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

Hovi, Aarne; Korpela, Ilkka

doi:10.5194/isprsarchives-xl-1-w1-145-2013

Cited by 7 publications

(16 citation statements)

References 12 publications

(14 reference statements)

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“…Multispectral waveform LiDAR offers a particular advantage in being able to characterise both vegetation structure and reflective properties (Hovi and Korpela 2014), which would also be conducive to studies of tree and forest condition. As a result, advances in these sensors will offer increased capabilities in structural and spectral sensitivities to changes in forest foliage and biomass that will no doubt result in increased future application to assessments of forest insect damage.…”

Section: Future Outlookmentioning

confidence: 99%

Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective

et al. 2016

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Section: Future Outlookmentioning

confidence: 99%

Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective

et al. 2016

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“…MOD09GA [50] provides an estimate of the surface spectral reflectance with a daily revisit frequency and 500-m spatial resolution. In addition to the reflectance values, the quality information is also embedded.…”

Section: Remote Sensing Datamentioning

confidence: 99%

“…The Levenberg-Marquardt optimization algorithm [49] was used to adjust the synaptic weights and neuron bias to obtain the best agreement between the output simulated by the NN and the corresponding value of LAI in the training dataset. After calibration, the two NNs (SAILNN and four-scaleNN) were applied to the MODIS daily surface reflectance products (MOD09GA) [50] to produce two LAI maps, denoted as SAIL LAI and four-scale LAI, respectively.…”

Section: The Neural Network Inversion Techniquementioning

confidence: 99%

Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection

Yin

Liu

et al. 2015

Remote Sensing

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Physically-based approaches for estimating Leaf Area Index (LAI) using remote sensing data rely on radiative transfer (RT) models. Currently, many RT models are freely available, but determining the appropriate RT model for LAI retrieval is still problematic. This study aims to evaluate the necessity of RT model selection for LAI retrieval and to propose a retrieval methodology using different RT models for different vegetation types. Both actual experimental observations and RT model simulations were used to conduct the evaluation. Each of them includes needleleaf forests and croplands, which have contrasting structural attributes. The scattering from arbitrarily inclined leaves (SAIL) model and the four-scale model, which are 1D and 3D RT models, respectively, were used to simulate the synthetic test datasets. The experimental test dataset was established through two field campaigns conducted in the Heihe River Basin. The results show that the realistic representation of canopy structure in RT models is very important for LAI retrieval.If an unsuitable RT model is used, then the root mean squared error (RMSE) will increase from 0.43 to 0.60 in croplands and from 0.52 to 0.63 in forests. In addition, an RT model's potential to retrieve LAI is limited by the availability of a priori information on RT model OPEN ACCESSRemote Sens. 2015, 7 4605 parameters. 3D RT models require more a priori information, which makes them have poorer generalization capability than 1D models. Therefore, physically-based retrieval algorithms should embed more than one RT model to account for the availability of a priori information and variations in structural attributes among different vegetation types.

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“…Such discrete systems generally do not specify the exploited pulse detection method [4,7,8] and only provide range information with a single backscatter intensity value, inherently neglecting the backscattering characteristics of the illuminated target. In contrast, highly accurate pulse detection and subsequent range measurement is potentially possible in full-waveform lidar, subject to the methods adopted for data post-processing.…”

Section: Introductionmentioning

confidence: 99%

“…These include classification [1,2], object extraction and 3D reconstruction [3], vegetation structure characterization [4,5] and digital elevation model (DEM) generation [6].…”

Section: Introductionmentioning

confidence: 99%

A Sparsity-Based Regularization Approach for Deconvolution of Full-Waveform Airborne Lidar Data

2016

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Full-waveform lidar systems capture the complete backscattered signal from the interaction of the laser beam with targets located within the laser footprint. The resulting data have advantages over discrete return lidar, including higher accuracy of the range measurements and the possibility of retrieving additional returns from weak and overlapping pulses. In addition, radiometric characteristics of targets, e.g., target cross-section, can also be retrieved from the waveforms. However, waveform restoration and removal of the effect of the emitted system pulse from the returned waveform are critical for precise range measurement, 3D reconstruction and target cross-section extraction. In this paper, a sparsity-constrained regularization approach for deconvolution of the returned lidar waveform and restoration of the target cross-section is presented. Primal-dual interior point methods are exploited to solve the resulting nonlinear convex optimization problem. The optimal regularization parameter is determined based on the L-curve method, which provides high consistency in varied conditions. Quantitative evaluation and visual assessment of results show the superior performance of the proposed regularization approach in both removal of the effect of the system waveform and reconstruction of the target cross-section as compared to other prominent deconvolution approaches. This demonstrates the potential of the proposed approach for improving the accuracy of both range measurements and geophysical attribute retrieval. The feasibility and consistency of the presented approach in the processing of a variety of lidar data acquired under different system configurations is also highlighted.

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

Cited by 7 publications

References 12 publications

Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective

Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective

Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection

A Sparsity-Based Regularization Approach for Deconvolution of Full-Waveform Airborne Lidar Data

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