Fusion of Multitemporal LiDAR Data for Individual Tree Crown Parameter Estimation on Low Density Point Clouds

Daniele Marinelli; Claudia Paris; Lorenzo Bruzzone

doi:10.1109/IGARSS.2018.8518441

Fusion of Multitemporal LiDAR Data for Individual Tree Crown Parameter Estimation on Low Density Point Clouds

Daniele Marinelli
, Claudia Paris
, Lorenzo Bruzzone

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

1 Citation (Scopus)

Abstract

The increasingly availability of Light Detection and Ranging (LiDAR) data acquired at different times can be used to analyze the forest dynamics at individual tree level. This often requires to deal with LiDAR point clouds having significantly different point densities. To address this issue, this paper presents a method for the fusion of multitemporal Li-DAR data which aims at using the information provided by high density LiDAR data (higher than 10 pts/m 2 ) to improve the single tree parameter estimation of low density data (up to 5 pts/m 2 ) acquired over the same forest at different times. The method first accurately characterizes the crown shapes on the high density data. Then, it uses the obtained estimates to drive the tree parameter estimation on the low density LiDAR data. The method has been tested on a multitemporal dataset acquired in coniferous forests located in the Italian Alps. Experimental results confirmed the effectiveness of the method.

Original language	English
Title of host publication	2018 IEEE International Geoscience and Remote Sensing Symposium
Subtitle of host publication	Proceedings
Publisher	IEEE
Pages	3999-4002
Number of pages	4
ISBN (Electronic)	978-1-5386-7150-4, 978-1-5386-7149-8
ISBN (Print)	978-1-5386-7151-1
DOIs	https://doi.org/10.1109/IGARSS.2018.8518441
Publication status	Published - 15 Nov 2018
Externally published	Yes
Event	38th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018: Observing, Understanding and Forcasting the Dynamics of Our Planet - Feria Valencia Convention & Exhibition Center, Valencia, Spain Duration: 22 Jul 2018 → 27 Jul 2018 Conference number: 38 https://www.igarss2018.org/

Conference

Conference	38th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Abbreviated title	2018
Country/Territory	Spain
City	Valencia
Period	22/07/18 → 27/07/18
Internet address	https://www.igarss2018.org/

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10.1109/IGARSS.2018.8518441

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@inproceedings{ccc0ef842c934933bf8eaa5e088c16bc,

title = "Fusion of Multitemporal LiDAR Data for Individual Tree Crown Parameter Estimation on Low Density Point Clouds",

abstract = "The increasingly availability of Light Detection and Ranging (LiDAR) data acquired at different times can be used to analyze the forest dynamics at individual tree level. This often requires to deal with LiDAR point clouds having significantly different point densities. To address this issue, this paper presents a method for the fusion of multitemporal Li-DAR data which aims at using the information provided by high density LiDAR data (higher than 10 pts/m 2 ) to improve the single tree parameter estimation of low density data (up to 5 pts/m 2 ) acquired over the same forest at different times. The method first accurately characterizes the crown shapes on the high density data. Then, it uses the obtained estimates to drive the tree parameter estimation on the low density LiDAR data. The method has been tested on a multitemporal dataset acquired in coniferous forests located in the Italian Alps. Experimental results confirmed the effectiveness of the method.",

author = "Daniele Marinelli and Claudia Paris and Lorenzo Bruzzone",

year = "2018",

month = nov,

day = "15",

doi = "10.1109/IGARSS.2018.8518441",

language = "English",

isbn = "978-1-5386-7151-1",

pages = "3999--4002",

booktitle = "2018 IEEE International Geoscience and Remote Sensing Symposium",

publisher = "IEEE",

address = "United States",

note = "38th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 : Observing, Understanding and Forcasting the Dynamics of Our Planet, 2018 ; Conference date: 22-07-2018 Through 27-07-2018",

url = "https://www.igarss2018.org/",

}

Marinelli, D, Paris, C & Bruzzone, L 2018, Fusion of Multitemporal LiDAR Data for Individual Tree Crown Parameter Estimation on Low Density Point Clouds. in 2018 IEEE International Geoscience and Remote Sensing Symposium: Proceedings., 8518441, IEEE, pp. 3999-4002, 38th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018, Valencia, Spain, 22/07/18. https://doi.org/10.1109/IGARSS.2018.8518441

Fusion of Multitemporal LiDAR Data for Individual Tree Crown Parameter Estimation on Low Density Point Clouds. / Marinelli, Daniele; Paris, Claudia; Bruzzone, Lorenzo.
2018 IEEE International Geoscience and Remote Sensing Symposium: Proceedings. IEEE, 2018. p. 3999-4002 8518441.

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

TY - GEN

T1 - Fusion of Multitemporal LiDAR Data for Individual Tree Crown Parameter Estimation on Low Density Point Clouds

AU - Marinelli, Daniele

AU - Paris, Claudia

AU - Bruzzone, Lorenzo

N1 - Conference code: 38

PY - 2018/11/15

Y1 - 2018/11/15

N2 - The increasingly availability of Light Detection and Ranging (LiDAR) data acquired at different times can be used to analyze the forest dynamics at individual tree level. This often requires to deal with LiDAR point clouds having significantly different point densities. To address this issue, this paper presents a method for the fusion of multitemporal Li-DAR data which aims at using the information provided by high density LiDAR data (higher than 10 pts/m 2 ) to improve the single tree parameter estimation of low density data (up to 5 pts/m 2 ) acquired over the same forest at different times. The method first accurately characterizes the crown shapes on the high density data. Then, it uses the obtained estimates to drive the tree parameter estimation on the low density LiDAR data. The method has been tested on a multitemporal dataset acquired in coniferous forests located in the Italian Alps. Experimental results confirmed the effectiveness of the method.

AB - The increasingly availability of Light Detection and Ranging (LiDAR) data acquired at different times can be used to analyze the forest dynamics at individual tree level. This often requires to deal with LiDAR point clouds having significantly different point densities. To address this issue, this paper presents a method for the fusion of multitemporal Li-DAR data which aims at using the information provided by high density LiDAR data (higher than 10 pts/m 2 ) to improve the single tree parameter estimation of low density data (up to 5 pts/m 2 ) acquired over the same forest at different times. The method first accurately characterizes the crown shapes on the high density data. Then, it uses the obtained estimates to drive the tree parameter estimation on the low density LiDAR data. The method has been tested on a multitemporal dataset acquired in coniferous forests located in the Italian Alps. Experimental results confirmed the effectiveness of the method.

UR - https://iris.unitn.it/handle/11572/224041

U2 - 10.1109/IGARSS.2018.8518441

DO - 10.1109/IGARSS.2018.8518441

M3 - Conference contribution

SN - 978-1-5386-7151-1

SP - 3999

EP - 4002

BT - 2018 IEEE International Geoscience and Remote Sensing Symposium

PB - IEEE

T2 - 38th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018

Y2 - 22 July 2018 through 27 July 2018

ER -

Fusion of Multitemporal LiDAR Data for Individual Tree Crown Parameter Estimation on Low Density Point Clouds

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