Using open-source software and digital imagery to efficiently and objectively quantify cover density of an invasive alien plant species

Carlier, Julien; Davis, Eithne; Ruas, Sara; Byrne, Dolores; Caffrey, J. M.; Coughlan, Neil E.; Dick, Jaimie T. A.; Lucy, Frances E.

doi:10.1016/j.jenvman.2020.110519

Cited by 13 publications

(8 citation statements)

References 38 publications

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“…For example, Bhola et al (2018) proposed a method integrating spectral clustering and spatial segmentation to detect power lines, which was evaluated on a small collection of aerial images. Carlier et al (2020) investigated the feasibility of using morphological spatial pattern analysis to replace manual visual estimation for plant cover measurement on a collection of 30 images. Chen & Liu (2021) trained a bottom-up model for slope damage detection with a dataset consisting of ,100 photos.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Detecting, extracting, and classifying objects of interest from images are not a unique problem in water quality management. Rather, the problem is widely encountered in various domains such as urban planning (Li & Yang 2020) and vegetation management (Carlier et al 2020). In computer vision, it is usually formulated as a problem of object detection or instance segmentation, which aims not only to tell if certain objects exist, but also locate the pixel area of each object of different classes on given images.…”

Section: Introductionmentioning

confidence: 99%

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Detecting, extracting and classifying foreign objects in inter-basin channels to ensure water supply safety

Chen

Liu

2021

Journal of Hydroinformatics

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Foreign objects (e.g., livestock, rafting, and vehicles) intruded into inter-basin channels pose threats to water quality and water supply safety. Timely detection of the foreign objects and acquiring relevant information (e.g., quantities, geometry, and types) is a premise to enforce proactive measures to control potential loss. Large-scale water channels usually span a long distance and hence are difficult to be efficiently covered by manual inspection. Applying unmanned aerial vehicles for inspection can provide time-sensitive aerial images, from which intrusion incidents can be visually pinpointed. To automate the processing of such aerial images, this paper aims to propose a method based on computer vision to detect, extract, and classify foreign objects in water channels. The proposed approach includes four steps, i.e., aerial image preprocessing, abnormal region detection, instance extraction, and foreign object classification. Experiments demonstrate the efficacy of the approach, which can recognize three typical foreign objects (i.e., livestock, rafting, and vehicle) with a robust performance. The proposed approach can raise early awareness of intrusion incidents in water channels for water quality assurance.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detecting, extracting and classifying foreign objects in inter-basin channels to ensure water supply safety

Chen

Liu

2021

Journal of Hydroinformatics

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“…The morphological segmentation of binary patterns (Soille and Vogt, 2009) provides an effective method for characterising spatial patterns with emphasis on connections between their parts as measured at varying analysis scales. The method is now widely used for the analysis of landscape patterns such as those related to the fragmentation of forests or other natural land cover classes, e.g., (Ossola et al, 2019;Carlier et al, 2020;Rincón et al, 2021;Modica et al, 2021). This can be explained by its effectiveness at capturing the complexity of binary patterns and their connections by partitioning the foreground and background pixels of the corresponding binary images into mutually exclusive classes with a clear semantic meaning.…”

Section: Introductionmentioning

confidence: 99%

Morphological Spatial Pattern Analysis: Open Source Release

Soille

Vogt²

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The morphological segmentation of binary patterns provides an effective method for characterising spatial patterns with emphasis on connections between their parts as measured at varying analysis scales. The method is widely used for the analysis of landscape patterns such as those related to the fragmentation of forests or other natural land cover classes. This can be explained by its effectiveness at capturing the complexity of binary patterns and their connections by partitioning the foreground pixels of the corresponding binary images into mutually exclusive classes. While the principles of the method are conceptually simple, the definition of the classes relies on a series of advanced mathematical morphology operations whose actual implementation is not straightforward. In this paper, we propose an open source code for MSPA and detail its main components in the form of pseudo-code. We demonstrate its effectiveness for asynchronous processing of tera-pixel images and the synchronous exploratory analysis and rendering with Jupyter notebooks.

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“…Niphadkar and Nagendra [17] reported that morphological, biochemical, phenological, or physiological plant features can improve remote sensing mapping. Carlier et al [18] presented an application of morphological image analysis to provide an objective method for detection and accurate cover assessment of an IAPS. They used top-down images captured using a hand-held digital camera with images that cover 1 m × 1 m. James and Bradshaw [19] based their work on images collected using UAVs, which allowed them to cover larger areas, but instead of using morphological image analysis, James and Bradshaw [19] used the U-net convolutional neural network to segment images semantically.…”

Section: Introductionmentioning

confidence: 99%

Camera Assisted Roadside Monitoring for Invasive Alien Plant Species Using Deep Learning

Dyrmann

Mortensen

Linneberg³

et al. 2021

Sensors

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Invasive alien plant species (IAPS) pose a threat to biodiversity as they propagate and outcompete natural vegetation. In this study, a system for monitoring IAPS on the roadside is presented. The system consists of a camera that acquires images at high speed mounted on a vehicle that follows the traffic. Images of seven IAPS (Cytisus scoparius, Heracleum, Lupinus polyphyllus, Pastinaca sativa, Reynoutria, Rosa rugosa, and Solidago) were collected on Danish motorways. Three deep convolutional neural networks for classification (ResNet50V2 and MobileNetV2) and object detection (YOLOv3) were trained and evaluated at different image sizes. The results showed that the performance of the networks varied with the input image size and also the size of the IAPS in the images. Binary classification of IAPS vs. non-IAPS showed an increased performance, compared to the classification of individual IAPS. This study shows that automatic detection and mapping of invasive plants along the roadside is possible at high speeds.

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Using open-source software and digital imagery to efficiently and objectively quantify cover density of an invasive alien plant species

Cited by 13 publications

References 38 publications

Detecting, extracting and classifying foreign objects in inter-basin channels to ensure water supply safety

Detecting, extracting and classifying foreign objects in inter-basin channels to ensure water supply safety

Morphological Spatial Pattern Analysis: Open Source Release

Camera Assisted Roadside Monitoring for Invasive Alien Plant Species Using Deep Learning

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