Symbolic regression-based allometric model development of a mangrove forest LAI using structural variables and digital hemispherical photography. (February 2022) - Paramanik, S.; Behera, M.D.; Dash, J.
Abstract: 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. Highlights: Cost-effective and accurate estimation of LAI is particularly important for under-assessed and multi-strata dense tropical forests such as mangroves to enhance our understanding of spatial and temporal dynamics of ecosystem processes. The allometric model for LAI estimation was developed for a dense mangrove forest in the BhitarKanika Wildlife Sanctuary (BWS), located along Eastern coast of India using three commonly measured structural variable: diameter at breast height (DBH), Tree Density (TD), and canopy Height (Ht) from 122 ESUs. Singlet variables were not capable enough to establish a statistically strong relationship whereas the combination of all three dependent variables (DBH, TD, and Ht) improved the results (coefficient of correlation = 0.71). No other regressions e.g., simple linear, non-linear, multiple and partial least square regression was well suited for better curve fitting whereas symbolic regression (SR) worked better than all. To the best of our knowledge, it is the first developed allometric model in a simple and non-destructive approach to estimate LAI for a mangrove ecosystem in India with a potential to expand to other mangrove forests.