A1 Refereed original research article in a scientific journal
Feasibility of Bi-Temporal Airborne Laser Scanning Data in Detecting Species-Specific Individual Tree Crown Growth of Boreal Forests
Authors: Poorazimy, Maryam; Ronoud, Ghasem; Yu, Xiaowei; Luoma, Ville; Hyyppä, Juha; Saarinen, Ninni; Kankare, Ville; Vastaranta, Mikko
Publisher: MDPI
Publishing place: BASEL
Publication year: 2022
Journal: Remote Sensing
Journal name in source: REMOTE SENSING
Journal acronym: REMOTE SENS-BASEL
Article number: 4845
Volume: 14
Issue: 19
Number of pages: 17
eISSN: 2072-4292
DOI: https://doi.org/10.3390/rs14194845(external)
Abstract
The tree crown, with its functionality of assimilation, respiration, and transpiration, is a key forest ecosystem structure, resulting in high demand for characterizing tree crown structure and growth on a spatiotemporal scale. Airborne laser scanning (ALS) was found to be useful in measuring the structural properties associated with individual tree crowns. However, established ALS-assisted monitoring frameworks are still limited. The main objective of this study was to investigate the feasibility of detecting species-specific individual tree crown growth by means of airborne laser scanning (ALS) measurements in 2009 (T1) and 2014 (T2). Our study was conducted in southern Finland over 91 sample plots with a size of 32 x 32 m. The ALS crown metrics of width (WD), projection area (A(2D)), volume (V), and surface area (A(3D)) were derived for species-specific individually matched trees in T1 and T2. The Scots pine (Pinus sylvestris), Norway spruce (Picea abies (L.) H. Karst), and birch (Betula sp.) were the three species groups that studied. We found a high capability of bi-temporal ALS measurements in the detection of species-specific crown growth (Delta), especially for the 3D crown metrics of V and A(3D), with Cohen's D values of 1.09-1.46 (p-value < 0.0001). Scots pine was observed to have the highest relative crown growth (r Delta) and showed statistically significant differences with Norway spruce and birch in terms of r Delta WD, r Delta A(2D), r Delta V, and r Delta A(3D) at a 95% confidence interval. Meanwhile, birch and Norway spruce had no statistically significant differences in r Delta WD, r Delta V, and r Delta A(3D) (p-value < 0.0001). However, the amount of r Delta variability that could be explained by the species was only 2-5%. This revealed the complex nature of growth controlled by many biotic and abiotic factors other than species. Our results address the great potential of ALS data in crown growth detection that can be used for growth studies at large scales.
The tree crown, with its functionality of assimilation, respiration, and transpiration, is a key forest ecosystem structure, resulting in high demand for characterizing tree crown structure and growth on a spatiotemporal scale. Airborne laser scanning (ALS) was found to be useful in measuring the structural properties associated with individual tree crowns. However, established ALS-assisted monitoring frameworks are still limited. The main objective of this study was to investigate the feasibility of detecting species-specific individual tree crown growth by means of airborne laser scanning (ALS) measurements in 2009 (T1) and 2014 (T2). Our study was conducted in southern Finland over 91 sample plots with a size of 32 x 32 m. The ALS crown metrics of width (WD), projection area (A(2D)), volume (V), and surface area (A(3D)) were derived for species-specific individually matched trees in T1 and T2. The Scots pine (Pinus sylvestris), Norway spruce (Picea abies (L.) H. Karst), and birch (Betula sp.) were the three species groups that studied. We found a high capability of bi-temporal ALS measurements in the detection of species-specific crown growth (Delta), especially for the 3D crown metrics of V and A(3D), with Cohen's D values of 1.09-1.46 (p-value < 0.0001). Scots pine was observed to have the highest relative crown growth (r Delta) and showed statistically significant differences with Norway spruce and birch in terms of r Delta WD, r Delta A(2D), r Delta V, and r Delta A(3D) at a 95% confidence interval. Meanwhile, birch and Norway spruce had no statistically significant differences in r Delta WD, r Delta V, and r Delta A(3D) (p-value < 0.0001). However, the amount of r Delta variability that could be explained by the species was only 2-5%. This revealed the complex nature of growth controlled by many biotic and abiotic factors other than species. Our results address the great potential of ALS data in crown growth detection that can be used for growth studies at large scales.
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