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

Daniele Marinelli; Claudia Paris; Lorenzo Bruzzone

doi:10.1109/IGARSS.2016.7729931

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 proceeding › Conference contribution › Academic › peer-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 language	Undefined
Title of host publication	2016 IEEE International Geoscience & Remote Sensing Symposium (IGARSS)
Subtitle of host publication	Proceedings
Publisher	IEEE
Pages	3595-3598
Number of pages	4
ISBN (Electronic)	978-1-5090-3332-4, 978-1-5090-3331-7
ISBN (Print)	978-1-5090-3333-1
DOIs	https://doi.org/10.1109/IGARSS.2016.7729931
Publication status	Published - 3 Nov 2016
Externally published	Yes
Event	IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016: Advancing the Understanding of Our Living Planet - Beijing, China Duration: 10 Jul 2016 → 15 Jul 2016 http://www.igarss2016.org/

Publication series

Name
ISSN (Electronic)	2153-7003

Conference

Conference	IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
Abbreviated title	IGARSS
Country/Territory	China
City	Beijing
Period	10/07/16 → 15/07/16
Internet address	http://www.igarss2016.org/

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10.1109/IGARSS.2016.7729931

Cite this

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title = "Fusion of high and very high density LiDAR data for 3D forest change detection",

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.",

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doi = "10.1109/IGARSS.2016.7729931",

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booktitle = "2016 IEEE International Geoscience & Remote Sensing Symposium (IGARSS)",

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note = "IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 ; Conference date: 10-07-2016 Through 15-07-2016",

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Marinelli, D, Paris, C & Bruzzone, L 2016, Fusion of high and very high density LiDAR data for 3D forest change detection. in 2016 IEEE International Geoscience & Remote Sensing Symposium (IGARSS): Proceedings., 7729931, IEEE, pp. 3595-3598, IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016, Beijing, China, 10/07/16. https://doi.org/10.1109/IGARSS.2016.7729931

Fusion of high and very high density LiDAR data for 3D forest change detection. / Marinelli, Daniele; Paris, Claudia; Bruzzone, Lorenzo.
2016 IEEE International Geoscience & Remote Sensing Symposium (IGARSS): Proceedings. IEEE, 2016. p. 3595-3598 7729931.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Fusion of high and very high density LiDAR data for 3D forest change detection

AU - Marinelli, Daniele

AU - Paris, Claudia

AU - Bruzzone, Lorenzo

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N2 - 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.

AB - 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.

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BT - 2016 IEEE International Geoscience & Remote Sensing Symposium (IGARSS)

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

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