An indirect method of estimating leaf area index in a tropical deciduous forest of India

Rapid, reliable and meaningful estimates of leaf area index (LAI) are essential to functional characterization of forest ecosystems including biomass and primary productivity studies. Accurate LAI estimates of tropical deciduous forest are required in studies of regional and global change modeling. Tropical deciduous forest due to higher species richness, multiple species association, varied phenophases, irregular stem densities and basal cover, multistoried canopy architecture and different micro-climatic conditions offers dynamism to the understanding of the LAI dynamics of different PFTs in an ecosystem. This investigation reports a new indirect method for measurement of leaf area index (LAI) in a topical moist deciduous forest in Himalayan foothills using LAI-2000 Plant Canopy Analyzer. We measured the LAI in two seasons (summer; leaf senescence stage and post-monsoon; full green stage) in three (dry miscellaneous, sal mixed and teak plantations) plant functional types (PFT) in Katerniaghat Wildlife Sanctuary, India. Ground LAI values ranged between 2.41 and 6.89, 1.17 and 7.71, and 1.92 and 5.19 during post-monsoon season and 1.36-4.49, 0.67-3.1 and 0.37-1.83 during summer season in dry miscellaneous, sal mixed and teak plantation, respectively. We observed strong correlation between LAI and community structural parameters (tree density, basal cover and species richness), with maximum with annual litter fall (R2 > 0.8) and aboveground biomass (AGB) (R2 > 0.75). We provided equations relating LAI with AGB, which can be utilized in future studies for this region and can be reasonably extrapolated to other regions with suitable statistical extrapolations. However, the relations between LAI and other parameters can be further improved with incorporation of data from optimized and seasonal sampling. Our indirect method of LAI estimation using litter fall as a proxy, offers repetitive potential for LAI estimate in other PFTs with relatively time and cost-effective way, thereby generating quicker and reliable data for model run for regional and global change studies.