An intensity, image-based method to estimate gap fraction, canopy openness and effective leaf area index from phase-shift terrestrial laser scanning

Grotti, Mirko; Calders, Kim; Origo, Niall; Puletti, Nicola; Alivernini, Alessandro; Ferrara, Carlotta; Chianucci, Francesco

doi:10.1016/j.agrformet.2019.107766

Cited by 36 publications

(31 citation statements)

References 42 publications

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“…Therefore, DHP is less suited to denser canopies, which are characterized by smaller gap fraction, as hemisphere portions with no gaps gave undefined results when gap fraction was inverted to estimate PAI. With reference to PAI e , the lower performance of DHP relative to DCP can also be attributed to the lower ability of DHP to detect smaller canopy elements with increasing optical distance, which results in an underestimation of PAI e in comparison with TLS, as observed in a previous study (Grotti et al 2020). The disadvantage of DCP relative to DHP is that estimates of LAI or PAI from DCP require independent measurements of leaf inclination angle to parametrize k. In the case study described, reliable PAI e estimates were obtained from DCP using measured leaf inclination angles collected in sample trees, which indicates a planophile leaf inclination distribution; however, closer inspection of the results suggested that sparser canopies have more inclined leaves, whereas denser canopies have more horizontal (planophile) distribution.…”

Section: Outlining Strategies For Sampling Overstory Canopymentioning

confidence: 66%

“…These improvements notwithstanding, canopy photography still has some major issues associated with the lack of standardization in field protocols, the dependence of the results on sky conditions, and camera exposure. Although recent and future advances in raw photography may greatly reduce the issue of camera exposure (Macfarlane et al 2014;Hwang et al 2016;Grotti et al 2020), the current main challenges in using canopy photography are as follows.…”

Section: Discussionmentioning

confidence: 99%

“…Image classification is closely related to camera exposure; indeed, previous studies (Grotti et al 2020;Macfarlane 2011;Macfarlane et al 2014) concluded that classification is of little importance if canopy and sky regions are well contrasted; therefore, shooting in raw mode and contrast-stretching the images can be an effective option for facilitating image classification while simultaneously minimizing the influence of camera exposure (Macfarlane et al 2014). Table 4 lists the main features of some available DHP software for performing image analysis and thresholding.…”

Section: Factors Affecting the Accuracy Of Digital Canopy Photographymentioning

confidence: 99%

“…The trial was conducted in seven 0.25 ha (50 m × 50 m) singlelayered beech (Fagus sylvatica L.) stands, which were sampled in summer 2018 in central Italy (Alpe di Catenaia, 43°66=N, 11°92=E). Estimates from photography were compared against benchmark values obtained from high-resolution terrestrial laser scanning (TLS) measurements collected in an independent study (Grotti et al 2020).…”

Section: Case Studymentioning

confidence: 99%

“…Gap fraction was obtained from automated Otsu's (1979) multiple classification of intensity TLS images derived from raw scan data (Fig. 7) using a procedure comparable with the standard image analysis processing of digital canopy images (for details, see Grotti et al 2020).…”

Section: Case Studymentioning

confidence: 99%

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An overview of in situ digital canopy photography in forestry

Chianucci

2019

Can. J. For. Res.

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Since the 1960s, canopy photography has been widely used in forestry. Hemispherical photography has been the most widely used technique, but a great drawback of this method is its perceived sensitivity to hemispherical image acquisition and processing. Over the last decade, several alternative photographic approaches using restricted view angle have been proposed. Cover photography acquired via a normal lens was the first of the recently introduced photographic techniques. Use of a restricted view (often fixed) lens has subsequently contributed to the extension of canopy photography to new sensors and platforms, which ultimately have provided answers to some previous challenges regarding within-crown clumping correction, isolated and urban tree measurements, understory assessment, operational leaf inclination angle measurements, and phenological monitoring. This study provides a comprehensive review of the use of canopy photography in forestry and describes the theory and definitions of the variables used to quantify canopy structure. A case study is presented to illustrate and compare the different features and performance of the existing overstory photographic techniques; the results make it possible to suggest sampling strategies for consistent overstory canopy photographic measurements. Emerging operational fields of canopy photography are also described and discussed.

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Section: Outlining Strategies For Sampling Overstory Canopymentioning

confidence: 66%

Section: Discussionmentioning

confidence: 99%

Section: Factors Affecting the Accuracy Of Digital Canopy Photographymentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

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An overview of in situ digital canopy photography in forestry

Chianucci

2019

Can. J. For. Res.

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Open Field Geometric Primitives’ Representation by a 2D Low-Cost LIDAR for Vineyard Sprayer Application Under Different Conditions

Ilari,

Favi,

Zingaretti

et al. 2023

Lecture Notes in Civil Engineering

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coveR: an R package for processing digital cover photography images to retrieve forest canopy attributes

Chianucci

Ferrara

Puletti

2022

Trees

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1.Digital Cover Photography (DCP) is an increasingly popular tool for estimating canopy cover and leaf area index (LAI). However, existing solutions to process canopy images are predominantly tailored for fisheye photography, whereas open-access tools for DCP are lacking. 2.We developed an R package (coveR) to support the whole processing of DCP images in an automated, fast, and reproducible way. The package functions, which are designed for stepby-step single-image analysis, can be performed sequentially in a pipeline, while also allowing simple implementation for batch-processing bunches of images. 3.A case study is presented to demonstrate the reliability of canopy attributes derived from coveR in pure beech (Fagus sylvatica L.) stands with variable canopy density and structure.Estimates of gap fraction and effective LAI from DCP were validated against reference measurements obtained from terrestrial laser scanning. 4.By providing a simple, transparent, and flexible image processing procedure, coveR supported the use of DCP for routine measurements and monitoring of forest canopy attributes.This, combined with the implementability of DCP in many devices, including smartphones, microcameras, and remote trail cameras, can greatly expand the accessibility of the method also by non-experts.

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An intensity, image-based method to estimate gap fraction, canopy openness and effective leaf area index from phase-shift terrestrial laser scanning

Cited by 36 publications

References 42 publications

An overview of in situ digital canopy photography in forestry

An overview of in situ digital canopy photography in forestry

Open Field Geometric Primitives’ Representation by a 2D Low-Cost LIDAR for Vineyard Sprayer Application Under Different Conditions

coveR: an R package for processing digital cover photography images to retrieve forest canopy attributes

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