Reflectance hyperspectral imaging for monitoring indoor living wall: A pilot study on data acquisition and processing

Indoor green walls (GW) are increasingly adopted as Nature-based Solutions (NbS) to improve buildings Indoor Environmental Quality (IEQ). However, their widespread adoption is limited by high maintenance costs, particularly in indoor environments where artificial lighting, irrigation, and drainage are required. In this context, continuous and non-destructive monitoring techniques are essential to support efficient maintenance strategies and future automation. This study investigates the application of reflectance hyperspectral imaging (HSI) as a monitoring tool for indoor GWs, with the aim of defining best practices for data acquisition and processing. Two living walls (LWs), an intensive type of GW, composed of four different plant species were installed inside a climatic chamber, where air temperature, relative humidity, and CO 2 concentration were controlled and monitored over a 5-week campaign. Hyperspectral data were acquired weekly and processed using six vegetation indices (VIs) targeting plant vigour, pigment content, photosynthetic efficiency, and water status. Two lighting configurations were tested during acquisition: halogen-only illumination, and a combination of the previous with LED lighting optimal for plant growth. Preliminary results show that vegetation remained healthy throughout the monitoring period, confirming successfully plant adaptation to the controlled indoor environment. VIs captured species physiological characteristics and subtle temporal variations, providing insights into the evolution of LWs. CO 2 decay experiments demonstrated enhanced photosynthetic activity under LED lighting, confirming the active role of LWs in CO 2 absorption when adequate photosynthetically active radiation (PAR) is provided. However, lighting conditions influenced spectral measurements, with plant-growth LED lighting introducing spectral noise. These findings highlight the strong potential of HSI for indoor GW monitoring and provide quantitative data for future automated maintenance systems aimed at improving GWs performance, reducing operational costs, and supporting sustainable building design.