Fusion of high and very high density LiDAR data for 3D forest change detection

Daniele Marinelli, Claudia Paris, Lorenzo Bruzzone

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

7 Citations (Scopus)

Abstract

Light Detection And Ranging (LiDAR) data have proven to be very effective in the estimation of parameters for forestry applications. However, little research has been done regarding the multitemporal analysis of these data. In this paper we propose a novel hierarchical change detection approach that first performs the detection of major changes (e.g., harvested trees) and then focuses on the detection of minor changes (e.g., single tree growth), using multitemporal LiDAR data having different point densities. Splitting the change detection problem allows us to analyze the different types of changes with different techniques. In particular, the detection of minor changes is carried out directly on the point clouds in order to exploit all the informative content of the LiDAR data. The approach has been tested on a dataset acquired in 2010 and 2014 on a complex forest area located in the Southern Italian Alps. The experimental results confirm the effectiveness of the proposed approach.
Original languageUndefined
Title of host publication2016 IEEE International Geoscience & Remote Sensing Symposium (IGARSS)
Subtitle of host publicationProceedings
PublisherIEEE
Pages3595-3598
Number of pages4
ISBN (Electronic)978-1-5090-3332-4, 978-1-5090-3331-7
ISBN (Print)978-1-5090-3333-1
DOIs
Publication statusPublished - 3 Nov 2016
Externally publishedYes
EventIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016: Advancing the Understanding of Our Living Planet - Beijing, China
Duration: 10 Jul 201615 Jul 2016
http://www.igarss2016.org/

Publication series

Name
ISSN (Electronic)2153-7003

Conference

ConferenceIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
Abbreviated titleIGARSS
Country/TerritoryChina
CityBeijing
Period10/07/1615/07/16
Internet address

Cite this