Symbolic regression-based allometric model development of a mangrove forest LAI using structural variables and digital hemispherical photography

The leaf area index (LAI) serves as a proxy to understand the dynamics of plant productivity, energy balance, and gas exchange. Cost-effective and accurate estimation of LAI is essential for under-assessed carbon-rich tropical forests, e.g., mangroves. Here, we developed allometric equations to estimate LAI using a combination of non-destructive, optical measurements through digital hemispherical photographs (DHP), and genetic programming-based Symbolic Regression (SR). We used three structural variables: diameter at breast height (DBH), tree density (TD), and canopy height (Ht) for a mangrove forest in the BhitarKanika Wildlife Sanctuary (BWS), located along the Eastern coast of India. Triplet combination using SR provided the best equation (R-2 = 0.51) than any singlet or duplet combination of the variables, and even it was better than Partial Least Square (PLS) based regression (R-2 = 0.42). To the best of our knowledge, the current study is the maiden attempt to develop an allometric model to estimate LAI for a mangrove ecosystem in India. In-situ measurements of structural variables such as DBH, Ht, and TD can be used for LAI estimates, as shown here. LAI estimates using cost-effective methods would greatly enhance our understanding of the spatial and temporal dynamics of mangrove ecosystems.