Biology subjects

Chianucci, F.

Publications and source records attributed to Chianucci, F..

5 recordsLinked to original sources

bioRxiv2022

LAD: an R package to estimate leaf angle distribution from measured leaf inclination angles

Leaf angle distribution (LAD) is an important factor for characterizing the optical features of vegetation canopies. The characterization of LAD requires direct measurements of leaf inclination angles, which can be obtained from manual clinometer measurements or leveled digital photography. Package LAD allows to calculate the Leaf angle distribution (LAD) function and the G-function from measured leaf inclination angles. Using the package, the LAD distribution and G-function is derived by fitting a two parameter Beta distribution. Summary leaf angle statistics and distribution type is also calculated, by comparing the obtained LAD against theoretical distribution by de Wit (1965).

ecology↗

bioRxiv2022

bRAW: an R package for digital raw canopy imagery

Digital photography is an increasingly popular tool to estimate forest canopy attributes. However, estimates of gap fraction, upon which calculations of canopy attributes are based, are sensitive to photographic exposure in upward-facing images. Recent studies have indicated that analyzing RAW imagery, rather than other inbuilt camera format (e.g. jpeg, png, tiff) allows to obtain largely-insensitive gap fraction retrieval from digital photography. The package bRaw implemented the method proposed by Macfarlane et al. (2014). They found that shooting raw with one stop of underexposure and applying a linear contrast stretch yielded largely insensitive results, thus providing a way for standardizing and optimizing photographic exposure. The package replicate the methodology and thus it provides an effective tool to use raw imagery in canopy photography.

plant biology↗

bioRxiv2022

hemispheR: an R package for fisheye canopy image analysis

Hemispherical photography is a relevant tool to estimate canopy attributes such as leaf area index (LAI). Advancements in digital photography and image processing tools have supported long-lasting use of digital hemispherical photography (DHP). While some open-source tools have been made available for DHP, very few solutions have been made available in R programming packages, and none of these allows a full processing workflow to retrieve LAI and other canopy attributes from fisheye images. To fill this gap, we developed an R package (hemispheR) to support the whole processing of DHP images in an automated, fast, and reproducible way. The package functions, which are designed for step-by-step single-image analysis, can be performed sequentially in a pipeline, while allowing inspecting the quality of each image processing step. The package allows to analyze both circular and fullframe fisheye images, collected either with upward facing (forest canopies) or downward facing (short canopies and crops) camera orientation. In addition, the package allows to implement two consolidated LAI methods (LAI-2000/2200 and 57{degrees} method). A case study is presented to demonstrate the reliability of canopy attributes derived from hemispheR in temperate deciduous forests with variable canopy density and structure. Canopy attributes were validated against either results obtained from a reference proprietary software, either by benchmarking measurements obtained from terrestrial laser scanning. Results indicated hemispheR provide reliable openness and leaf area index values in forest canopies as compared with reference values. By providing a simple, transparent, and flexible image processing procedure, hemispheR supported the use of DHP for routine measurements and monitoring of forest canopy attributes. Hosting the package in a Git repository will further support development of the package, through either collaborative coding or forking projects.

ecology↗

bioRxiv2022

coveR: An R package for processing Digital Cover Photography images to retrieve forest canopy attributes

O_LIDigital 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. C_LIO_LIWe 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 step-by-step single-image analysis, can be performed sequentially in a pipeline, while also allowing simple implementation for batch-processing bunches of images. C_LIO_LIA 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. C_LIO_LIBy 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, micro-cameras, and remote trail cameras, can greatly expand the accessibility of the method also by non-experts. C_LI

plant biology↗

bioRxiv2021

With great power comes great responsibility: an analysis of sustainable forest management quantitative indicators in the DPSIR framework

The monitoring of environmental policies in Europe has taken place since the 1980s and still remains a challenge for decision- and policy-making. For forests, it is concretized through the publication of a State Of Europes Forests every five years, the last report just been released. However, the process lacks a clear analytical framework and appears limited to orient and truly assess sustainable management of European forests. We classified the 34 quantitative sustainable forest management indicators in the Driver-Pressure-State-Impact-Response (DPSIR) framework to analyse gaps in the process. In addition, we classified biodiversity-related indicators in the simpler Pressure-State-Response (PSR) framework. We showed that most of the sustainable forest management indicators assess the state of European forests, but almost half could be classified in another DPSIR category. For biodiversity, most indicators describe pressures, while direct taxonomic state indicators are very few. Our expert-based classification show that sustainable forest management indicators are unbalanced regarding the DPSIR framework. However, completing this framework with other indicators would help to have a better view and more relevant tools for decision-making. The results for biodiversity were comparable, but we showed that some indicators from other criteria than the one dedicated to biodiversity could also help understanding threats and actions concerning it. Such classification helps in the decision process, but is not sufficient to fully support policy initiative. In particular, the next step would be to better understand the links between DPSIR and PSR categories.

ecology↗