Evaluating the reliability of bi-temporal canopy height model generated from airborne laser scanning for monitoring forest growth in boreal forest region

The discrepancies in data across different phases and the unexplored optimal spatial resolution present challenges when using multi-temporal canopy height models to accurately discern actual forest growth. In this study, we evaluated the reliability of bi-temporal CHMs to characterize growth changes in a boreal natural forest over a four-year period. A maximum mosaic method was introduced to construct a CHM from various flight strips, aimed at minimizing data alignment errors. Subsequently, the canopy height and height changed derived from six different height percentile metrics and five spatial resolutions were evaluated. The results showed that higher resolution (e.g., < 2 m) and lower height metrics (e.g., 85th height percentile) consistently underestimated. Tree growth correlation with individual segmentation surpassed field-measurements at all resolutions and height metrics. for the optimal resolution and height metrics, the results suggest that using a 95th height percentile the 10 m scale effectively represents both canopy height (R-2012(2) = 0.95, RMSE2012 = 0.88 m, rRMSE(2012 ) = 5.83%; R-2016(2) = 0.96, RMSE2016 = 1.11 m, rRMSE(2016) = 6.91%) and height changes (R-2 = 0.59, RMSE = 0.86 m, rRMSE = 18.38%). This study demonstrates the necessity of carefully evaluating data characteristics and resolutions when employing multi-temporal CHM for forest dynamics monitoring.