Comparison of smartphone and drone lidar methods for characterizing spatial variation in PAI in a tropical forest

Tamara E. Rudic; Lindsay A. Mcculloch; Katherine C. Cushman

doi:10.3390/rs12111765

Comparison of smartphone and drone lidar methods for characterizing spatial variation in PAI in a tropical forest

Tamara E. Rudic, Lindsay A. Mcculloch, Katherine C. Cushman

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8 Scopus citations

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 PAI_eff, 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_eff 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.

Original language	English
Article number	1765
Journal	Remote Sensing
Volume	12
Issue number	11
DOIs	https://doi.org/10.3390/rs12111765
State	Published - Jun 1 2020
Externally published	Yes

Funding

Lidar collection was supported by an award to J.R. Kellner through the National Science Foundation Grants for Rapid Response Research (RAPID) program and by an award to S. Saatchi and J.R. Kellner from the National Aeronautics and Space Administration. T.E.R. was supported by the Brown University Presidential Scholars Program. L.A.M. was supported by the Institute at Brown for Environment and Society Seed Grant. K.C.C. was supported by an NSF Graduate Research Fellowship, the Brown Presidential Fellowship, the Institute at Brown for Environment and Society at Brown University and the Department of Energy's NGEE-Tropics initiative. We thank Jim Kellner for leading the drone lidar collection over La Selva; Matteo Detto for providing the original lidar LAI algorithm code; and Aeroscout GmbH (Benedikt Imbach and Carlo Zgraggen), John Burley and Orlando Vargas for their assistance in the fieldwork for this project. We also thank Markus Birrer, Christoph Eck, Cristoph Falleger, Henry Johnson and Stephen Porder. Funding: Lidar collection was supported by an award to J.R. Kellner through the National Science Foundation Grants for Rapid Response Research (RAPID) program and by an award to S. Saatchi and J.R. Kellner from the National Aeronautics and Space Administration. T.E.R. was supported by the Brown University Presidential Scholars Program. L.A.M. was supported by the Institute at Brown for Environment and Society Seed Grant. K.C.C. was supported by an NSF Graduate Research Fellowship, the Brown Presidential Fellowship, the Institute at Brown for Environment and Society at Brown University and the Department of Energy’s NGEE-Tropics initiative.

Keywords

Hemispherical photography
La selva biological station
Leaf area index
Lidar
Tropical forest

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title = "Comparison of smartphone and drone lidar methods for characterizing spatial variation in PAI in a tropical forest",

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.",

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AU - Mcculloch, Lindsay A.

AU - Cushman, Katherine C.

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Y1 - 2020/6/1

N2 - 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.

AB - 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.

KW - Hemispherical photography

KW - La selva biological station

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ER -

Comparison of smartphone and drone lidar methods for characterizing spatial variation in PAI in a tropical forest

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