Influence of Lidar Full-Waveform Density and Voxel Size on Forest Stand Estimates

Crespo-Peremarch, Pablo; Ruiz, Luis Á.

doi:10.1109/igarss.2018.8517594

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Some of them are forest structure variables such as diameter at breast height (DBH) [12,13], basal area [14,15], stem volume [16,17], stem density [13][14][15], stand volume [18,19], fractional cover/gap fraction, and leaf area index (LAI) [20,21]; forest mass variables, such as biomass components [13,17,19]; and forest fuel variables, such as mean and dominant tree heights [18,22], canopy base height, canopy fuel load, and canopy bulk density [19,23]. Moreover, ALS D data have also been used in combination with multispectral or hyperspectral images to classify tree species [24,25] and fuel types [24,26,27]. The forest fuel variables mentioned (i.e., canopy bulk density, canopy fuel load, canopy base height, and dominant tree height) describe the forest fuel load as well as the horizontal and vertical structure.…”

Section: Introductionmentioning

confidence: 99%

A Full-Waveform Airborne Laser Scanning Metric Extraction Tool for Forest Structure Modelling. Do Scan Angle and Radiometric Correction Matter?

Crespo-Peremarch

Ruiz

2020

Remote Sensing

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In the last decade, full-waveform airborne laser scanning (ALSFW) has proven to be a promising tool for forestry applications. Compared to traditional discrete airborne laser scanning (ALSD), it is capable of registering the complete signal going through the different vertical layers of the vegetation, allowing for a better characterization of the forest structure. However, there is a lack of ALSFW software tools for taking greater advantage of these data. Additionally, most of the existing software tools do not include radiometric correction, which is essential for the use of ALSFW data, since extracted metrics depend on radiometric values. This paper describes and presents a software tool named WoLFeX for clipping, radiometrically correcting, voxelizing the waves, and extracting object-oriented metrics from ALSFW data. Moreover, extracted metrics can be used as input for generating either classification or regression models for forestry, ecology, and fire sciences applications. An example application of WoLFeX was carried out to test the influence of the relative radiometric correction and the acquisition scan angle (1) on the ALSFW metric return waveform energy (RWE) values, and (2) on the estimation of three forest fuel variables (CFL: canopy fuel load, CH: canopy height, and CBH: canopy base height). Results show that radiometric differences in RWE values computed from different scan angle intervals (0°–5° and 15°–20°) were reduced, but not removed, when the relative radiometric correction was applied. Additionally, the estimation of height variables (i.e., CH and CBH) was not strongly influenced by the relative radiometric correction, while the model obtained for CFL improved from R2 = 0.62 up to R2 = 0.79 after applying the correction. These results show the significance of the relative radiometric correction for reducing radiometric differences measured from different scan angles and for modelling some stand-level forest fuel variables.

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Section: Introductionmentioning

confidence: 99%

A Full-Waveform Airborne Laser Scanning Metric Extraction Tool for Forest Structure Modelling. Do Scan Angle and Radiometric Correction Matter?

Crespo-Peremarch

Ruiz

2020

Remote Sensing

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Influence of voxel size on forest canopy height estimates using full-waveform airborne LiDAR data

Wang

Luo

et al. 2020

For. Ecosyst.

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Background Forest canopy height is a key forest structure parameter. Precisely estimating forest canopy height is vital to improve forest management and ecological modelling. Compared with discrete-return LiDAR (Light Detection and Ranging), small-footprint full-waveform airborne LiDAR (FWL) techniques have the capability to acquire precise forest structural information. This research mainly focused on the influence of voxel size on forest canopy height estimates. Methods A range of voxel sizes (from 10.0 m to 40.0 m interval of 2 m) were tested to obtain estimation accuracies of forest canopy height with different voxel sizes. In this study, all the waveforms within a voxel size were aggregated into a voxel-based LiDAR waveform, and a range of waveform metrics were calculated using the voxel-based LiDAR waveforms. Then, we established estimation model of forest canopy height using the voxel-based waveform metrics through Random Forest (RF) regression method. Results and conclusions The results showed the voxel-based method could reliably estimate forest canopy height using FWL data. In addition, the voxel sizes had an important influence on the estimation accuracies (R2 ranged from 0.625 to 0.832) of forest canopy height. However, the R2 values did not monotonically increase or decrease with the increase of voxel size in this study. The best estimation accuracy produced when the voxel size was 18 m (R2 = 0.832, RMSE = 2.57 m, RMSE% = 20.6%). Compared with the lowest estimation accuracy, the R2 value had a significant improvement (33.1%) when using the optimal voxel size. Finally, through the optimal voxel size, we produced the forest canopy height distribution map for this study area using RF regression model. Our findings demonstrate that the optimal voxel size need to be determined for improving estimation accuracy of forest parameter using small-footprint FWL data.

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Influence of Lidar Full-Waveform Density and Voxel Size on Forest Stand Estimates

Cited by 2 publications

References 12 publications

A Full-Waveform Airborne Laser Scanning Metric Extraction Tool for Forest Structure Modelling. Do Scan Angle and Radiometric Correction Matter?

A Full-Waveform Airborne Laser Scanning Metric Extraction Tool for Forest Structure Modelling. Do Scan Angle and Radiometric Correction Matter?

Influence of voxel size on forest canopy height estimates using full-waveform airborne LiDAR data

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