Prediction of leaf area index using thermal infrared data acquired by UAS over a mixed temperate forest

Stobbelaar, Philip; Neinavaz, Elnaz; Nyktas, Panagiotis

doi:10.1016/j.jag.2022.103049

Cited by 1 publication

(1 citation statement)

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“…However, in previous research, the echo intensity needed to be corrected, and in mountainous areas with a high slope, the echo intensity was greatly affected by distance and incidence angle, resulting in a decrease in the estimation accuracy of LAI. In 2022, Stobbelaar et al [40] inverted the LAI of mixed broadleaf-conifer forest based on seven vegetation indices and compared it with the LAI value estimated using surface reflectance, proving that the prediction accuracy of modeling with additional variables was higher. However, due to the heterogeneity of leaf characteristics and growth rhythm among different tree species [41], it was difficult to avoid the influence of the vegetation's canopy size, diameter at breast height (DBH) and tree height parameters, resulting in large fluctuations in the estimated results of LAIe.…”

Section: Introductionmentioning

confidence: 99%

A LiDAR-Driven Effective Leaf Area Index Inversion Method of Urban Forests in Northeast China

Zhai,

Ding,

Ren

et al. 2023

Forests

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Leaf area index (LAI) stands as a pivotal parameter for the quantitative assessment of vegetation growth dynamics, and the rapid acquisition of the effective leaf area index (LAIe) in different scales is crucial for forest ecological monitoring. In this study, forest structure parameters were derived from fusion point cloud data obtained through Airborne Laser Scanning and Terrestrial Laser Scanning in three coniferous forests. The influence of point diameter on the extraction of different forest structure parameters was examined, and an in-depth analysis of the correlations between these parameters and measured LAIe was undertaken. The LAIe inversion model was constructed, and its performance for different forest types was studied. The results show that the precision of the extracted forest structure parameters was highest when the point diameter was set to 0.1 cm. Among the 10 forest structure parameters, internal canopy structures such as canopy openness (CO), gap fraction (GF) and canopy closure (CC) were significantly correlated with measured LAIe (p < 0.01), and the correlations between different forest types were significantly different. In addition, the multiparameter LAIe inversion model was able to distinguish forest type and thus better stimulate measured LAIe; also, it appeared closer to the 1:1 relationship line than the voxel model. This study made up for the inefficiency of LAIe measurement with optical instruments and the inaccuracy of passive remote sensing measurement and proved the possibility of LAIe extraction at a large scale via LiDAR in the future.

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Section: Introductionmentioning

confidence: 99%