Luo Tian

Gap fraction ( p (θ)) and extinction coefficient ( k ) are critical variables to predict canopy structure parameters, such as the leaf area index (LAI). The discrete airborne LiDAR scanner provides an entirely new method to estimate p (θ) and k of a canopy. However, it is widely recognized that estimating p (θ), derived from the laser penetration index (LPI), and estimating k , are strongly influenced by the characteristics of the LiDAR sensor, observation geometry, and canopy properties. This study conducted a global sensitivity analysis to comprehensively assess the influences of factors deriving from the above three sources on p (θ) and k , using the point cloud data simulated by the computer simulation model. Also, the applicability of point cloud for estimating p (θ) and for estimating k using the normal vector reconstruction method under different conditions was investigated. Results showed that the influence of explored factors on different LPIs of the canopy is dominated by some single factor, but the specific factors are LPI- and LAI-dependent. In contrast, the influences from explored factors on k showed an identical pattern across different support region sizes and leaf angular distribution types. The retrieved k was influenced by pulse density-related factors. The estimated p (θ) accuracy varied with the form of LPI and the range of LAI. The capability of successfully estimating k relied on the leaf angular distribution. The estimated k was influenced by the inherent target selection effect of LiDAR return, which requires further investigation.

The leaf area index (LAI) is a vital parameter for quantifying the material and energy exchange between terrestrial ecosystems and the atmosphere. The Global Ecosystem Dynamics Investigation (GEDI), with its mission to produce a near-global map of forest structure, provides a product of the effective leaf area index (referred to as GEDI LAIe). However, it is unclear about the performance of GEDI LAIe across different temperate forest types and the degree of factors influencing GEDI LAIe performance. This study assessed the accuracy of GEDI LAIe in temperate forests and quantifies the effects of various factors, such as the difference of gap fraction (DGF) between GEDI and discrete point cloud Lidar of the National Ecological Observatory Network (NEON), sensor system parameters, and characteristics of the canopy, topography, and soil. The reference data for the LAIe assessment were derived from the NEON discrete point cloud Lidar, referred to as NEON Lidar LAIe, covering 12 forest types across 22 sites in the Continental United States (the CONUS). Results showed that GEDI underestimated LAIe (Bias: −0.56 m²/m²), with values of the mean absolute error (MAE), root mean square error (RMSE), percent bias (%Bias), and percent RMSE (%RMSE) of 0.70 m²/m², 0.89 m²/m², −0.20, and 0.31, respectively. Among forest types, the underestimation of GEDI LAIe in broadleaf forests and mixed forests was generally greater than that in coniferous forests, which showed a moderate error (%RMSE: 0.33~0.52). Factor analysis indicated that multiple factors explained 52% variance of the GEDI LAIe error, among which the DGF contributed the most with a relative importance of 49.82%, followed by characteristics of canopy and soil with a relative importance of 23.20% and 16.18%, respectively. The DGF was a key pivot for GEDI LAIe error; that is, other factors indirectly influence the GEDI LAIe error by affecting the DGF first. Our findings demonstrated that the GEDI LAIe product has good performance, and the factor analysis is expected to shed some light on further improvements in GEDI LAIe estimation.

The leaf area index (LAI) is an essential input parameter for quantitatively studying the energy and mass balance in soil-vegetation-atmosphere transfer systems. As an active remote sensing technology, light detection and ranging (LiDAR) provides a new method to describe forest canopy LAI. This paper reviewed the primary LAI retrieval methods using point cloud data (PCD) obtained by discrete airborne LiDAR scanner (DALS), its validation scheme, and its limitations. There are two types of LAI retrieval methods based on DALS PCD, i.e., the empirical regression and the gap fraction (GF) model. In the empirical model, tree height-related variables, LiDAR penetration indexes (LPIs), and canopy cover are the most widely used proxy variables. The height-related proxies are used most frequently; however, the LPIs proved the most efficient proxy. The GF model based on the Beer-Lambert law has been proven useful to estimate LAI; however, the suitability of LPIs is site-, tree species-, and LiDAR system-dependent. In the local validation in previous studies, poor scalability of both empirical and GF models in time, space, and across different DALS systems was observed, which means that field measurements are still needed to calibrate both types of models. The method to correct the impact from the clumping effect and woody material using DALS PCD and the saturation effect for both empirical and GF models still needs further exploration. Of most importance, further work is desired to emphasize assessing the transferability of published methods to new geographic contexts, different DALS sensors, and survey characteristics, based on figuring out the influence of each factor on the LAI retrieval process using DALS PCD. In addition, from a methodological perspective, taking advantage of DALS PCD in characterizing the 3D structure of the canopy, making full use of the ability of machine learning methods in the fusion of multisource data, developing a spatiotemporal scalable model of canopy structure parameters including LAI, and using multisource and heterogeneous data are promising areas of research.

Accurate and continuous monitoring of leaf area index (LAI), a widely-used vegetation structural parameter, is crucial to characterize crop growth conditions and forecast crop yield. Meanwhile, advancements in collecting field LAI measurements have provided strong support for validating remote-sensing-derived LAI. This paper evaluates the performance of LAI retrieval from multi-source, remotely sensed data through comparisons with continuous field LAI measurements. Firstly, field LAI was measured continuously over periods of time in 2018 and 2019 using LAINet, a continuous LAI measurement system deployed using wireless sensor network (WSN) technology, over an agricultural region located at the Heihe watershed at northwestern China. Then, cloud-free images from optical satellite sensors, including Landsat 7 the Enhanced Thematic Mapper Plus (ETM+), Landsat 8 the Operational Land Imager (OLI), and Sentinel-2A/B Multispectral Instrument (MSI), were collected to derive LAI through inversion of the PROSAIL radiation transfer model using a look-up-table (LUT) approach. Finally, field LAI data were used to validate the multi-temporal LAI retrieved from remote-sensing data acquired by different satellite sensors. The results indicate that good accuracy was obtained using different inversion strategies for each sensor, while Green Chlorophyll Index (CIgreen) and a combination of three red-edge bands perform better for Landsat 7/8 and Sentinel-2 LAI inversion, respectively. Furthermore, the estimated LAI has good consistency with in situ measurements at vegetative stage (coefficient of determination R² = 0.74, and root mean square error RMSE = 0.53 m² m⁻²). At the reproductive stage, a significant underestimation was found (R² = 0.41, and 0.89 m² m⁻² in terms of RMSE). This study suggests that time-series LAI can be retrieved from multi-source satellite data through model inversion, and the LAINet instrument could be used as a low-cost tool to provide continuous field LAI measurements to support LAI retrieval.

Candida albicans is an opportunistic human pathogen, and its pathogenicity is associated with hyphal formation. Previous studies have shown that at neutral-to-alkaline pH, hyphal growth is dependent on the Rim101 pathway whose activation requires Snf7, a member of the ESCRT system. In this work, we described the purification and characterization of the C. albicans Vps4, an AAA ATPase required for recycling of the ESCRTs. Its role on hyphal growth has been investigated. Our data suggest deletion of Vps4 decreases overall hyphal growth at pH 7 and increases the growth of multiple hyphae induced by serum, which indicates that the ESCRTs may make a Rim101-independent contribution to hyphal growth. Furthermore, DBeQ, an inhibitor of the AAA ATPase p97, was shown to inhibit the ATPase activity of Vps4 with an IC₅₀ of about 11.5 μM. To a less degree, it also inhibits hyphal growth. Our work may provide a new strategy to control C. albicans infection.