Simulating the impact of discrete-return lidar system and survey characteristics over young conifer and broadleaf forests

Disney, Mathias; Kalogirou, Vasileios; Lewis, P.; Prieto-Blanco, A.; Hancock, Steven; Pfeifer, Marion

doi:10.1016/j.rse.2010.02.009

Cited by 120 publications

(84 citation statements)

References 56 publications

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“…In spite of the uncertainties, the results are promising and can serve as basis for attempts to calibrate and validate simulation models so that they could be used e.g. in planning of LiDAR acquisitions (Disney et al, 2010).…”

Section: Validation and Sensitivity Analysis Of The Simulatormentioning

confidence: 98%

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Real and Simulated Waveform Recording Lidar Data in Boreal Juvenile Forest Vegetation

Hovi

Korpela

2013

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Airborne small-footprint LiDAR is replacing field measurements in regional-level forest inventories, but auxiliary field work is still required for the optimal management of young stands. Waveform (WF) recording sensors can provide a more detailed description of the vegetation compared to discrete return (DR) systems. Furthermore, knowing the shape of the signal facilitates comparisons between real data and those obtained with simulation tools. We performed a quantitative validation of a Monte Carlo ray tracing (MCRT) -based LiDAR simulator against real data and used simulations and empirical data to study the WF recording LiDAR for the classification of boreal juvenile forest vegetation. Geometric-optical models of three common species were used as input for the MCRT model. Simulated radiometric and geometric WF features were in good agreement with the real data, and interspecies differences were preserved. We used the simulator to study the effects of sensor parameters on species classification performance. An increase in footprint size improved the classification accuracy up to a certain footprint size, while the emitted pulse width and the WF sampling rate had minor effects. Analyses on empirical data showed small improvement in performance compared to existing studies, when classifying seedling stand vegetation to four operational classes. The results on simulator validation serve as a basis for the future use of simulation models e.g. in LiDAR survey planning or in the simulation of synthetic training data, while the empirical findings clarify the potential of WF LiDAR data in the inventory chain for the operational forest management planning in Finland.

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Section: Validation and Sensitivity Analysis Of The Simulatormentioning

confidence: 98%

“…The MCRT is considered as one of the most accurate methods for radiative transfer modeling in vegetation (Widlowski et al, 2008). There have been applications to LiDAR as well (Disney et al, 2010;Morsdorf et al, 2009), but comparisons to real data are often lacking.…”

Section: Introductionmentioning

confidence: 99%

Real and Simulated Waveform Recording Lidar Data in Boreal Juvenile Forest Vegetation

Hovi

Korpela

2013

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Previous work has demonstrated the value of lidar modelling of vegetation for discrete return lidar (Disney et al, 2010) and full waveform systems (Ni-Meister et al, 2001;North et al, 2010;Sun & Ranson, 2000). Using a simulation approach, the sensitivity of lidar data to surface structural and optical properties can be explored to improve our understanding and interpretation of the estimation of lidar-derived biophysical parameters.…”

Section: Waveform Simulation and Multispectral Lidarmentioning

confidence: 99%

Lidar Remote Sensing for Biomass Assessment

Rosette¹,

Suárez²,

Nelson³

et al. 2012

Remote Sensing of Biomass - Principles and Applications

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“…It is recognised the presented technique does not account for factors caused by an increased flying height such as increase in laser footprint size or attenuation of laser power caused by increased atmospheric thickness (Goodwin et al, 2006), as well as acquisition at oblique viewing angles (Lovell et al, 2005) or with a different instrument. Physical models such as that presented by Disney et al (2010) could potentially be used to more accurately model different acquisition scenarios. However this would require significant effort to recreate landscape scale variance in native forest structure and is therefore beyond the scope of this study.…”

Section: Discussionmentioning

confidence: 99%

“…The use of 3D tree reconstruction, either through inventory measurements (Woodgate, 2015) or with terrestrial laser scanning (Burt et al, 2013), could provide a more robust validation dataset. This could be further coupled with a radiative transfer model which would allow parameterisation of both the forest and the ALS acquisition (Disney et al, 2010).…”

Section: Outlined Inmentioning

confidence: 99%

Assessment of forest canopy vertical structure with multi-scale remote sensing: from the plot to the large area

Wilkes

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Simulating the impact of discrete-return lidar system and survey characteristics over young conifer and broadleaf forests

Cited by 120 publications

References 56 publications

Real and Simulated Waveform Recording Lidar Data in Boreal Juvenile Forest Vegetation

Real and Simulated Waveform Recording Lidar Data in Boreal Juvenile Forest Vegetation

Lidar Remote Sensing for Biomass Assessment

Assessment of forest canopy vertical structure with multi-scale remote sensing: from the plot to the large area

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