Stabilizing and Optimizing of Automatic Leaf Area Index Estimation in Temporal Forest

Under climate change, the importance of ecosystem monitoring has been repeatedly emphasized over the past decades. Leaf Area Index (LAI), a key ecosystem variable linking the atmosphere and rhizosphere, has been widely studied through various LAI measurement methods. As satellite-based LAI products continue to advance, the demand for extensive and periodic in situ LAI observations has also increased. In this study, we evaluated the combinations of binarization techniques and temporal filtering to reduce variability in an automatic in situ LAI observation network using fisheye lens imagery, which was established by the National Institute of Forest Science (NIFoS). Compared to the widely used methods such as Otsu thresholding (Otsu) and K-means clustering (K-means), the deep learning (DL) method showed more stable LAI time series under field conditions. Under different illumination conditions, mean LAI values fluctuated significantly—from 0.89 to 3.15—depending on image acquisition time. Furthermore, sixteen temporal filtering methods were tested to identify a reasonable range of LAI values, with optimal post-processing strategies suggested: seven-day moving average for maximum LAI (LAI different range among filtering methods −6.1~−1.5) and a three-day moving average excluding rainy days for minimum LAI (LAI different range among filtering methods 0~0.9). This study highlights uncertainties in canopy classification methods, the effects of acquisition timing and lighting, and the necessity of outlier filtering in automatic LAI networks. Despite these challenges, the need for automated LAI observation system is growing, particularly in complex and fragmented forests such as those found in South Korea.