Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Raising the Bar: Height Threshold and Grid Resolution Influence Repeatability of Crown Closure Estimation by Airborne Laser Surveys

Version 1 : Received: 30 December 2020 / Approved: 31 December 2020 / Online: 31 December 2020 (11:55:01 CET)

How to cite: Racine, E.B.; Leboeuf, A.; Bégin, J. Raising the Bar: Height Threshold and Grid Resolution Influence Repeatability of Crown Closure Estimation by Airborne Laser Surveys. Preprints 2020, 2020120792 (doi: 10.20944/preprints202012.0792.v1). Racine, E.B.; Leboeuf, A.; Bégin, J. Raising the Bar: Height Threshold and Grid Resolution Influence Repeatability of Crown Closure Estimation by Airborne Laser Surveys. Preprints 2020, 2020120792 (doi: 10.20944/preprints202012.0792.v1).

Abstract

Monitoring crown closure evolution using multi-temporal Light Detection and Ranging (LiDAR) surveys is a method that we expect to be increasingly adopted given the availability of LiDAR sensors and the accumulating survey archives. However, little attention was devoted to comparing crown closure estimates from independent surveys. Although survey parameters cannot be modified after the data collection, we speculate that the error associated to crown closure estimates comparison can be reduced by selecting optimal post-survey parameters. In this study, we compared crown closure estimates of three airborne LiDAR surveys from 2018 (40 pt/m²) used as a reference, and two lower-density surveys from 2016 (4.5 pt/m²) and 2018 (2 pt/m²). We studied the effect of the height threshold used to separate canopy points and the grid resolution, using skewness and variance of lagged difference of crown closure. Crown closure estimates using low height thresholds were more different across surveys, resulting in higher root mean squared error (RMSE), bias and more different variograms. Results show that optimal height threshold was 3 m and grid resolution was 25 m, although there was room for decision (RMSE of 7% and 5%, and bias of 4% and 0% for 2016 and 2018 low-density surveys).

Subject Areas

Boreal forest; Multi-temporal LiDAR remote sensing; Crown Closure Monitoring; Tree Density; Gap Fraction; Vertical canopy cover; Gap probability; Spatial Autocorrelation; Height threshold; Spatial grid resolution

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