| Summary: | Estimating leaf area index (LAI) and assessing spatial variation in LAI across a landscape is crucial to many ecological studies. Several direct and indirect methods of LAI estimation have been developed and compared; however, many of these methods are prohibitively expensive and/or time consuming. Here, we examine the feasibility of using the free image processing software CAN-EYE to estimate effective plant area index (PAI<sub>eff</sub>) from hemispherical canopy images taken with an extremely inexpensive smartphone clip-on fisheye lens. We evaluate the effectiveness of this inexpensive method by comparing CAN-EYE smartphone PAI<sub>eff</sub> estimates to those from drone lidar over a lowland tropical forest at La Selva Biological Station, Costa Rica. We estimated PAI<sub>eff</sub> from drone lidar using a method based in radiative transfer theory that has been previously validated using simulated data; we consider this a conservative test of smartphone PAI<sub>eff</sub> reliability because above-canopy lidar estimates share few assumptions with understory image methods. Smartphone PAI<sub>eff</sub> varied from 0.1 to 4.4 throughout our study area and we found a significant correlation (<i>r</i> = 0.62, <i>n</i> = 42, <i>p</i> < 0.001) between smartphone and lidar PAI<sub>eff</sub>, which was robust to image processing analytical options and smartphone model. When old growth and secondary forests are assumed to have different leaf angle distributions for the lidar PAI<sub>eff</sub> algorithm (spherical and planophile, respectively) this relationship is further improved (<i>r</i> = 0.77, <i>n</i> = 42, <i>p</i> < 0.001). However, we found deviations in the magnitude of the PAI<sub>eff</sub> estimations depending on image analytical options. Our results suggest that smartphone images can be used to characterize spatial variation in PAI<sub>eff</sub> in a complex, heterogenous tropical forest canopy, with only small reductions in explanatory power compared to true digital hemispherical photography.
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