Detection and correction of LiDAR raster data from the Italian national remote sensing programme and production of a suitable CHM to forest volume estimation in Calabria (southern Italy)

Floris, F. J. T.; Penasa, P; Michelini, Manuela; Puletti, P

doi:10.3832/efor2328-014

Cited by 2 publications

(1 citation statement)

References 36 publications

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“…Porzioni del CHM attribuibili a elementi non arborei, quali pale eoliche, viadotti, costruzioni rurali e altre, sono state escluse dall'analisi, mentre valori eccessivamente elevati del CHM in corrispondenza dello strato arboreo, ritenuti outlier, sono stati modificati tramite interpolazione spaziale con i dati vicini (parametro mediana). Approfondimenti su metodi e procedure adottati per la produzione automatizzata di CHM il più possibile privi di dati aberranti sono riportati in Floris et al (2017). Un esempio di CHM è riportato in Fig.…”

Section: Dati Lidarunclassified

Models of stand volume and biomass estimation based on LiDAR data for the main forest types in Calabria (southern Italy)

Scrinzi¹,

Floris²,

Clementel³

et al. 2017

Forest@

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Models of stand volume and biomass estimation based on LiDAR data for the main forest types in Calabria (southern Italy). The AlForLab project is part of the Cluster MEA (Materials Energy Environment) addressed to the Calabria Region. Estimating the main dendrometric variables of Calabrian forests using models based on publicly available remote sensed data is one of the main purposes of the project. This paper describes the procedures used to develop several thematic maps (raster and vector) of timber volume and phytomass to be used in planning and management activities at both regional and forest property scale, as well as for felling plans, logging projects etc. We used public LiDAR data at medium-low resolution (1.6 pts m -2 ), acquired on about 90% of Calabrian territory in the frame of a national remote sensing programme of the Italian Ministry of the Environment. Field data from the second National Forest Inventory (INFC 2005) on 311 sample points were used for model calibration, as well as new field data acquired specifically for AlForLab project on 143 angle count samples. A series of regression models to predict volume and its corresponding aboveground biomass (dry and fresh weight) were developed and digital maps at different spatial resolutions were produced, as well as their estimate uncertainties. These models and their mapping products are also an important part of the new-establishing forest Decision Support System CFOR. The adopted models, though based on the same mathematical equation, have specific coefficients for different species and groups of species, according to a forest type classification system compatible with the fourth level of Corine Land Cover. In this way it is possible to apply the models without accessing more detailed forest type maps. All estimation methods and procedures are consistent with national forest inventory models, and with the other new tools proposed by AlForLab project to estimate timber volume, such as the regional tariffs and the field sampling inventory procedures. R 2 adjusted values (for models at the highest typological detail) are between 60% and 85%, whereas uncertainties of timber volume estimate (ESS%) range from 25% (for main forest species) up to 50% (for less spread forest types). All processing steps to produce digital maps were performed on open-source environment (R and QGIS).Keywords: ALS, LiDAR, Timber Volume, Forest Biomass, Estimation Models, CHM, AlForLab Received: Feb 09, 2017; Accepted: Apr 19, 2017; Published online: May 15, 2017 Citation: Scrinzi G, Floris A, Clementel F, Bernardini V, Chianucci F, Greco S, Michelini T, Penasa A, Puletti N, Rizzo M, Turco R, Corona P, 2017. Modelli di stima del volume e delle fitomasse del soprassuolo arboreo delle principali formazioni forestali della Calabria mediante dati LiDAR. Forest@ 14: 175-187 [online 2017-05-15]

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Section: Dati Lidarunclassified

Models of stand volume and biomass estimation based on LiDAR data for the main forest types in Calabria (southern Italy)

Scrinzi¹,

Floris²,

Clementel³

et al. 2017

Forest@

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Sustainable forest management and forest biomass estimates in Calabria (Southern Italy): a case study

Iovino¹,

Cruscomagno²,

Nicolaci³

et al. 2017

Forest@

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IntroduzioneNegli ultimi decenni il bosco ha assunto un significato e un ruolo sostanzialmente diverso da quello del passato, costituendo un bene ambientale, economico e culturale di rilevante interesse sociale (Ciancio 2003). La multifunzionalità delle foreste, unitamente alle considerazioni sulle nuove dimensioni scientifiche e culturali assunte dai boschi, trovano compiutezza nei principi della Gestione Forestale Sostenibile (MCPFE 1993). Questa si concretizza con la mediazione tra gli interessi produttivi, le necessità ambientali e le esigenze sociali legate al patrimonio, riuscendo contemporaneamente a garantire l'approvvigionamento di materie prime e di prodotti fo- it).Abstract: Sustainable forest management and forest biomass estimates in Calabria (Southern Italy): a case study. Based on LiDAR (Light Detection And Ranging) data, estimates of volume and biomass potentially available from coppice and high forest stands under different silvicultural options were estimated in a case study located in the Pollino National Park (Calabria -Southern Italy) within the framework of the ALForLAB project.Estimates of volume and biomass potentially available were elaborated separately for the main silvicultural types: beech, Calabrian pine and Mediterranean pines high forest stands on one hand, and for traditional coppices, outgrown coppice and coppice in conversion to high forest on the other. The estimates were performed taking into duly account both of the forest regional law and the restrictions due to the protected area (Natural Park). The overall standing volume ranged from 4.8 up to 7.7 M m 3 , while the volume potentially available ranged from 3.7 up to 6 M m 3 . Estimates of mass potentially available under the the adoption of different silvicultural system and options, in a sustainable forest management context, and according to the main stand structure types were analysed and discussed.

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Detection and correction of LiDAR raster data from the Italian national remote sensing programme and production of a suitable CHM to forest volume estimation in Calabria (southern Italy)

Cited by 2 publications

References 36 publications

Models of stand volume and biomass estimation based on LiDAR data for the main forest types in Calabria (southern Italy)

Models of stand volume and biomass estimation based on LiDAR data for the main forest types in Calabria (southern Italy)

Sustainable forest management and forest biomass estimates in Calabria (Southern Italy): a case study

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