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.

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

BORGOGNO MONDINO, Enrico Corrado
First
;
FISSORE, VANINA;LESSIO, ANDREA;MOTTA, Renzo
Last
2016

Abstract

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.
9
901
909
http://www.sisef.it/iforest/pdf/?id=ifor1992-009
ALS, LiDAR, CHM, Data Quality, Vertical Errors, Slope Effect, Forest Applications
BORGOGNO MONDINO, Enrico Corrado; Fissore, Vanina; Lessio, Andrea; Motta, Renzo
File in questo prodotto:
File Dimensione Formato  
iForest_ifor1992-009_Borgogno_EDITORIAL.pdf

Accesso aperto

Tipo di file: PDF EDITORIALE
Dimensione 1.15 MB
Formato Adobe PDF
1.15 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1611516
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 7
  • ???jsp.display-item.citation.isi??? 8
social impact