Binary Image Processing for Cloud Detection in All-Sky Monochrome Imagery: Applications to Light Pollution Studies

Artificial light at night (ALAN) alters natural nocturnal environments and affects ecosystems, human health, and night sky visibility, while cloud cover strongly modulates night sky brightness (NSB) by enhancing or attenuating skyglow. This study investigated the relationship between cloudiness and NSB at three locations in southern Poland representing contrasting light environments: urban Kraków, high-altitude Kasprowy Wierch, and rural Chochołów. Cloudiness was quantified using a custom binary image-processing method applied to monochrome all-sky camera images acquired under automatic exposure conditions. The procedure included exposure-time normalisation, dual-threshold segmentation with hysteresis, and site-specific histogram-based classification. Cloud-cover time series were synchronised with continuous SQM-LU night sky brightness measurements collected during moonless astronomical nights. An independent comparison was performed using a Hukseflux NR01 net radiometer and a radiative cloudiness proxy derived from surface–sky temperature differences. Increasing cloudiness was associated with brighter night skies at all sites, with the strongest effect observed in Kraków (r=−0.97; slope ≈−2.8 mag/arcsec2), a weaker response at Kasprowy Wierch (r=−0.77; slope ≈−0.4 mag/arcsec2), and a moderate relationship in Chochołów (r=−0.48; slope ≈−0.7 mag/arcsec2). Camera-derived cloudiness showed moderate agreement with the radiometric cloudiness proxy (robust R2=0.56, rs=−0.79). The proposed method proved practically useful across diverse lighting conditions, demonstrating its usefulness for long-term light pollution studies based on monochrome all-sky imagery.