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Uncrewed aircraft system spherical photography for the vertical characterization of canopy structural traits.

ABSTRACT: The plant area index (PAI) is a structural trait that succinctly parametrizes the foliage distribution of a canopy and is usually estimated using indirect optical techniques such as digital hemispherical photography. Critically, on-the-ground photographic measurements forgo the vertical variation of canopy structure which regulates the local light environment. Hence new approaches are sought for vertical sampling of traits. We present an uncrewed aircraft system (UAS) spherical photographic method to obtain structural traits throughout the depth of tree canopies. Our method explained 89% of the variation in PAI when compared with ground-based hemispherical photography. When comparing UAS vertical trait profiles with airborne laser scanning data, we found highest agreement in an open birch (Betula pendula/pubescens) canopy. Minor disagreement was found in dense spruce (Picea abies) stands, especially in the lower canopy. Our new method enables easy estimation of the vertical dimension of canopy structural traits in previously inaccessible spaces. The method is affordable and safe and therefore readily usable by plant scientists.

SUBMITTER: Ribas Costa VA

PROVIDER: S-EPMC9303749 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Uncrewed aircraft system spherical photography for the vertical characterization of canopy structural traits.

Ribas Costa Vicent Agustí VA Durand Maxime M Robson T Matthew TM Porcar-Castell Albert A Korpela Ilkka I Atherton Jon J

The New phytologist 20220222 2

The plant area index (PAI) is a structural trait that succinctly parametrizes the foliage distribution of a canopy and is usually estimated using indirect optical techniques such as digital hemispherical photography. Critically, on-the-ground photographic measurements forgo the vertical variation of canopy structure which regulates the local light environment. Hence new approaches are sought for vertical sampling of traits. We present an uncrewed aircraft system (UAS) spherical photographic meth ...[more]

PMID: 35090193

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Project description:IntroductionCanopy species need to shift their ecological adaptation to improve light and water resources utilization, and the study of intraspecific variations in plant leaf functional traits based at individual scale is of great significance for evaluating plant adaptability to climate change.MethodsIn this study, we evaluate how leaf functional traits of giant trees relate to spatial niche specialization along a vertical gradient. We sampled the tropical flagship species of Parashorea chinensis around 60 meters tall and divided their crowns into three vertical layers. Fourteen key leaf functional traits including leaf morphology, photosynthetic, hydraulic and chemical physiology were measured at each canopy layer to investigate the intraspecific variation of leaf traits and the interrelationships between different functional traits. Additionally, due to the potential impact of different measurement methods (in-situ and ex-situ branch) on photosynthetic physiological parameters, we also compared the effects of these two gas exchange measurements.Results and discussionIn-situ measurements revealed that most leaf functional traits of individual-to-individual P. chinensis varied significantly at different canopy heights. Leaf hydraulic traits such as midday leaf water potential (MWP) and leaf osmotic potential (OP) were insignificantly correlated with leaf photosynthetic physiological traits such as maximal net assimilation rate per mass (A mass). In addition, great discrepancies were found between in-situ and ex-situ measurements of photosynthetic parameters. The ex-situ measurements caused a decrease by 53.63%, 27.86%, and 38.05% in A mass, and a decrease of 50.00%, 19.21%, and 27.90% in light saturation point compared to the in-situ measurements. These findings provided insights into our understanding of the response mechanisms of P. chinensis to micro-habitat in Xishuangbanna tropical seasonal rainforests and the fine scale adaption of different resultant of decoupled traits, which have implications for understanding ecological adaption strategies of P. chinensis under environmental changes.

Project description:Hemispherical photography (HP), implemented with cameras equipped with "fisheye" lenses, is a widely used method for describing forest canopies and light regimes. A promising technological advance is the availability of low-cost fisheye lenses for smartphone cameras. However, smartphone camera sensors cannot record a full hemisphere. We investigate whether smartphone HP is a cheaper and faster but still adequate operational alternative to traditional cameras for describing forest canopies and light regimes. We collected hemispherical pictures with both smartphone and traditional cameras in 223 forest sample points, across different overstory species and canopy densities. The smartphone image acquisition followed a faster and simpler protocol than that for the traditional camera. We automatically thresholded all images. We processed the traditional camera images for Canopy Openness (CO) and Site Factor estimation. For smartphone images, we took two pictures with different orientations per point and used two processing protocols: (i) we estimated and averaged total canopy gap from the two single pictures, and (ii) merging the two pictures together, we formed images closer to full hemispheres and estimated from them CO and Site Factors. We compared the same parameters obtained from different cameras and estimated generalized linear mixed models (GLMMs) between them. Total canopy gap estimated from the first processing protocol for smartphone pictures was on average significantly higher than CO estimated from traditional camera images, although with a consistent bias. Canopy Openness and Site Factors estimated from merged smartphone pictures of the second processing protocol were on average significantly higher than those from traditional cameras images, although with relatively little absolute differences and scatter. Smartphone HP is an acceptable alternative to HP using traditional cameras, providing similar results with a faster and cheaper methodology. Smartphone outputs can be directly used as they are for ecological studies, or converted with specific models for a better comparison to traditional cameras.

Project description:BackgroundThe technology of cotton defoliation is essential for mechanical cotton harvesting. Agricultural unmanned aerial vehicle (UAV) spraying has the advantages of low cost, high efficiency and no mechanical damage to cotton and has been favored and widely used by cotton planters in China. However, there are also some problems of low cotton defoliation rates and high impurity rates caused by unclear spraying amounts of cotton defoliants. The chemical rate recommendation and application should be based upon crop canopy volume rather than on land area. Plant height and leaf area index (LAI) is directly connected to plant canopy structure. Accurate dynamic monitoring of plant height and LAI provides important information for evaluating cotton growth and production. The traditional method to obtain plant height and LAI was s a time-consuming and labor-intensive task. It is very difficult and unrealistic to use the traditional measurement method to make the temporal and spatial variation map of plant height and LAI of large cotton fields. With the application of UAV in agriculture, remote sensing by UAV is currently regarded as an effective technology for monitoring and estimating plant height and LAI.ResultsIn this paper, we used UAV RGB photos to build dense point clouds to estimate cotton plant height and LAI following cotton defoliant spraying. The results indicate that the proposed method was able to dynamically monitor the changes in the LAI of cotton at different times. At 3 days after defoliant spraying, the correlation between the plant height estimated based on the constructed dense point cloud and the measured plant height was strong, with [Formula: see text] and RMSE values of 0.962 and 0.913, respectively. At 10 days after defoliant spraying, the correlation became weaker over time, with [Formula: see text] and RMSE values of 0.018 and 0.027, respectively. Comparing the actual manually measured LAI with the estimated LAI based on the dense point cloud, the [Formula: see text] and RMSE were 0.872 and 0.814 and 0.132 and 0.173 at 3 and 10 days after defoliant spraying, respectively.ConclusionsDense point cloud construction based on UAV remote sensing is a potential alternative to plant height and LAI estimation. The accuracy of LAI estimation can be improved by considering both plant height and planting density.

Dataset Information

Uncrewed aircraft system spherical photography for the vertical characterization of canopy structural traits.

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Uncrewed aircraft system spherical photography for the vertical characterization of canopy structural traits.

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