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  1. Pubblicazioni

UAV LiDAR Survey for Forest Structure Metrics Estimation in Planning Scenario. A Case Study on a Laricio Pine Forest in the Sila Mountains (Southern Italy)

Contributo in Atti di convegno
Data di Pubblicazione:
2023
Abstract:
Home Computational Science and Its Applications – ICCSA 2023 Workshops Conference paper UAV LiDAR Survey for Forest Structure Metrics Estimation in Planning Scenario. A Case Study on a Laricio Pine Forest in the Sila Mountains (Southern Italy) Giandomenico De Luca, Salvatore Praticò, Gaetano Messina, Enrico Borgogno-Mondino & Giuseppe Modica Conference paper First Online: 29 June 2023 430 Accesses Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14107)) Abstract LiDAR (light detection and ranging) sensors, mounted on UAVs (unmanned aerial vehicles), are a consolidated technology for the remote sensing of the urban and/or natural structural parameters. This study investigates a practical aspect of the advantages of drone UAV LiDAR systems in estimating the main dendrometric parameters in a Calabrian laricio pine forest. In particular, the criteria of hypsometric dendrometry were applied, in which, unlike the classic criteria based on field surveys only, the variable to be determined is the diameter of the individual trees rather than the height. In fact, this last variable is definitely more available for more shafts than the diameter, thanks to LiDAR surveys. Dendrometric variables, such as trees’ density, diameter and height, were measured adopting classic field-based methods for all the trees within some survey sample areas (SSAs), and other mean parameters were then obtained (e.g., mean basal area, mean diameter, mean Lorey’s height, above ground trees’ volume). The same parameters, retrieved by integrating hypsometric dendrometry and LiDAR data, were thus compared, and the degree of correlation/error was calculated. The error degree, obtained by comparing the field-measured diameters with the respective ones predicted using the LiDAR-based hypsometric model (R2 = 0.62; RMSE = 11.30 cm; Bias = 0.48 cm), confirmed the reliability of LiDAR systems for their practical application in the professional forestry sector.
Tipologia CRIS:
04A-Conference paper in volume
Keywords:
airborne laser scanning (ALS), forest management, above-ground biomass, dendrometry, remote sensing, drone, image processing
Elenco autori:
De Luca, Giandomenico; Praticò, Salvatore; Messina, Gaetano; Borgogno-Mondino, Enrico; Modica, Giuseppe
Autori di Ateneo:
BORGOGNO MONDINO Enrico Corrado
Link alla scheda completa:
https://iris.unito.it/handle/2318/1961794
Titolo del libro:
Lecture Notes in Computer Science, LNCS Volume 14107
Pubblicato in:
LECTURE NOTES IN COMPUTER SCIENCE
Journal
LECTURE NOTES IN COMPUTER SCIENCE
Series
  • Dati Generali
  • Aree Di Ricerca

Dati Generali

URL

https://link.springer.com/chapter/10.1007/978-3-031-37114-1_23

Aree Di Ricerca

Settori (3)


PE10_14 - Earth observations from space/remote sensing - (2022)

SH7_10 - GIS, spatial analysis; big data in geographical studies - (2022)

PIANETA TERRA, AMBIENTE, CLIMA, ENERGIA e SOSTENIBILITA' - Protezione e prevenzione del territorio dai rischi naturali, ambientali e antropici
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