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Article

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

by
Diogo N. Cosenza
1,*,
Luísa Gomes Pereira
2,3,
Juan Guerra-Hernández
1,4,
Adrián Pascual
1,
Paula Soares
1 and
Margarida Tomé
1
1
Forest Research Centre, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisboa, Portugal
2
Águeda School of Technology and Management (ESTGA), Aveiro University, Apartado 473, 3754-909 Águeda, Portugal
3
Centre for Research in Geospatial Science (CICGE), Porto University, 4099-002 Porto, Portugal
4
3edata, Centro de Iniciativas Empresariais, Fundación CEL, 27004 Lugo, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 918; https://doi.org/10.3390/rs12060918
Submission received: 29 January 2020 / Revised: 4 March 2020 / Accepted: 10 March 2020 / Published: 12 March 2020
(This article belongs to the Special Issue LiDAR Remote Sensing of Forest Resources and Wildland Fires)

Abstract

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.
Keywords: point classification; ALS; forest modeling point classification; ALS; forest modeling
Graphical Abstract

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MDPI and ACS Style

Cosenza, D.N.; Gomes Pereira, L.; Guerra-Hernández, J.; Pascual, A.; Soares, P.; Tomé, M. Impact of Calibrating Filtering Algorithms on the Quality of LiDAR-Derived DTM and on Forest Attribute Estimation through Area-Based Approach. Remote Sens. 2020, 12, 918. https://doi.org/10.3390/rs12060918

AMA Style

Cosenza DN, Gomes Pereira L, Guerra-Hernández J, Pascual A, Soares P, Tomé M. Impact of Calibrating Filtering Algorithms on the Quality of LiDAR-Derived DTM and on Forest Attribute Estimation through Area-Based Approach. Remote Sensing. 2020; 12(6):918. https://doi.org/10.3390/rs12060918

Chicago/Turabian Style

Cosenza, Diogo N., Luísa Gomes Pereira, Juan Guerra-Hernández, Adrián Pascual, Paula Soares, and Margarida Tomé. 2020. "Impact of Calibrating Filtering Algorithms on the Quality of LiDAR-Derived DTM and on Forest Attribute Estimation through Area-Based Approach" Remote Sensing 12, no. 6: 918. https://doi.org/10.3390/rs12060918

APA Style

Cosenza, D. N., Gomes Pereira, L., Guerra-Hernández, J., Pascual, A., Soares, P., & Tomé, M. (2020). Impact of Calibrating Filtering Algorithms on the Quality of LiDAR-Derived DTM and on Forest Attribute Estimation through Area-Based Approach. Remote Sensing, 12(6), 918. https://doi.org/10.3390/rs12060918

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