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.

Fusion of high and very high density LiDAR data for 3D forest change detection / Marinelli, Daniele; Paris, Claudia; Bruzzone, Lorenzo. - ELETTRONICO. - (2016), pp. 3595-3598. ((Intervento presentato al convegno IGARSS 2016 tenutosi a Beijing nel 10th-15th July 2016 [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

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.
2016 IEEE International Geoscience & Remote Sensing Symposium Proceedings
Piscataway, USA
IEEE
978-1-5090-3332-4
Marinelli, Daniele; Paris, Claudia; Bruzzone, Lorenzo
Fusion of high and very high density LiDAR data for 3D forest change detection / Marinelli, Daniele; Paris, Claudia; Bruzzone, Lorenzo. - ELETTRONICO. - (2016), pp. 3595-3598. ((Intervento presentato al convegno IGARSS 2016 tenutosi a Beijing nel 10th-15th July 2016 [10.1109/IGARSS.2016.7729931].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/165771
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