Are the new gridded DSM/DTMs of the Piemonte Region (Italy) proper for forestry? A fast and simple approach for a posteriori metric assessment

doi:10.3832/ifor1992-009

Are the new gridded DSM/DTMs of the Piemonte Region (Italy) proper for forestry? A fast and simple approach for a posteriori metric assessment

Enrico Borgogno Mondino, Vanina Fissore , Andrea Lessio, Renzo Motta

iForest - Biogeosciences and Forestry, Volume 9, Issue 6, Pages 901-909 (2016)
doi: https://doi.org/10.3832/ifor1992-009
Published: Aug 29, 2016 - Copyright © 2016 SISEF

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Aerial LiDAR (Light Detection and Ranging) derived data are widely adopted for the study and characterization of forests. In particular, LiDAR derived-CHM (Canopy Height Model) has proved essential in identifying tree height variability and estimating many forest features such as biomass and wood volume. However, CHM quality may be affected by internal limits and anomalies caused by raw data (point cloud) processing (i.e., vertical errors), which are quite often disregarded by users, thus generating potentially erroneous results in their applications. In this work, an auto-consistent procedure for the fast evaluation of CHM accuracy has been developed based on the assessment of internal anomalies affecting CHM data obtained by differencing gridded DSM (Digital Surface Model) and DTM (Digital Terrain Model). To this purpose, a CHM was generated using the gridded DTMs and DSMs provided by the Cartographic Office of the Piemonte Region (north-western Italy). We estimated the local potential CHM error over the whole region, and demonstrated its strictly dependence on the terrain morphometry, particularly slope. The relationship between potential CHM error and slope was modeled separately for mountain, hill and flat terrain contexts, and used to produce a potential error map over the whole region. Our results showed that approximately 20% of the regional territory suffers from CHM uncertainty (in particular high elevation areas, including the treeline), though the majority of regional forest categories was affected by negligible CHM error. The potential consequences of CHM error in forest applications were evaluated, concluding that the tested LiDAR dataset provide a reliable basis for forest applications in most of the regional territory.

Keywords

ALS, LiDAR, CHM, Data Quality, Vertical Errors, Slope Effect, Forest Applications

Authors’ address

(1)

DISAFA, University of Torino, l.go Paolo Braccini 2, I-10095, Grugliasco, TO (Italy)

Corresponding author

Vanina Fissore
vanina.fissore@unito.it

Citation

Borgogno Mondino E, Fissore V, Lessio A, Motta R (2016). Are the new gridded DSM/DTMs of the Piemonte Region (Italy) proper for forestry? A fast and simple approach for a posteriori metric assessment. iForest 9: 901-909. - doi: 10.3832/ifor1992-009

Academic Editor

Matteo Garbarino

Paper history

Received: Jan 21, 2016
Accepted: May 13, 2016

First online: Aug 29, 2016
Publication Date: Dec 14, 2016
Publication Time: 3.60 months

Open Access

This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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