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.
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.
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 (ΩE)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.
1. LAI estimated using litter traps
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.
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.
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.
2. Needle technique for taking LAI measurements
3. Plant canopy analyzer optical device for performing LAI estimation
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.
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).
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.
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 |
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 |
Höhe (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 |
BA1,3 (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, BA1.3: 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.
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.
The authors have nothing to disclose.
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).
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/ |