Unmanned aerial vehicle (UAV) derived structure-from-motion photogrammetry point clouds for oil palm (<i>Elaeis guineensis</i>) canopy segmentation and height estimation

Fawcett, Dominic; Azlan, Benjamin; Hill, Timothy; Kho, Lip Khoon; Bennie, Jonathan; Anderson, Karen

doi:10.1080/01431161.2019.1591651

Cited by 59 publications

(45 citation statements)

References 46 publications

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“…The PMF was applied using the implementation of the lidR package [45], where the filter works at point cloud level without any rasterization process. The PMF has been commonly applied to ALS data [18,26], and the release of the lidR package has also promoted this filter to process photogrammetric point clouds [46]. The package uses two parameters: the sequence of windows size, and the sequence of threshold, which is the height value below which a return is classified as a ground return.…”

Section: The Progressive Morphological Filter (Pmf)mentioning

confidence: 99%

Impact of Calibrating Filtering Algorithms on the Quality of LiDAR-Derived DTM and on Forest Attribute Estimation through Area-Based Approach

et al. 2020

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Ground point filtering of the airborne laser scanning (ALS) returns is crucial to derive digital terrain models (DTMs) and to perform ALS-based forest inventories. However, the filtering calibration requires considerable knowledge from users, who normally perform it by trial and error without knowing the impacts of the calibration on the produced DTM and the forest attribute estimation. Therefore, this work aims at calibrating four popular filtering algorithms and assessing their impact on the quality of the DTM and the estimation of forest attributes through the area-based approach. The analyzed filters were the progressive triangulated irregular network (PTIN), weighted linear least-squares interpolation (WLS) multiscale curvature classification (MCC), and the progressive morphological filter (PMF). The calibration was established by the vertical DTM accuracy, the root mean squared error (RMSE) using 3240 high-accuracy ground control points. The calibrated parameter sets were compared to the default ones regarding the quality of the estimation of the plot growing stock volume and the dominant height through multiple linear regression. The calibrated parameters allowed for producing DTM with RMSE varying from 0.25 to 0.26 m, against a variation from 0.26 to 0.30 m for the default parameters. The PTIN was the least affected by the calibration, while the WLS was the most affected. Compared to the default parameter sets, the calibrated sets resulted in dominant height equations with comparable accuracies for the PTIN, while WLS, MCC, and PFM reduced the models’ RMSE by 6.5% to 10.6%. The calibration of PTIN and MCC did not affect the volume estimation accuracy, whereas calibrated WLS and PMF reduced the RMSE by 3.4% to 7.9%. The filter calibration improved the DTM quality for all filters and, excepting PTIN, the filters increased the quality of forest attribute estimation, especially in the case of dominant height.

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Section: The Progressive Morphological Filter (Pmf)mentioning

confidence: 99%

Impact of Calibrating Filtering Algorithms on the Quality of LiDAR-Derived DTM and on Forest Attribute Estimation through Area-Based Approach

et al. 2020

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“…The locations of 496 palm tree tops were derived using structure-from-motion photogrammetry in a previous study conducted on this site 15 . Median NDVI and CHL values were extracted within a 2.5 m buffer around each point, the buffer size was chosen to minimise the impact of overlapping palm fronds or visible ground between palms.…”

Section: Per-palm Ndvi and Chl Valuesmentioning

confidence: 99%

Investigating impacts of calibration methodology and irradiance variations on lightweight drone-based sensor derived surface reflectance products

Fawcett

Anderson

2019

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI

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The miniaturisation of multispectral sensors in recent years have resulted in a proliferation of applications particularly in vegetation-focused studies using lightweight drones. Multi-camera arrays (MCAs), capable of capturing information over different wavelength intervals using separate cameras with specific band-pass filters, are now commonplace in this field. However, data from MCAs require a considerable amount of geometric and radiometric corrections if high quality reflectance products are to be delivered. Some aspects of this workflow can be handled by commercial software packages (e.g. Pix4D and Agisoft Metashape), using black box algorithms, however radiometric uncertainties within products are not reported to the end-user by the software. We present the results of two experiments using a low-cost MCA complete with irradiance sensor (Parrot Sequoia), which set out to assess the accuracy and consistency of hemispherical-conical surface reflectance factors from MCA data. Using reference panels in the field, we found that the empirical line method (ELM) generated the smallest RMSEs (0.0037) when compared to simplified single-panel based workflows; while for the latter there was little difference between using a calibrated Spectralon® panel or grey card imaged prior to the flight (0.0215 vs 0.0154 average over the four bands). Errors for a vegetated target within the survey flight were larger and comparable for all cases. Furthermore, a study on median vegetation index values for single vegetation canopies showed that illumination correction using irradiance data still yields significant differences in resulting values between two acquisitions during changing direct and diffuse irradiance conditions. We therefore highlight the importance of critical assessment prior to integrating drone derived MCA-measured reflectance factors into further geospatial workflows.

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“…UAVs are cost-effective tools to generate inventories for improved plantation management. Fawcett et al (2019) used lightweight UAVs to capture data on 2, 7 and 10 years old oil palm trees at peat soil sites in Sarawak, Malaysia. Canopy height models were evaluated for automated palm identification using a local height maxima technique.…”

Section: Age Estimationmentioning

confidence: 99%

Preface

Kanniah

Cracknell

2019

International Journal of Remote Sensing

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Unmanned aerial vehicle (UAV) derived structure-from-motion photogrammetry point clouds for oil palm (Elaeis guineensis) canopy segmentation and height estimation

Abstract: (2019) Unmanned aerial vehicle (UAV) derived structure-frommotion photogrammetry point clouds for oil palm (Elaeisguineensis) canopy segmentation and height estimation,

Cited by 59 publications

References 46 publications

Impact of Calibrating Filtering Algorithms on the Quality of LiDAR-Derived DTM and on Forest Attribute Estimation through Area-Based Approach

Impact of Calibrating Filtering Algorithms on the Quality of LiDAR-Derived DTM and on Forest Attribute Estimation through Area-Based Approach

Investigating impacts of calibration methodology and irradiance variations on lightweight drone-based sensor derived surface reflectance products

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