Quick LAI Method Comparison Chart
|Method ||Relative Cost ||Temporal Sampling ||Suitability for Tall Canopies ||Suitability for Short Canopies ||Spacial Scaling ||Ease of Collecting Samples ||Vertical Profiling Samples|
|Destructive harvest ||H* ||Single ||L ||H ||L ||VL ||Yes|
|Litter traps ||M* ||Single ||H ||L ||L - M ||M ||No|
|Hemispherical photography ||M ||Single ||H ||L ||M ||M ||No|
|PAR inversion (LP-80) ||M ||Both* ||H* ||H ||M ||H ||Yes|
|Vegetation index ||L - VH ||Continous ||M** ||VH ||M -H ||VH ||No|
| ||*Labor intensive ||**Single with LP-80 |
Continuous with subcanopy PAR sensors
|*Requires access to top of canopy or large open area |
**Requires access to top of canopy
| || || || |
Table 1. KEY: VL = very low, L = low, M = moderate, H = high, VH = very high
Accuracy: 10% or better for spectral irradiance and radiance values
Dimensions: 43 x 40 x 27 mm
Calibration: NIST traceable calibration to known spectral irradiance and radiance
Measurement type: < 300 ms
Connector type: 3.5 mm (stereo) plug or stripped and tinned wires
Communication: SDI-12 digital sensor
Data logger compatibility: (not exclusive) METER Em50/60 series, Campbell Scientific
NDVI bands: Centered at 630 nm and 800 nm with 50 nm and 40 nm Full Width Half Maximum (FWHM), respectively
Operating environment: 0 to 50°C, 0 to 100% relative humidity
Probe length: 86.5 cm
Number of sensors: 80
Overall length: 102 cm (40.25 in)
Microcontroller dimensions: 15.8 x 9.5 x 3.3 cm (6.2 x 3.75 x 1.3 in)
PAR range: 0 to >2,500 µmol m-2 s-1
Resolution: 1 µmol m-2 s-1
Minimum spatial resolution: 1cm
Data storage capacity: 1MB RAM, 9000 readings
Unattended logging interval: User selectable, between 1 and 60 minutes
Instrument weight: 1.22 kg (2.7 lbs)
Data retrieval: Direct via RS-232 cable
Power: 4 AA alkaline cells
External PAR sensor connector: Locking 3-pin circular connector (2 m cable)
Extension cable option: 7.6 m (25 ft)
More resources that answer the questions: What is leaf area index and how to measure leaf area index.
Campbell, Gaylon S., and John M. Norman. “The light environment of plant canopies.” In An Introduction to Environmental Biophysics, pp. 247-278. Springer New York, 1998.
Garrigues, Sébastien, N. V. Shabanov, K. Swanson, J. T. Morisette, F. Baret, and R. B. Myneni. “Intercomparison and sensitivity analysis of Leaf Area Index retrievals from LAI-2000, AccuPAR, and digital hemispherical photography over croplands.” Agricultural and Forest Meteorology 148, no. 8 (2008): 1193-1209.
Gitelson, Anatoly A. “Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation.” Journal of Plant Physiology 161, no. 2 (2004): 165-173.
Hyer, Edward J., and Scott J. Goetz. “Comparison and sensitivity analysis of instruments and radiometric methods for LAI estimation: assessments from a boreal forest site.” Agricultural and Forest Meteorology 122, no. 3 (2004): 157-174.
Jiang, Zhangyan, Alfredo R. Huete, Kamel Didan, and Tomoaki Miura. “Development of a two-band enhanced vegetation index without a blue band.” Remote Sensing of Environment 112, no. 10 (2008): 3833-3845.
Kucharik, Christopher J., John M. Norman, and Stith T. Gower. “Measurements of branch area and adjusting leaf area index indirect measurements.” Agricultural and Forest Meteorology 91, no. 1 (1998): 69-88.
Lang, A. R. G., and Xiang Yueqin. “Estimation of leaf area index from transmission of direct sunlight in discontinuous canopies.” Agricultural and Forest Meteorology 37, no. 3 (1986): 229-243.
Norman, J. M., and P. G. Jarvis. “Photosynthesis in Sitka spruce (Picea sitchensis (Bong.) Carr.). III. Measurements of canopy structure and interception of radiation.” Journal of Applied Ecology (1974): 375-398.
Rouse Jr, J_W, R. H. Haas, J. A. Schell, and D. W. Deering. “Monitoring vegetation systems in the Great Plains with ERTS.” (1974).
Qi, Jiaguo, Abdelghani Chehbouni, A. R. Huete, Y. H. Kerr, and Soroosh Sorooshian. “A modified soil adjusted vegetation index.” Remote Sensing of Environment 48, no. 2 (1994): 119-126.
The LP-80: pain-free leaf area index
DR. GAYLON S. CAMPBELL
Leaf area index (LAI) is just a single number—a statistical snapshot of a canopy taken at one particular time. But that one number can lead to significant insight, because it can be used to model and understand key canopy processes, including radiation interception, energy conversion, momentum, gas exchange, precipitation interception, and evapotranspiration.
Older LAI methods are tedious
Leaf area index is defined as the one-sided green leaf area of a canopy or plant community per unit ground area. It can be found by harvesting and measuring the area of every leaf in a canopy covering one unit area of ground. In 1981, Anderson developed a less destructive method for finding LAI. Using hemispherical photographs looking upwards, she estimated the fraction of light that penetrated the canopy and applied a predictive mathematical model to approximate leaf area index.
Evaluating “fisheye” canopy pictures was tedious work. An assistant would usually lay a grid over each picture and count what fraction of the squares was light. One lab tech recalls, “After too many hours looking at those pictures, I used to dream in checkers.” The “checkers” evaluation allowed investigators to find the probability that a random beam of light would penetrate that particular section of canopy.
Getting a value for leaf area index is often just a point along the way. If you plan to use LAI to model environmental interactions of the canopy, measuring photosynthetically active radiation (PAR) may be a more direct route. That’s because many of these models are using LAI to predict PAR in the first place. It’s possible to go back the other way—to use PAR to estimate LAI. But why do that if PAR is the number you really want? You may want to evaluate whether LAI is the most useful parameter for your particular application. It is sometimes more straightforward, and usually more accurate, to simply measure intercepted PAR and use that data directly in an appropriate model.
The mathematical model that converts this fraction of light into an estimate of leaf area index is relatively simple. To understand how it works, picture holding a leaf with an area of ten square centimeters horizontally over a large white square. It would cast a shadow of ten square centimeters. Then, randomly place an identically sized leaf over the square. In all probability, the shadow cast would now be twenty square centimeters, although there is a small chance that the leaves might overlap. When a third leaf is added, the probability of overlap increases. As more and more leaves are randomly placed, eventually, the white square will be completely shaded. And though leaf area will increase as leaves are added, the shaded area will remain constant because all light has been intercepted.
The LP-80 solves the equation for you
The equation describing this phenomenon (see Solving the Equation below for its mathematical derivation) is