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Pubblicazioni Scientifiche
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Pubblicazioni per anno
Influence of voxel size and point cloud density on crown cover estimation in poplar plantations using terrestrial laser scanning
Mostra abstract
Accurate estimates of crown cover (CC) are central for a wide range of forestry studies. As direct measurements do not exist to retrieve this variable in the field, CC is conventionally determined from optical measurements as the complement of gap fraction close to the zenith. As an alternative to passive optical measurements, active sensors like terrestrial Light Detection And Ranging (LiDAR) allows for characterizing the 3D canopy structure with unprecedented detail. We evaluated the reliability of terrestrial LiDAR (TLS) to estimate CC using a voxel-based approach. Specifically, we tested how different voxel sizes (ranging from 5-20 cm) and voxel densities (1-9 points/dm<sup>3</sup>) influenced the retrieval of CC. Results were compared against benchmark values obtained from digital cover photography (DCP). The trial was performed in hybrid poplar plantations in Northern Italy. Results indicate that TLS can be used for obtaining accurate estimates of CC, but the choice of voxel size and point density is critical for achieving such accuracy. In hybrid poplars, the best performance was obtained using voxel size of 10 cm and point density of 8 points/dm<sup>3</sup>. The combined ability of measuring and mapping CC also holds great potential to use TLS for calibrating and upscaling results using coarser-scale remotely sensed products. © 2021 Centro di Ricerca per la Selvicoltura, Consiglio per la Ricerca in Agricoltura e l'Analisi dell'Economia Agraria. All rights reserved.
One to rule them all? Assessing the performance of sustainable forest management indicators against multitaxonomic data for biodiversity conservation
Paillet
,
Yoan
,
Zapponi
,
Livia
,
Schall
,
Peter
,
Monnet
,
Jean Matthieu
,
Ammer
,
Christian
,
Balducci
,
Lorenzo
,
Boch
,
Steffen
,
Brazaitis
,
Gediminas
,
Campanaro
,
Alessandro
,
Chianucci
,
Francesco
,
Doerfler
,
Inken
,
Fischer
,
Markus
,
Gosselin
,
Marion
,
Goßner
,
Martin M.
,
Heilmann-Clausen
,
Jacob
,
Hofmeister
,
Jeňýk
,
Hošek
,
Jan
,
Jung
,
Kirsten G.
,
Kepfer-Rojas
,
Sebastian
,
Ódor
,
Péter
,
Tinya
,
Flóra
,
Trentanovi
,
Giovanni
,
Vacchiano
,
Giorgio
,
Vandekerkhove
,
Kris
,
Weisser
,
Wolfgang W.
,
Wohlwend
,
Michael Rudolf
,
Burrascano
,
Sabina
Mostra abstract
Several regional initiatives and reporting efforts assess the state of forest biodiversity through broad-scale indicators based on data from national forest inventories. Although valuable, these indicators are essentially indirect and evaluate habitat quantity and quality rather than biodiversity per se. Therefore, their link to biodiversity may be weak, which decreases their usefulness for decision-making. For several decades, Forest Europe indicators assessed the state of European forests, in particular their biodiversity. However, no extensive study has been conducted to date to assess their performance – i.e. the capacity of the indicators to reflect variations in biodiversity – against multitaxonomic data. We hypothesized that no single biodiversity indicator from Forest Europe can represent overall forest biodiversity, but that several indicators would reflect habitat quality for at least some taxa in a comprehensive way. We tested the set of Forest Europe's indicators against the species richness of six taxonomic and functional groups across several hundreds of sampling units over Europe. We showed that, while some indicators perform relatively well across groups (e.g. deadwood volume), no single indicator represented all biodiversity at once, and that a combination of several indicators performed better. Forest Europe indicators were chosen for their availability and ease of understanding for most people. However, we showed that gaps in the monitoring framework persist, and that surveying certain taxa along with stand structure is necessary to support policymaking and tackle forest biodiversity loss at the large scale. Adding context (e.g. forest type) may also contribute to increase the performance of biodiversity indicators. © 2024 Elsevier Ltd