Global Leaf Area Index Research over the Past 75 Years: A Comprehensive Review and Bibliometric Analysis

Ma, Jun; Zhang, Jianpeng; Wang, Jinliang; Хромых, В. В.; Li, Jie; Zhong, Xuzheng

doi:10.3390/su15043072

Cited by 2 publications

(1 citation statement)

References 167 publications

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“…It is defined as half of the total leaf area on the ground at the unit level [1]. It is closely related to vegetation photosynthesis [2] and the carbon cycle [3] and is a key parameter in quantitatively describing the growth and development characteristics of forest leaves [4]. LAI provides an effective method for quantitative growth analysis of plant communities and has been widely applied to crop yield estimation [5], the breeding of improved varieties [6] and forest productivity comparation at the landscape level [7].…”

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%

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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Model for Inverting the Leaf Area Index of Green Plums by Integrating IoT Environmental Monitoring Data and Leaf Relative Content of Chlorophyll Values

Yu,

Tong,

Huang

et al. 2024

Agriculture

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The quantitative inversion of the leaf area index (LAI) of green plum trees is crucial for orchard field management and yield prediction. The data on the relative content of chlorophyll (SPAD) in leaves and environmental data from orchards show a significant correlation with LAI. Effectively integrating these two data types for LAI inversion is important to explore. This study proposes a multi−source decision fusion LAI inversion model for green plums based on their adjusted determination coefficient (MDF−ADRS). First, three statistical methods—Pearson, Spearman rank, and Kendall rank correlation analyses—were used to measure the linear relationships between variables, and the six environmental factors most highly correlated with LAI were selected from the orchard’s environmental data. Then, using multivariate statistical analysis methods, LAI inversion models based on environmental feature factors (EFs−PM) and SPAD (SPAD−PM) were established. Finally, a weight optimization allocation strategy was employed to achieve a multi−source decision fusion LAI inversion model for green plums. This strategy adaptively allocates weights based on the predictive performance of each data source. Unlike traditional models that rely on fixed weights or a single data source, this approach allows the model to increase the influence of a key data source when its predictive strength is high and reduce noise interference when it is weaker. This dynamic adjustment not only enhances the model’s robustness under varying environmental conditions but also effectively mitigates potential biases when a particular data source becomes temporarily unreliable. Our experimental results show that the MDF−ADRS model achieves an R2 of 0.88 and an RMSE of 0.39 in the validation set, outperforming other fusion methods. Compared to the EFs−PM and SPAD−PM models, the R2 increased by 0.19 and 0.26, respectively, and the RMSE decreased by 0.16 and 0.22. This model effectively integrates multiple sources of data from green plum orchards, enabling rapid inversion and improving the accuracy of green plum LAI estimation, providing a technical reference for monitoring the growth and managing the production of green plums.

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Global Leaf Area Index Research over the Past 75 Years: A Comprehensive Review and Bibliometric Analysis

Cited by 2 publications

References 167 publications

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

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

Model for Inverting the Leaf Area Index of Green Plums by Integrating IoT Environmental Monitoring Data and Leaf Relative Content of Chlorophyll Values

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