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Poster De Conférence Année : 2022

Using pixel- or superpixel-based segmentation for efficient classification of green roof digital images and rapid estimation of plant species abundance

Résumé

Green roofs are “nature-based solutions” that may provide numerous ecosystem services in the context of urban greening. Vegetation development in strong interactions with green roof media and microbial communities play a key role in the ecosystem multifunctionality of these green infrastructures. Consequently, research on plant selection has increased in recent years in order to expand the list of species able to grow on green roofs in cold or arid climates, with special efforts to clarify the relationships between species diversity, functional traits and ecosystem services. There is still a need, however, for carrying out long-term vegetation survey to avoid premature conclusions regarding plant adaptation as well as to better understand the ecosystem functioning and ecological trajectories of these engineered ecosystems. Nevertheless, vegetation cover determination on green roofs may rapidly become time-consuming and quite tedious, especially in the case of densely covered mixed species roofs or due to plant species (sometimes overlapping) with similar leaf shape and colours (e.g. sedums). Therefore, the aim of this work was to investigate the ability of two freely available plugins, which were recently developed for the opensource image analysis software Fiji (a distribution of the free ImageJ platform, initially dedicated to biological image analysis), to rapidly and efficiently perform supervised machine-learning for the classification of green roof vegetation from photographs. Two workflows are thus described, depending on the use of the “Trainable Weka Pixel Segmentation” or the “Trainable Superpixel Segmentation”, and compared with the traditional “point frame measurement” method for plant abundance determination at the species level. Both machine-learning based methods performed very well regarding image classification efficiency and reproducibility (using fast random forest algorithm), with the best results obtained with a minimum image label number of 16 per object category to be classified (pouzzolane, moss, sedum and grass species). Moreover, preliminary image segmentation into superpixels with jSLIC (Simple Linear Iterative Clustering) with an initial grid size of 1 (and a minimum label number of 4 per object category to be classified) allowed for highly satisfactory classification results in a few seconds only, which would facilitate the use of such imagery tool for routine survey.
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Dates et versions

hal-04484074 , version 1 (29-02-2024)

Identifiants

  • HAL Id : hal-04484074 , version 1

Citer

Didier Técher. Using pixel- or superpixel-based segmentation for efficient classification of green roof digital images and rapid estimation of plant species abundance. SFE² GfÖ EEF Joint meeting, International Conference on Ecological Sciences, 2022, Metz, France. . ⟨hal-04484074⟩

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