Article
Environmental Sciences
Maxime Soma, Francois Pimont, Jean-Luc Dupuy
Summary: The need for fine scale description of vegetation structure is increasing as the importance of Leaf Area Density (LAD) as a critical parameter to understand ecosystem functioning is recognized. Terrestrial Laser Scanning (TLS) has shown great potential for retrieving foliage area at various scales, but measurements remain sensitive to factors like voxel size and heterogeneity in sampling. The study aimed at disentangling biases and errors in plot-scale measurements of LAD with TLS in a simulated vegetation scene, finding that no scenario was unbiased and that an intermediate voxel size of 0.5m was the best option for reasonable measurement errors.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Geochemistry & Geophysics
Maryia Halubok, Adam K. Kochanski, Rob Stoll, Brian Bailey
Summary: This study evaluated different methods for estimating the 3-D distribution of leaf area using LiDAR data. The Beer's law-based method consistently outperformed the PDF-based methods. The Beer's law approach showed decreasing errors with an increase in the number of voxel statistical samples and leaf area index, while heterogeneity increased errors in LAD inversion.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Agronomy
Tiangang Yin, Bruce D. Cook, Douglas C. Morton
Summary: In this study, a data processing workflow named PVlad was developed to estimate leaf area index (LAI) and voxel-based leaf area density (LAD) using apparent reflectance from airborne laser scanning (ALS) point clouds. This workflow reduces the need for field measurements and captures structural differences in forests of different ages and conditions.
AGRICULTURAL AND FOREST METEOROLOGY
(2022)
Article
Plant Sciences
Behrokh Nazeri, Melba M. Crawford, Mitchell R. Tuinstra
Summary: This study investigates the effectiveness of using LiDAR data combined with statistical and plant structure features, along with ground reference values, to estimate LAI for sorghum and maize at different times using wheeled vehicles and drones. Predictive models show R-2 results ranging from around 0.4 in the early season to 0.6 to 0.80 for sorghum and maize in more mature growth stages.
FRONTIERS IN PLANT SCIENCE
(2021)
Article
Geochemistry & Geophysics
Hailan Jiang, Shiyu Cheng, Guangjian Yan, Andres Kuusk, Ronghai Hu, Yiyi Tong, Xihan Mu, Donghui Xie, Wuming Zhang, Guoqing Zhou, Felix Morsdorf
Summary: The clumping effect significantly affects LAI estimation, with different types and shapes of canopies being impacted differently. Neglecting within-crown clumping can lead to considerable underestimation of LAI, highlighting the need for further research on this aspect to improve future LAI retrieval methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Yiru Ma, Qiang Zhang, Xiang Yi, Lulu Ma, Lifu Zhang, Changping Huang, Ze Zhang, Xin Lv
Summary: This study utilized a UAV equipped with a hyperspectral sensor to obtain hyperspectral images of cotton canopy and constructed an LAI monitoring model based on spectral reflectance and vegetation indices. The results showed that the model achieved the best performance after noise reduction and feature selection.
Article
Agronomy
Lijun Su, Wanghai Tao, Yan Sun, Yuyang Shan, Quanjiu Wang
Summary: This paper analyzes the relationship between Leaf Area Index (LAI) and crop biomass production and yields. The researchers established universal models for LAI and accurately predicted LAI changes in extremely arid grape-growing areas using various models. The Michaelis-Menten model and quadratic polynomial function were used to predict dynamic changes in grapevine LAI, biomass, yields, and harvest index. This study provides insights for improving water use efficiency and determining optimal irrigation quotas in grape cultivation.
Article
Remote Sensing
Jie Zhang, Jinyan Tian, Xiaojuan Li, Le Wang, Beibei Chen, Huili Gong, Rongguang Ni, Bingfeng Zhou, Cankun Yang
Summary: This study is the first to use photon counting LiDAR to inverse leaf area index (LAI), showing the feasibility and accuracy of this method. The results demonstrate satisfactory agreements between ICESat-2 derived LAI and MODIS/Sentinel-2 derived LAI, indicating the reliability of ICESat-2 and its advantages over MODIS in terms of LAI estimation.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Review
Construction & Building Technology
A. De Bock, B. Belmans, S. Vanlanduit, J. Blom, A. A. Alvarado-Alvarado, A. Audenaert
Summary: The leaf area index (LAI) is a crucial parameter in Vertical Greening Systems (VGS) that quantifies the total leaf area in the canopy and determines the co-benefits of VGS. However, there is limited understanding of the LAI parameter itself, its determination process, and the monitoring techniques for continuous LAI monitoring in VGS. This paper focuses on the LAI of VGS and its monitoring techniques, providing an overview of existing techniques and proposing guidelines for standardized LAI determination and reporting in VGS.
BUILDING AND ENVIRONMENT
(2023)
Article
Environmental Sciences
Qiaosi Li, Frankie Kwan Kit Wong, Tung Fung, Luke A. Brown, Jadunandan Dash
Summary: Remote sensing technology is an effective method for LAI estimation, especially for inaccessible areas like mangrove forests. This study explored the potential of Sentinel-2 imagery, airborne hyperspectral imagery, and LiDAR data for estimating the LAI of overstory and understory in a multi-layered mangrove stand. The results showed that the models for overstory estimation performed better than understory estimation. A red-edge VI derived from hyperspectral imagery delivered the highest accuracy for overstory estimation, while the combination of LiDAR metrics and Sentinel-2 VIs performed best for understory estimation. It was found that HSI was less affected by the understory, and LiDAR data provided separate information for upper and lower canopy, reducing noise and improving understory estimation.
Article
Environmental Sciences
Yongkang Lai, Xihan Mu, Weihua Li, Jie Zou, Yuequn Bian, Kun Zhou, Ronghai Hu, Linyuan Li, Donghui Xie, Guangjian Yan
Summary: The clumping effect is the main issue causing heterogeneity in vegetation canopies and underestimation of LAI. A method using fractal dimension to correct for the clumping effect and improve the accuracy of LAI indirect measurements is proposed.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Environmental Sciences
Jinling Song, Xiao Zhu, Jianbo Qi, Yong Pang, Lei Yang, Lihong Yu
Summary: This study presents a new method for quantifying understory Leaf Area Index (LAI) in temperate forests by combining the advantages of point cloud and full-waveform LiDAR data. Through a series of steps including automatically determining height boundaries, deconvolution, waveform decomposition, and modification of LiDAR equations, the understory LAI was estimated successfully. Validation against ground-based measurements showed a good estimation with an R-2 of 0.54 and a root-mean-square error (RMSE) of 0.21.
Article
Engineering, Electrical & Electronic
Ameni Mkaouar, Abdelaziz Kallel, Zouhaier Ben Rabah, Thouraya Sahli Chahed
Summary: This study proposed a method based on TLS point cloud to jointly estimate foliage density and leaf angle distribution, utilizing direct/inverse radiative transfer modeling and shuffled complex evolution method. The estimated values are close to the actual values, demonstrating the effectiveness of the approach in forest canopy characterization.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Environmental Sciences
Beibei Shen, Lei Ding, Leichao Ma, Zhenwang Li, Alim Pulatov, Zheenbek Kulenbekov, Jiquan Chen, Saltanat Mambetova, Lulu Hou, Dawei Xu, Xu Wang, Xiaoping Xin
Summary: This study improved the LAI inversion model of Inner Mongolia grassland based on machine learning algorithms incorporating empirical knowledge. Normalized Difference Phenology Index was found to contribute the most to LAI estimation. Twelve LAI estimation models were built based on different input variables, and Random Forest Regression demonstrated higher prediction accuracy compared to other algorithms.
Article
Environmental Sciences
Wanyi Lin, Hua Yuan, Wenzong Dong, Shupeng Zhang, Shaofeng Liu, Nan Wei, Xingjie Lu, Zhongwang Wei, Ying Hu, Yongjiu Dai
Summary: This study reprocessed the MODIS LAI data and found that the reprocessed data performed better in validation against reference maps and had smoother and more consistent time series. Compared to C5, MODIS C6.1 LAI showed improvements in ground validation but little improvement in spatial and temporal continuity. Therefore, the reprocessed C6.1 data is recommended as a substitute for the reprocessed C5 data in land surface and climate modeling.