An Improved Method for Estimating Clumping Index by Digital Hemispheric Photography With Field Measurements

Clumping index (CI) field measurements based on the logarithmic gap fraction averaging (LX) method are widely used. However, some challenges regarding this method have been recognized; e.g., CI overestimation or underestimation occurs in the sampling units where there is no measurement gap, which creates major uncertainties in CI field measurements. To address this issue, we proposed an improved eight-connected LX method that replaces null gap units with the arithmetic mean of the gaps in the eight connected neighboring units, considering the neighboring connections of the natural foliage extension. To validate this method, we designed two controlled experimental schemes based on simulated digital hemispheric photography (DHP) images through the LargE-Scale Remote Sensing Data and Image Simulation Framework (LESS) model considering the leaf area index (LAI) and leaf angle distribution (LAD), respectively, together with collected field measurements. The results showed that our method could almost prevent overestimation and improve underestimated CIs by nearly 20%. In addition, CIs of our method had the smallest error compared to the 'true' CIs (error < 0.1). In conclusion, our method can significantly improve the data quality of the simulation results relative to the existing methods and present potentials in the CI measurements of upcoming field campaigns.