Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Jakub &#268;ern&#253;; Radek Pokorn&#253;; Pavel Haninec; Pavel Bedn&#225;&#345;

doi:10.3791/59757

JoVE Journal > Environment

환경과학

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Published: August 29, 2019

doi:

10.3791/59757

Jakub Černý¹, Radek Pokorný², Pavel Haninec^2,3, Pavel Bednář¹

¹Forestry and Game Management Research Institute,Research Station at Opočno, ²Department of Silviculture,Mendel University in Brno, ³Department of Forest Botany, Dendrology and Geobiocenology,Mendel University in Brno

Summary

An accurate estimation of leaf area index (LAI) is crucial for many models of material and energy fluxes within plant ecosystems and between an ecosystem and the atmospheric boundary layer. Therefore, three methods (litter traps, needle technique, and PCA) for taking precise LAI measurements were in the presented protocol.

Abstract

Accurate estimations of leaf area index (LAI), defined as half of the total leaf surface area per unit of horizontal ground surface area, are crucial for describing the vegetation structure in the fields of ecology, forestry, and agriculture. Therefore, procedures of three commercially used methods (litter traps, needle technique, and a plant canopy analyzer) for performing LAI estimation were presented step-by-step. Specific methodological approaches were compared, and their current advantages, controversies, challenges, and future perspectives were discussed in this protocol. Litter traps are usually deemed as the reference level. Both the needle technique and the plant canopy analyzer (e.g., LAI-2000) frequently underestimate LAI values in comparison with the reference. The needle technique is easy to use in deciduous stands where the litter completely decomposes each year (e.g., oak and beech stands). However, calibration based on litter traps or direct destructive methods is necessary. The plant canopy analyzer is a commonly used device for performing LAI estimation in ecology, forestry, and agriculture, but is subject to potential error due to foliage clumping and the contribution of woody elements in the field of view (FOV) of the sensor. Eliminating these potential error sources was discussed. The plant canopy analyzer is a very suitable device for performing LAI estimations at the high spatial level, observing a seasonal LAI dynamic, and for long-term monitoring of LAI.

Introduction

LAI, defined as half of the total leaf surface area per unit of horizontal ground surface area¹, is a key variable used in many bio-geophysical and chemical exchange models focused on carbon and water fluxes²^,³^,⁴. LAI is directly proportional to the active surface of leaves where it drives primary production (photosynthesis), transpiration, energy exchange, and other physiological attributes connected with a range of ecosystem processes in plant communities⁵.

Numerous approaches and instruments for performing LAI estimation have been developed, and they are currently available on the market⁶^,⁷^,⁸^,⁹. Ground-based methods for performing LAI estimation can be grouped into two main categories: (i) direct, and (ii) indirect methods¹⁰^,¹¹^,¹². The first group includes methods measuring leaf area directly, while the indirect methods infer LAI from measurements of more readily measurable parameters, using radiative transfer theory (in terms of time, labour-intensiveness, and technology)¹³^,¹⁴.

This protocol deals with the practical use of litter traps and the needle technique, as non-destructive semi-direct methods¹⁰; and the optical device plant canopy analyzer as an indirect method⁶^,⁷ for performing LAI estimation on a chosen sample from temperate deciduous forest stands in Central Europe (see its structural and dendrometric characteristics in Appendix A and Appendix B).

In deciduous forests and crops, it is possible to perform non-destructive semi-direct LAI estimation using litter traps¹¹ distributed below the canopy layer¹⁵. Litter traps provide precise LAI values for deciduous species in which LAI reaches a plateau within the growing season. However, for species that can replace leaves during the growing season, such as poplar, the method overestimates LAI¹¹. This method assumes that the content of the traps represents the average amount of leaves that fall during a leaf-fall period in the stand¹⁶, especially during the autumn months. Traps are opened boxes or nets (Figure 1) with a predetermined sufficient size (minimum 0.18 m², but preferably over 0.25 m²)¹⁰^,¹⁷, lateral sides preventing the wind from blowing leaves into/out of the traps, and with a perforated bottom avoiding decomposition of the leaves; which are located below the canopy layer of the studied stand, however, above the ground surface¹¹. The distribution of the traps can be either random¹⁸ or systematic in transects¹⁹ or a regular spacing grid²⁰. The number and distribution of traps are a crucial methodological step for performing an accurate LAI estimation reflecting the unique stand structure, spatial homogeneity, expected wind speed and direction, especially in the case of sparse stands (or alleys and orchards), and the work capacity for evaluating data. The precision of LAI estimation increases with the rising frequency of traps within studied stands¹¹^,²¹ (see Figure 2).

The recommended frequency of collecting samples of the litter-fall from each trap is at least monthly¹⁰ and even twice per week in periods of heavy fall, which may coincide with heavy rainfall. It is necessary to prevent decomposition of the litter in the traps and the leaching of nutrients from the material during rain episodes in the case of chemical analysis. After collecting leaves in a field, a mixed sub-sample is used for estimating the specific leaf area (SLA, cm² g^-1)²², defined as the fresh projected area of leaves to its dry mass weight ratio. The rest of the collected litter is dried to a constant weight and used for calculating the dry mass of the litter as g cm^-2 in the lab. Leaf dry mass on each collection date is converted into the leaf area by multiplying the collected biomass by SLA or leaf dry mass per area (LMA, g cm^-2) as the inverse parameter to SLA²³^,²⁴. A fresh projected area of particular leaves can be determined using a planimetric approach. The planimetric method is based on the dependency between the area of a specific leaf and the area covered by the leaf in the horizontal surface. The leaf is horizontally fixed to the scan screen, and its average is measured using a leaf area meter. Then, its area is calculated. Many leaf area meters based on different measurement principles are available on the market. Some of them include, for instance, the LI-3000C Portable Leaf Area Meter, which uses the orthogonal projection method, and the LI-3100C Area Meter, which measures leaf average using a fluorescent light source and a semi-conducted scanning camera. The next device, the CI-202 portable laser leaf area meter, codes a leaf length using a code reader. Besides them, the AM350 and BSLM101 Portable Leaf Area Meters are also commonly used for performing accurate leaf area estimation.

Furthermore, leaf area meters based on systems that analyze video exist. These leaf area meters consist of a video camera, a digitalisation frame, a screen, and a PC, including suitable software for making the data analysis such as WD3 WinDIAS Leaf Image Analysis System¹¹. Currently, conventional scanners connected to a PC can be used for an estimating leaf area. Afterwards, the leaf area is calculated as a multiple of the number of black pixels and its size depends on the selected resolution (dots per inch – dpi), or the leaf area is measured through specific software, for instance, WinFOLIA. Finally, the total dry mass of leaves collected within a known ground surface area is converted into the LAI by multiplying by SLA and a shrinkage coefficient²⁵ which reflects the changes in the area of fresh and dried leaves. Shrinkage depends on the tree species, water content and leaf softness. The shrinkage of leaves in length and width (what affect the projected area) is usually up to 10%²⁶, for instance, it ranges from 2.6 to 6.8% for oak²⁷. Sorting leaves by species for weighing and establishing the specific leaf area ratio is necessary to determine the contribution of each species to the total LAI²⁸.

LAI determination by the needle technique is an inexpensive method derived from the inclined point quadrat method²⁹^,³⁰^,³¹^,³². In deciduous stands, it is an alternative for performing LAI estimation without using traps¹⁰ based on the assumption that the total leaf number and their area in a tree are equal to what is collected on the soil surface after a complete leaf-fall²⁰. A thin sharp needle is pierced vertically into the litter lying on the ground immediately after the leaf-fall¹⁰. After the complete leaf-fall, the leaves are collected from the ground onto a needle of a vertical probe, are related to the contact number and equal the actual LAI value. An intensive sampling (100-300 sampling points per studied stand per field probe) by the needle technique is required to quantify a mean contact number and to derive the LAI value correctly¹⁰^,²⁰^,³³.

The plant canopy analyzer (e.g., LAI-2000 or LAI-2200 PCA) is a commonly used portable instrument for performing an indirect LAI estimation by taking a measurement of the light transmission throughout the canopy⁷ within the filtered blue portion of the light spectrum (320-490 nm)³⁴^,³⁵ to minimize the contribution of the light which has passed through the leaves, was scattered by the canopy and is passing through the foliage⁷^,³⁴. In the blue part of the light spectrum, the maximum contrast between the leaf and sky is achieved, and the foliage appears black against the sky³⁴. Therefore, it is based on the canopy gap fraction analysis⁷. The instrument has been widely used for making eco-physiological studies in plant communities such as crops³⁶, grasslands³⁷, coniferous stands⁸, and deciduous stands³⁸. The plant canopy analyzer uses a fisheye optical sensor with a FOV of 148° ³⁵ to project a hemispherical image of the canopy onto silicon detectors to arrange them into five concentric rings³⁹ with central zenith angles of 7°, 23°, 38°, 53°, and 68° ⁹^,⁴⁰^,⁴¹. Five view caps (i.e., 270°, 180°, 90°, 45°, and 10°) can be used to restrict the azimuth view of the optical sensor²⁷ to avoid shading by obstacles in an open area (for the above-referenced reading) or the operator in the sensor’s FOV during LAI estimation can adjust the FOV sensor to an open area for above-canopy readings. Measurements using the plant canopy analyzer are taken above (or in a sufficiently extended open area) and below the studied canopy⁷. The same view caps must be used for both above and below readings to avoid biases of gap fraction estimation³⁴. The LAI-2000 PCA produces an effective leaf area index (LAIe) as introduced by Chen et al.⁴², or rather an effective plant area index (PAIe) as woody elements are included in the sensor reading value. In deciduous stands with flat leaves, the LAIe is the same as the hemi-surface LAI. In the case of evergreen forest stands, the LAIe is necessary to correct for the clumping effect at the shoot level (SPAR, STAR)⁴³, the clumping index at scales larger than the shoot (Ω_E)⁴⁴, and the contribution of woody elements including stems and branches (i.e., woody-to-total area ratio),⁴⁵ which cause a systematic LAI underestimation²⁰. The clumping index on a higher spatial scale than the shoot or leaf could be quantified as an apparent clumping index (ACF), which can be estimated using the plant canopy analyzer when more restrictive view caps are used²⁷. As these authors state that this ACF is deduced from a ratio of LAI values calculated from transmittance by different procedures for homogeneous and non-homogeneous canopies according to Lang⁴⁶, we presume that this clumping index describes rather canopy homogeneity. Besides the ACF calculation, new diffuser caps that enable a more extensive application of LAI-2200 PCA in respect of weather conditions, a user menu instead of Fct codes, and the possibility to take many more measurements per file session are among the main technological upgrades compared to the former LAI-2000 PCA³⁴^,⁴⁷. Measurements and subsequent internal software calculations are based on four assumptions: (1) light blocking plant elements including leaves, branches, and stems, are randomly distributed in the canopy, (2) foliage is an optically black body that absorbs all the light it receives, (3) all plant elements are the same projection to the horizontal ground surface as a simple geometric convex shape, (4) plant elements are small compared to the area covered by each ring¹¹.

Protocol

1. LAI estimated using litter traps

First, perform a field survey, investigating the site conditions and structure of the studied stands (i.e., inclination and exposition of the slope, forest or vegetation type, forest or vegetation density, homogeneity of the canopy closure, the crown size, and the crown base height).
Select a suitable litter trap type for positioning below the canopy by choosing the mesh size of the net based on the size of the assimilation apparatus of the studied stands (i.e., the mesh size has to be smaller than the size of the captured assimilation apparatus), then number and distribute the traps within the studied stands, and subsequently label them.
1. Typically, use a number of traps ranging from 15 to 25 per investigated stand²⁵^,⁴⁸ with a capturing area ranging from 0.18 m² up to 0.5 m² or more, especially for tree species with large leaves such as poplar¹⁰^,¹⁷^,⁴⁸.
2. Place the traps at regular spacing throughout the studied stand within one or two mutually perpendicular transects or a regular grid (Figure 2). The proper sampling design, procedure, and analysis of the litter-fall are also described by Ukonmaanaho et al.¹⁷ or Fleck et al.²¹.
  1. Determine the distance between the traps on crown sizes, canopy closure and stand texture.
  2. Increase the number of litter traps both with rising stand area and stand heterogeneity in texture.

Figure 1: Different types of litter traps´ construction and their location within the stand.
From the left: woody, plastic, plastic boxes, and metal construction. Please click here to view a larger version of this figure.

Install the traps at the beginning of the growing season (soon after the leaf flushing because a leaf-fall can occur due to either damage by insects or extremely dry weather events in the summer period).
1. Firmly fix each of the traps above the ground surface and below the stand canopy so that there are no changes of the capturing area. Maintain each of the traps in a horizontal position and stable normalized capturing area. Examples of different kinds of traps are presented in Figure 1 or, for instance, in Ukonmaanaho et al.¹⁷.

Figure 2: The regular schematic pattern of litter trap distribution in forest stands with distinct homogeneity.
The homogeneity decreases from the left. Please click here to view a larger version of this figure.

Place the traps above the ground surface (minimum 0.1 m) to enable air to blow beneath the collecting part of the traps. Commonly, the height of the traps is 1 m above the ground surface²⁵^,³⁸^,⁴⁹.
Choose the time step for litter collection with regards to the typical course of weather in the studied site and litter-fall intensity. The standard time step ranges from 1 to 4 weeks (a shorter time step should be used during rainy weather to eschew litter decomposition and during intensive leaf falls).
1. During each of the measurements, check the strength of the trap frames, the compactness of the nets or boxes, and the levelling of the whole trap (i.e., the horizontal position of the trap).
Place the collected litter from each of the traps into previously labelled paper bags.
1. Transport all samples preferably in cool boxes, or if necessary, temporarily stored at 4 °C, but not frozen¹⁷ due to damage to the leaf tissues.
After transporting the samples to the lab, separate the assimilation apparatus from the other litter components (branches, seeds, bark, flowers; according to tree species if necessary).
Immediately after sorting, analyze a part of a mixed (intermingled) sample of each litter trap for performing SLA estimation (i.e., the ratio between the projected area of leaves and its dry mass weight).
1. Given that different tree species and even types of foliage (sunny and shaded) with differences in properties occur within the crown vertical profile, thoroughly mix the sample from each trap before selecting leaves for performing SLA (LMA) estimation¹¹. As there is a difference between fresh and dry leaf projected area due to shrinkage, estimate a shrinkage correction coefficient from the subsample of fresh (green) leaves²⁶.
  1. Collect leaves proportionally (similarly as in the mixed subsample from the trap) from all tree species located in the stand.
2. Separate the sub-sample counting at least 100-200 leaves from all used traps²¹^,²⁷ for performing SLA estimation.
  1. Place leaves in a flat, straight manner either onto the scan board or the leaf area meter, and it is necessary to eschew overlapping the leaves there.
  2. As the dried litter leaves can fold or curl, soak them in hot water (60-70 °C) for a short time¹⁷^,²¹. This has been found to flatten leaves sufficiently for taking measurements, but especially after a long time of soaking, they lose weight.
  3. If the scanner or leaf area meter does not enable upper illumination (to avoid reflectance and shadowing), use a suitable distribution of leaves either on a scan board or a leaf area meter conveyor (i.e. leaves are placed perpendicularly to the scan headlight) so that shadows do not form during scan headlight movement because it is difficult to remove the shadows during subsequent data processing.
  4. If a scanner connected to a PC is used, use a resolution of the black-and-white pictures of 200 dpi at a minimum based on sufficient accuracy of the area.
    1. To avoid reflectance, which is visible as light pixels within the leaves, when an ordinary scanner is used, adjust the scan brightness to reach an appropriate threshold (Figure 3). Software (e.g., WinFOLIA) then estimates leaf area by counting the dark pixels in the scan and converts them using the known dpi resolution.
3. Dry this sub-sample designated for SLA estimation for 48 hours at 80 or 105 °C to attain a constant weight. Use a ventilated oven with a thermostat to homogenize and maintain the internal temperature (e.g., IncuMax CV150).
  NOTE: The water content in leaves remains as fixed water in cells when oven-drying occurs at lower temperatures. When drying at 105 °C, no water remains in the plant sample¹⁷.
4. Weigh the dry-mass of this sub-sample using lab scales with a high degree of accuracy of 1 g at a minimum.
  1. Check the levelling of the lab scales and avoid external effects (e.g., the blowing of strong wind in the lab during weighing).
5. Calculate the SLA value as the fresh projected area of leaves of the sub-sample designated for SLA estimation divided by its dry mass weight.

Figure 3: The scan of a leaf sample with an example of a correct quality scan (on left side) and an incorrect scan (right side)
when brightness should be adjusted to eliminate reflectance visible as white pixels inside the leaf bodies and/or where surface dirt (a) and any edge effect (b) should be deleted before making an analysis of area.

Oven-dry the rest of the sample (i.e., collected leaves) for each trap for 48 hours at the same temperature that was used for SLA estimation, i.e., at 80 or 105 °C to reach a constant weight.
Multiply the dry mass weight of the rest of the sample for each particular litter trap by the correct SLA value to reach the total projected leaf area per trap.
Repeat steps from 1.5 to 1.9 for each of the studied stands and each litter collection date.
Calculate the LAI as the ratio of the cumulative total leaf area estimated using litter traps and the capturing area of the litter traps.

2. Needle technique for taking LAI measurements

Initially, perform a field survey, investigate the site conditions and the structure of the studied stands (i.e., inclination and exposition of the slope, forest or vegetation type, forest or vegetation density, homogeneity of the canopy closure, the crown size, and the crown base height).
Immediately after a complete leaf-fall, prepare all necessary equipment including a sufficiently long sharp metallic needle with as small a diameter as possible (maximally 2.0 mm in diameter).
Select a suitable number of randomly distributed sampling points (at least 100)¹⁰^,²⁰^,³⁸ based on the canopy structure of each studied stand.
NOTE: Generally, the more sampling points, the higher the accuracy of the LAI estimation in the studied stand (the number of sampling points should increase in relation to the size of the investigated plot and the structure of the canopy).
Using the metallic needle, puncture the leaves at a more or less similar angle through the layer of freshly fallen leaves that are lying on the ground surface at each of the probed sampling points.
1. Use any angle of stab since these fallen leaves have no relationships to their previous position within the canopy.
Check to make sure only freshly fallen leaves are present on the needle. In the case of the presence of partially decomposed leaves from the previous year, remove them from the needle.
Count the number of leaves pierced by the needle with each stab at each sampling point.
Repeat steps from 2.4 to 2.6 for all probed sampling points.
Count the total of all leaves pierced by the needle within the whole stand (i.e., for at least 100 sampling points).
Divide this sum by the number of stabs (i.e., counting the arithmetic mean). The resulted arithmetic average is equal to the actual LAI value at the stand level. Note: The average number of all fresh leaves collected on the needle corresponds to the true LAI value of the investigated forest stand.

3. Plant canopy analyzer optical device for performing LAI estimation

At the beginning, perform a field survey, including investigating the site conditions and the structure of the studied stands (i.e., inclination and exposition of the slope, forest or vegetation type, forest or vegetation density, homogeneity of the canopy closure, the crown size, and the crown base height).
Find a suitable open area (clearing) with identical sky conditions as above the observed plot, located a maximum distance of 1 km away²¹, which is required for above-canopy sensor readings.
1. As the plant canopy analyzer enables the user to use a different FOV in both azimuth (by restriction view caps) as well as zenith (through software processing by ring masking) directions, apply the same cap (and its orientation) for both above- and below-canopy readings.
2. Derive the size of the open area and the utilization of the appropriate view cap from the scope of the FOV. The known FOV of the sensor from the vertical in the zenith orientation and the estimation of the height of the nearest obstacles (trees, terrain, buildings) provide the most suitable solution, where the sufficient size of the open area can be calculated according to equation 1:
  Y = H∙tg∙α (1),
  Where Y is the required distance from the nearest barrier; H means the height of the obstacle; α denotes the FOV in a direction from vertical (Figure 4). Instead of the open area, a tower higher than the stand being investigated stand can be used for taking above-canopy readings²¹.
  1. Take the slope and heterogeneity of the terrain into consideration when calculating the size of the open area.

Figure 4: A schematic depiction of the sensor´s FOV (a grey area).
α is the sensor´s FOV; H denotes the height of the nearest obstacle; Y means the horizontal distance between the operator and the obstacle⁶³.

Based on the structural parameters of the stand (canopy homogeneity), determine a suitable sampling point number, the location of equidistant sampling points situated in either transect, or a grid for taking below-canopy readings in the studied stand⁹.
1. Deduce the appropriate distance from the variability of below-canopy readings in the field.
  1. Slowly move with the sensor below the canopy in transect and watch the variability of the most upper ring readings. Slight variability interrupted by higher values is a common result. Half of the distance between these peak values in variability should be considered appropriate.
2. If an observation of the seasonal LAI dynamic is being performed, use permanent fixation of transects or sampling points within the studied stand (e.g., by wooden stakes or geological metal sticks).
  NOTE: The number and spacing of transects depend on the particular canopy structure of the stand (Figure 5).
3. In homogenous stands, a sufficient number of transects ranges from 1 to 3. In the case of high heterogeneity, apply a regular grid of sampling points. Choose the orientation of transects with regards to slope and distribution of the trees in the stand, especially in case of row spacing. Spacing among particular sampling points is determined with respect to the heterogeneity of the stand, the crown sizes, the crown base height, and the sensor´s FOV (Figure 6). In homogenous stands, the number of sampling points commonly ranges between 5 and 36 ⁴⁶^,⁵⁰. Particular sampling designs are also described by Baret et al.⁵¹; Majasalmi et al.⁵²; Woodgate et al.⁵⁰; Fleck et al.²¹; Calders et al.⁵³.
  1. With a sloping terrain, orient the sensor view along level curves.

Figure 5: Layouts of measurements in pure deciduous stands.
(A), (B) Layouts of the optimal placing of particular transects in a pure plantation established by line planting (i.e., rectangular spacing). (C) The layout of the optimal placing of particular transects in a pure plantation established by line planting at triangular spacing. (D) The layout of the optimal placing of particular transects in a pure plantation established by line planting with two distinctly different parts. (E) The layout of the optimal placing of particular transects in a stand with four markedly distinct parts of the stand. (F) The layout of the optimal placing of particular transects in a pure plantation established by line planting with two different parts. (G) The layout of the optimal placing of particular transects in a pure plantation established by line planting with three markedly distinct parts representing 50%, 25%, and 25% of the whole area of the stand. (H) The layout of placing transects in stands established by natural regeneration, where approximately 12 measurement points per transect are sufficient from the accuracy point of view. Grey transects could be alternatively omitted from the measurement.

Figure 6: A schematic depiction of a spacing choice between measurement points within transects with regards to FOV, stand density, and height of the crown base.
a: suitable spacing distance in the case of the schematically displayed sensor height and view, and crown base height, c: unsuitable spacing distance as some canopy parts (d – in white) are not visible by the sensor. Thus, the spacing should be corrected (by b, i.e., a = c – b), c*: also corrected, suitable spacing distance due to the corrected enlarged sensor view angle (fine dashed line).

Although some possibilities and corrections of LAI estimation under sunny conditions are presented⁴⁷^,⁵⁴, conduct all measurements under a diffuse light sky (standard overcast) and windless conditions⁵⁵^,⁵⁶ (see Figure 7). Despite the fact that the plant canopy analyzer enables correction of light scattering for measurements under sunny conditions²¹, the sensor’s producer recommends using it under standard overcast conditions³⁴.
1. Use the plant canopy analyzer out of direct sunlight, as sunlit foliage might appear as bright pixels on the image and improperly classify as the sky (the penumbra effect). Ideally, take the measurements under entirely overcast conditions (with uniform cloud cover), when diffuse light is evenly scattered throughout the sky.
2. Reflectance is also obviously higher under sunlight compared to diffuse sky conditions. As an alternative, take measurements before sunrise or after sunset, when the sun is hidden below the horizon, and the vegetation is not backlit by the sun (keep in mind that during these times of the day, the light environment changes rapidly). However, also keep in mind that, due to the sensitivity of the sensor, reading values should be higher than ca. 3 in an open area.
3. Avoid rain because raindrops on the sensor affect the precision of measurements. A wet canopy reflects more light, which can lead to LAI underestimation.
4. Prevent heavy wind because moving plant elements might influence below-canopy readings, and thus they could cause incorrect results.
5. Avoid foggy conditions within the canopy as well.

Figure 7: Optimal weather conditions for performing LAI estimation using a plant canopy analyzer. Please click here to view a larger version of this figure.

If an observation of a seasonal course of the LAI is not needed, take all measurements from June to mid-September because the LAI of most tree species reaches its maximum value and remains (except during dry summers). Therefore, this period is the most suitable for making a LAI comparison during the growing season³⁸^,⁵⁷^,⁵⁸^,⁵⁹.
NOTE: This period should be shorter or modified under drought conditions of on leaf fall or senescence.
Estimate the woody area index (WAI, Figure 10) during the leaf-off period (i.e., both before bud breaking in early spring and after complete leaf-fall in late autumn).
NOTE: Given that the plant canopy analyzer has only one visible band (320-490 nm)³⁴^,³⁵ and cannot distinguish leaf and woody components, the results obtained during a growing season represent a plant area index (PAI) which is the sum of the LAI and WAI (PAI = LAI + WAI)⁶⁰. Therefore, subtract the mean value of both WAI measurements taken in a leaf-off period from each of the PAI measurements estimated in the leaf-on period to obtain correct LAI values (LAI = PAI – WAI)²⁰^,³⁸.
1. Perform the above canopy readings as the first measurement of each stand transect or grid in a sufficient open area (see step 3.2).
  NOTE: It is possible to take dual-mode measurements because the LAI-2000 PCA (or its improved versions LAI-2200 PCA and LAI-2200C) enable making simultaneous estimations with two sensors together (i.e., one for below- and one for above-readings). In this case, the sensors should be calibrated in accordance with the instruction manual (LI-COR 2011). Briefly, it is recommended that the user connect both sensors to one control unit to unify readings and time, placing the sensor for above-canopy readings at the top of a tripod in an open area, levelling it, and using the same restriction view cap. The sensor view orientation should be the same in the azimuth direction as was used for taking below-canopy readings.
2. Perform the below-canopy readings in the spatial measurement design described in detail in 3.3. The sensor is usually held from 0.5 to 2.0 m above the ground²¹^,³⁸, i.e., above understory vegetation, below the canopy and with the visible sensor bubble-level.
  1. A bubble level is a component of the sensor. Use the restriction view caps if the sensor is held below 2.0 m to exclude the operator from the FOV. Use the identical view cap for both below- and above-readings.
  2. Use a minimum distance between the sensor and the nearest element of the plant´s above-ground parts (stems, branches) of at least four times the diameter or width of the component.
3. Calculate the WAI values from field measured raw data using the LAI-2200 File Viewer (FV2200) freeware, which is available at https://www.licor.com/env/products/leaf_area/LAI-2200C/software.html.
  1. Restrict the sensor´s FOV in zenith direction to the upper three rings (i.e., 0-43°) to exclude an edge effect and big gap sizes²⁰^,⁶¹^,⁶².
  2. Process the data using the standard algorithms for LAI-2000 PCA, and set parameters for making evaluations using FV2200 according to the user manual³⁴.
4. Determine annual WAI value as the arithmetic mean of both measurements performed before the beginning of the growing season (i.e., before bud breaking) and after complete leaf-fall (Figure 10).
Estimate PAI using the same procedure as was used for making the WAI estimation (from step 3.6.1-3.6.3.)
Calculate the actual LAI value at the stand level as the difference between the mean PAI and WAI values (LAI = PAI – WAI)²⁰^,³⁸.

Representative Results

Average LAI values at the stand level of all studied stands in the 2013 growing season are presented in Figure 8. On all plots except A, the highest values were measured by litter traps, which serve as the reference level. Contrarily, the highest mean LAI value was estimated through the needle technique on plot A. All differences between LAI values estimated using litter traps and a plant canopy analyzer were not significant (p > 0.05; Figure 8, left). On plots B, C, and D, the needle technique significantly underestimated the LAI obtained from the litter traps. Conversely, on plot A, this technique overestimated the LAI measured using the litter traps, however, at not a significant level (p = 0.01; Figure 8, middle). Significant differences among LAI values estimated by the plant canopy analyzer and the needle technique were found in all cases (Figure 8, right).

Figure 8: A comparison of the statistically significant differences among average LAI values estimated using litter traps, the needle technique, and LAI-2000 PCA approaches.
A-C: European beech plots, D: sycamore maple plot, p < 0.05 (*), p < 0.001 (**), p ˃ 0.05 (ns). The whiskers show standard deviations. This figure has been modified with permission³⁸. Please click here to view a larger version of this figure.

Deviations of under- or over-estimation of the LAI obtained by the plant canopy analyzer and the needle technique, both compared to the LAI values obtained from the litter traps deemed as the reference level are displayed in Figure 9. Underestimations of the LAI values measured using litter traps and the plant canopy analyzer on plots A, B, C, and D were 15.3%, 11.0%, 18.9%, and 5.8%, respectively. The mean deflection of LAI values on beech plots and all investigated plots together were 15.1% and 12.7%, respectively. On plots B, C, and D, the needle technique underestimated LAI obtained from the litter traps by 41.0%, 38.0%, and 40.0%, respectively. Contrarily, on plot A, an overestimation of 13.0% was found between the LAI values obtained by the needle technique and the litter traps. The mean deflections of LAI values on beech and all studied plots regardless of tree species composition were 39.7% and 26.5%, respectively.

Figure 9: Mean deflection of LAI values estimated using the needle technique and an LAI-2000 PCA from LAI values obtained from litter traps deemed as the reference.
A-C: European beech plots, D: sycamore maple plot, ALL – average deviation of all plots regardless of tree species. The whiskers show standard deviations. This figure has been modified with permission³⁸. Please click here to view a larger version of this figure.

After the complete leaf-fall and before bud break (i.e., in April), the WAI can be readily measured using a plant canopy analyzer. Average seasonal values of the WAI for plots A, B, C, and D reached 1.33, 0.26, 0.99, and 0.38, respectively (Figure 10). The most rapid LAI development was noted during the period from bud break occurring in April until the beginning of May (part 1, Figure 10). From May until the end of June (part 2, Figure 10), continuation of the fast LAI development of leaves was observed; however, with less intensity compared to part 1. From the second half of June until the end of July, the LAI value declined by 0.46 on plot B. Plot A was deliberately selected for more detailed LAI monitoring where seasonal LAI measurements were taken at shorter time intervals. Therefore, the stagnation of LAI was more evident during the summer months on this plot (part 3, Figure 10). In all studied forest stands, leaves started to fall at the end of September, illustrated by the decrease in the LAI curve (part 4, Figure 10).

Figure 10: Seasonal LAI dynamics during the 2013 growing season.
LAI: leaf area index, WAI: woody area index, A–C: European beech plots, D: maple plot, DOY: day of the year. Empty diamonds signify average seasonal WAI subtracted from the PAI to obtain the correct LAI (LAI = PAI – WAI). Period 3 appears to be the most suitable phase to compare the LAI of deciduous stands during the whole growing season. The whiskers display standard deviations of LAI estimation, and the grey area signifies the confidence interval of the mean LAI curve. This figure has been modified with permission³⁸. Please click here to view a larger version of this figure.

Plot	A	B	C	D
Geographic coordinates	49°26'29.946" N	49°19'27.6" N	49°19'32.6" N	49°19'20.7" N
Geographic coordinates	16°42'06.237" E	16°43'4.3" E	16°43'54.8" E	16°43'48.2" E
Altitude	600 m a. s. l.	450 m a. s. l.		460 m a. s. l.
Bedrock	Acid granodiorit	Granodiorit
Soil classification (soil type)	Modal oligotrophic Cambisol	Modal mesotrophic Cambisol
Mean annual precipitation (mm)	592	596
Mean annual temperature (°C)	7.0	7.0
Slope orientation	NW	W	N	NW
Slope inclination (%)	10	15	20	10
Forest type	Abieto-Fagetum oligo-mesotrophicum; Nutrient Medium Fir-Beech	Fagetum calcarium; Limestone beech	Fagetum mesotrophicum; Nutrient-rich Beech	Fagetum illimerosum mesotrophicum; Loamy beech

Appendix A: Characteristics of the study plots. A–C: European beech, D: sycamore maple. The forest type classification is based on ecological factors (i.e., the soil and climate) and their relationships to forest stands. Each of the plots had an area of 400 m² (20 x 20 m). This table has been modified with permission³⁸.

Plot	A	B	C	D
Age of the stand (years)	46	19	77	13
Stand density (trees ha^-1)	2300	2700	900	5800
Height (m)	18.3 ± 4.6	6.0 ± 1.3	22.6 ± 11.3	5.6 ± 0.8
DBH (cm)	13.4 ± 5.7	7.0 ± 1.3	24.1 ± 4.1	3.9 ± 1.6
BA_1,3 (m² ha^-1)	38.8 ± 0.01	10.4 ± 0.01	40.9 ± 0.10	6.9 ± 0.01
Tree species representation (%)	EB (100)	EB (100)	EB (100)	SM (100)

Appendix B: Structural characteristics (Average ± SD) of the investigated stands. A–C: European beech plots, D: sycamore maple plot, DBH: diameter at the breast height, BA_1.3: basal area at the breast height at the end of the 2013 growing season, EB: European beech, SM: sycamore maple. This table has been modified with permission³⁸.

Discussion

Litter traps are deemed as one of the most accurate methods for performing LAI estimation⁸, but they are more labor-intensive and time-consuming than the indirect methods³⁵^,⁶⁴ which were incorporated into this protocol. Within the entire LAI estimation procedure using litter traps, a precise estimation of the SLA is the most critical point¹⁰ because the SLA can vary with plant species⁶⁵, date and year, length of time in the traps, weather⁶⁶, and site fertility⁶⁷. Although litter traps are usually considered as the reference level, and a calibration tool for indirect methods³⁸^,⁴⁹, a possible discrepancy of LAI estimation using litter traps can occur due to wind flow, the number and distribution of the traps within the stand regardless of canopy cover and stand structure, the size of the stand area,⁶⁸^,⁶⁹ or it can also be caused by a deflection of the litter trap from its level, horizontal position. Furthermore, LAI values obtained by litter traps can also be affected by weather and climate⁷⁰, especially by decomposition of the litter-fall¹⁰^,¹¹ or the withering of leaves in traps, which can be elicited by severe drought during summer months. Therefore, a shrinkage correction coefficient should be applied in this case²⁵^,²⁶^,²⁷. The sufficient number of litter traps for performing an estimation of LAI ranged between 15 and 25 ²⁵^,⁴⁸, however, the higher total capturing area of traps per investigated stand, the more precise LAI estimation. Litter traps do not enable users to estimate leaf distribution within the vertical profile of crowns¹¹, or to determine an accurate LAI value at a single moment in time during the growing season⁶⁰, however in the leaf-fall period, it is useful in estimating the dynamics of LAI and for making an inter-annual comparison of its dynamics⁴⁸^,⁷¹. Although a precise LAI estimation by litter traps is related to the complete annual leaf-fall¹⁶, this approach has also already been successfully applied in mixed evergreen-deciduous forests⁷².

The needle technique is effortless to use and applicable merely for deciduous forest stands and is suitable especially for forest stands of large-leaf species such as oak (Quercus sp.) or beech (Fagus sp.) genus. It is the easiest to use on sites where the litter wholly decomposes every year¹⁰. If a thin and sharp needle is used, this method provides precise LAI estimations. The main advantages of the needle technique are its straightforward use, not needing a leaf area meter or balance, and it being much less time-consuming than using classical litter traps²⁰. Moreover, it is attractive for application, because the assumption of random leaf distribution is not necessary and owing to its non-destructive character¹¹. Nevertheless, LAI measurements based on this method systematically underestimate LAI values obtained from litter traps (by 6-37%),²⁰ which is also supported by Černý et al.³⁸. The underestimation of LAI (Figure 8, Figure 9) can be mainly caused by either the diameter of the used needle, or a micro-relief of the ground surface below the studied canopy where leaves can be blown by wind either into a terrain depression or out from tiny bumps of the surface, or a combination of both mentioned factors. Besides these shortcomings, the needle method is complicated to use in a deciduous conifer tree species such as Larch sp. due to the size and shape of its assimilation apparatus.

The plant canopy analyzer is one of the indirect optical non-destructive methods. The main advantage of its easy field application for LAI estimation consists in the possibility to take repeated measurements, which makes it possible to evaluate the LAI seasonal course during the entire growing season,¹¹ and it allows for a large-scale implementation and long-term monitoring of the LAI²⁸. The LAI-2000 PCA demands relatively specific weather conditions for performing a precise LAI estimation (step 3.4). This potential drawback is markedly eliminated by the improved versions, LAI-2200 PCA and LAI-2200C, which are more robust with respect to the synoptic situation when making a LAI estimation⁴¹ due to its better ability to perform light scattering conversion⁴⁷. Despite this fact, LAI estimation using these sensors is recommended either under standard overcast conditions³⁴ or sunny conditions where a stable bright sky with the sun high above the horizon²¹. This method requires measuring only 12⁵² up to 25 sampling points²¹ per stand to reach the required level of accuracy. However, optical gap fraction-based measurements are not suitable for stands with a high leaf area because these indirect LAI estimations are saturated at LAI values around 6¹⁴. For performing a precise LAI estimation, another potential weakness of the LAI-2×00 PCA approach is the requirement for an above-canopy reference reading⁶. However, this drawback can be eliminated by the possibility of taking simultaneous and automatized measurements in dual mode when two sensors are controlled by one unit of the LAI-2000 PCA⁷³ or its improved successors LAI-2200 PCA and LAI-2200C³⁴^,⁴¹.

The use of the plant canopy analyzer to estimate the WAI in leafless periods and its subtraction from optical PAI (i.e., effective plant area index) in leafy period seems to be practical⁷². In contrast, the potential of this instrument is restricted by its general tendency towards underestimating LAI in discontinuous and heterogeneous canopies¹⁵^,²⁰^,⁴³^,⁴⁹^,⁷⁴ which is mainly ascribed to the contribution of woody materials and clumping effects within the canopy¹⁰^,⁷². On the contrary, overestimation of the LAI can be observed in stands composed of species (e.g., poplar) that can replace their leaves during the growing season¹¹. Deblonde et al.⁷⁵ quantified the woody material by direct destructive methods which are very time-consuming and labor intensive. It is also possible to estimate the woody contribution using the indirect measurement distinguishing it within the near-infrared band⁷⁶, or by terrestrial laser scanning either using a laser scanner⁷⁷ or point clouds of LIDAR⁷⁸. LAI underestimation was especially seen within those canopies with a non-random distribution (e.g., evergreen forest) where the plant canopy analyzer underestimates LAI values by approximately 35-40% due to foliage clumping at the shoot level³⁹^,⁷⁹. As one of the possible methods for performing an accurate LAI estimation, Chen et al.⁸ and Leblanc et al.⁸⁰ recommend combining a plant canopy analyzer and The Tracing Radiation and Architecture of Canopies (TRAC), which quantifies the clumping effect and woody components. However, it is currently also possible to correct clumping either by the finite-length averaging method⁸¹ or gap-size distribution method⁸² or a combination of the gap-size distribution and the finite-length averaging methods⁸³ or path length distribution method⁸⁴ as stated by Yan et al.³⁵ in their review study. Although significant progress has been accomplished in the development of LAI calculations using indirect optical methods, some challenges remain, especially involving the estimation of leaf angle distribution where the application of active laser scanning technology is one of the methods which can detect it, but its three-dimensional information has not yet been fully explored and implemented³⁵.

Disclosures

The authors have nothing to disclose.

Acknowledgements

We are indebted to the editorial board of the Journal of Forestry Research for encouraging and authorizing us to use the representative results in this protocol from the article published there. We also kindly thank two anonymous reviewers for their valuable comments, which have substantially improved the manuscript. The research was funded by the Ministry of Agriculture of the Czech Republic, institutional support MZE-RO0118 and the National Agency of Agricultural Research (Project No. QK1810126).

Materials

Area Meter	LI-COR Biosciences Inc., NE, USA	LI-3100C	https://www.licor.com/env/products/leaf_area/LI-3100C/
Computer Image Analysis System	Regent Instruments Inc., CA	WinFOLIA	http://www.regentinstruments.com/assets/images_winfolia2/WinFOLIA2018-s.pdf
File Viewer	LI-COR Biosciences Inc., NE, USA	FV2200C Software	https://www.licor.com/env/products/leaf_area/LAI-2200C/software.html
Laboratory oven	Amerex Instruments Inc., CA, USA	CV150	https://www.labcompare.com/4-Drying-Ovens/2887-IncuMax-Convection-Oven-250L/?pda=4\|2887_2_0\|\|\|
Leaf Image Analysis System	Delta-T Devices, UK	WD3 WinDIAS	https://www.delta-t.co.uk/product/wd3/
Litter traps	Any	NA	See Fig. 2
Needle	Any	NA	Maximum diameter of 2 mm
Plant Canopy Analyser	LI-COR Biosciences Inc., NE, USA	LAI-2000 PCA	LAI-2200 PCA or LAI-2200C as improved versions of LAI-2000 PCA can be used, see: https://www.licor.com/env/products/leaf_area/LAI-2200C/
Portable Laser Leaf Area Meter	CID Bio-Science, WA, USA	CI-202	https://cid-inc.com/plant-science-tools/leaf-area-measurement/ci-202-portable-laser-leaf-area-meter/
Portable Leaf Area Meter	ADC, BioScientic Ltd., UK	AM350	https://www.adc.co.uk/products/am350-portable-leaf-area-meter/
Portable Leaf Area Meter	Bionics Scientific Technogies (P). Ltd., India	BSLM101	http://www.bionicsscientific.com/measuring-meters/leaf-area-index-meter.html
Portable Leaf Area Meter	LI-COR Biosciences Inc., NE, USA	LI-3000C	https://www.licor.com/env/products/leaf_area/LI-3000C/

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Černý, J., Pokorný, R., Haninec, P., Bednář, P. Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands. J. Vis. Exp. (150), e59757, doi:10.3791/59757 (2019).