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

Green roofs are nature-based solutions that provide numerous ecosystem services in the context of urban green infrastructures. Plant species diversity and the associated vegetation communities, in strong interactions with green roof substrate, play a central role in the green infrastructure functioning. In order to better understand the influence of vegetation in relation with the co-benefits provided by green roofs as well as to select suitable species for these usually harsh environments, it is essential to be able to achieve accurate and long-term monitoring of plant communities. In this short communication, two free plugins recently developed for the open-source image analysis software Fiji (a distribution of the freely available ImageJ platform, initially dedi-cated to biological image analysis) were investigated for their capacity to rapidly and efficiently perform su-pervised machine-learning for the classification of green roof vegetation photographs, with the aim of estimating individual plant species abundance. Two simple methods are thus described using the Trainable Weka Pixel Segmentation (Arganda-Carreras et al., 2017) or the Trainable Superpixel Segmentation (Salinas Colina et al., 2018), which allowed for rapid, efficient and reproducible classification and estimation of multispecies colonized green roof regardless the color or shape similarities among species or ground cover materials. Finally, recom-mendations are made for the use of the Trainable Superpixel Segmentation which is particularly convenient for quick and efficient green roof image analysis.

Automated summary

Green roofs are nature-based solutions that provide ecosystem services in urban green infrastructures. Using trainable superpixel segmentation can efficiently classify and estimate green roof vegetation photographs.