Estimating plot volume using lidar height and intensity distributional parameters

Doo Ahn Kwak, Guishan Cui, Woo-Kyun Lee, Hyun Kook Cho, Seong Woo Jeon, Seung Ho Lee

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Abstract

This study explored the feasibility of height distributional metrics and intensity values extracted from low-density airborne light detection and ranging (lidar) data to estimate plot volumes in dense Korean pine (Pinus koraiensis) plots. Multiple linear regression analyses were performed using lidar height and intensity distributional metrics. The candidate variables for predicting plot volume were evaluated using three data sets: total, canopy, and integrated lidar height and intensity metrics. All intensities of lidar returns used were corrected by the reference distance. Regression models were developed using each data set, and the first criterion used to select the best models was the corrected Akaike Information Criterion (AICc). The use of three data sets was statistically significant at R2 = 0.75 (RMSE = 52.17 m3 ha-1), R2 = 0.84 (RMSE = 45.24 m3 ha-1), and R2 = 0.91 (RMSE = 31.48 m3 ha-1) for total, canopy, and integrated lidar distributional metrics, respectively. Among the three data sets, the integrated lidar metrics-derived model showed the best performance for estimating plot volumes, improving errors up to 42% when compared to the other two data sets. This is attributed to supplementing variables weighted and biased to upper limits in dense plots with more statistical variables that explain the lower limits. In all data sets, intensity metrics such as skewness, kurtosis, standard deviation, minimum, and standard error were employed as explanatory variables. The use of intensity variables improved the accuracy of volume estimation in dense forests compared to prior research. Correction of the intensity values contributed up to a maximum of 58% improvement in volume estimation when compared to the use of uncorrected intensity values (R2 = 0.78, R2 = 0.53, and R2 = 0.63 for total, canopy, and integrated lidar distributional metrics, respectively). It is clear that the correction of intensity values is an essential step for the estimation of forest volume.

Original languageEnglish
Pages (from-to)4601-4629
Number of pages29
JournalInternational Journal of Remote Sensing
Volume35
Issue number13
DOIs
Publication statusPublished - 2014 Jan 1

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canopy
Akaike information criterion
skewness
detection
parameter

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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Estimating plot volume using lidar height and intensity distributional parameters. / Kwak, Doo Ahn; Cui, Guishan; Lee, Woo-Kyun; Cho, Hyun Kook; Jeon, Seong Woo; Lee, Seung Ho.

In: International Journal of Remote Sensing, Vol. 35, No. 13, 01.01.2014, p. 4601-4629.

Research output: Contribution to journalArticle

Kwak, Doo Ahn ; Cui, Guishan ; Lee, Woo-Kyun ; Cho, Hyun Kook ; Jeon, Seong Woo ; Lee, Seung Ho. / Estimating plot volume using lidar height and intensity distributional parameters. In: International Journal of Remote Sensing. 2014 ; Vol. 35, No. 13. pp. 4601-4629.
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abstract = "This study explored the feasibility of height distributional metrics and intensity values extracted from low-density airborne light detection and ranging (lidar) data to estimate plot volumes in dense Korean pine (Pinus koraiensis) plots. Multiple linear regression analyses were performed using lidar height and intensity distributional metrics. The candidate variables for predicting plot volume were evaluated using three data sets: total, canopy, and integrated lidar height and intensity metrics. All intensities of lidar returns used were corrected by the reference distance. Regression models were developed using each data set, and the first criterion used to select the best models was the corrected Akaike Information Criterion (AICc). The use of three data sets was statistically significant at R2 = 0.75 (RMSE = 52.17 m3 ha-1), R2 = 0.84 (RMSE = 45.24 m3 ha-1), and R2 = 0.91 (RMSE = 31.48 m3 ha-1) for total, canopy, and integrated lidar distributional metrics, respectively. Among the three data sets, the integrated lidar metrics-derived model showed the best performance for estimating plot volumes, improving errors up to 42{\%} when compared to the other two data sets. This is attributed to supplementing variables weighted and biased to upper limits in dense plots with more statistical variables that explain the lower limits. In all data sets, intensity metrics such as skewness, kurtosis, standard deviation, minimum, and standard error were employed as explanatory variables. The use of intensity variables improved the accuracy of volume estimation in dense forests compared to prior research. Correction of the intensity values contributed up to a maximum of 58{\%} improvement in volume estimation when compared to the use of uncorrected intensity values (R2 = 0.78, R2 = 0.53, and R2 = 0.63 for total, canopy, and integrated lidar distributional metrics, respectively). It is clear that the correction of intensity values is an essential step for the estimation of forest volume.",
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