Harmonizing multi-temporal airborne laser scanning point clouds to derive periodic annual height increments in temperate mixedwood forests

Abstract

When combining multi-temporal airborne laser scanning (ALS) data sets, forest height growth assessments can be compro-mised due to variations in ALS acquisitions. Herein, we demonstrate the importance of assessing and harmonizing the vertical alignment of multi-temporal ALS data sets used for height growth calculations. Using four ALS acquisitions (2005–2018) in a temperate mixedwood forest, we developed an ALS data harmonization approach and quantified the impact of the harmonization on derived height periodic annual increment (PAI), comparing the ALS-derived PAI to PAI derived from non-harmonized ALS data sets and field measurements. We found significant differences in PAI derived from harmonized and non-harmonized data, and these differences were greater for shorter growth intervals. Data harmonization resulted in a consistent PAI series that reduced uncertainties associated with the different ALS acquisitions. Although overall there was a strong relationship between field and ALS height measures (R2 ≥ 0.88), we found a weak relationship between the field-and ALS-derived PAI (R2 = 0.12). We identified systematic errors in field-based tree height measures in plots with complex crowns, tall trees, and restricted visibility. We demonstrate the need for harmonizing multi-temporal ALS data sets for the generation of PAI and, likewise, highlight the need of carefully scrutinize field-measured heights and associated increments.