounted sensors offer an extraordinary opportunity to bridge the existing gap between field observations and traditional air-and space-borne remote sensing, by providing high spatial detail over relatively large areas in a cost-effective way and an entirely new capacity for enhanced temporal retrieval. As well as showcasing recent advances in the field, there is also a need to identify and understand the potential limitations of UAS technology. For these platforms to reach their monitoring potential, a wide spectrum of unresolved issues and application-specific challenges require focused community attention. Indeed, to leverage the full potential of UAS-based approaches, sensing technologies, measurement protocols, postprocessing techniques, retrieval algorithms, and evaluation techniques need to be harmonized. The aim of this paper is to provide an overview of the existing research and applications of UAS in natural and agricultural ecosystem monitoring in order to identify future directions, applications, developments, and challenges.

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Section: Monitoring Of Natural Ecosystemsmentioning

confidence: 99%

On the Use of Unmanned Aerial Systems for Environmental Monitoring

et al. 2018

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“…UAV‐based invasive species mapping has yielded high accuracies using different data types, like RGB or VNIR information (Michez et al. ; Alvarez‐Taboada et al. ; Baena et al.…”

Section: Introductionmentioning

confidence: 99%

How canopy shadow affects invasive plant species classification in high spatial resolution remote sensing

Lopatin

Dološ

Kattenborn

2019

Remote Sens Ecol Conserv

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Plant invasions can result in serious threats for biodiversity and ecosystem functioning. Reliable maps at very‐high spatial resolution are needed to assess invasions dynamics. Field sampling approaches could be replaced by unmanned aerial vehicles (UAVs) to derive such maps. However, pixel‐based species classification at high spatial resolution is highly affected by within‐canopy variation caused by shadows. Here, we studied the effect of shadows on mapping the occurrence of invasive species using UAV‐based data. MaxEnt one‐class classifications were applied to map Acacia dealbata, Ulex europaeus and Pinus radiata in central‐south Chile using combinations of UAV‐based spectral (RGB and hyperspectral), 2D textural and 3D structural variables including and excluding shaded canopy pixels during model calibration. The model accuracies in terms of area under the curve (AUC), Cohen's Kappa, sensitivity (true positive rate) and specificity (true negative rate) were examined in sunlit and shaded canopies separately. Bootstrapping was used for validation and to assess statistical differences between models. Our results show that shadows significantly affect the accuracies obtained with all types of variables. The predictions in shaded areas were generally inaccurate, leading to misclassification rates between 65% and 100% even when shadows were included during model calibration. The exclusion of shaded areas from model calibrations increased the predictive accuracies (especially in terms of sensitivity), decreasing false positives. Spectral and 2D textural information showed generally higher performances and improvements when excluding shadows from the analysis. Shadows significantly affected the model results obtained with any of the variables used, hence the exclusion of shadows is recommended prior to model calibration. This relatively easy pre‐processing step enhances models for classifying species occurrences using high‐resolution spectral imagery and derived products. Finally, a shadow simulation showed differences in the ideal acquisition window for each species, which is important to plan revisit campaigns.

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“…Species classification of the tree crowns from extremely high resolution images (less than 5 cm) has been studied less [Gini et al, 2014;Lisein et al, 2015]. Michez et al [2016] achieved overall accuracies of 72%, 68%, and 97% for I. glandulifera, Japanese knotweed, and H. mantegazzianum, respectively, in riparian invasive species classification. Object-oriented image analysis was presented in 1980 by Landgrebe [1980].…”

Section: Introductionmentioning

confidence: 99%

Automatic Segment-Level Tree Species Recognition Using High Resolution Aerial Winter Imagery

Kuzmin

Korhonen

Manninen

et al. 2016

European Journal of Remote Sensing

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Our objective was to automatically recognize the species composition of a boreal forest from high-resolution airborne winter imagery. The forest floor was covered by snow so that the contrast between the crowns and the background was maximized. The images were taken from a helicopter flying at low altitude so that fine details of the canopy structure could be distinguished. Segments created by an object-oriented image processing were used as a basis for a linear discriminant analysis, which aimed at separating the three dominant tree species occurring in the area: Scots pine, Norway spruce, and downy birch. In a cross validation, the classification showed an overall accuracy of 81.9%, and a kappa coefficient of 0.73.

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Mapping of riparian invasive species with supervised classification of Unmanned Aerial System (UAS) imagery

Cited by 115 publications

References 36 publications

On the Use of Unmanned Aerial Systems for Environmental Monitoring

On the Use of Unmanned Aerial Systems for Environmental Monitoring

How canopy shadow affects invasive plant species classification in high spatial resolution remote sensing

Automatic Segment-Level Tree Species Recognition Using High Resolution Aerial Winter Imagery

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