LAI-2000 Plant Canopy Analyzer Research Articles

Continuous Leaf Area Index (LAI) Observation in Forests: Validation, Application, and Improvement of LAI-NOS

The leaf area index (LAI) is one of the core parameters reflecting the growth status of vegetation. The continuous long-term observation of the LAI is key when assessing the dynamic changes in the energy exchange of ecosystems and the vegetation’s response indicators to climate change. The errors brought about by non-standard operations in manual LAI measurements hinder the further research utilization of this parameter. The long-term automatic LAI observation network is helpful in reducing errors from manual measurements. To further test the applicability of automatic LAI observation instruments in forest environments, this study carried out comparative validation research of the LAI-NOS (LAI automatic network observation system) at the Wanglang Mountain Ecological Remote Sensing Comprehensive Observation Station, China, comparing it with the results measured by the LAI-2200 Plant Canopy Analyzer (LI-COR, Lincoln, NE, USA), the LAI-probe handheld instrument, and a fisheye lens digital camera (DHP method). Instead of using the original “smoothest window” method, a new method, the “sunrise–sunset” method, is used to extract daily LAI-NOS LAI, and the corresponding confidence level is used to filter the data. The results of the data analysis indicate the following: LAI-NOS has a high data stability. The automatically acquired daily data between two consecutive days has a small deviation and significant correlations. Single-angle/multi-angle LAI measurement results of the LAI-NOS have good correlations with the LAI-2200 (R2 = 0.512/R2 = 0.652), the LAI-probe (R2 = 0.692/R2 = 0.619), and the DHP method (R2 = 0.501/R2 = 0.394). The daily LAI obtained from the improved method, when compared to the original method, both show the same vegetation growth trend. However, the improved method has a smaller dispersion. This study confirms the stability and accuracy of automatic observation instruments in mountainous forests, demonstrating the distinct advantages of automatic measurement instruments in the long-term ground observation of LAIs.

Estimation of Leaf Area Index in a Typical Northern Tropical Secondary Monsoon Rainforest by Different Indirect Methods

The leaf area index (LAI) is a crucial indicator for quantifying forest productivity and community ecological processes. Satellite remote sensing can achieve large-scale LAI monitoring, but it needs to be calibrated and validated according to the in situ measurements on the ground. In this study, we attempted to use different indirect methods to measure LAI in a tropical secondary forest. These methods included the LAI-2200 plant canopy analyzer (LAI-2200), Digital Hemispherical Photography (DHP), Tracing Radiation and Architecture of Canopies (TRAC), and Terrestrial Laser Scanning (TLS) (using single-station and multi-station measurements, respectively). Additionally, we tried to correct the measured LAI by obtaining indicators of woody components and clumping effects. The results showed that the LAI of this forest was large, with estimated values of 5.27 ± 1.16, 3.69 ± 0.74, 5.86 ± 1.09, 4.93 ± 1.33, and 3.87 ± 0.89 for LAI-2200, DHP, TRAC, TLS multi-station, and TLS single-station, respectively. There was a significant correlation between the different methods. LAI-2200 was significantly correlated with all other methods (p < 0.01), with the strongest correlation with DHP (r = 0.684). TRAC was significantly correlated with TLS single-station (p < 0.01, r = 0.283). TLS multi-station was significantly correlated with TLS single-station (p < 0.05, r = 0.266). With the multi-station measurement method, TLS could maximize the compensation for measurement bias due to the shadowing effects. In general, the clumping index of this forest was 0.94 ± 0.05, the woody-to-total area ratio was 3.23 ± 2.22%, and the total correction coefficient was 1.03 ± 0.07. After correction, the LAI estimates for all methods were slightly higher than before, but there was no significant difference among them. Based on the performance assessment of existing ground-based methods, we hope to enhance the inter-calibration between methods to improve their estimation accuracy under complex forest conditions and advance the validation of remote sensing inversion of the LAI. Moreover, this study also provided a practical reference to promote the application of LiDAR technology in tropical forests.

An Automated Hemispherical Scanner for Monitoring the Leaf Area Index of Forest Canopies

The leaf area index (LAI) is an important structural parameter of plant canopies used in terrestrial biosphere models. Optical methods are commonly used for measuring LAI due to their non-destructive nature, convenience, and rapidity. In the present study, a novel instrument, named the Automated Hemispherical Scanner (AHS), was developed to measure plant area index (PAI) for monitoring daily changes in LAI in forest ecosystems. In the AHS, an optical sensor driven by a pair of servomotors is used to observe hemispherical light transmission continuously at adjustable intervals, and a blue filter is used to reduce the multiple scattering effect of light on the measured transmission. A set of algorithms was developed to screen the direct radiation transmitted through the canopy and to compute the transmissions from the diffuse radiation at seven zenith (0–60) and seven azimuth (0–150) angles for calculating PAI. Field experiments were conducted to verify the reliability of the AHS in three forests of Northeast China against an existing instrument named the LAI-2200 Plant Canopy Analyzer. The PAI values obtained using the AHS agreed well (R2 = 0.927, root mean square error = 0.41) with those from the LAI-2200. Since both instruments use the same gap fraction theory for calculating the PAI from diffuse radiation transmissions obtained from multiple angles, the agreement of these two instruments means that the AHS can reliably measure the transmittance of diffuse radiation and the theory has been implemented correctly. Compared with LAI-2200, the AHS has the advantage of automated and continuous measurements, and therefore it is suitable for monitoring variations in PAI over extended periods, such as the whole growing season. Compared with widely used digital photographic techniques, the AHS also avoids the requirement of determining a suitable photographic exposure, which is often problematic in the field with variable sky conditions. With these advantages, the AHS could be deployed in forest growth monitoring networks.

Field Measurement of Effective Leaf Area Index using Optical Device in Vegetation Canopy

Leaf area index (LAI) is an essential canopy variable describing the amount of foliage in an ecosystem. The parameter serves as the interface between green components of plants and the atmosphere, and many physiological processes occur there, primarily photosynthetic uptake, respiration, and transpiration. LAI is also an input parameter for many models involving carbon, water, and the energy cycle. Moreover, ground-based in situ measurements serve as the calibration method for LAI obtained from remote sensing products. Therefore, straightforward indirect optical methods are necessary for making precise and rapid LAI estimates. The methodological approach, advantages, controversies, and future perspectives of the newly developed LP 110 optical device based on the relation between radiation transmitted through the vegetation canopy and canopy gaps were discussed in the protocol. Furthermore, the instrument was compared to the world standard LAI-2200 Plant Canopy Analyzer. The LP 110 enables more rapid and more straightforward processing of data acquired in the field, and it is more affordable than the Plant Canopy Analyzer. The new instrument is characterized by its ease of use for both above- and below-canopy readings due to its greater sensor sensitivity, in-built digital inclinometer, and automatic logging of readings at the correct position. Therefore, the hand-held LP 110 device is a suitable gadget for performing LAI estimation in forestry, ecology, horticulture, and agriculture based on the representative results. Moreover, the same device also enables the user to take accurate measurements of incident photosynthetically active radiation (PAR) intensity.

Velocity uncertainty quantification based on Riparian vegetation indices in open channels colonized by Phragmites australis

One of the main purposes of Ecohydraulics is to predict the effects of riparian vegetation on aquatic ecosystems within real water channels. The interaction between water flow and riparian plants significantly affects flow dynamics, hydraulic conveyance, and water quality of vegetated water bodies. This study aimed at quantifying analytically the uncertainty in flow average velocity estimations associated with the uncertainty of Leaf Area Index (LAI) of Phragmites australis (Cav.) Trin. ex Steudel covering a vegetated channel. The impacts of this species on the hydrodynamics of vegetated streams are far to be completely understood. The uncertainty in flow average velocity was assessed through the first-order second-moment statistical method, by comparing direct and indirect LAI measurements of mature Phragmites australis plants. Indirect LAI values were obtained using the LICOR® LAI-2000 Plant Canopy Analyzer device. The results of this study suggest that the uncertainties in flow average velocity estimations are comparable to those associated with experimental measurements of streamwise velocity components retrieved in real vegetated flows fully covered by mature Phragmites australis plants.

Error Analysis of LAI Measurements with LAI-2000 Due to Discrete View Angular Range Angles for Continuous Canopies

As a widely used ground-based optical instrument, the LAI-2000 or LAI-2200 plant canopy analyzer (PCA) (Li-Cor, Inc., Lincoln, NE) is designed to measure the plant effective leaf area index (Le) by measuring the canopy gap fraction at several limited or discrete view zenith angles (VZAs) (usually five VZAs: 7, 23, 38, 53, and 68°) based on Miller’s equation. Miller’s equation requires the probability of radiative transmission through the canopy to be measured over the hemisphere, i.e., VZAs in the range from 0 to 90°. However, the PCA view angle ranges are confined to several limited ranges or discrete sectors. The magnitude of the error produced by the discretization of VZAs in the leaf area index measurements remains difficult to determine. In this study, a theoretical deduction was first presented to definitely prove why the limited or discrete VZAs or ranges can affect the Le measured with the PCA, and the specific error caused by the limited or discrete VZAs was described quantitatively. The results show that: (1) the weight coefficient of the last PCA ring is the main cause of the error; (2) the error is closely related to the leaf inclination angles (IAs)—the Le measured with the PCA can be significantly overestimated for canopies with planophile IAs, whereas it can be underestimated for erectophile IAs; and (3) the error can be enhanced with the increment of the discrete degree of PCA rings or VZAs, such as using four or three PCA rings. Two corrections for the error are presented and validated in three crop canopies. Interestingly, although the leaf IA type cannot influence the Le calculated by Miller’s equation in the hemispheric space, it affects the Le measured with the PCA using the discrete form of Miller’s equation for several discrete VZAs.

Continuous estimation of leaf area index and the woody-to-total area ratio of two deciduous shrub canopies using fisheye webcams in a semiarid loessial region of China

Automatic and accurate measurements of the leaf area index (LAI) can provide information about key parameters of vegetation structure in an ecosystem. Little attention, however, has been paid to LAI dynamics for shrub canopies in both arid and semiarid regions compared to forests and crops. One of the main sources of error when measuring shrub LAI from the indirect method is the woody part. The influence of woody parts on LAI estimations can be expressed as the woody-to-total area ratio, α; there are also relatively few studies on α value of shrubs, especially the dynamic changes of α. This study used a new cost-effective tool, the fisheye webcam, to derive daily LAI and α in the fields of Caragana korshinskii and Salix psammophila on the Loess Plateau in China. The LAI data were compared with measurements acquired by an LAI-2200 Plant Canopy Analyzer and direct methods. The LAI-2200 data were consistent with the effective plant area index (PAIeff) estimated using upward-pointing webcams (UPWs) (R2 = 0.89, root mean square error (RMSE) = 0.21) but not downward-pointing webcams (DPWs) (R2 = 0.52, RMSE = 0.68). Shrub PAIeff derived from the DPWs was much smaller than PAIeff derived from the UPWs over the season (y = 0.48x–0.03, R2 = 0.79, RMSE = 0.85). LAI derived from the direct methods was strongly linearly correlated with the PAI minus WAI obtained from upward digital hemispherical photography (R2 = 0.94, RMSE = 0.62 for C. korshinskii and R2 = 0.99, RMSE = 0.31 for S. psammophila). This result indicates that the upward digital hemispherical photography for deriving PAI and WAI performs good, which can be utilized to calculate LAI accurately. The woody-to-total area ratio (α) of deciduous shrub canopies was a dynamic parameter that varied throughout the season, decreasing as the canopy leaves developed and increasing when they fell. α = 1 for the leaf-off season and < 0.1 when the foliage peaked. α differed little between the two shrub canopies (R2 = 0.95, RMSE = 0.07), and it could be used to convert PAI to LAI for C. korshinskii and S. psammophila. The UPWs demonstrated excellent potential for continuous LAI measurement for the two shrub canopies, but the DPWs should be further tested before use. The fisheye-webcam is inexpensive and can easily monitor LAI at field scale and can be used to evaluate LAI products derived from remote sensing.

Scale-dependent changes in the contributions of biotic and abiotic factors to leaf area index in a natural forest in northeast China

The leaf area index (LAI) is one of the most commonly used parameters for quantifying canopy structure and predicting vegetation growth. However, it remains unclear whether the relative contributions of biotic factors and abiotic factors to stand LAI are modulated by the study scale (or plot size). Additionally, whether tree size affects the contributions of biotic factors to the LAI is unknown. In this study, we measured LAI with a LAI-2200 plant canopy analyzer in subplots of three scales (i.e., 10 m × 10 m, 20 m × 20 m and 30 m × 30 m) in a 9 ha old-growth forest plot in northeast China. We also measured biotic factors for large trees (diameter at breast height, DBH > 9 cm) and small trees (DBH ≤ 9 cm) as well as abiotic factors (e.g., soil properties and light availability quantified by hillshade) in each subplot for three scales. With structural equation modeling (SEM), we found that an increase in the basal area of large trees directly reduced the stand LAI at all three scales and indirectly reduced the LAI by limiting the growth of small trees, but increased stand density (or basal area) for small trees increased the stand LAI. The increased species richness of small trees enhanced the LAI at 10 m × 10 m and 20 m × 20 m; in contrast, the increased species richness of large trees only significantly reduced the LAI at 20 m × 20 m. Increases in a soil property (total phosphorus) directly reduced the LAI at all three scales, but increased light increased the LAI. We also found that the contribution of large trees to LAI was higher at smaller scales but that of small trees increased with increasing scale; the contribution of soil properties to the LAI increased with increasing scale, but light made higher contributions at smaller scales. These results clearly suggest that the relative contributions of biotic factors and abiotic factors to stand LAI are scale-dependent and that tree size plays a key role in revealing the relationships between biotic factors and stand LAI in natural forests.

Automatic Proba-V Processor: TREX—Tool for Raster Data Exploration

The processing tool TREX, standing for ‘Tool for Raster data EXploration’ is presented and evaluated in the Biebrza wetlands in northeastern Poland. TREX was designed for the automatization of processing satellite data from the Proba-V satellite into maps of NDVI or LAI in any defined by the user projection, spatial resolution, or extent. The open source and access concept of TREX encourages the potential community of users to collaborate, develop, and integrate the tool with other satellite imagery and models. TREX reprojects, shifts, and resamples original data obtained from the Proba-V satellite to deliver reliable maps of NDVI and LAI. Validation of TREX in Biebrza wetlands resulted in correlations between 0.79 and 0.92 for NDVI data (measured with ASD Field Spec 4) and 0.92 for LAI data (measured with LiCOR—LAI-2000 Plant Canopy Analyzer).

Multi-temporal dataset of stand and canopy structural data in temperate and Mediterranean coppice forests

We provided long-term stand and canopy structural data from permanent monitoring plots representative of some most diffuse temperate and Mediterranean forests, under different coppice management regimes. Periodic inventories were performed in the surveyed plots since the 1970s. Annual litterfall production and its partitioning (leaf, woody, reproductive parts) and optical canopy measurements using the LAI-2000 Plant Canopy Analyzer were performed every year in fully equipped plots since the 1990s. These data can be used for evaluating the influence of coppice management in the stand and canopy structure, the parametrization of radiative transfer models that require accurate ground truth data, and the calibration of high to medium resolution remotely sensed data. Dataset access is at https://doi.org/10.17632/z8zm3ytkcx.2 . Associated metadata is available at https://agroenvgeo.data.inra.fr/geonetwork/srv/eng/catalog.search#/metadata/2bd2d77f-3cf8-43da-b1b5-9f8196dc017f .

Impact of leaf area index from various sources on estimating gross primary production in temperate forests using the JULES land surface model

The effects of various types of leaf area index (LAI) on simulations of stand scale gross primary production (GPP) in evergreen needleleaf (TCK) and deciduous broadleaf (TBK) temperate forests were analyzed using the Joint UK Land Environment Simulator (JULES) land surface model (LSM). LAI was observed during the period from 2015 to 2017 with an LAI-2200 plant canopy analyzer. Other LAIs included the enhanced vegetation index (EVI) of two satellites with different temporal and spatial resolutions (Landsat and MODIS), the LAI product of MODIS, and the JULES phenology model. These LAIs were compared with in situ observations of LAI, and their effects on GPP simulation were compared with simulated GPP using in situ observed LAI (GPPobs). Our results show that the JULES phenology model does not adequately express the LAI of either TCK or TBK forests. This misrepresentation was improved through simple modifications. LAI data from a remote-sensing EVI corresponded relatively well with the observations, whereas the MODIS LAI product, with a relatively low spatial resolution (500 m), could not discern the complex heterogeneity of a temperate forest and provided a 31% higher maximum value. Compared with GPPobs, the difference in annual GPP was smaller than in LAI, ranging from −3.48% to 6.0% in TCK and −6.52% to 10.9% in TBK. The differences in seasonal GPP estimates were up to 14.6% in spring for TCK and −17.4% in autumn for TBK. Our study identified limitations of the JULES leaf phenology model, and suggested modifications to reduce the differences in GPP estimation for temperate forests at the stand scale. In addition, we suggested using sufficiently high spatial resolution data, especially for LAI, as inadequately represented forest cover types resulted in seasonal GPP discrepancies.

Characterizing light across a strip shelterwood in a mixed conifer forest

A knowledge of how light varies within gaps is fundamental to understanding variation in survival and growth of regenerating trees. Simple indices based on a small number of instantaneous measurements by portable sensors or cameras are widely used for this purpose. But the effectiveness of these indices for estimating growing season transmittance is influenced by stand characteristics and local weather conditions. This study was conducted in two small (50 m × 150 m) clearings in a mixed stand of the interior cedar hemlock zone of southern British Columbia to evaluate several commonly used light estimation techniques, including: (1) hemispherical photography; (2) LAI-2000 plant canopy analyzer; and, (3) midday measurements of % photosynthetic photon flux density (hourly average PPFD) under sunny and overcast skies in a strip-shelterwood system. While diffuse indices from hemispherical methods (LAI-2000 and photograph) performed poorly, their direct and total indices (photograph) gave lower variability (lower rmse) and stronger correlations (higher r2) with growing season transmittance. Sky conditions under which light measurements were recorded by photosynthetically active radiation (PAR) sensors influenced the relationships between PAR sensor indices and growing season transmittance. For example, measuring light around midday on a sunny day provided stronger correlation with growing season transmittance than overcast day measurements. Moreover, rather than using one midday light measurement on a sunny or an overcast day, an average of two measurements increased r2 and reduced bias while measuring light three times on a sunny day (morning, noon and afternoon) gave the highest r2 and the lowest variability. Several indices also correlated strongly with monthly light levels, measured from May – September. Based on the finding that indices that accounted for direct beam contribution gave the best outcomes, we suggest that canopy conditions (degree of heterogeneity), stand orientation and gap sizes should be considered in selecting an instantaneous light index for studies in high latitude forests.

Landsat 8 Based Leaf Area Index Estimation in Loblolly Pine Plantations

Leaf area index (LAI) is an important biophysical parameter used to monitor, model, and manage loblolly pine plantations across the southeastern United States. Landsat provides forest scientists and managers the ability to obtain accurate and timely LAI estimates. The objective of this study was to investigate the relationship between loblolly pine LAI measured in situ (at both leaf area minimum and maximum through two growing seasons at two geographically disparate study areas) and vegetation indices calculated using data from Landsat 7 (ETM+) and Landsat 8 (OLI). Sub-objectives included examination of the impact of georegistration accuracy, comparison of top-of-atmosphere and surface reflectance, development of a new empirical model for the species and region, and comparison of the new empirical model with the current operational standard. Permanent plots for the collection of ground LAI measurements were established at two locations near Appomattox, Virginia and Tuscaloosa, Alabama in 2013 and 2014, respectively. Each plot is thirty by thirty meters in size and is located at least thirty meters from a stand boundary. Plot LAI measurements were collected twice a year using the LI-COR LAI-2200 Plant Canopy Analyzer. Ground measurements were used as dependent variables in ordinary least squares regressions with ETM+ and OLI-derived vegetation indices. We conclude that accurately-located ground LAI estimates at minimum and maximum LAI in loblolly pine stands can be combined and modeled with Landsat-derived vegetation indices using surface reflectance, particularly simple ratio (SR) and normalized difference moisture index (NDMI), across sites and sensors. The best resulting model (LAI = −0.00212 + 0.3329SR) appears not to saturate through an LAI of 5 and is an improvement over the current operational standard for loblolly pine monitoring, modeling, and management in this ecologically and economically important region.

Estimating Snow Depth and Leaf Area Index Based on UAV Digital Photogrammetry.

This study presents a novel approach in the application of Unmanned Aerial Vehicle (UAV) imaging for the conjoint assessment of the snow depth and winter leaf area index (LAI), a structural property of vegetation, affecting the snow accumulation and snowmelt. The snow depth estimation, based on a multi-temporal set of high-resolution digital surface models (DSMs) of snow-free and of snow-covered conditions, taken in a partially healthy to insect-induced Norway spruce forest and meadow coverage area within the Šumava National Park (Šumava NP) in the Czech Republic, was assessed over a winter season. The UAV-derived DSMs featured a resolution of 0.73–1.98 cm/pix. By subtracting the DSMs, the snow depth was determined and compared with manual snow probes taken at ground control point (GCP) positions, the root mean square error (RMSE) ranged between 0.08 m and 0.15 m. A comparative analysis of UAV-based snow depth with a denser network of arranged manual snow depth measurements yielded an RMSE between 0.16 m and 0.32 m. LAI assessment, crucial for correct interpretation of the snow depth distribution in forested areas, was based on downward-looking UAV images taken in the forest regime. To identify the canopy characteristics from downward-looking UAV images, the snow background was used instead of the sky fraction. Two conventional methods for the effective winter LAI retrieval, the LAI-2200 plant canopy analyzer, and digital hemispherical photography (DHP) were used as a reference. Apparent was the effect of canopy density and ground properties on the accuracy of DSMs assessment based on UAV imaging when compared to the field survey. The results of UAV-based LAI values provided estimates were comparable to values derived from the LAI-2200 plant canopy analyzer and DHP. Comparison with the conventional survey indicated that spring snow depth was overestimated, and spring LAI was underestimated by using UAV photogrammetry method. Since the snow depth and the LAI parameters are essential for snowpack studies, this combined method here will be of great value in the future to simplify snow depth and LAI assessment of snow dynamics.

An Approach for Indirect Determination of Leaf Area Index

Abstract. Leaf area index (LAI) is important in crop models, but determination of LAI can be challenging for crops and mixtures of crops. The purpose of this study was to determine LAI indirectly from ground cover (determined from photographic images) and canopy height using defined relationships based on crop coefficients. Images of plant canopies were obtained to estimate the green cover fraction. An equation was derived to determine LAI from ground cover and plant height, and the technique was tested on different crops. For three of the measurement dates, the data were compared to LAI measurements obtained with a LAI-2200 plant canopy analyzer. The trend was the same for the two LAI procedures, but there was some scatter. The photographic technique was adequate for LAI determination. Keywords: Ground cover, Plant canopy cover, Plant height.

Influence of automatic exposure on the estimation of seasonal variations in leaf area index measured by digital hemispherical photograph.

Automatic exposure is one of the important error sources during measurement of leaf area index (LAI) by digital hemispherical photography (DHP). This study was conducted in a mixed broadleaved-Korean pine (Pinus koraiensis) forest, a secondary birch (Betula platyphylla) forest, a Korean pine plantation and a Dahurian larch (Larix gmelinii) plantation in the Xiaoxing'an Mountains. LAI was measured using DHP and LAI-2200 plant canopy analyzer in the middle of June to September. We compared LAI values measured through these two methods, and then tested whether the forest type and study period had a significant influence on the correlations between the measured values of those two methods. We constructed empirical models for correcting the errors caused by automatic exposure for LAI values measured through DHP at different study periods in different forest types. The results showed that LAI from DHP was underestimated by 20%-49% rela-tive to that from LAI-2200 in four study periods of the four forest types. Forest type had no significant effect on the construction of empirical models between these two measuring methods of LAI, whereas study period showed significant effects. Two classified empirical models (A and B) were constructed, which were suitable for correcting the LAI from DHP in June and September, July and August in four forest types, respectively. After being corrected by the classified empirical models, LAI from DHP of the four forest types increased by 45%-79%, and the measurement accuracy could be improved to 83%-94%. Classified empirical models between LAI from DHP and LAI-2200 could effectively correct the influence of automatic exposure on DHP and greatly improve its mea-surement accuracy, and provide a technical support for rapid and effective measurement of seasonal changes of LAI in different forest types.

Estimation of Gap Fraction and Foliage Clumping in Forest Canopies

The gap fractions of three mature hemi-boreal forest stands in Estonia were estimated using the LAI-2000 plant canopy analyzer ( LI-COR Biosciences, Lincoln, NE, USA), the TRAC instrument (Edgewall, Miami, FL, USA), Cajanus’ tube, hemispherical photos, as well as terrestrial (TLS) and airborne (ALS) laser scanners. ALS measurements with an 8-year interval confirmed that changes in the structure of mature forest stands are slow, and that measurements in the same season of different years should be well comparable. Gap fraction estimates varied considerably depending on the instruments and methods used. None of the methods considered for the estimation of gap fraction of forest canopies proved superior to others. The increasing spatial resolution of new ALS devices allows the canopy structure to be analyzed in more detail than was possible before. The high vertical resolution of point clouds improves the possibility of estimating the stand height, crown length, and clumping of foliage in the canopy. The clumping/regularity of the foliage in a forest canopy is correlated with tree height, crown length, and basal area. The method suggested herein for the estimation of foliage clumping allows the leaf area estimates of forest canopies to be improved.

Estimation of the directional and whole apparent clumping index (ACI) from indirect optical measurements

Canopy clumping index (CI) indicates the non-random distribution of foliage components in space, and is an important structural parameter for better understanding the radiative transfer process in a canopy. The apparent clumping index (ACI), calculated using the logarithmic gap fraction averaging method, is reported by the LAI-2200 Plant Canopy Analyzer. While LAI-2200 calculates the gap fraction and ACI from different conical rings, calculation of ACI for other geometric units (e.g., an angular cell or an azimuth sector) and instruments has been lacked. Building upon the LAI-2200 ACI, this study compares the ACIs calculated for different geometric spaces from different optical instruments. The field data obtained from seasonal continuous measurements with LAI-2200, digital hemispheric photography (DHP), and AccuPAR at a paddy rice field in northeast China were used to calculate the directional ACIs at different levels—a directional cell (ΩA(θ, ϕ)), a concentric ring (ΩA(θ)), an azimuth sector (ΩA(ϕ)), and over the horizontal landscape (ΩA(ν)). The whole ACIs were calculated from the directional ACIs with an angular integration method, a simple averaging method, a non-linear correction method, and a variance-to-mean ratio method. The directional ACIs for paddy rice generally follow the order of ΩA(θ, ϕ) < ΩA(θ) and ΩA(ϕ) < ΩA(ν), displaying an increase of foliage randomness with the segment size. The ΩA(θ, ϕ) estimated from DHP indicates canopy clumping at the finest level and is consistent with the CIs estimated from the logarithmic averaging method (ΩLX) and the ratio method (the effective leaf area index (LAIe) divided by the LAI). The ACI metrics expand the current CI metrics and can be obtained with different optical instruments. The expanded metrics can be applied in the canopy radiative transfer modeling and in the estimation of canopy biophysical parameters for other vegetation ecosystems.

Decreasing the error in the measurement of the ecosystem effective leaf area index of a Pinus massoniana forest

Decreasing the forest ecosystem leaf-area index error (LAIe) helps accurately estimate the growth and light energy utilization of aboveground foliage. Analyzing light transmission in forest ecosystems can effectively determine LAIe. The LAI-2200 plant canopy analyzer (PCA) is used extensively for rapid field-effective LAI (LAIe) measurements and primarily to measure forest canopy LAIe values. However, sometimes this parameter must also be measured in forests with small clearings. In this study, we used the LAI-2200 PCA to obtain one A-value and four B-values each for the canopy, herbaceous layer, and forest ecosystem LAIe. Field measurements showed that the three LAIe types were obviously different. In certain quadrats, the average herbaceous layer (Dicranopteris dichotoma Bernh.) LAIe apparently exceeded that of the Pinus massoniana forest ecosystem. The sources of this error were measuring and recording A-value readings for small canopies and underestimating the ecosystem LAIe. We obtained similar coefficients of determination for both the pre-recomputation and post-recomputation of the canopy and forest ecosystem LAIe (R2 ≥ 0.96 and R2 ≥ 0.99, respectively); thus, the error was decreased. Measuring field LAIe with the LAI-2200 PCA and recomputation should compensate for LAIe underestimation in complex forest ecosystems.

Parameterization of the vertical distribution of leaf area index (LAI) in rice (Oryza sativa L.) using a plant canopy analyzer

Monitoring the vertical distribution of leaf area index (LAI) is an effective method for evaluating canopy photosynthesis and biomass productivity. In this study, we proposed a novel method to characterize LAI vertical distribution non-destructively by utilizing LAI-2200 plant canopy analyzer, followed by the application of statistical moment equations. Field experiments were conducted with 5 rice cultivars under 2 fertilizer treatments in 2013 and with 3 rice cultivars under 3 plant density treatments in 2014. LAI readings obtained by a plant canopy analyzer for non-destructive stratified measurements were relatively consistent with LAI estimations using the stratified clipping method for every cultivar and treatment. The parameters calculated using the statistical moment equations numerically showed the changes in LAI vertical distribution with plant growth up to the heading stage. The differences in the parameters also quantified the effect of cultivar, fertilizer, and plant density treatments. These results suggest that the non-destructive stratified measurements and the statistical moments evaluated in this study provide quantitative, reliable information on the dynamics of LAI vertical distribution. The method is expected to be utilized by researchers in various research fields sharing common interests.