Abstract
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 (PAIeff) 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 PAIeff estimates to those from drone lidar over a lowland tropical forest at La Selva Biological Station, Costa Rica. We estimated PAIeff 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 PAIeff reliability because above-canopy lidar estimates share few assumptions with understory image methods. Smartphone PAIeff varied from 0.1 to 4.4 throughout our study area and we found a significant correlation (r = 0.62, n = 42, p < 0.001) between smartphone and lidar PAIeff, 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 PAIeff algorithm (spherical and planophile, respectively) this relationship is further improved (r = 0.77, n = 42, p < 0.001). However, we found deviations in the magnitude of the PAIeff estimations depending on image analytical options. Our results suggest that smartphone images can be used to characterize spatial variation in PAIeff in a complex, heterogenous tropical forest canopy, with only small reductions in explanatory power compared to true digital hemispherical photography.
Highlights
Leaf area index (LAI) is a characteristic describing vegetated ecosystems that is widely utilized in the development of Earth system and climate models and in studies of ecophysiology, demography, biogeochemistry and atmosphere/biosphere interactions [1,2,3,4,5]
If models 2–4 outperformed model 1 (∆AIC > 2), we considered there to be evidence that there is significant residual variation in smartphone PAIeff that is explained by phone model and/or forest type
The highest concordance between smartphone and lidar PAIeff in old growth and secondary forest was found using leaf angle distribution (LAD) in the lidar PAIeff calculation that varied by forest type—for old growth locations, mean absolute error (MAE) between smartphone and lidar PAIeff was more than twice as large when using a planophile LAD in the lidar PAIeff calculation than when using a spherical or erectophile LAD; for secondary forest locations, MAE was more than twice as large when using a spherical LAD than a planophile or erectophile LAD (Table 2)
Summary
Leaf area index (LAI) is a characteristic describing vegetated ecosystems that is widely utilized in the development of Earth system and climate models and in studies of ecophysiology, demography, biogeochemistry and atmosphere/biosphere interactions [1,2,3,4,5]. Several direct and indirect measurements of LAI have been developed and utilized including destructive harvesting, litterfall collection and weighing, digital hemispherical photography (DHP), canopy analyzers (such as the LiCOR LAI 2000), terrestrial laser scanning (TLS), airborne lidar (light detection and ranging) and spaceborne lidar [7,8,9,10,11,12,13,14]. Direct methods (destructive harvesting) are time consuming and limited in spatial extent while indirect methods to estimate LAI, including LiCOR LAI-2000, DHP, TLS or airborne lidar methods, are complicated by leaf spatial distribution, leaf angle distribution (LAD) and the contribution of non-photosynthetic tissue to light attenuation [7]. DHP can be a less expensive and labor-intensive alternative to other ground-based measurement techniques that requires only a camera, a hemispherical lens and LAI computation software
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