Gold – A novel deconvolution algorithm with optimization for waveform LiDAR processing

Zhou, Tan; Popescu, Sorin C.; Krause, Keith; Sheridan, R.; Putman, E.

doi:10.1016/j.isprsjprs.2017.04.021

Cited by 39 publications

(31 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This may suggest that more attention should be paid to composite waveforms when the absolute vertical related information is used. Compared to the point cloud from DR LiDAR data, the waveform-based point cloud is generally more dense and informative [17,34]. In the present study, waveform metrics from raw and composite waveforms have demonstrated that both are capable of providing a more promising prospect of potential applications such as tree species identification than waveform-based point cloud data Therefore, this result indirectly confirms that waveform LiDAR data are more useful for vegetation characterization than DR LiDAR data.…”

Section: Waveform Metrics and Feature Selectionsupporting

confidence: 73%

“…The entire study area was covered by two perpendicular direction flight lines, with 12 and 19 flight lines in an east-west direction and north-south direction, respectively. Major technical specifications of the flight campaign have been reported in in the study of Zhou et al [34].…”

Section: Study Sitementioning

confidence: 99%

“…As part of the preprocessing step of tree segmentation and metrics extraction, we first applied the Gaussian decomposition (Equation (1)) for waveforms to obtain a point cloud as described in our previous study [34]. The present study will not go into details of how the point cloud is obtained, whereas major steps of the procedure are the preprocessing of waveforms, the fitting waveform with a mixture of Gaussian functions and geolocation transformation.…”

Section: Waveform Decompositionmentioning

confidence: 99%

See 2 more Smart Citations

Bayesian and Classical Machine Learning Methods: A Comparison for Tree Species Classification with LiDAR Waveform Signatures

et al. 2017

Self Cite

View full text Add to dashboard Cite

A plethora of information contained in full-waveform (FW) Light Detection and Ranging (LiDAR) data offers prospects for characterizing vegetation structures. This study aims to investigate the capacity of FW LiDAR data alone for tree species identification through the integration of waveform metrics with machine learning methods and Bayesian inference. Specifically, we first conducted automatic tree segmentation based on the waveform-based canopy height model (CHM) using three approaches including TreeVaW, watershed algorithms and the combination of TreeVaW and watershed (TW) algorithms. Subsequently, the Random forests (RF) and Conditional inference forests (CF) models were employed to identify important tree-level waveform metrics derived from three distinct sources, such as raw waveforms, composite waveforms, the waveform-based point cloud and the combined variables from these three sources. Further, we discriminated tree (gray pine, blue oak, interior live oak) and shrub species through the RF, CF and Bayesian multinomial logistic regression (BMLR) using important waveform metrics identified in this study. Results of the tree segmentation demonstrated that the TW algorithms outperformed other algorithms for delineating individual tree crowns. The CF model overcomes waveform metrics selection bias caused by the RF model which favors correlated metrics and enhances the accuracy of subsequent classification. We also found that composite waveforms are more informative than raw waveforms and waveform-based point cloud for characterizing tree species in our study area. Both classical machine learning methods (the RF and CF) and the BMLR generated satisfactory average overall accuracy (74% for the RF, 77% for the CF and 81% for the BMLR) and the BMLR slightly outperformed the other two methods. However, these three methods suffered from low individual classification accuracy for the blue oak which is prone to being misclassified as the interior live oak due to the similar characteristics of blue oak and interior live oak. Uncertainty estimates from the BMLR method compensate for this downside by providing classification results in a probabilistic sense and rendering users with more confidence in interpreting and applying classification results to real-world tasks such as forest inventory. Overall, this study recommends the CF method for feature selection and suggests that BMLR could be a superior alternative to classical machining learning methods.

show abstract

Section: Waveform Metrics and Feature Selectionsupporting

confidence: 73%

Section: Study Sitementioning

confidence: 99%

Section: Waveform Decompositionmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian and Classical Machine Learning Methods: A Comparison for Tree Species Classification with LiDAR Waveform Signatures

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…Many studies have mapped forest canopy height using remote sensing data [1][2][3][4][5][6][7][8][9]. The majority of these are based on the light detection and ranging (LiDAR), synthetic aperture radar (SAR) or digital photogrammetry (DP) techniques [5,8,10].…”

Section: Introductionmentioning

confidence: 99%

“…Researchers have demonstrated the feasibility of airborne laser scanning (ALS) in mapping forest canopy height under various conditions [10][11][12]. The Geoscience Laser Altimeter System (GLAS) onboard NASA's Ice, Cloud, and land Elevation Satellite (ICESat) produced the most commonly used spaceborne LiDAR data, which can also be used to map forest height [13].…”

Section: Introductionmentioning

confidence: 99%

Mapping Forest Canopy Height in Mountainous Areas Using ZiYuan-3 Stereo Images and Landsat Data

Liu

Cao

Dang

et al. 2019

Forests

View full text Add to dashboard Cite

Forest canopy height is an important parameter for studying biodiversity and the carbon cycle. A variety of techniques for mapping forest height using remote sensing data have been successfully developed in recent years. However, the demands for forest height mapping in practical applications are often not met, due to the lack of corresponding remote sensing data. In such cases, it would be useful to exploit the latest, cheaper datasets and combine them with free datasets for the mapping of forest canopy height. In this study, we proposed a method that combined ZiYuan-3 (ZY-3) stereo images, Shuttle Radar Topography Mission global 1 arc second data (SRTMGL1), and Landsat 8 Operational Land Imager (OLI) surface reflectance data. The method consisted of three procedures: First, we extracted a digital surface model (DSM) from the ZY-3, using photogrammetry methods and subtracted the SRTMGL1 to obtain a crude canopy height model (CHM). Second, we refined the crude CHM and correlated it with the topographically corrected Landsat 8 surface reflectance data, the vegetation indices, and the forest types through a Random Forest model. Third, we extrapolated the model to the entire study area covered by the Landsat data, and obtained a wall-to-wall forest canopy height product with 30 m × 30 m spatial resolution. The performance of the model was evaluated by the Random Forest's out-of-bag estimation, which yielded a coefficient of determination (R 2 ) of 0.53 and a root mean square error (RMSE) of 3.28 m. We validated the predicted forest canopy height using the mean forest height measured in the field survey plots. The validation result showed an R 2 of 0.62 and a RMSE of 2.64 m.Moderate Resolution Imaging Spectroradiometer (MODIS) data [2,3,[14][15][16]. The SAR is well known for its high temporal resolution, because it is less affected by weather and illumination conditions [17]. The SAR-based techniques for forest canopy height mapping include radargrammetry, interferometry SAR (InSAR), and polarimetric interferometric SAR (PolInSAR). The radargrammetry is based on SAR stereo images [5,18,19]. The InSAR is based on the phase differences between two complex SAR images [6,20]. The PolInSAR includes phase difference methods and the model-based methods, which rely on the complex coherent coefficient model [7,21]. In these methods, the TerraSAR-X (X-band), Radarsat-2 (C-band), and ALOS-2 (L-band) are commonly used SAR data sources. It is expected that the techniques for mapping forest canopy height using the LiDAR and SAR will be further developed when data from the ICESat-2 [22], the GEDI [23], the Biomass (P-band) (due for launch in 2021) [24], and the Tandem-L (L-band) (due for launch in 2022) [25] missions become available. The airborne DP systems have a large field of view, high cruising altitude, fast data acquisition speed, and easy flight planning [26]. The spaceborne DP systems can perform continuous and repetitive observations. Several spaceborne systems, such as the WorldView series, provide sub-meter resolution ...

show abstract

Introducing Alternative Algorithms for the Determination of the Distribution of Relaxation Times

Bergmann

Schlüter

2022

ChemPhysChem

View full text Add to dashboard Cite

Impedance spectroscopy is a powerful characterization method to evaluate the performance of electrochemical systems. However, overlapping signals in the resulting impedance spectra oftentimes cause misinterpretation of the data. The distribution of relaxation times (DRT) method overcomes this problem by transferring the impedance data from the frequency domain into the time domain, which yields DRT spectra with an increased resolution. Unfortunately, the determination of the DRT is an ill-posed problem, and appropriate mathematical regularizations become inevitable to find suitable solutions.The Tikhonov algorithm is a widespread method for computing DRT data, but it leads to unlikely spectra due to necessary boundaries. Therefore, we introduce the application of three alternative algorithms (Gold, Richardson Lucy, Sparse Spike) for the determination of stable DRT solutions and compare their performances. As the promising Sparse Spike deconvolution has a limited scope when using one single regularization parameter, we furthermore replaced the scalar regularization parameter with a vector. The resulting method is able to calculate well-resolved DRT spectra.

show abstract

Gold – A novel deconvolution algorithm with optimization for waveform LiDAR processing

Cited by 39 publications

References 34 publications

Bayesian and Classical Machine Learning Methods: A Comparison for Tree Species Classification with LiDAR Waveform Signatures

Bayesian and Classical Machine Learning Methods: A Comparison for Tree Species Classification with LiDAR Waveform Signatures

Mapping Forest Canopy Height in Mountainous Areas Using ZiYuan-3 Stereo Images and Landsat Data

Introducing Alternative Algorithms for the Determination of the Distribution of Relaxation Times

Contact Info

Product

Resources

About