iForest - Biogeosciences and Forestry


Spatially explicit estimation of forest age by integrating remotely sensed data and inverse yield modeling techniques

Ludovico Frate (1), Maria Laura Carranza (1)   , Vittorio Garfì (2), Mirko Di Febbraro (1), Daniela Tonti (3), Marco Marchetti (3), Marco Ottaviano (3), Giovanni Santopuoli (3), Gherardo Chirici (4)

iForest - Biogeosciences and Forestry, Volume 9, Issue 1, Pages 63-71 (2015)
doi: https://doi.org/10.3832/ifor1529-008
Published: Jul 25, 2015 - Copyright © 2015 SISEF

Research Articles

In this work we present an innovative method based on the application of inverse yield models for producing spatially explicit estimations of forest age. Firstly, a raster growing stock volume map was produced using the non-parametric k-Nearest Neighbors estimation method on the basis of IRS LISS-III remotely sensed imagery and field data collected in the framework of a local forest inventory. Secondly, species specific inverted yield equations were applied to estimate forest age as a function of growing stock volume. The method was tested in 128.000 ha of even-aged forests in central Italy (Molise region). The accuracy of the method was assessed using an independent dataset of 305 units from a local standwise forest inventory. The results demonstrated that the forest age map was accurate, with a root mean square error of 15.8 years (30% of the mean of field values), thus at least useful for supporting forest management purposes, such as the assessment of harvesting potential, and of ecosystem services. Thanks to the use of remotely sensed data and spatial modeling, the approach we propose is cost-effective and easily replicable for vast regions.


k-Nearest Neighbors, Mapping, Forest Inventory, Growing Stock, IRS LISS-III

Authors’ address

Ludovico Frate
Maria Laura Carranza
Mirko Di Febbraro
Envix Lab, Dipartimento di Bioscienze e Territorio (DiBT), Università degli Studi del Molise, I-86090 Pesche, Isernia (Italy)
Vittorio Garfì
Global Ecology Lab, Dipartimento di Bioscienze e Territorio (DiBT), Università degli Studi del Molise, I- 86090 Pesche, Isernia (Italy)
Daniela Tonti
Marco Marchetti
Marco Ottaviano
Giovanni Santopuoli
Natural Resource & Environmental Planning Lab, Dipartimento di Bioscienze e Territorio (DiBT), Università degli Studi del Molise, I- 86090 Pesche, Isernia (Italy)
Gherardo Chirici
geoLAB - Laboratorio di Geomatica, Dipartimento di Gestione dei Sistemi Agrari, Alimentari e Forestali (GESAAF), Università degli Studi di Firenze, I-50145 Firenze (Italy)

Corresponding author

Maria Laura Carranza


Frate L, Carranza ML, Garfì V, Febbraro MD, Tonti D, Marchetti M, Ottaviano M, Santopuoli G, Chirici G (2015). Spatially explicit estimation of forest age by integrating remotely sensed data and inverse yield modeling techniques. iForest 9: 63-71. - doi: 10.3832/ifor1529-008

Academic Editor

Matteo Garbarino

Paper history

Received: Dec 12, 2014
Accepted: Apr 14, 2015

First online: Jul 25, 2015
Publication Date: Feb 21, 2016
Publication Time: 3.40 months

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